CN116757054A - Photovoltaic power station optimizing installation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a photovoltaic power station optimizing and installing method, a device, electronic equipment and a storage medium, and relates to the technical field of power station design, wherein the photovoltaic power station optimizing and installing method comprises the following steps: acquiring current gradient information of an installation area, and determining initial installation information according to the current gradient information; constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information; and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters. The application improves the optimizing and installing effect of the photovoltaic power station.
Description
Technical Field
The application relates to the technical field of power station design, in particular to a photovoltaic power station optimizing and installing method, a device, electronic equipment and a storage medium.
Background
Along with the rapid development of the photovoltaic power station, the use requirement of a user on the photovoltaic power station is higher and higher, the normal use of the photovoltaic power station is expected to be met, the income of the photovoltaic power station is improved, and the requirements on optimizing and installing the photovoltaic power station are also changed.
The traditional photovoltaic power station optimizing installation method is characterized in that the installation angle is designed manually based on the actual environment angle, and meanwhile, the installation distance is determined according to the installation angle so as to install the photovoltaic power station. The photovoltaic power station optimizing installation method has great defects, and the problem that the photovoltaic power station optimizing installation scheme for determining the installation angle and the installation distance according to personal experience cannot reach the optimal scheme for optimizing installation of the photovoltaic power station. That is, the photovoltaic power station optimizing installation method can not reach the optimal scheme of photovoltaic power station optimizing installation due to the photovoltaic power station optimizing installation scheme determined by personal experience, and therefore the photovoltaic power station installation effect is poor.
Disclosure of Invention
The application mainly aims to provide a photovoltaic power station optimizing installation method, a photovoltaic power station optimizing installation device, electronic equipment and a storage medium, and aims to solve the technical problem of poor photovoltaic power station installation effect.
In order to achieve the above object, the present application provides a photovoltaic power station optimizing installation method, which includes the steps of:
acquiring current gradient information of an installation area, and determining initial installation information according to the current gradient information;
Constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information;
and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
Optionally, the step of constructing the objective penalty function based on the preset constraint condition includes:
determining all constraint functions in preset constraint conditions; the preset constraint conditions comprise an installation angle, an installation azimuth angle, an installation interval and a component capacity ratio;
and constructing a target penalty function based on the preset main function and each constraint function.
Optionally, the step of determining the installation design parameter set according to the objective penalty function includes:
determining target value constraint conditions of the target penalty function, and determining all constraint condition parameters in the target penalty function;
and determining installation design parameters according to the constraint condition parameters and the target value constraint conditions in sequence, and carrying out iteration based on each installation design parameter to obtain an installation design parameter set.
Optionally, the step of determining the installation design parameter sequentially according to the constraint condition parameter and the target value constraint condition includes:
Determining an optimizing target value of the constraint condition parameter, and detecting whether the optimizing target value meets the target value constraint;
if the optimizing target value meets the target value constraint condition, sequentially updating the constraint condition parameters, and executing the step of determining the optimizing target value of the constraint condition parameters, wherein the parameter corresponding to the optimizing target value is taken as an installation design parameter;
if the optimizing target value does not meet the target value constraint condition, updating the value of the constraint condition parameter, and executing the step of determining the optimizing target value of the constraint condition parameter based on the updated value of the constraint condition parameter.
Optionally, the step of determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm includes:
determining all installation combination parameters in the installation parameter set, and determining the optimal power station design benefit in all the installation combination parameters based on a preset optimizing algorithm;
and taking the installation combination parameters corresponding to the optimal power station design benefit as optimal installation parameters.
Optionally, the preset optimizing algorithm includes a particle swarm algorithm and a genetic algorithm, and the step of determining the best power station design benefit in the installation combination parameters based on the preset optimizing algorithm includes:
Determining an algorithm iteration formula of the particle swarm algorithm, and determining the optimal power station design benefit in each installation combination parameter or based on the algorithm iteration formula;
and determining an algorithm initial population of the genetic algorithm, and determining the optimal power station design benefit in each installation combination parameter based on the cross variation of the algorithm initial population.
Optionally, after the step of determining the optimal installation parameter in the installation parameter set according to a preset optimizing algorithm, the method includes:
detecting whether the optimal installation parameters are matched with preset angle design parameters or not; the preset angle design parameters comprise that the installation angles of adjacent rows of components are the same;
if the optimal installation parameters are matched with preset angle design parameters, merging adjacent arrays with the same installation angle in the optimal installation parameters to obtain new optimal installation parameters;
and determining new power station design benefits corresponding to the new optimal installation parameters, and updating the optimal installation parameters based on the new power station design benefits and the power station design benefits corresponding to the optimal installation parameters.
Optionally, after the step of determining the optimal installation parameter in the installation parameter set according to a preset optimizing algorithm, the method further includes:
Detecting whether the power station design benefit of the optimal installation parameters meets a preset convergence condition; the preset convergence condition comprises that the design benefit of the power station reaches a preset value;
if the power station design benefit of the optimal installation parameters does not meet a preset convergence condition, updating the initial installation information based on the optimal installation parameters, and executing the step of determining an installation design parameter set according to the target penalty function and the initial installation information based on the updated initial installation information;
and if the power station design benefit of the optimal installation parameters meets a preset convergence condition, outputting the optimal installation parameters.
Optionally, after the step of obtaining the current gradient information of the installation area, the method includes:
if the current gradient information is matched with the preset mountain gradient characteristics, executing the step of determining initial installation information according to the current gradient information;
and if the current gradient information is matched with the preset mountain gradient characteristics and the preset flat gradient characteristics, determining mountain gradient information and flat gradient information in the current gradient information, executing the step of determining initial installation information according to the current gradient information based on the mountain gradient information, and executing the step of determining initial installation information according to the current gradient information based on the flat gradient information.
In addition, in order to achieve the above object, the present application also provides a photovoltaic power station optimizing and installing device, the photovoltaic power station optimizing and installing device includes:
the initial value determining module is used for acquiring current gradient information of the installation area and determining initial installation information according to the current gradient information;
the function processing module is used for constructing a target penalty function based on preset constraint conditions and determining an installation design parameter set according to the target penalty function and the initial installation information;
and the optimizing algorithm module is used for determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
The application also provides photovoltaic power station optimizing and installing equipment, which comprises the following electronic equipment:
at least one processor;
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the photovoltaic power plant optimizing installation method described above.
The application also provides a storage medium, wherein the storage medium is stored with a program for realizing the photovoltaic power station optimizing and installing method, and the program for realizing the photovoltaic power station optimizing and installing method is executed by a processor to realize the steps of the photovoltaic power station optimizing and installing method.
