CN114565242A - Comprehensive low-carbon energy base site selection intelligent optimization method and device - Google Patents
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Abstract
The invention provides an intelligent optimization method, a device, electronic equipment and a medium for site selection of a comprehensive low-carbon energy base, wherein the method comprises the following steps: constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost; selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm based on a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation during the next generation evolution; and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit. The method has the advantages of high solving precision, strong robustness and the like, so that the requirements of high benefit and low cost of building and site selection of the comprehensive low-carbon energy base can be met.
Description
Technical Field
The invention relates to the technical field of comprehensive energy, in particular to a comprehensive low-carbon energy base site selection intelligent optimization method, a comprehensive low-carbon energy base site selection intelligent optimization device, electronic equipment and a comprehensive low-carbon energy base site selection intelligent optimization medium.
Background
The comprehensive low-carbon energy system comprises a plurality of energy forms at the power supply side, and the initial energy forms with different characteristics such as natural gas, wind energy, hydrogen energy, nuclear energy, solar energy and the like are basically gathered. Similarly, the use of clean energy can greatly reduce carbon emission, and higher requirements are provided for each link of production, scheduling, consumption and the like of the whole comprehensive low-carbon energy system. Wherein electric power production is the first problem of synthesizing low carbon energy development, and reasonable construction scheme can make whole comprehensive energy system have higher economic benefits, can guarantee lower carbon emission, improves clean energy utilization and rate, guarantees ecological environment's robustness.
With the large-scale development of comprehensive low-carbon energy, a plurality of energy base construction schemes cannot meet the requirements of the existing comprehensive energy system, and the construction of the comprehensive low-carbon energy base is a nondeterministic polynomial time-solvable judgment problem. In order to maximize the economic benefit of building and site selection of the comprehensive low-carbon energy base and reduce the construction cost, the optimal site selection problem of the comprehensive low-carbon energy base needs to be solved under the condition that the construction scale of the complex comprehensive low-carbon energy base is continuously increased.
Disclosure of Invention
The invention aims to provide an intelligent optimization method, device, electronic equipment and medium for site selection of a comprehensive low-carbon energy base.
Specifically, the invention is realized by the following technical scheme:
in a first aspect, the invention provides an intelligent optimization method for site selection of a comprehensive low-carbon energy base, which comprises the following steps:
constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost;
selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm based on a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation during the next generation evolution;
and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
Further, the objective function is represented as:
fi＝EC(x(i,:),i)*TCT
in the formula (f)iThe benefit value of the ith artificial moth is represented, x (i): represents the individual code of the ith artificial moth, EC represents a low-carbon energy base construction cost matrix, and TC represents an electric power transmission cost matrix.
Further, the elite moth population optimization algorithm comprises the following steps:
initializing basic control parameters and a moth colony population;
calculating a fitness value according to a target evaluation function to start an algorithm iteration process;
judging whether the maximum iteration times is reached, and if so, obtaining an optimal solution; if not, continuing to return to the step of calculating the fitness value according to the target evaluation function and starting the algorithm iteration process.
Further, the calculating a fitness value according to the objective evaluation function to start an algorithm iteration process includes: and replacing the artificial moth with the worst fitness with the artificial moth with the best fitness in the next generation of evolution.
Further, the input quantity of the elite moth colony optimization algorithm comprises the scale of the moth colony, the iteration times, the space dimension, the upper and lower bounds of the single dimension variable of the artificial moth, and the output quantity comprises an optimal comprehensive low-carbon energy base site selection scheme.
Further, the number of rows of the low-carbon energy base construction cost matrix represents the number of types of the energy bases, the number of columns represents the number of target urban areas to be selected, and the number of columns of the power transmission cost matrix represents the number of target urban areas to be selected.
Further, the way of generating the initial solution of the moth population is as follows:
xi,j＝λ*(UB-LB)+LB
in the formula, xi,jAnd (3) taking the initial solution of the moth population, wherein lambda is a random number between 0 and 1, UB is the upper bound of the single dimension variable of the artificial moth, and LB is the lower bound of the single dimension variable of the artificial moth.
In a second aspect, the invention provides an intelligent optimization device for site selection of a comprehensive low-carbon energy base, comprising:
the target function construction unit is used for constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost of each urban area;
the optimal scheme calculation unit is used for selecting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm according to a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation when the next generation evolves;
and the intelligent optimization unit is used for carrying out site selection on the comprehensive low-carbon energy base according to the site selection scheme of the comprehensive low-carbon energy base with the highest economic benefit.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the comprehensive low-carbon energy base site selection intelligent optimization method according to the first aspect.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the integrated low carbon energy base site selection intelligent optimization method according to the first aspect.
