Optimal dip angle optimization design method based on plane roof photovoltaic system

Abstract

The invention discloses an optimal dip angle optimization design method based on a plane roof photovoltaic system, which comprises the following steps: taking local past load demand data as reference, and combining a long-short-term memory artificial neural network algorithm to predict the local electricity load demand in the next year; the optimal inclination angle is optimized according to the predicted load demand, the obtained optimal inclination angle can improve the utilization rate of the electric energy load in one year, and the problem of excessive power shortage in individual months is avoided. In addition, the capacity of the photovoltaic device per unit occupied area under the actual installation cost is considered, and the photovoltaic dip angle when the capacity of the photovoltaic device per unit occupied area is maximum is calculated. And finally, optimizing the two schemes by using a game theory method, so that the finally obtained optimal inclination angle not only can improve the utilization rate of an electric energy load in one year, but also can obtain the capacity of a photovoltaic device per unit occupied area.

Description

Technical Field

[0001] The invention relates to the technical field of power grid data processing, and is particularly applicable to an optimal tilt angle optimization design method based on a flat roof photovoltaic system. Background Art

[0002] Under the background of "dual carbon", solar energy is a good alternative to traditional energy. Driven by the energy crisis, large-capacity distributed photovoltaic power generation has begun to be connected to the power grid. In the photovoltaic power generation system, photovoltaic components, commonly known as solar panels, are the core part of the system and the most valuable part of the system. Its function is to convert the sun's radiation capacity into electrical energy, or send it to the battery for storage, or drive the load to work. The most widely used photovoltaic power station in my country is the fixed grid-connected photovoltaic power station. In the use and design of fixed grid-connected photovoltaic power stations, the surface of the array needs to be tilted at a certain angle so that the installation inclination angle is equal to or close to the optimal inclination angle, so as to obtain the maximum solar radiation throughout the year. Therefore, the optimal inclination angle is one of the key design parameters of fixed grid-connected photovoltaic power stations.

[0003] In actual use, many experts have found that there is a connection between the installation inclination angle and its geographical latitude. Taking the equator as the orientation, the general optimal photovoltaic installation inclination angle is approximately the local latitude. In addition, the optimal inclination angle is also affected by solar radiation. Some scholars have quantified the uniformity of radiation distribution throughout the year, calculated the inclination angle under the conditions of taking into account uniformity and maximum, and calculated the percentage of increased radiation relative to the horizontal plane based on the assumption of sunny days. However, this method will reduce photovoltaic power generation in winter, and may cause excessive power shortages in some months. At the same time, due to excessive focus on the maximum solar radiation that can be obtained by the optimal inclination angle of photovoltaic panels, the influence of factors such as system usage frequency, centralized installation, and later operation and maintenance for different load requirements has been ignored, which reduces the guiding significance of this method for engineering practice. Summary of the invention

[0004] The purpose of the present invention is to provide an optimal tilt angle optimization design method based on a flat roof photovoltaic system, aiming to provide a multi-objective optimization method for the optimal tilt angle optimization design of a flat roof photovoltaic system, so as to avoid excessive power shortage in individual months, and combine the influencing factors such as frequency of use, centralized installation, and later operation and maintenance to improve the power conversion efficiency of distributed photovoltaics on flat slope roofs and reduce the investment and maintenance costs of the "source-grid-load-storage" system.

