CN117933105B - Optimization method and system for heliostat field layout - Google Patents
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Abstract
The invention discloses a heliostat field layout optimization method and system, wherein the method comprises the following steps: initializing the heliostat field to obtain an initialized population; partitioning and processing the initialized population by adopting a K-time intra-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set; performing secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set; and circularly executing the competition algorithm in the K subareas and the competition algorithm among the K subareas until the candidate parameter with the highest optical efficiency is obtained, and determining the optimal heliostat field layout. The invention takes the optical efficiency of the heliostat field as an optimization target, improves the efficiency of heliostat field layout optimization calculation through the K-time regional competition algorithm and the K-time inter-regional competition algorithm, and improves the resource utilization rate to the greatest extent.
Description
Technical Field
The invention belongs to the technical field of solar power generation, and particularly relates to an optimization method and system for heliostat field layout.
Background
As a representative of renewable energy technology, the solar tower power station has the characteristics of cleanness, high efficiency and strong energy storage capacity, and is a future energy source with great potential.
A typical solar tower power plant is made up of a variety of components including heliostats, support structures, receiving towers, heat transfer systems, heat storage and power generation components, and the like. Among these components, the heliostat field that concentrates sunlight onto the heat receiver at the top of the tower is a key subsystem of a solar tower power plant, and optimizing the layout of the heliostats plays a critical role in improving the overall efficiency of the solar tower power plant.
Optical efficiency is a key indicator for evaluating heliostat fields. Solar fields with heliostats typically consist of thousands of heliostats, each of which requires calculation of its optical efficiency. The layout of heliostats in the field has a significant impact on these optical efficiencies, so solving the problem of large-scale optimization of the field design is critical to improving the energy efficiency of the solar system. However, the high dimensionality of this problem results in an exponential expansion of the search space, making it difficult for the prior art to efficiently and accurately provide the best solution for heliostat field layout design.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides an optimization method and an optimization system for heliostat field layout, is suitable for large heliostat field layout, takes the optical efficiency of a heliostat field as an optimization target, improves the optimization calculation efficiency of the heliostat field layout through a K-time regional competition algorithm and a K-time inter-regional competition algorithm, and improves the resource utilization rate to the greatest extent.
The invention provides the following technical scheme:
in a first aspect, a method for optimizing a heliostat field layout is provided, comprising:
Initializing layout is carried out on the heliostat field, and an initialized population formed by a plurality of candidate parameter sets is obtained;
partitioning and processing the initialized population by adopting a K-time intra-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set;
Performing secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set;
And executing the competition algorithm in the K subareas and the competition algorithm among the K subareas again based on the secondary winner set, and circulating according to the competition algorithm until the candidate parameter with the highest optical efficiency is obtained, so as to determine the optimal heliostat field layout.
Further, the K-time intra-area competition algorithm includes:
Dividing the initialized population into 2 K areas according to the rule of K-time partition;
And performing intra-area competition in the divided areas, and selecting the candidate parameter with highest optical efficiency in each area.
Further, the K-time intra-area competition algorithm specifically includes:
each group of candidate parameters in the initializing population P corresponds to one particle, and then the initializing population P is divided into two areas, so that the average value of the optical efficiency of the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
assuming that the currently selected particle is i, randomly selecting a particle j in the region;
Calculating optical efficiencies fitness_i and fitness_j of the particle i and the particle j;
judging and comparing the sizes of the fitness_i and the fitness_j;
If the fitness_i is greater than or equal to the fitness_j, updating the speed and the position of the particle i and the particle j, putting the particle i into the winner set W1, and putting the particle j into the loser set L1; otherwise, putting the particle j into the winner set W1, and putting the particle i into the loser set L1;
After the traversal is completed, the winner set W1 is obtained.
Further, the K-time inter-partition competition algorithm includes:
Each group of candidate parameters in the winner set W1 corresponds to one particle, and then the winner set W1 is divided into two areas, so that the average value of the optical efficiency corresponding to the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
Assuming that the currently selected particle is p, randomly selecting a particle q from the winner set W1;
Calculating optical efficiencies fitness_p and fitness_q of the particles p and the particles q;
judging and comparing the sizes of the fitness_p and the fitness_q;
if the fitness_p is greater than or equal to the fitness_q, updating the speed and the position of the particle p and the particle q, putting the particle p into the winner set W2, and putting the particle q into the loser set L2; otherwise, putting the particle q into the winner set W2, and putting the particle p into the loser set L2;
After the traversal is completed, a secondary winner set W2 is obtained.