According to the comprehensive low-carbon energy base site selection intelligent optimization method, the device electronic equipment and the medium, the comprehensive low-carbon energy base construction benefit evaluation function is constructed according to the low-carbon energy base construction cost and the power transmission cost, the elite moth colony optimization algorithm is adopted to select the comprehensive low-carbon energy base site selection scheme with the highest economic benefit based on the comprehensive low-carbon energy base construction benefit evaluation function, and the comprehensive low-carbon energy base site selection scheme with the highest economic benefit is adopted to select the comprehensive low-carbon energy base site.
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In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a comprehensive low-carbon energy base site selection intelligent optimization method according to an embodiment of the invention;
FIG. 2 is a flow chart of the steps of an Elite moth population optimization algorithm according to an embodiment of the present invention;
FIG. 3 is a comparison graph of the construction site selection cost index values of the comprehensive low-carbon energy base using the cuckoo search algorithm and the simulated annealing algorithm in the method of the embodiment of the invention relative to the prior art;
fig. 4 is a schematic diagram of an integrated low-carbon energy base site selection intelligent optimization device according to an embodiment of the invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The various terms or phrases used herein have the ordinary meaning as is known to those skilled in the art, and even then, it is intended that the present invention not be limited to the specific terms or phrases set forth herein. To the extent that the terms and phrases referred to herein have a meaning inconsistent with the known meaning, the meaning ascribed to the present invention controls; and have the meaning commonly understood by a person of ordinary skill in the art if not defined herein.
Fig. 1 is a flowchart of an intelligent optimization method for site selection of a comprehensive low-carbon energy base according to an embodiment of the invention. Referring to fig. 1, the comprehensive low-carbon energy base site selection intelligent optimization method may include the steps of:
step 101: constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost;
step 102: selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm based on a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation during the next generation evolution;
step 103: and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
Specifically, in step 101, the low-carbon energy base construction cost EC and the power transmission cost TC of each urban area may be obtained by evaluating a survey or referring to a related feasibility report, and then a comprehensive low-carbon energy base construction benefit evaluation function is constructed according to the low-carbon energy base construction cost EC and the power transmission cost TC, for example, the benefit evaluation function (i.e., an objective function) may be expressed as:
fi＝EC(x(i,:),i)*TCT
in the formula (f)iThe benefit value of the ith artificial moth is represented, x (i): represents the individual code of the ith artificial moth, EC represents a low-carbon energy base construction cost matrix, and TC represents an electric power transmission cost matrix.
In step 102, an elite moth population optimization algorithm can be adopted to select the site selection scheme of the comprehensive low-carbon energy base with the highest economic benefit based on the comprehensive low-carbon energy base construction benefit evaluation function. As shown in fig. 2, the specific steps of the elite moth population optimization algorithm may include:
in the initialization stage of the moth colony, the scale of the moth colony is M-45, the search space dimension is D-13, 5 alternative energy base types are available, and 13 target urban areas are to be selected. The upper and lower bounds of the single dimension variable of the artificial moth are [1,5 ]]Maximum number of iterations G max100. And randomly initializing an initial solution of the moth population, wherein the encoding mode is a positive integer. The way of generating the initial solution of the moth population is as follows:
xi,j＝λ*(UB-LB)+LB (1)
the specific kth artificial moth code can be expressed as:
xk＝[1 5 3 4 4 2 1 1 3 5 2 2 4]
fi＝EC(x(i,:),i)*TCT (2)
wherein x (i,: means the individual code of the i-th artificial moth. EC represents a low-carbon energy base construction cost matrix of each urban area, and TC represents an electric power transmission cost matrix.
It should be noted that the number of rows of the low-carbon energy base construction cost matrix EC represents the number of types of energy bases, the number of columns represents the number of target urban areas to be selected, and the number of columns of the electric power transmission cost matrix TC represents the number of target urban areas to be selected.
Specifically, for example, the cost index of the i-th artificial moth is calculated as fi. Let the position code of the i-th artificial moth be xi＝[3 4 5 2 1 2 2 1 1 5 4 3 1]。
It is assumed that a low-carbon energy base construction cost matrix EC (5 rows and 13 columns) and a power transmission cost matrix TC (1 row and 13 columns) can be obtained by evaluating the low-carbon energy base construction cost and the power transmission cost for each urban area. In order to facilitate the calculation of the algorithm, a standardization method is designed, and the corresponding cost is treated as a real number between [0,1] and is used for representing the cost index, wherein the closer to 1, the higher the cost is, and the closer to 0, the lower the cost is.