[0005] To achieve the above object, the present invention adopts the following technical solutions: The optimal tilt angle optimization design method based on a flat roof photovoltaic system of the present invention comprises the following steps: S1, based on the sunshine percentage of ground meteorological stations or satellite remote sensing data, establish the correlation between solar radiation and slope surface; S2, obtaining the declination angle, hour angle and solar time of the installation location; S3, obtaining the parameters of the photovoltaic modules to be installed and the historical electricity load demand data of the installation site; S4, based on the data of steps S1 and S2, a plane slope solar radiation calculation model is established to obtain the optimal photovoltaic tilt angle without considering the load demand; S5, based on the historical electricity load demand data of the installation site, use the long short-term memory artificial neural network algorithm to predict the electricity load demand of the installation site in the next year; in order to avoid excessive power shortage in some months in the next year, optimize the optimal photovoltaic tilt angle and obtain the optimal installation tilt angle considering the load demand; S6, establishing a dynamic shadow area calculation model according to the data in step S2; and obtaining an optimal installation inclination angle for maximizing the photovoltaic device capacity per unit area based on the dynamic shadow area calculation model; S7, taking the single panel installation cost and benefit corresponding to the optimal installation inclination angle considering the load demand and the optimal installation inclination angle with the maximum photovoltaic device capacity per unit area as evaluation factors, respectively, using the network analytic hierarchy process to determine the subjective weight of the evaluation factor, and using the entropy weight method to determine the objective weight of the evaluation factor; S8, combining and weighting the evaluation factors according to a game theory algorithm to obtain a combined weight of the evaluation factors; S9, performing weighted summation on the installation inclination angles corresponding to the combined weights of the evaluation factors to obtain an optimal installation angle.

[0006] Furthermore, the plane slope solar radiation calculation model expression in step S4 is: In the formula, is the direct radiation, is the diffuse radiation from the sky, Designing the tilt angle for photovoltaic panels, is the latitude of the installation location; is the solar declination of the installation location; is the hour angle; n is the number of days in a year starting from New Year's Day; is the ground reflectivity, which is taken as 0.2.

[0007] Furthermore, the optimization of the optimal photovoltaic tilt angle in step S5 includes respectively calculating the average absolute error S between the photovoltaic power generation rate of each month and the electricity load demand of each month in the next year corresponding to different tilt angles within the range of ±10° of the optimal photovoltaic tilt angle determined in step S4, and the tilt angle corresponding to the minimum value of S is the optimal installation tilt angle considering the load demand.

[0008] Furthermore, in step S7, the network analytic hierarchy process for determining the subjective weights of the evaluation factors includes: S7.1.1, constructing a pairwise comparison matrix X of the evaluation factors, and calculating a weight vector M of each evaluation factor; S7.1.2, multiply the comparison matrix X by the weight vector M of each evaluation factor to obtain a super weighted matrix; S7.1.3, calculating successive powers of the super-weighted matrix until the matrix tends to be stable, and obtaining a limit super-matrix, wherein each column in the limit super-matrix corresponds to the subjective weight of each evaluation factor.

[0009] Furthermore, in step S8, according to the game theory algorithm, the sum of the deviations between the subjective weight and the objective weight is minimized to obtain the weight coefficient of the subjective weight of each evaluation factor; and the combined weight of each evaluation factor is calculated.

[0010] The advantage of the present invention is that, based on the existing optimal photovoltaic installation inclination research, the local past load demand data is used as a reference, and the long short-term memory artificial neural network (LSTM) algorithm is combined to predict the local electricity load demand in the next year; the optimal inclination is optimized according to the predicted load demand, and the optimal inclination obtained can improve the utilization rate of the electric energy load in a year and avoid the problem of excessive power shortage in individual months. In addition, when the shadow shielding time of the front and rear row components is consistent, the distance from the front edge of the front row fixed bracket component to the front edge of the adjacent rear row fixed bracket component is also reduced accordingly. At this time, the footprint of the 1 MW photovoltaic array will also be different. Considering factors such as the actual installation cost, the capacity of photovoltaic devices per unit area will also be affected. This patent calculates the photovoltaic inclination angle when the capacity of photovoltaic devices per unit area is maximized. Finally, the game theory method is used to optimize the above two schemes so that the optimal inclination angle finally obtained can not only improve the utilization rate of the electric energy load in a year, but also obtain the capacity of photovoltaic devices per unit area. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 The present invention is a flow chart of the method.