Further, after comparing the optical efficiencies corresponding to the two particles, the velocity and position updates are performed according to the following formula:
；
；
wherein, And/>Respectively representing the speed and the position of the loser particles at the t+1st iteration in the region,/>And/>Respectively representing the velocity and the position of the loser particles after the t-th iteration in the region,/>Representing the position of winning particles after the t-th iteration in the region,/>Is the average position of all particles in the region traversed by the t-th iteration,/>Is control/>Parameters of the effect; /(I)Representing three random vectors, D being the number of candidate parameters;
per is the term of random perturbation, Obeys a normal distribution, wherein,Represents the degree of deviation of the optical efficiency corresponding to the particle position at the t+1st iteration from the optical efficiency at the previous iteration,/>In order to control the factor of the disturbance,Indicating the optical efficiency of the particle as determined by the selected parameter corresponding to the position of the t +1 iteration,The optical efficiency obtained from the candidate parameter corresponding to the average position of the particles in the present region at the t-th iteration is shown.
Further, the method for making the average value of the optical efficiency corresponding to the particles in each region the same includes:
the particles in the region are arranged in a non-descending order according to the corresponding optical efficiency value;
calculating the sum of the optical efficiencies corresponding to all particles;
dividing the sum of the optical efficiencies corresponding to all particles by 2 to obtain an adaptability average value of each region;
initializing an array with the length of 2 to represent the cumulative sum of the optical efficiency of each area, wherein the initial value is 0;
sequentially traversing all particles in the ordered sequence, and adding the current particles to the area with the smallest optical efficiency accumulation until all particles are distributed;
thus, two divided areas are obtained, and the average value of the optical efficiency corresponding to the particles in each area is the same.
Further, the preset termination condition of executing the K-time intra-regional competition algorithm and the K-time inter-regional competition algorithm loops is that the total algebra N of the iteration is calculated, and the output result is terminated once the iteration is completed for N times.
Further, the optical efficiency is an average value of total optical efficiency of each heliostat in the heliostat field, wherein a calculation formula of total optical efficiency of a single heliostat in the heliostat field is as follows:
；
In the method, in the process of the invention, Is the total optical efficiency of a single heliostat,/>Is the shadow shading efficiency of a single heliostat,/>Is the cosine efficiency of a single heliostat,/>Is the atmospheric optical efficiency of a single heliostat,/>Is the truncation efficiency of a single heliostat,/>Is the reference optical efficiency of a single heliostat.
Further, the method for initializing layout of heliostat field includes: the layout of the heliostat field is initialized using a radially staggered layout of heliostats based on a circular solar heliostat field.
In a second aspect, there is provided an optimization system for heliostat field layout, comprising:
the initialization module is used for carrying out initialization layout on the heliostat field to obtain an initialization population formed by a plurality of candidate parameter sets;
the first data processing module is used for partitioning and processing the initialized population by adopting a competition algorithm in K times of regions, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set;
The second data processing module is used for carrying out secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set;
and the optimal solution determining module is used for executing the competition algorithm in the K times of subareas and the competition algorithm between the K times of subareas again based on the secondary winner set, and circulating the steps until the candidate parameter with the highest optical efficiency is obtained, so as to determine the optimal heliostat field layout.
Compared with the prior art, the invention has the beneficial effects that:
The method takes the optical efficiency of the heliostat field as an optimization target, adopts a K-time regional competition algorithm to partition and process the initialized population to obtain candidate parameters with highest optical efficiency in each region, and performs regional competition in each region and randomly matches particles for competition in each region, so that the exploration characteristics of the particles are amplified; the invention also adopts the competition algorithm among K times of subareas to carry out secondary subareas and treatments on the winner set, thereby enhancing the convergence of particles; and the candidate parameters with highest optical efficiency are obtained by circulating the competition algorithm in the K subareas and the competition algorithm among the K subareas, and the optimal heliostat field layout is finally determined, so that the method is particularly suitable for large heliostat field layout, the efficiency of heliostat field layout optimization calculation can be improved, and the resource utilization rate is furthest improved.