According to the technical scheme, the current gradient information of the installation area is obtained, and initial installation information is determined according to the current gradient information; constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information; and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters. The installation design parameter set is determined through the current gradient information and the constructed target penalty function, and then the optimal installation parameter is found in the installation design parameter set through the optimizing algorithm, so that the phenomenon that the optimal scheme of photovoltaic power station optimizing installation cannot be achieved according to the photovoltaic power station optimizing installation scheme with the installation angle and the installation distance determined according to personal experience can be avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic structural diagram of photovoltaic power station optimizing installation equipment in a hardware operation environment according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a first embodiment of a photovoltaic power plant optimizing installation method of the present application;
FIG. 3 is a schematic view of a photovoltaic power plant optimizing installation device module of the present application;
FIG. 4 is a schematic flow chart of a photovoltaic power plant optimizing installation method;
FIG. 5 is a schematic illustration of a photovoltaic power plant optimizing installation method of the present application;
fig. 6 is a schematic view of a scenario of photovoltaic power plant optimizing installation.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a photovoltaic power station optimizing installation device in a hardware operation environment according to an embodiment of the present invention.
As shown in fig. 1, the photovoltaic power plant optimizing installation apparatus may include: processor 0003, such as central processing unit (CentralProcessingUnit, CPU), communication bus 0001, fetch interface 0002, processing interface 0004, memory 0005. Wherein a communication bus 0001 is used to enable connected communication between these components. The acquisition interface 0002 may comprise an information acquisition device, an acquisition unit such as a computer, and the optional acquisition interface 0002 may also comprise a standard wired interface, a wireless interface. Processing interface 0004 may optionally comprise a standard wired interface, a wireless interface. The memory 0005 may be a high-speed random access memory (RandomAccessMemory, RAM) or a stable nonvolatile memory (Non-VolatileMemory, NVM), such as a disk memory. The memory 0005 may alternatively be a storage device separate from the aforementioned processor 0003.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the photovoltaic power plant optimizing installation, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, the memory 0005 as a storage medium may include an operation device, an acquisition interface module, an execution interface module, and a photovoltaic power plant optimizing installation program.
In the photovoltaic power plant optimizing installation apparatus shown in fig. 1, the communication bus 0001 is mainly used for realizing connection communication between components; the acquisition interface 0002 is mainly used for connecting a background server and carrying out data communication with the background server; the processing interface 0004 is mainly used for connecting a deployment end (user end) and carrying out data communication with the deployment end; the processor 0003 and the memory 0005 in the photovoltaic power station optimizing and installing equipment can be arranged in the photovoltaic power station optimizing and installing equipment, the photovoltaic power station optimizing and installing equipment calls the photovoltaic power station optimizing and installing program stored in the memory 0005 through the processor 0003, and the photovoltaic power station optimizing and installing method provided by the embodiment of the invention is executed.
For clarity and conciseness of the following description of the embodiments, a brief description of implementation of a photovoltaic power station optimizing installation method is first provided:
At present, the assembly arrangement mode of the mountain power station is installed along with the slope, the actual installation angle is installed according to the current angle of the mountain, and the installation interval is installed according to the minimum distance from winter to 6 hours of shadow to shelter from according to the current installation inclination angle. Because the uncertainty of mountain power station slope leads to the angle of arranging of each array of every row to be different, and the interval of each array of arranging is arranged according to the minimum interval that current installation angle calculated, and the generated energy and the income of whole power station can not reach a preferred value, and then cause the effect that the installation reached can not guarantee optimally, refer to fig. 6, fig. 6 is a scene schematic diagram of photovoltaic power station optimizing installation, there is a large amount of positions of uninstalled photovoltaic module in the diagram, and then the generated energy and the income of whole region can not reach a preferred value. On the other hand, if the optimal scheme for installation needs to be determined, test calculation is needed to be manually performed on all the schemes, and finally the optimal scheme for installation is obtained. This causes a problem of large time cost and labor investment for the whole installation. Therefore, based on the problems, the technical scheme of the application can solve the problems of high time cost and high labor investment, and simultaneously solves the problem of poor installation effect of common installation.
According to the photovoltaic power station optimizing installation method, the current gradient information of an installation area is obtained, and initial installation information is determined according to the current gradient information; constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information; and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters. The installation design parameter set is determined through the current gradient information and the constructed target penalty function, and then the optimal installation parameter is found in the installation design parameter set through the optimizing algorithm, so that the phenomenon that the optimal scheme of photovoltaic power station optimizing installation cannot be achieved according to the photovoltaic power station optimizing installation scheme with the installation angle and the installation distance determined according to personal experience can be avoided.
Based on the hardware structure, the embodiment of the photovoltaic power station optimizing and installing method is provided.
The embodiment of the invention provides a photovoltaic power station optimizing and installing method, and referring to fig. 2, fig. 2 is a flow diagram of a first embodiment of the photovoltaic power station optimizing and installing method.
In this embodiment, the photovoltaic power station optimizing and installing method includes:
step S10, current gradient information of an installation area is obtained, and initial installation information is determined according to the current gradient information;
based on the condition that the installation of the existing power station is only carried out according to the gradient, the embodiment combines various designs such as angles, intervals, capacity ratios, azimuth angles and the like of different arrays through various algorithms, and carries out constraint according to the gradient of a mountain to obtain the corresponding combination such as optimal arrangement capacity or economic indexes and the like. The design scheme of the embodiment carries out integral optimizing design on the angle range allowing installation of each array of each row, the interval range between each array of each row and the installation capacity on the basis of experience values, and ensures that the income of the power station reaches a better level.