[0012] Figure 2 The present invention is a flow chart for determining the optimal installation inclination angle considering load requirements in the method of the present invention. DETAILED DESCRIPTION

[0013] The technical solutions in the embodiments of the present invention are described clearly and completely below. 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.

[0014] like Figure 1 As shown, the optimal tilt angle optimization design method based on the flat roof photovoltaic system of the present invention comprises the following steps: S1, based on the sunshine percentage of ground meteorological stations or satellite remote sensing data, establish the correlation between solar radiation and slope surface.

[0015] S2, obtaining geographic data such as the declination angle δ, hour angle ω and solar time of the installation site.

[0016] Formula (1) n is the number of days in a year starting from New Year's Day.

[0017] Formula (2) Formula (3) is the local standard time; is the local longitude; is the standard longitude; DS is daylight saving time, which is 1 when it is in effect, otherwise it is 0.

[0018] According to formula (2) and the specific sunrise time and sunset time, the sunrise hour angle and sunset hour angle can be calculated.

[0019] S3, obtaining the parameters of the photovoltaic modules to be installed and the historical electricity load demand data of the installation site.

[0020] S4, based on the data of steps S1 and S2, a plane slope solar radiation calculation model is established to obtain the optimal photovoltaic tilt angle without considering the load demand.

[0021] The calculation model expression of plane slope solar radiation is: Formula (4) In the formula, H T Total daily radiation is the direct radiation, is the diffuse radiation from the sky, Designing the tilt angle for photovoltaic panels, is the latitude of the installation location; is the solar declination of the installation location; is the hour angle; n is the number of days in a year starting from New Year's Day; is the ground reflectivity, which is taken as 0.2.

[0022] S5, based on the historical electricity load demand data of the installation site, use the long short-term memory artificial neural network algorithm to predict the electricity load demand of the installation site in the next year; in order to avoid excessive power shortage in some months in the next year, optimize the optimal photovoltaic tilt angle and obtain the optimal installation tilt angle considering the load demand. The specific process is as follows Figure 2 shown.

[0023] like Figure 2 As shown in the figure, the specific steps of establishing a long short-term memory artificial neural network algorithm to predict the power load demand of the installation site in the next year are: S5.1, collect the monthly power load conditions of the installation site over the years, pre-process the collected data, and divide the sample data into training set and test set.

[0024] S5.2, use the training set data to train the long short-term memory artificial neural network algorithm to obtain a prediction model for predicting the electricity load demand of the installation site in the next year, and determine whether the accuracy of the prediction model reaches a preset value. If the preset accuracy is not reached, update the prediction model parameters and continue training until the prediction accuracy of the prediction model reaches the preset value.

[0025] S5.3, using the test set data to verify the prediction results of the prediction model, and obtain the final prediction model, which is used to output the electricity load demand of the installation site in the next year.

[0026] Then, based on the optimal photovoltaic tilt angle obtained by S4 without considering the load demand, the average absolute error S between the photovoltaic panel power generation rate of each month at each different tilt angle and the monthly electricity load demand of the next year is calculated within the range of ±10°, and the tilt angle corresponding to the minimum value of S is taken as the optimal installation tilt angle considering the load demand. The expression of S is Formula (5) in, is the load demand value predicted for the i-th month; is the power generation of the photovoltaic panel at a certain inclination angle in the i-th month. n is the total number of natural months.

[0027] S6, establishing a dynamic shadow area calculation model according to the data in step S2; and obtaining an optimal installation inclination angle for maximizing the photovoltaic device capacity per unit area based on the dynamic shadow area calculation model.