Drawings
FIG. 1 is a flow chart of a heliostat field layout optimization method in an embodiment of the invention;
FIG. 2 is a schematic diagram of a heliostat field initialization layout in accordance with an embodiment of the invention;
FIG. 3 is a diagram illustrating a K-time partition contention mechanism according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
As shown in fig. 1, the present embodiment provides a method for optimizing a heliostat field layout, which includes the following steps:
And step 1, initializing layout is carried out on the heliostat field, and an initialized population P formed by a plurality of candidate parameter sets is obtained.
The method for initializing the layout comprises the following steps: the heliostat radial staggered layout based on the circular solar heliostat field is used for initializing the layout of the heliostat field, so that the initialized population is closer to the optimal solution. The initialization is performed here using the tempo algorithm （C. J. Noone, M. Torrilhon, A. Mitsos. Heliostat field optimization: A new computationally efficient model and biomimetic layout [J]. Solar Energy, 2012, 86(2): 792–803） of the prior art resulting heliostat field layout, with a specific initialization arrangement shown in fig. 2.
In this embodiment, a coding scheme is also proposed for illustrating the number and composition of a set of candidate parameters. The coding scheme consists of two parts: the first part includes all essential parameters of the heliostat, representing the properties of the heliostat, which are common to all heliostats, such as: the length L H of the heliostats, the width L W of the heliostats, the installation height of the heliostats and the number of the heliostats; the second part represents the coordinates of the heliostat, which is a critical part requiring optimization, which is extremely important for the optical efficiency of the heliostat field.
And step 2, partitioning and processing the initialized population by adopting a K-time intra-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set.
Step 2.1: the initialization population is divided into 2 K regions according to the rule of K partitions.
As shown in fig. 3, each set of candidate parameters in the initializing population P corresponds to one particle, and then the initializing population P is divided into two areas, so that the average value of the optical efficiency of the particles in each area is the same; and then carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation.
The method for making the average value of the optical efficiency corresponding to the particles in each region the same is as follows:
(1) The particles in the region are arranged in a non-descending order according to the corresponding optical efficiency value;
(2) Calculating the sum of the optical efficiencies corresponding to all particles;
(3) Dividing the sum of the optical efficiencies corresponding to all particles by 2 to obtain an adaptability average value of each region;
(4) Initializing an array with the length of 2 to represent the cumulative sum of the optical efficiency of each area, wherein the initial value is 0;
(5) Sequentially traversing all particles in the ordered sequence, and adding the current particles to the area with the smallest optical efficiency accumulation until all particles are distributed;
(6) Thus, two divided areas are obtained, and the average value of the optical efficiency corresponding to the particles in each area is the same.
Step 2.2: for the segmented 2 K regions, each of them is traversed in turn.
Step 2.3: for the region currently being traversed, each particle in the region is traversed.
Step 2.4: assuming that the currently selected particle is i, a particle j is randomly selected in the region.
Step 2.5: the optical efficiencies fitness_i and fitness_j of particle i and particle j are calculated.
Step 2.6: the sizes of the fitness_i and the fitness_j are judged and compared.
Step 2.7: if the fitness_i is greater than or equal to the fitness_j, updating the speed and the position of the particle i and the particle j, putting the particle i into the winner set W1, and putting the particle j into the loser set L1; otherwise, particle j is placed in winner set W1 and particle i is placed in loser set L1.
After comparing the optical efficiency of the two particles, the velocity and position updates are performed according to the following formula:
；
；
wherein, And/>Respectively representing the speed and the position of the loser particles at the t+1st iteration in the region,/>And/>Respectively representing the velocity and the position of the loser particles after the t-th iteration in the region,/>Representing the position of winning particles after the t-th iteration in the region,/>Is the average position of all particles in the region traversed by the t-th iteration,/>Is control/>Parameters of the effect; /(I)Representing three random vectors, D being the number of candidate parameters;
per is the term of random perturbation, Obeys a normal distribution, wherein,Represents the degree of deviation of the optical efficiency corresponding to the particle position at the t+1st iteration from the optical efficiency at the previous iteration,/>In order to control the factor of the disturbance,Indicating the optical efficiency of the particle as determined by the selected parameter corresponding to the position of the t +1 iteration,The optical efficiency obtained from the candidate parameter corresponding to the average position of the particles in the present region at the t-th iteration is shown.