In this embodiment, the set of installation design parameters satisfying the per-constraint conditions is determined by introducing a penalty function. The installation design parameter set at least comprises parameters such as an installation angle, an installation distance, an installation azimuth angle, a component capacity ratio and the like, if the installation angle is 30 degrees, the installation interval is 9 meters, the installation azimuth angle is 5 degrees in the west and is used as the installation design parameter set to install an array of the power station, the information such as the component capacity ratio, the cost, the generating capacity and the like can be determined based on the above installed power station, and further the optimal installation design parameter is selected based on the information such as the component capacity ratio, the cost, the generating capacity and the like, and the limiting condition refers to the limiting condition on the information such as the component capacity ratio, the cost, the generating capacity and the like after the installation design parameter corresponds to the installation, and the limiting condition is not limited one by one. The initial value of the penalty function is determined by acquiring current gradient information of an installation area, determining initial installation information based on the current gradient information, and further taking the initial installation information as an initial value of a penalty function (namely, a target penalty function mentioned later), wherein the installation area refers to an area of a power station to be installed, and can be a mountain area or a flat area, the current gradient information refers to an angle of the current gradient, the gradient of the current gradient information can be calculated according to the current mountain contour points or contour lines and the like, the installable angles of each array in the same row are calculated according to the gradient, so that each row of the current gradient information is fully arranged as much as possible, and meanwhile, the installation angle and the installation interval in adjacent rows (according to the shadow of 6 hours in winter) are calculated according to the installation angle of each row, and the obtained installation angle and installation interval and other related parameters, such as a capacity ratio, are taken as the initial value of the penalty function, and the installation angle of the power station in common use is directly determined according to the gradient, and then the installation interval and other parameters are determined, that is, namely, the installation mode of the common power station is installed according to the corresponding installation of the power station, and photovoltaic power station is carried out according to the current installation, and then the optimal gradient is carried out, and the optimal gradient is carried out according to the installation parameter. The initial installation information refers to information such as an installation pitch, an installation azimuth, and the like, which are determined based on an installation gradient in the current gradient information. Finally, initial installation information can be determined to provide initial values for subsequent punishment functions, further basis can be provided for subsequent screening of installation design parameters meeting limiting conditions, and the problem that the installation effect of the photovoltaic power station is poor can be further solved through punishment function screening.
Step S20, constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information;
in this embodiment, by constructing the objective penalty function based on a preset constraint condition, and further determining the installation design parameter set according to the objective penalty function and the initial installation information, where the preset constraint condition includes an installation angle, an installation azimuth angle, an installation interval, and a component capacity ratio, and the objective penalty function is a penalty function constructed based on the preset constraint condition, and is different from the penalty function in that the constraint condition includes at least constraints of various parameter information of installation of the power station such as the installation angle, the installation azimuth angle, the installation interval, and the component capacity ratio, that is, determining an installation design parameter set based on a commonly used penalty function and the initial installation information as the penalty function. The installation design parameter set refers to a set of parameters meeting a function and determined based on a target penalty function, namely installation parameter information meeting constraint conditions, wherein the installation parameter information refers to various parameter information of installation of a power station such as an installation angle, an installation azimuth angle, an installation interval, a component capacity ratio and the like, for example, the constraint condition [15,60] of the installation angle, the installation angle of the installation design parameter set determined by the target penalty function is [40, 50], and then an optimal installation parameter can be found in a small range by an optimizing algorithm, meanwhile, the constraint condition can be determined according to the weight of the constraint condition, for example, the weight of the azimuth angle is the highest in 0-10 degrees, the weight of the azimuth angle is the lowest in 10-20 degrees, and then the azimuth angle can be controlled to be limited to the highest in 0-10 priorities by the target penalty function, so that the accuracy of the installation design parameter set can be ensured, meanwhile, the installation design parameter set can be determined by the target penalty function and the initial installation information, and then the optimal installation parameter can be found, and the problem of the power station installation effect of the photovoltaic power station can be solved.
And step S30, determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
In this embodiment, after the installation design parameter set is finally obtained, an optimal installation parameter in the installation design parameter set is determined based on a preset optimizing algorithm, where the preset optimizing algorithm at least includes a particle swarm algorithm and a genetic algorithm, the optimal installation parameter refers to an installation parameter of a power station with optimal power generation capacity and cost in the installation design parameter set, the installation parameter refers to various parameter information of installation of the power station such as an installation angle, an installation azimuth angle, an installation interval, a component capacity ratio and the like, the optimal installation parameter refers to an optimal scheme comprehensively considered from the power generation capacity and the cost, and the optimal installation parameter is not a single pursuit cost or the power generation capacity, and may be an installation parameter of all array components for determining the whole installation area, or may be determined separately in each small area, and the array refers to a row for arranging the power station. The installation angle, the space and the arrangement capacity of each array are optimized, so that the manual arrangement is replaced according to experience, and the economic benefit of the power station is increased; meanwhile, the calculation of the generated energy and the economic index is carried out on each array through an algorithm, the calculation time is greatly shortened, the requirement time requirement of the power station design is met, the optimal installation parameters are screened out through a punishment function and an optimizing algorithm, the design of combining according to experience is exceeded, and the problem that the installation effect of the photovoltaic power station is poor when the existing power station is installed is solved.
Further, this embodiment also provides a technical scheme flow diagram of a photovoltaic power station optimizing installation method, referring to fig. 4, in this embodiment, the whole scheme carries out generating capacity and cost calculation by introducing a penalty function and an optimizing algorithm, and then an optimal installation scheme is determined, because the penalty function needs to be introduced, installation design angles of each array in the same row are calculated according to the current mountain slope, and then minimum installation intervals in adjacent rows are calculated according to the installation angles of each row, the minimum installation intervals are not shielded by shadows of 6 hours from winter, that is, initial values of the penalty function are the minimum installation intervals corresponding to the installation design angles and the installation design angles determined for the current mountain slope, and then the generating capacity and cost are calculated by introducing the penalty function, and the calculation mode is a common calculation mode, for example, after the component arrangement angles and the intervals of the whole power station are determined, the corresponding generating capacity penalty function and cost can be known, and then the data which do not meet the conditions can be removed through the constraint of the generating capacity and the cost, for example, the initial angle installation range is (10 degrees, 60 degrees) is included, namely, the initial value of the penalty function is the installation angle is determined for the current mountain slope, namely the installation angle is determined for the installation design angle and the installation angle is corresponding to the installation parameter is directly, but the optimal parameter is directly calculated