[0028] When the shadow shielding time of the front and rear row components is the same, the distance from the front edge of the front row fixed bracket component to the front edge of the adjacent rear row fixed bracket component will also be reduced accordingly. At this time, the footprint of the 1 MW photovoltaic array will also be different. Considering factors such as the actual installation cost, the capacity of photovoltaic devices per unit area will also be affected. Use PVsyst software to simulate the shadow, analyze the shadow formation mechanism based on the spatial projection method, and establish a dynamic shadow area calculation model. The specific steps are as follows: S6.1, calculate the width C of the shadow of the photovoltaic array in the plane slope model; Formula (6) is the width of the photovoltaic array; is the height difference between the foundations of the photovoltaic series; L is the height of the photovoltaic array support; is the slope angle of the plane slope. S6.2, calculate the shadow length D of the photovoltaic array in the plane slope model; Formula (7) is the length of the photovoltaic array; S6.3, then the area occupied by the photovoltaic panels is : Formula (8) S6.4, using PVsyst simulation software to analyze and calculate The photovoltaic panel installation inclination angle that achieves the minimum value is recorded as β.

[0029] S7, taking the single-panel installation cost and benefit corresponding to the optimal installation inclination angle considering load demand and the optimal installation inclination angle with the largest photovoltaic device capacity per unit area as evaluation factors, respectively, using the network hierarchy analysis method to determine the subjective weight of the evaluation factor, and using the entropy weight method to determine the objective weight of the evaluation factor.

[0030] The network analytic hierarchy process for determining the subjective weight of the evaluation factor includes: S7.1.1, construct a pairwise comparison matrix X of the evaluation factors, and calculate the weight vector M of each evaluation factor.

[0031] Among them, four evaluation factors are determined based on the single panel installation cost and benefits corresponding to the optimal installation inclination angle considering load demand and the optimal installation inclination angle with the largest photovoltaic device capacity per unit area, namely, the single panel installation cost corresponding to the optimal installation inclination angle considering load demand, which is related to the initial investment of photovoltaic power generation projects; The revenue corresponding to the optimal installation inclination angle considering load demand is an important indicator for measuring the feasibility of photovoltaic power generation projects; the single-panel installation cost corresponding to the optimal installation inclination angle with the largest photovoltaic device capacity per unit area reflects the cost required to maximize the power generation capacity per unit area, which is related to the efficiency of land resource utilization and cost control; the revenue corresponding to the optimal installation inclination angle with the largest photovoltaic device capacity per unit area represents the profit that can be obtained when the capacity per unit area is maximized at a specific installation angle, which is extremely critical for comprehensively evaluating the economic benefits of the installation plan.

[0032] The pairwise comparison matrix X of the evaluation factors is constructed as follows: In the formula, a ijIndicates the importance of the i-th factor compared to the j-th factor. w1 is the important weight of the cost of a single photovoltaic panel considering the installation angle of load demand; w2 is the important weight of the profit of a single photovoltaic panel considering the installation angle of load demand; w3 is the important weight of the cost of the installation angle with the maximum capacity per unit area; w4 is the important weight of the profit of the installation angle with the maximum capacity per unit area.

[0033] S7.1.2, multiply the comparison matrix X by the weight vector M of each evaluation factor to obtain a super weighted matrix.

[0034] The pairwise comparison matrix X of the above evaluation factors is a reciprocal matrix with a ij =1 / a ji characteristics, there is consistency between elements.

[0035] Assume there exists a positive vector M of the same order as the matrix X; is the maximum eigenvalue of matrix X, M is The characteristic vector of , solve the matrix X, and the solution obtained is the weight vector corresponding to each evaluation factor . Multiplying the matrix X by the priority weights generated from the criteria, denoted as W=X×M, we can get the super-weighted matrix. Each element in the super-weighted matrix is ​​the relative importance of each factor in the network hierarchy, denoted as Mi'=(v1,v2,v3,v4). v1,v2,v3,v4 are the relative importance of each factor.

[0036] S7.1.3, calculating successive powers of the super-weighted matrix until the matrix tends to be stable, and obtaining a limit super-matrix, wherein each column in the limit super-matrix corresponds to the subjective weight of each evaluation factor.

[0037] In order to capture all the impact paths between these evaluation factors, including direct and indirect impacts, the super-weighted matrix needs to be upgraded to the extreme super-matrix.