Step 2.8: after the traversal is finished, the particle set is updated, and the winner set W1 is obtained.
And 3, performing secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain the secondary winner set.
Step 3.1, each group of candidate parameters in the winner set W1 corresponds to one particle, and then the winner set W1 is divided into two areas, so that the average value of the optical efficiencies corresponding to the particles in each area is the same, wherein the method for making the average value of the optical efficiencies corresponding to the particles in each area the same is the same as that in step 2.1.
And 3.2, performing the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation.
And 3.3, traversing each of the segmented 2 K areas in turn.
Step 3.4, for the region currently being traversed, traversing each particle in the region.
Step 3.5, assuming that the currently selected particle is p, a particle q is randomly selected from the winner set W1.
Step 3.6, calculating optical efficiencies fitness_p and fitness_q of the particles p and the particles q.
Step 3.7, judging and comparing the sizes of the fitness_p and the fitness_q.
Step 3.8, if the fitness_p is more than or equal to the fitness_q, updating the speed and the position of the particle p and the particle q, putting the particle p into the winner set W2, and putting the particle q into the loser set L2; otherwise, particle q is placed in winner set W2 and particle p is placed in loser set L2. The method of updating the speed and the position of the particles is the same as that of the step 2.7.
And 3.9, after the traversal is finished, obtaining a secondary winner set W2.
And 4, setting a total algebra N of calculation iteration as a preset termination condition, judging whether the preset termination condition is met, if not, executing the K-time intra-regional competition algorithm of the step 2 and the K-time inter-regional competition algorithm of the step 3 again based on the secondary winner set, and repeating the steps until the iteration is completed for N times, outputting the optimal solution, namely the candidate parameter with highest optical efficiency, and then determining the optimal heliostat field layout.
The optical efficiency in the step 2 to the step 3 is an average value of the total optical efficiency of each heliostat in the heliostat field, and the calculation method comprises the following steps: substituting a group of candidate parameters into a calculation formula of each optical efficiency of a single heliostat in a heliostat field, calculating the product of each optical efficiency to obtain the total optical efficiency of the single heliostat in the heliostat field, and then calculating the average value of the total optical efficiency of each heliostat in the heliostat field, wherein the larger the optical efficiency is, the larger the adaptability of the group of candidate parameters is. The calculation formula of the total optical efficiency of a single heliostat in the heliostat field is as follows:
；
；
；
；
；
；
In the method, in the process of the invention, Is the total optical efficiency of a single heliostat,/>Is the shadow shading efficiency of a single heliostat,/>Is the cosine efficiency of a single heliostat,/>Is the atmospheric optical efficiency of a single heliostat,/>Is the truncation efficiency of a single heliostat,/>Is the reference optical efficiency of a single heliostat; /(I)Is the distance from the center of the mirror surface of each heliostat to the center of the heat collector; /(I)Is the angle formed by the incident light of the sun and the normal vector of the heliostat mirror surface.
In order to balance diversity and convergence of populations, K-time zoning competitive population optimization (KSPCSO) combined with CSO is provided to solve heliostat field layout problem. KSPCSO is divided into K sub-zoned intra-zone contention and inter-zone contention. The population is divided into subgroups by adopting a K-time grouping strategy, then, the search capability of particles is enhanced by the competition in the areas, and the convergence speed of the population is increased by the competition among the areas.
Example 2
The embodiment provides an optimization system for heliostat field layout, comprising:
the initialization module is used for carrying out initialization layout on the heliostat field to obtain an initialization population formed by a plurality of candidate parameter sets;
the first data processing module is used for partitioning and processing the initialized population by adopting a competition algorithm in K times of regions, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set;
The second data processing module is used for carrying out secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set;
and the optimal solution determining module is used for executing the competition algorithm in the K times of subareas and the competition algorithm between the K times of subareas again based on the secondary winner set, and circulating the steps until the candidate parameter with the highest optical efficiency is obtained, so as to determine the optimal heliostat field layout.