according to the more cost, and the optimal parameter is calculated, because the cost is more than the optimal parameter is calculated. And then, carrying out repeated multi-round calculation on the rest large-batch combined data based on an introduction algorithm, wherein the algorithm means that the optimizing algorithm at least can comprise a particle swarm algorithm, a genetic algorithm, a simulated annealing algorithm or a combination of a plurality of algorithms, and mainly carries out optimizing on the rest large-batch combined data to determine optimal installation parameters, wherein the optimizing algorithm is taken as an example of the particle swarm algorithm, and then the optimal installation parameters are searched for in the rest large-batch combined data through iteration, and the basis of the optimal installation parameters can be set as an optimization scheme of generating capacity and cost. After determining the optimal installation parameters, setting a convergence condition according to project precision, and carrying out multiple tests again through a penalty function and an optimizing algorithm until convergence, wherein the convergence condition can be a convergence condition of a limiting condition, such as that an angle of the optimal installation parameters is 35 degrees, further, an angle installation range near 35 degrees is used as an initial angle installation range, a smaller angle change range can be set, such as that a first execution penalty function determines 5 degrees as a change unit, a second execution penalty function determines 1 degree as a change unit, the convergence condition can also be an optimal condition for determining the generating capacity and the cost, such as that the generating capacity and the cost at 35 degrees and 33 degrees are the same, and an optimal convergence according to the angle is 34 degrees, such as that the generating capacity and the cost converge at 34 degrees, can be determined based on the penalty function and the optimizing algorithm. And finally, screening the installation angle, the space and the per-capacity of each array in each row corresponding to the optimal economic index through judgment of the convergence condition, namely, determining the optimal scheme of generating capacity and cost, wherein the optimal scheme comprehensively considers the optimal value between the two, and the optimal value is not the direct generating capacity unilateral value, so that the problem that the generating capacity and the cost are not optimal due to poor installation effect of the photovoltaic power station can be solved. For example, referring to fig. 5, fig. 5 is a schematic diagram of a photovoltaic power plant optimizing installation method, and it is assumed that the arrangement of a part of areas in a mountain area, such as the (a) diagram, is shown that only the installation angles of the arrays in two rows are different, the different distances between the two rows are shown (actually, because of the slight differences of the topography, the arrangement of each of the arrangeable areas is as many as possible in each row; at this time, the installation angle of each array, the spacing between the arrays and the arrangement capacity at this time are initial values of optimizing, namely initial values of penalty functions, for design optimizing. The optimizing logic is as follows: if a part of the small arrays are not arranged due to the topography, the shading of the arrays of the adjacent rows is reduced, the arrangement of the arrays of the adjacent rows can be increased, or the spacing of the arrays of the adjacent rows is reduced, so that the overall capacity is increased or decreased; and meanwhile, the optimal design inclination angle and the optimal design pitch are found out for the angles and the corresponding pitches of each array, and finally the optimal income of the whole power station can be achieved. The installation result shown in the figure (b) is realized through the method flow, and compared with the figure (a), the installation result shown in the figure (a) is shown to be the whole area according to the least effective utilization, so that the problem of poor installation effect of the photovoltaic power station is solved. Assuming that three arrays are installed in the current row, namely shadow positions in the three figures, each array is installed at an angle of 32,29,35 degrees respectively, and each array in the adjacent row corresponds to 6 hours of shadow non-shielding minimum spacing of 10,11,9 meters respectively; the method comprises the steps of carrying out optimization on each array installation angle through an algorithm, wherein the angles (optimizing ranges are respectively [32,60], [29,60], [35,60 ]) and the distances (optimizing ranges are respectively [10, 15], [11,15], [9,15], the maximum distance is set to be 15 meters, different projects are set to be different maximum distances according to actual conditions), simultaneously carrying out optimization on the corresponding arrangement capacity (the full arrangement capacity is initial, the distance is changed and the arrangement capacity is changed) under each distance, further determining to reduce the installation angle to be [40,50], [40,55], [35,40], reducing the distance range corresponding to the installation angle to be [12, 15], [13,15], [11,15], finally determining the optimal installation parameters according to the set of the ranges based on the optimizing algorithm, and if the different path data and two arrays are identical, such as one array is 25 degrees and the other array is 24 degrees, then carrying out calculation on the arrangement by adjusting the corresponding array 24 degrees to 25 degrees in the algorithm. And furthermore, the optimal installation parameters can be obtained through the calling of the punishment function and the optimizing algorithm, and the problem of poor installation effect of the photovoltaic power station is solved.
According to the embodiment, the current gradient information of the installation area is obtained, and initial installation information is determined according to the current gradient information; constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information; and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters. The installation design parameter set is determined through the current gradient information and the constructed target penalty function, and then the optimal installation parameter is found in the installation design parameter set through the optimizing algorithm, so that the phenomenon that the optimal scheme of photovoltaic power station optimizing installation cannot be achieved according to the photovoltaic power station optimizing installation scheme with the installation angle and the installation distance determined according to personal experience can be avoided.
Further, based on the first embodiment of the photovoltaic power station optimizing and installing method, the second embodiment of the photovoltaic power station optimizing and installing method is provided, and the step of constructing the objective penalty function based on the preset constraint condition comprises the following steps:
step a, determining all constraint functions in preset constraint conditions; the preset constraint conditions comprise an installation angle, an installation azimuth angle, an installation interval and a component capacity ratio;
step b, constructing a target penalty function based on a preset main function and each constraint function;
in this embodiment, when the objective penalty function is constructed, by determining all constraint functions in the preset constraint conditions, the constraint functions refer to penalty functions in the objective penalty function; the preset constraint conditions at least comprise installation parameter information such as installation angle, installation azimuth angle, installation interval, component capacity ratio and the like, and the constraint conditions can be increased or decreased based on actual conditions. Meanwhile, a target punishment function is constructed through a preset main function and each constraint function, wherein the preset main function refers to the main function of the target punishment function, such as the corresponding relation between the power station benefit and the power station installation parameter, and the target installation parameter set is further screened through introducing the punishment function, so that a basis is provided for subsequent optimizing. The objective penalty function after construction of the penalty function is as follows:
Where p (x) is a constraint function, λ is a penalty factor, f (x) is a primary function, m is a target value, and x is a variable number such as an installation angle, an installation azimuth angle, an installation pitch, a component capacity ratio, and the like. Different from the common punishment function, the target punishment function is provided with a plurality of constraint conditions, such as that x1 corresponds to an installation angle, x2 corresponds to an installation interval, x3 corresponds to an installation azimuth angle and the like, so that x1-xn is constrained through the constraint function, a small range meeting the constraint conditions can be determined in a large range, and a basis is provided for subsequent optimization to find the optimal installation parameters.
Further, the step of determining an installation design parameter set according to the objective penalty function includes:
step c, determining target value constraint conditions of the target penalty function, and determining all constraint condition parameters in the target penalty function;
and d, determining installation design parameters according to the constraint condition parameters and the target value constraint conditions in sequence, and carrying out iteration based on each installation design parameter to obtain an installation design parameter set.