[0038] This can be achieved by calculating successive powers of the super-weighted matrix. For example, you can first calculate the square of the super-weighted matrix and then observe the changes in the matrix; then calculate the cube and observe again; and so on, until the matrix tends to be stable.

[0039] In this process, we will notice that the values ​​of the matrix elements will change as the power increases, which reflects the dynamics and complexity of the influence between the evaluation factors. When the super-weighted matrix reaches the limit state, we get the limit super-matrix. This limit super-matrix contains the direct and indirect influence paths between all evaluation factors. The relative limit ranking of each element in the network layer below the control element to element i in the limit super-matrix is ​​the subjective weight of the evaluation factor.

[0040] The specific process of determining the objective weight of the evaluation factor by the entropy weight method is as follows: By collecting the actual values ​​of photovoltaic power plants that have been built and put into use, an evaluation set X2 = (x ij ) 4×4 , where x ij is the actual value of the i-th evaluation factor on the j-th sample. The evaluation factors include the cost of a single photovoltaic panel at the installation angle considering load demand, the profit of a single photovoltaic panel at the installation angle considering load demand, the cost of the maximum installation angle considering the capacity per unit area, and the profit of the maximum installation angle considering the capacity per unit area.

[0041] Then the evaluation set is normalized. The standardized formula for the profit evaluation factor (set as index i=2,4) is , here x i Represents the value of the i-th profit evaluation factor on all samples. The purpose of this formula is to map the profit data to the [0,1] interval so that the profit data of different power plants are comparable. The larger the value, the better the profit situation.

[0042] For the cost index (set as index i=1,3), the standardized formula is After this processing, the cost data is also mapped to the [0,1] interval, but contrary to profit, the smaller the value, the lower the cost. Through these standardization processes, we get the standardized matrix C2 = (c ij ) 4×4 ,c ij is the standard value of the i-th indicator on the j-th sample.

[0043] Then the proportion of the jth sample under the i-th indicator to the indicator is f ij , the calculation method is For example, for the data of the first indicator (the cost of a single photovoltaic panel considering the installation angle of load demand) in four photovoltaic power station samples, the standardized cost value corresponding to each power station is divided by the sum of the standardized cost values ​​of this indicator of the four power stations to obtain the proportion of each power station under this indicator.

[0044] Then calculate the entropy value H of the i-th indicator i , the calculation method is The entropy value reflects the degree of confusion or uncertainty of the indicator data. The larger the entropy value, the more uniform the data distribution of the indicator is, and the less information is provided.

[0045] Finally, calculate the difference coefficient 1-Hi. The difference coefficient indicates the degree of information difference of the i-th indicator. The larger the difference coefficient, the more effective information the indicator provides. Then determine the entropy weight Ni of the i-th indicator: In fact, this is the normalization of the difference, which is consistent with the previous normalization logic.

[0046] S8, combining and weighting the evaluation factors according to the game theory algorithm to obtain the combined weights of the evaluation factors. That is, when considering the actual situation of photovoltaic panel installation, both the load demand and the photovoltaic panel installation cost should be taken into account. In order to comprehensively consider various factors and make the weight determination more scientific and reasonable, in the process of determining the optimal inclination angle of the photovoltaic panel, a combined weighting method combining the subjective weight factor with the entropy weight method weight factor is adopted.

[0047] The subjective weight is obtained by the limit supermatrix Mi'=(v1,v2,v3,v4). The objective weight is obtained by the entropy weight method weight N i Assume ξ is the weight coefficient, which ranges from 0 to 1 and is used to balance the relative importance of subjective weight and entropy weight in the combined weight. Based on the game theory algorithm, we expect the deviation of subjective and objective weights to be minimized, which is achieved by constructing a specific optimization objective function. By finding the combined weight coefficient ξ, according to Calculate the combined weight of each evaluation factor.