The optimization system for heliostat field layout provided in this embodiment is used to implement the optimization method for heliostat field layout in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (6)
1. A method for optimizing a heliostat field layout, comprising:
Initializing layout is carried out on the heliostat field, and an initialized population formed by a plurality of candidate parameter sets is obtained;
partitioning and processing the initialized population by adopting a K-time intra-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set;
Performing secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set;
Based on the secondary winner set, executing the competition algorithm in the K-time subareas and the competition algorithm among the K-time subareas again, and circulating the steps until the candidate parameter with the highest optical efficiency is obtained, and determining the optimal heliostat field layout;
The K-time intra-area competition algorithm specifically comprises the following steps:
each group of candidate parameters in the initializing population P corresponds to one particle, and then the initializing population P is divided into two areas, so that the average value of the optical efficiency of the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
assuming that the currently selected particle is i, randomly selecting a particle j in the region;
Calculating optical efficiencies fitness_i and fitness_j of the particle i and the particle j;
judging and comparing the sizes of the fitness_i and the fitness_j;
If the fitness_i is greater than or equal to the fitness_j, updating the speed and the position of the particle i and the particle j, putting the particle i into the winner set W1, and putting the particle j into the loser set L1; otherwise, putting the particle j into the winner set W1, and putting the particle i into the loser set L1;
after the traversal is finished, obtaining a winner set W1;
The K times of inter-regional competition algorithm comprises the following steps:
Each group of candidate parameters in the winner set W1 corresponds to one particle, and then the winner set W1 is divided into two areas, so that the average value of the optical efficiency corresponding to the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
Assuming that the currently selected particle is p, randomly selecting a particle q from the winner set W1;
Calculating optical efficiencies fitness_p and fitness_q of the particles p and the particles q;
judging and comparing the sizes of the fitness_p and the fitness_q;
if the fitness_p is greater than or equal to the fitness_q, updating the speed and the position of the particle p and the particle q, putting the particle p into the winner set W2, and putting the particle q into the loser set L2; otherwise, putting the particle q into the winner set W2, and putting the particle p into the loser set L2;
after the traversing is finished, a secondary winner set W2 is obtained;
The method for making the average value of the optical efficiency corresponding to the particles in each region the same comprises the following steps:
the particles in the region are arranged in a non-descending order according to the corresponding optical efficiency value;
calculating the sum of the optical efficiencies corresponding to all particles;
dividing the sum of the optical efficiencies corresponding to all particles by 2 to obtain an adaptability average value of each region;
initializing an array with the length of 2 to represent the cumulative sum of the optical efficiency of each area, wherein the initial value is 0;
sequentially traversing all particles in the ordered sequence, and adding the current particles to the area with the smallest optical efficiency accumulation until all particles are distributed;
thus, two divided areas are obtained, and the average value of the optical efficiency corresponding to the particles in each area is the same.
2. The method of claim 1, wherein the speed and position updates are performed according to the following formula after comparing the optical efficiencies of the two particles:
；
；
wherein, And/>Respectively representing the velocity and position of the loser particles at the t+1st iteration in the region,And/>Respectively representing the velocity and the position of the loser particles after the t-th iteration in the region,/>Representing the position of winning particles after the t-th iteration in the region,/>Is the average position of all particles in the region traversed by the t-th iteration,/>Is control/>Parameters of the effect; /(I)Representing three random vectors, D being the number of candidate parameters;
per is the term of random perturbation, Obeys a normal distribution, wherein,Represents the degree of deviation of the optical efficiency corresponding to the particle position at the t+1st iteration from the optical efficiency at the previous iteration,/>In order to control the factor of the disturbance,Indicating the optical efficiency of the particle as determined by the selected parameter corresponding to the position of the t +1 iteration,The optical efficiency obtained from the candidate parameter corresponding to the average position of the particles in the present region at the t-th iteration is shown.
3. The method of claim 1, wherein the predetermined termination condition for performing the K-time intra-zone competition algorithm and the K-time inter-zone competition algorithm loop is to calculate the total algebra N of the iterations, and the algorithm terminates the output result once the iteration is completed N times.