In this embodiment, by determining the target constraint condition of the target penalty function, all constraint condition parameters in the target penalty function are determined, the installation design parameters are determined sequentially according to the constraint condition parameters and the target constraint condition, and iteration is performed based on each installation design parameter to obtain the installation design parameter set. The target constraint condition refers to benefits obtained by installation parameters of the power station, such as constraint of cost and power generation capacity, such as constraint of the cost to be controlled at M and the power generation capacity to be controlled at N for the designed power station, and further a small-range constraint condition parameter meeting the target constraint condition can be selected from large-range constraint condition parameters, the constraint condition parameter refers to the power station parameter, such as parameter information at least comprising installation angles, installation azimuth angles, installation intervals, component capacity ratios and the like, namely constraint parameters corresponding to constraint conditions, namely x1-xn in a formula (1), and the formula of the target constraint condition is as follows:
Wherein Dj is a target value (such as a gross interest rate, LCOE, total income, etc. or other indexes), djl is a constraint condition of the target value, dj can determine, for example, the gross interest rate, LCOE, total income, etc. according to the determined installation parameters, djl is a self-defined target value, if the gross interest rate is defined to be more than H, the target value constraint condition of installation of the power station is met, k is an updated penalty factor lambda, that is, the condition is met, the penalty factor lambda is not met, the target value constraint condition can enable the penalty factor to be updated when the whole installation parameter does not meet the condition, and then the next cycle judgment is carried out, the whole formula has the meaning of continuously updating the penalty factor, enabling the whole target penalty function to approach to a numerical value, and further determining the installation parameter corresponding to the satisfied numerical value. Namely, the small-range power station installation parameters meeting the target value constraint conditions can be screened out from the large-range power station installation parameters through the target value constraint conditions.
Illustratively, for a target value, the variable (x 1 ,x 2 ,x 3 ,...x n ) The weight relation affecting the target value can be marked, if the weight relation of the mark installation azimuth x3 is that the weight of the deviation of the left direction and the right direction is 5 degrees to be maximum, the weight is smaller as the other deviation angles are larger, namely the deviation of the azimuth is limited, and the actual deviation is defined according to the actual situation, so that the accuracy of the installation parameters of the power station can be ensured.
Further, the step of determining the installation design parameter sequentially according to the constraint condition parameter and the target value constraint condition includes:
step e, determining an optimizing target value of the constraint condition parameter, and detecting whether the optimizing target value meets the target value constraint condition;
f, if the optimizing target value meets the target value constraint condition, sequentially updating the constraint condition parameters, and executing the step of determining the optimizing target value of the constraint condition parameters, wherein the parameter corresponding to the optimizing target value is taken as an installation design parameter;
and g, if the optimizing target value does not meet the target value constraint condition, updating the value of the constraint condition parameter, and executing the step of determining the optimizing target value of the constraint condition parameter based on the updated value of the constraint condition parameter.
In this embodiment, by determining the optimizing target value of the constraint condition parameter, it is further detected whether the optimizing target value meets the target value constraint condition, and when the optimizing target value meets the target value constraint condition, the constraint condition parameter is updated sequentially, and at the same time, the step of determining the optimizing target value of the constraint condition parameter is performed based on the updated constraint condition parameter, and the parameter corresponding to the optimizing target value is taken as the installation design parameter. When the optimizing target value does not meet the target value constraint condition, the value of the constraint condition parameter is updated, and the step of determining the optimizing target value of the constraint condition parameter is executed. The optimizing target value refers to a corresponding target value determined based on a value of a constraint condition parameter, for example, if the value of the constraint condition parameter is about 30 degrees, the target value corresponding to 30 degrees can be determined, for example, the cost is G tens of thousands of yuan, if G tens of thousands of yuan meet the constraint condition of the target value, for example, the requirement of being lower than G1 tens of thousands of yuan, the optimizing target value is determined according to the next constraint condition parameter, for example, the installation angle is x1, if the value of x1 is determined, for example, after 30 degrees (x 2-xn is unchanged), x2 (x 3-xn and x1 are unchanged) is determined until all xn is determined, for example, the value of x1 is not satisfied, for example, 30 degrees, the value of x1 is changed, for example, 35 degrees is performed, and the subsequent steps are performed, for example, the installation parameter set meeting the condition can be determined based on the change of the single constraint condition parameter. If the constraint condition parameter is marked as 0, the constraint condition parameter marking which needs to determine the optimizing target value is marked as 1, the parameter marked as 0 is unchanged, the variable parameter in the state of 1 starts to calculate the optimizing target value of the target penalty function, then judges whether the target constraint condition is met, if so, exits the optimizing of the constraint condition parameter which needs to determine the optimizing target value, and further carries out optimizing of other parameters in sequence; and then repeatedly iterating the variable parameters for a plurality of times, for example, obtaining n groups of objective functions fi (xi), and obtaining a plurality of groups of installation design parameter sets of design parameters through iteration, for example, numerical recombination of different constraint condition parameters, wherein the installation design parameter sets are as follows:
The matrix represents an installation design parameter set formed by k-m groups of power station installation parameters, namely the power station installation parameters meeting the cost, the generated energy or other conditions are selected from a large constraint range, and then optimization can be carried out based on the power station installation parameters to obtain optimal installation parameters so as to solve the problem of poor installation effect of the photovoltaic power station.
Further, based on the first embodiment and/or the second embodiment of the photovoltaic power station optimizing and installing method, a third embodiment of the photovoltaic power station optimizing and installing method is provided, and the photovoltaic power station optimizing and installing method comprises the following steps:
determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm, wherein the step comprises the following steps:
step h, all installation combination parameters in the installation parameter set are determined, and the optimal power station design benefit in all the installation combination parameters is determined based on a preset optimizing algorithm;
and i, taking the installation combination parameters corresponding to the optimal power station design benefit as optimal installation parameters.
In this embodiment, by determining all installation combination parameters in the installation parameter set, an optimal power station design benefit in all installation combination parameters is determined based on a preset optimizing algorithm, and finally, the installation combination parameter corresponding to the optimal power station design benefit is used as an optimal installation parameter. The installation combination parameters refer to the combination of parameters installed on the power station, for example, one row of the matrix is a group of installation combination parameters, the optimal power station design benefit refers to the benefit of the optimal power station determined after the power station is installed based on the installation combination parameters, for example, the comprehensive consideration of cost, power generation capacity and the like is optimal, instead of single pursuit of cost or power generation capacity, the installation combination parameters corresponding to the optimal power station design benefit are finally taken as optimal installation parameters, for example, the optimal power station design benefit of the last row in the matrix is comprehensively considered, and the installation combination parameters of the last row are taken as optimal installation parameters. Through optimizing algorithm (such as particle swarm, genetic algorithm, or mixture of optimizing algorithms), design multiple combinations of artificial experience value arrangement combination and other array installation angles, adjacent row spacing (spacing calculated according to step 2) and the like, whether the design information such as adjacent row array length, parallel group serial number, capacity ratio and the like meets the requirements or not is calculated in a target penalty function according to each design combination, further a required design set is determined, the sampled combinations such as each design angle, spacing, height, capacity ratio and the like are calculated to obtain optimal installation parameters, finally according to optimizing results, namely the optimal installation parameters, actual installation is carried out by determining the optimal installation inclination angles, spacing and arrangement capacity of each array, further investment of manpower and other cost in power station installation parameter determination can be reduced, and meanwhile optimal installation parameters are found for power station installation, the benefit of the whole power station can reach the optimal, and further the problem that the photovoltaic power station installation effect is not optimal is solved.