[0048] S9, performing weighted summation on the installation inclination angles corresponding to the combined weights of the evaluation factors to obtain the optimal installation angle, and the calculation formula is: , The optimal angle of a single photovoltaic panel for the installation angle considering the load demand; To consider the maximum installation angle per unit area capacity (i.e. including the shadow area).

Claims

1. A method for optimizing the optimal tilt angle of a flat roof photovoltaic system, characterized by: The following steps are included: S1, based on the sunshine percentage of ground meteorological stations or satellite remote sensing data, establish the correlation between solar radiation and slope surface; S2, obtaining the declination angle, hour angle and solar time of the installation location; S3, obtaining the parameters of the photovoltaic modules to be installed and the historical electricity load demand data of the installation site; S4, based on the data of steps S1 and S2, a plane slope solar radiation calculation model is established to obtain the optimal photovoltaic tilt angle without considering the load demand; S5, based on the historical electricity load demand data of the installation site, use the long short-term memory artificial neural network algorithm to predict the electricity load demand of the installation site in the next year; in order to avoid excessive power shortage in some months in the next year, optimize the optimal photovoltaic tilt angle and obtain the optimal installation tilt angle considering the load demand; S6, establishing a dynamic shadow area calculation model according to the data in step S2; and obtaining an optimal installation inclination angle for maximizing the photovoltaic device capacity per unit area based on the dynamic shadow area calculation model; S7, taking the single panel installation cost and benefit corresponding to the optimal installation inclination angle considering the load demand and the optimal installation inclination angle with the maximum photovoltaic device capacity per unit area as evaluation factors, respectively, using the hierarchical analysis method to determine the subjective weight of the evaluation factor, and using the entropy weight method to determine the objective weight of the evaluation factor; S8, combining and weighting the evaluation factors according to a game theory algorithm to obtain a combined weight of the evaluation factors; S9, performing weighted summation on the installation inclination angles corresponding to the combined weights of the evaluation factors to obtain an optimal installation angle.

2. The optimal tilt angle optimization design method based on a flat roof photovoltaic system according to claim 1 is characterized in that: The calculation model expression of the plane slope solar radiation in step S4 is: In the formula, is the direct radiation, is the diffuse radiation from the sky, Designing the tilt angle for photovoltaic panels, is the latitude of the installation location; is the solar declination of the installation location; is the hour angle; n is the number of days in a year starting from New Year's Day; is the ground reflectivity, which is taken as 0.

2.

3. The optimal tilt angle optimization design method based on a flat roof photovoltaic system according to claim 1 is characterized in that: The optimization of the best photovoltaic tilt angle in step S5 includes respectively calculating the average absolute error S between the photovoltaic power generation rate of each month and the electricity load demand of each month in the next year corresponding to different tilt angles within the range of ±10° of the best photovoltaic tilt angle determined in step S4, and the tilt angle corresponding to the minimum value of S is the best installation tilt angle considering the load demand.

4. The optimal tilt angle optimization design method based on a flat roof photovoltaic system according to claim 1 is characterized in that: In step S7, the network analytic hierarchy process for determining the subjective weights of the evaluation factors includes: S7.1.1, constructing a pairwise comparison matrix X of the evaluation factors, and calculating a weight vector M of each evaluation factor; S7.1.2, multiply the comparison matrix X by the weight vector M of each evaluation factor to obtain a super weighted matrix; S7.1.3, calculating successive powers of the super-weighted matrix until the matrix tends to be stable, and obtaining a limit super-matrix, wherein each column in the limit super-matrix corresponds to the subjective weight of each evaluation factor.

5. The optimal tilt angle optimization design method based on a flat roof photovoltaic system according to claim 1 is characterized in that: In step S8, according to the game theory algorithm, the sum of the deviations between the subjective weight and the objective weight is minimized to obtain the weight coefficient of the subjective weight of each evaluation factor; Calculate the combined weight of each evaluation factor.

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