4. The method of claim 1, wherein the optical efficiency is an average of total optical efficiencies of each heliostat in the heliostat field, and wherein the formula for calculating the total optical efficiency of a single heliostat in the heliostat field is:
；
In the method, in the process of the invention, Is the total optical efficiency of a single heliostat,/>Is the shadow shading efficiency of a single heliostat,/>Is the cosine efficiency of a single heliostat,/>Is the atmospheric optical efficiency of a single heliostat,/>Is the cut-off efficiency of a single heliostat,Is the reference optical efficiency of a single heliostat.
5. The method of optimizing a heliostat field layout of claim 1, wherein the method of initializing the layout of the heliostat field comprises: the layout of the heliostat field is initialized using a radially staggered layout of heliostats based on a circular solar heliostat field.
6. An optimization system for heliostat field layout, comprising:
the initialization module is used for carrying out initialization layout on the heliostat field to obtain an initialization population formed by a plurality of candidate parameter sets;
the first data processing module is used for partitioning and processing the initialized population by adopting a competition algorithm in K times of regions, extracting candidate parameters with highest optical efficiency in each partition, and summarizing to obtain a winner set;
The second data processing module is used for carrying out secondary partition and processing on the winner set by adopting a K-time inter-partition competition algorithm, extracting candidate parameters with highest optical efficiency in each secondary partition, and summarizing to obtain a secondary winner set;
the optimal solution determining module is used for executing the competition algorithm in the K times of subareas and the competition algorithm among the K times of subareas again based on the secondary winner set, and circulating the steps until the candidate parameter with the highest optical efficiency is obtained, so as to determine the optimal heliostat field layout;
The K-time intra-area competition algorithm specifically comprises the following steps:
each group of candidate parameters in the initializing population P corresponds to one particle, and then the initializing population P is divided into two areas, so that the average value of the optical efficiency of the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
assuming that the currently selected particle is i, randomly selecting a particle j in the region;
Calculating optical efficiencies fitness_i and fitness_j of the particle i and the particle j;
judging and comparing the sizes of the fitness_i and the fitness_j;
If the fitness_i is greater than or equal to the fitness_j, updating the speed and the position of the particle i and the particle j, putting the particle i into the winner set W1, and putting the particle j into the loser set L1; otherwise, putting the particle j into the winner set W1, and putting the particle i into the loser set L1;
after the traversal is finished, obtaining a winner set W1;
The K times of inter-regional competition algorithm comprises the following steps:
Each group of candidate parameters in the winner set W1 corresponds to one particle, and then the winner set W1 is divided into two areas, so that the average value of the optical efficiency corresponding to the particles in each area is the same;
then, carrying out the same segmentation operation on each segmented region, and obtaining 2 K regions after K times of segmentation;
Traversing each of the segmented 2 K regions in turn;
Traversing each particle in the region for the region currently being traversed;
Assuming that the currently selected particle is p, randomly selecting a particle q from the winner set W1;
Calculating optical efficiencies fitness_p and fitness_q of the particles p and the particles q;
judging and comparing the sizes of the fitness_p and the fitness_q;
if the fitness_p is greater than or equal to the fitness_q, updating the speed and the position of the particle p and the particle q, putting the particle p into the winner set W2, and putting the particle q into the loser set L2; otherwise, putting the particle q into the winner set W2, and putting the particle p into the loser set L2;
after the traversing is finished, a secondary winner set W2 is obtained;
The method for making the average value of the optical efficiency corresponding to the particles in each region the same comprises the following steps:
the particles in the region are arranged in a non-descending order according to the corresponding optical efficiency value;
calculating the sum of the optical efficiencies corresponding to all particles;
dividing the sum of the optical efficiencies corresponding to all particles by 2 to obtain an adaptability average value of each region;
initializing an array with the length of 2 to represent the cumulative sum of the optical efficiency of each area, wherein the initial value is 0;
sequentially traversing all particles in the ordered sequence, and adding the current particles to the area with the smallest optical efficiency accumulation until all particles are distributed;
thus, two divided areas are obtained, and the average value of the optical efficiency corresponding to the particles in each area is the same.
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