Further, the preset optimizing algorithm includes a particle swarm algorithm and a genetic algorithm, and the step of determining the optimal power station design benefit in the installation combination parameters based on the preset optimizing algorithm includes:
step j, determining an algorithm iteration formula of the particle swarm algorithm, and determining the optimal power station design benefit in each installation combination parameter or based on the algorithm iteration formula;
and k, determining an algorithm initial population of the genetic algorithm, and determining the optimal power station design benefit in each installation combination parameter based on the cross variation of the algorithm initial population.
In this embodiment, when determining the optimum power station design benefit based on the optimizing algorithm, the optimum installation and combination parameters are mainly determined by an algorithm iteration formula or cross variation, that is, the optimum installation and combination parameters are selected from the installation and combination parameters of the above matrix, and genetic algorithm, particle swarm algorithm or other algorithm combinations can be used. The following takes a particle swarm algorithm as an example, wherein the update speed formula of the particles in the algorithm is as follows: v (t+1) =v (t) ×w+c1× (pbest-X) +c2× (gbest-X)
Wherein pbest is a historical optimal position, gbest is a group optimal position, w is an inertia coefficient, c1 is an individual cognitive weight coefficient, c2 is a group cognitive weight coefficient, V (t) is a current particle speed, V (t+1) is a particle speed of the next period, and X is a current particle position. Updating the pbest in a new iteration period when the individual position is due to its historical optimal position; similarly, gbest is updated when the optimal position in the current population is better than the historical optimal position of the population. And the individual position and the velocity of the particle are directly related, the new position of the particle can be derived by:
X(t+1)＝X(t)+V(t+1)
Wherein X (t+1) is the new position of the particle, X (t) is the current position of the particle, and V (t+1) is the new speed of the particle. By setting convergence conditions and updating particle velocity, e.g. x k1 ,x k2 ,x k3 ,...x kn Corresponding to the convergence junction result B; x is x m1 ,x m2 ,x m3 ,...x mn The corresponding convergence result is M; and comparing convergence results corresponding to the parameters to obtain the optimal power station design benefit. The optimizing algorithm can also use a genetic algorithm, and the optimal power station design benefit is obtained by determining the initial population of the algorithm, namely the initial population of the genetic algorithm, and carrying out cross variation. Other optimization algorithms can be used for determination, and are not limited herein. The optimal power station design benefit is determined through an optimizing algorithm, and then the power station installation combination parameter corresponding to the optimal power station design benefit can be used as an optimal installation parameter, so that the problem of poor installation effect of the photovoltaic power station is solved.
Further, based on the first embodiment, the second embodiment and/or the third embodiment of the photovoltaic power station optimizing and installing method of the present invention, a fourth embodiment of the photovoltaic power station optimizing and installing method of the present invention is provided, and the photovoltaic power station optimizing and installing method includes:
after the step of determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm, the method comprises the following steps:
Step S301, detecting whether the optimal installation parameters are matched with preset angle design parameters; the preset angle design parameters comprise that the installation angles of adjacent rows of components are the same;
step S302, if the optimal installation parameters are matched with preset angle design parameters, merging adjacent arrays with the same installation angle in the optimal installation parameters to obtain new optimal installation parameters;
in this embodiment, after obtaining the optimal installation parameters, in order to determine the optimization of the cost and the discharge amount, it is detected whether the optimal installation parameters match with preset angle design parameters, where the preset angle design parameters include the installation angles of the adjacent rows of components are the same or similar, for example, the difference is not more than 5 °. When the optimal installation parameters are matched with the preset angle design parameters, adjacent arrays with the same installation angle in the optimal installation parameters are combined to obtain new optimal installation parameters, the component row refers to the arrays, the new optimal installation parameters refer to the arrays, the adjacent arrays in the optimal installation parameters are combined into one array, the multiple arrays with the same or similar installation angles are combined, for example, the optimal installation parameters correspond to 5 arrays, the installation angles of the first array and the second array are 49 degrees, the first array and the second array are combined into one array, and then the new optimal installation parameters only comprising 4 arrays are obtained, wherein the installation angles can be preset values which float above and below 49 degrees, for example, 5 degrees. And further provides a basis for searching the optimal installation parameters.
Step S303 determines a new plant design benefit corresponding to the new optimal installation parameter, and updates the optimal installation parameter based on the new plant design benefit and the plant design benefit corresponding to the optimal installation parameter.
In this embodiment, when the optimal installation parameter does not match the preset angle design parameter, the optimal installation parameter is directly selected, otherwise, the step of determining the new optimal installation parameter by the above steps exists. And then through confirming the new power station design benefit corresponding to new optimum installation parameter, new power station design benefit refers to the benefit such as cost, generating capacity corresponding to new optimum installation parameter, finally, based on the optimum installation parameter between new power station design benefit and power station design benefit corresponding to optimum installation parameter, update optimum installation parameter, namely, carry on optimizing from the cost and generating capacity two aspects and confirm the optimum value between new power station design benefit and update optimum installation parameter, if new power station design benefit is A1, power station design benefit is A2, if find A1 is superior to A2, will update optimum installation parameter, will new optimum installation parameter as optimum installation parameter, if find A2 is superior to A1, if otherwise, will not carry out the step of updating, confirm the optimum installation parameter directly and just as the optimum scheme, and then can optimize out the optimum installation parameter, so as to follow-up carry on the installation of power station based on the optimum installation parameter, solve the problem that the photovoltaic power station installation effect is not good.
Further, after the step of determining the optimal installation parameter in the installation parameter set according to the preset optimizing algorithm, the method further includes:
step S311, detecting whether the power station design benefit of the optimal installation parameters meets a preset convergence condition; the preset convergence condition comprises that the design benefit of the power station reaches a preset value;
step S312, if the power station design benefit of the optimal installation parameters does not meet the preset convergence condition, updating the initial installation information based on the optimal installation parameters, and executing the step of determining an installation design parameter set according to the objective penalty function and the initial installation information based on the updated initial installation information;
step S313, if the power station design benefit of the optimal installation parameter meets the preset convergence condition, outputting the optimal installation parameter.
In this embodiment, after obtaining the optimal installation parameters, it is further detected whether the power station design benefit of the optimal installation parameters meets a preset convergence condition, where the preset convergence condition includes that the power station design benefit reaches a preset numerical value or the convergence of the design, when the convergence condition is met, the optimal installation parameters are output, otherwise, when the convergence condition is not met, the initial installation information is updated based on the optimal installation parameters, and the step of determining the installation design parameter set according to the objective penalty function and the initial installation information is performed based on the updated initial installation information. That is, on the one hand, when the unit value of the change of the optimal installation parameter is larger, for example, the installation angle is 10 ° unit change, the unit change of 5 ° or 1 ° unit change is performed for the second time, and then the accurate optimal installation parameter can be determined, for example, the situation that the optimal installation parameter is accurate to 1 ° is determined, on the other hand, when two optimal values of the optimal installation parameter exist, for example, the installation angle is continuously optimal from 60 ° to 40 ° and is continuously optimal from 10 ° to 30 °, but the installation angle in the finally determined optimal installation parameter is 30 ° or 40 °, the penalty function can be continued to determine the convergence value near 30 ° or 40 °, if the final convergence value can be determined to 35 °, the final convergence value can be used as the optimal installation parameter of the whole optimizing scheme, so that the power station can be installed based on the optimal installation parameter, and the problem that the photovoltaic installation effect of the optimal power station is not optimal can be solved by finding the optimal installation parameter while the efficiency of the power station parameter design is improved.
Further, based on the first embodiment, the second embodiment, the third embodiment, and/or the fourth embodiment of the photovoltaic power plant optimizing installation method of the present invention, a fifth embodiment of the photovoltaic power plant optimizing installation method of the present invention is provided, and after the step of obtaining the current gradient information of the installation area, the method includes:
step S101, if the current gradient information is matched with the preset mountain gradient characteristics, executing the step of determining initial installation information according to the current gradient information;
step S102, if the current gradient information is matched with the preset mountain gradient feature and the preset level gradient feature, determining mountain gradient information and level gradient information in the current gradient information, performing the step of determining initial installation information according to the current gradient information based on the mountain gradient information, and performing the step of determining initial installation information according to the current gradient information based on the level gradient information.
In this embodiment, when the initial value of the penalty function is processed, the actual condition of the current gradient information is detected, and when the current gradient information is matched with the preset slope characteristics of the mountain land, the step of determining the initial installation information according to the current gradient information is performed, because the installation of the area divided into the flat land and the mountain land is separately set during the whole optimizing installation, mainly because the initial values of the two are different, and the two are different, for example, the slope of the mountain land exists, which is equivalent to the limit of the slope between the mountain land and the flat land. When the current gradient information is matched with the preset mountain gradient characteristics and the preset flat gradient characteristics, the whole power station optimizing installation step is separately executed, the step of determining initial installation information according to the current gradient information is executed based on the mountain gradient information by determining the mountain gradient information and the flat gradient information in the current gradient information, and the step of determining initial installation information according to the current gradient information is executed based on the flat gradient information, namely the step of executing power station optimizing installation by taking the mountain gradient information and the flat gradient information as the current gradient information respectively. The preset mountain slope characteristics refer to the mountain slope characteristics, such as defining a slope of more than 30 degrees as the mountain, the preset flat slope characteristics refer to the flat slope characteristics, such as defining a slope of less than 30 degrees as the flat, the mountain slope information refers to the mountain slope information, and the flat slope information refers to the flat slope information, so that the optimizing efficiency of the whole optimizing installation can be improved.
The invention also provides a photovoltaic power station optimizing and installing device, referring to fig. 3, the photovoltaic power station optimizing and installing device comprises:
the initial value determining module A01 is used for acquiring current gradient information of the installation area and determining initial installation information according to the current gradient information;
the function processing module A02 is used for constructing a target penalty function based on preset constraint conditions and determining an installation design parameter set according to the target penalty function and the initial installation information;
and the optimizing algorithm module A03 is used for determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
Optionally, the function processing module a02 is further configured to:
determining all constraint functions in preset constraint conditions; the preset constraint conditions comprise an installation angle, an installation azimuth angle, an installation interval and a component capacity ratio;
and constructing a target penalty function based on the preset main function and each constraint function.
Optionally, the function processing module a02 is further configured to:
determining target value constraint conditions of the target penalty function, and determining all constraint condition parameters in the target penalty function;
And determining installation design parameters according to the constraint condition parameters and the target value constraint conditions in sequence, and carrying out iteration based on each installation design parameter to obtain an installation design parameter set.
Optionally, the function processing module a02 is further configured to:
determining an optimizing target value of the constraint condition parameter, and detecting whether the optimizing target value meets the target value constraint;
if the optimizing target value meets the target value constraint condition, sequentially updating the constraint condition parameters, and executing the step of determining the optimizing target value of the constraint condition parameters, wherein the parameter corresponding to the optimizing target value is taken as an installation design parameter;
if the optimizing target value does not meet the target value constraint condition, updating the value of the constraint condition parameter, and executing the step of determining the optimizing target value of the constraint condition parameter based on the updated value of the constraint condition parameter.
Optionally, the optimizing algorithm module a03 is further configured to:
determining all installation combination parameters in the installation parameter set, and determining the optimal power station design benefit in all the installation combination parameters based on a preset optimizing algorithm;
And taking the installation combination parameters corresponding to the optimal power station design benefit as optimal installation parameters.
Optionally, the optimizing algorithm module a03 is further configured to:
determining an algorithm iteration formula of the particle swarm algorithm, and determining the optimal power station design benefit in each installation combination parameter or based on the algorithm iteration formula;
and determining an algorithm initial population of the genetic algorithm, and determining the optimal power station design benefit in each installation combination parameter based on the cross variation of the algorithm initial population.
Optionally, the optimizing algorithm module a03 is further configured to:
detecting whether the optimal installation parameters are matched with preset angle design parameters or not; the preset angle design parameters comprise that the installation angles of adjacent rows of components are the same;
if the optimal installation parameters are matched with preset angle design parameters, merging adjacent arrays with the same installation angle in the optimal installation parameters to obtain new optimal installation parameters;
and determining new power station design benefits corresponding to the new optimal installation parameters, and updating the optimal installation parameters based on the new power station design benefits and the power station design benefits corresponding to the optimal installation parameters.
Optionally, the optimizing algorithm module a03 is further configured to:
detecting whether the power station design benefit of the optimal installation parameters meets a preset convergence condition; the preset convergence condition comprises that the design benefit of the power station reaches a preset value;
if the power station design benefit of the optimal installation parameters does not meet a preset convergence condition, updating the initial installation information based on the optimal installation parameters, and executing the step of determining an installation design parameter set according to the target penalty function and the initial installation information based on the updated initial installation information;
and if the power station design benefit of the optimal installation parameters meets a preset convergence condition, outputting the optimal installation parameters.
Optionally, the initial value determining module a01 is further configured to:
if the current gradient information is matched with the preset mountain gradient characteristics, executing the step of determining initial installation information according to the current gradient information;
and if the current gradient information is matched with the preset mountain gradient characteristics and the preset flat gradient characteristics, determining mountain gradient information and flat gradient information in the current gradient information, executing the step of determining initial installation information according to the current gradient information based on the mountain gradient information, and executing the step of determining initial installation information according to the current gradient information based on the flat gradient information.
The method executed by each program module may refer to each embodiment of the photovoltaic power station optimizing installation method of the present invention, and will not be described herein.
The present invention also provides an electronic device including:
at least one processor;
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the photovoltaic power plant optimizing installation method described above.
The invention also provides a storage medium.
The photovoltaic power station optimizing installation program is stored on the storage medium, and the photovoltaic power station optimizing installation program is executed by the processor to realize the steps of the photovoltaic power station optimizing installation method.
The method implemented when the photovoltaic power station optimizing and installing program running on the processor is executed may refer to each embodiment of the photovoltaic power station optimizing and installing method of the present invention, which is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (12)
1. The photovoltaic power station optimizing and installing method is characterized by comprising the following steps of:
acquiring current gradient information of an installation area, and determining initial installation information according to the current gradient information;
constructing a target penalty function based on preset constraint conditions, and determining an installation design parameter set according to the target penalty function and the initial installation information;
and determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
2. The photovoltaic power plant optimizing installation method according to claim 1, wherein the step of constructing the objective penalty function based on a preset constraint condition includes:
determining all constraint functions in preset constraint conditions; the preset constraint conditions comprise an installation angle, an installation azimuth angle, an installation interval and a component capacity ratio;
and constructing a target penalty function based on the preset main function and each constraint function.
3. The photovoltaic power plant optimizing installation method of claim 1, wherein said step of determining an installation design parameter set from said objective penalty function comprises:
Determining target value constraint conditions of the target penalty function, and determining all constraint condition parameters in the target penalty function;
and determining installation design parameters according to the constraint condition parameters and the target value constraint conditions in sequence, and carrying out iteration based on each installation design parameter to obtain an installation design parameter set.
4. A photovoltaic power plant optimizing installation method as claimed in claim 3, wherein said step of determining installation design parameters in turn from said constraint parameters and said target value constraints comprises:
determining an optimizing target value of the constraint condition parameter, and detecting whether the optimizing target value meets the target value constraint;
if the optimizing target value meets the target value constraint condition, sequentially updating the constraint condition parameters, and executing the step of determining the optimizing target value of the constraint condition parameters, wherein the parameter corresponding to the optimizing target value is taken as an installation design parameter;
if the optimizing target value does not meet the target value constraint condition, updating the value of the constraint condition parameter, and executing the step of determining the optimizing target value of the constraint condition parameter based on the updated value of the constraint condition parameter.
5. The photovoltaic power plant optimizing installation method according to claim 1, wherein the step of determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm comprises:
determining all installation combination parameters in the installation parameter set, and determining the optimal power station design benefit in all the installation combination parameters based on a preset optimizing algorithm;
and taking the installation combination parameters corresponding to the optimal power station design benefit as optimal installation parameters.
6. The photovoltaic power plant optimizing installation method of claim 5, wherein the preset optimizing algorithm comprises a particle swarm algorithm and a genetic algorithm, and the step of determining the best power plant design benefit in each installation combination parameter based on the preset optimizing algorithm comprises the following steps:
determining an algorithm iteration formula of the particle swarm algorithm, and determining the optimal power station design benefit in each installation combination parameter or based on the algorithm iteration formula;
and determining an algorithm initial population of the genetic algorithm, and determining the optimal power station design benefit in each installation combination parameter based on the cross variation of the algorithm initial population.
7. The photovoltaic power plant optimizing installation method according to claim 1, wherein after the step of determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm, the method comprises:
Detecting whether the optimal installation parameters are matched with preset angle design parameters or not; the preset angle design parameters comprise that the installation angles of adjacent rows of components are the same;
if the optimal installation parameters are matched with preset angle design parameters, merging adjacent arrays with the same installation angle in the optimal installation parameters to obtain new optimal installation parameters;
and determining new power station design benefits corresponding to the new optimal installation parameters, and updating the optimal installation parameters based on the new power station design benefits and the power station design benefits corresponding to the optimal installation parameters.
8. The photovoltaic power plant optimizing installation method according to claim 1, wherein after the step of determining the optimal installation parameters in the installation parameter set according to a preset optimizing algorithm, further comprising:
detecting whether the power station design benefit of the optimal installation parameters meets a preset convergence condition; the preset convergence condition comprises that the design benefit of the power station reaches a preset value;
if the power station design benefit of the optimal installation parameters does not meet a preset convergence condition, updating the initial installation information based on the optimal installation parameters, and executing the step of determining an installation design parameter set according to the target penalty function and the initial installation information based on the updated initial installation information;
And if the power station design benefit of the optimal installation parameters meets a preset convergence condition, outputting the optimal installation parameters.
9. The photovoltaic power plant optimizing installation method of any one of claims 1-8, wherein after the step of obtaining current grade information of the installation area, it comprises:
if the current gradient information is matched with the preset mountain gradient characteristics, executing the step of determining initial installation information according to the current gradient information;
and if the current gradient information is matched with the preset mountain gradient characteristics and the preset flat gradient characteristics, determining mountain gradient information and flat gradient information in the current gradient information, executing the step of determining initial installation information according to the current gradient information based on the mountain gradient information, and executing the step of determining initial installation information according to the current gradient information based on the flat gradient information.
10. Photovoltaic power plant optimizing installation device, its characterized in that, photovoltaic power plant optimizing installation device includes:
the initial value determining module is used for acquiring current gradient information of the installation area and determining initial installation information according to the current gradient information;
The function processing module is used for constructing a target penalty function based on preset constraint conditions and determining an installation design parameter set according to the target penalty function and the initial installation information;
and the optimizing algorithm module is used for determining the optimal installation parameters in the installation design parameter set according to a preset optimizing algorithm so as to install the photovoltaic power station based on the optimal installation parameters.
11. An electronic device, the electronic device comprising:
at least one processor;
a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the photovoltaic power plant optimizing installation method of any one of claims 1 to 9.
12. A storage medium, characterized in that a program for realizing the photovoltaic power plant optimizing installation method is stored on the storage medium, the program for realizing the photovoltaic power plant optimizing installation method being executed by a processor to realize the steps of the photovoltaic power plant optimizing installation method according to any one of claims 1 to 9.
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