TC＝[0.960,0.548,0.139,0.150,0.258,0.841,0.255,0.815,0.244,0.930,0.350,0.197,0.252]
Therefore, the benefit value of the ith artificial moth can be calculated as:
wherein TCTIs a transposed matrix of the matrix TC, and obtains a cost index value f through matrix operationi＝3.5956。
updating the positions of the pathfinder moths according to a formula (3) to a formula (11) updated by the pathfinder moth stage positions, calculating the fitness value of the pathfinder moth individuals by using an objective function, comparing the fitness value with the fitness value of the initial population, selecting the superior individuals as light sources, and guiding the movement of the moth population main body;
in the exploration moth stage, a moth group selects a plurality of moths as the guidance moths in the next stage in a roulette mode, and the position updating formula is as follows:
the diversity cross point is provided, and the strategy of cross operation can expand the diversity of the population. First, individuals of the g-th generation were taken in the j-dimension from the following distribution:
in the formula (I), the compound is shown in the specification,
the number of the tie moths is Mp，
Indicates the individual code of the lead moth. Then, in order to measure whether the variation mechanism is carried out on the lead moth, the following distribution is obeyed:
when the moths forming the exploratory moth group are scattered, the moths are included in cpPerforming mutation operation on the population, and following the following formula:
the crossing rate of the moth population changes dynamically as the iteration progresses. At Mc∈MpIn the cross mechanism of (1), provided by the elite moth population optimization algorithm
Is for perturbing the selected principal vector
Generated, the formula is as follows:
wherein
Further deltauIs represented as follows:
in order to obtain a better flight path, each pathfinder moth updates the position of the pathfinder moth by performing interactive operation with the mutated sub-vector. The complete flight path may be defined as follows:
with the roulette selection mechanism, after the iterations, the fitness value of each generation is recalculated and compared to the fitness value of the pathfinder moth. The moths with better fitness values are continuously kept in the process of the next iteration, and the process for solving the minimum value is represented as follows:
Ppcost f for evaluating site selection schemepThe relationship between the two is expressed as follows:
fitness value f of the objective functionpThen the intensity of the light is expressed by the formula:
the number of exploratory moths decreases as the number of iterations increases during the exploratory moth phase. And (3) flying the exploration moth around the locating cost found in the stage of exploring the road moth in a logarithmic spiral way according to a formula (13), calculating the fitness value of the objective function, and if the fitness value is superior to the current optimal fitness value, converting the exploration moth into the road moth.
Fitness evaluation of exploration moths is inferior to that of pathfinder moths, MsThe number of pathworms is reduced as the number of iterations G increases. The formula is expressed as:
Ms＝round((M-Mp)) (12)
after the search of the pathfinder moth is finished, the fitness information is transmitted to the exploration moths, and the exploration moths update the positions of the exploration moths. Each exploratory moth will surround the current lead moth xpMaking spiral. The formula for exploring the updated location of moths is as follows:
in the formula, theta is a spiral shape constant to define the spiral shape of the moth, and the numeric area of theta is [ r,0.7 ]]R ═ 0.5-G/G. The classification of each moth varies with the number of iterations. Therefore, when each moth finds a position with a relatively low addressing cost, the position can be possibly changed into a pathfinder moth. That is, a new lead moth x is generated at this stagep。
as the number of exploratory moths decreased, the number of observation moths increased, the number of which was expressed as Mo＝M-Ms-Mp. The position of the moth is updated by a Gaussian walking calculation formula (14) and a learning mechanism calculation formula (15) in the stage of observing the moth, and the updated position is updatedAnd calculating a fitness function value according to the target function, and comparing the fitness function value with the fitness value calculated in the exploration moth stage, so that the better observed moth is converted into the exploration moth, and the worse observed moth is used as the pathfinding moth. The equations (14) (15) are as follows:
wherein the content of the first and second substances,
in the formula, xi1For the number of randomly generated populations in a Gaussian distribution, bestgThe position of the best moth population in this stage (including pathfinder moth and exploratory moth), xi2，ξ3Is [0,1]]A random number in between.
Wherein i ∈ {1,2, …, MHAnd 2.5H/H is a social factor, and 1.5-H/H is a cognitive factor. r1, r2 is [0,1]]A random number in between, and a random number,
the positions of the leaders and moths with better fitness are randomly selected based on the roulette probability.
according to the sequence from big to small of the benefit value, selecting the artificial moths with the best fitness, namely
When the next generation evolves, it replaces the worst artificial moth, keeping its current excellent code from being destroyed.
Finally, the best scheme x is obtainedbestThe method is used for the scheme decision of site selection of the comprehensive low-carbon energy base.
In step 102, the above-mentioned best scenario x may be employedbestThe site selection of the comprehensive low-carbon energy base is carried out (namely, the site selection scheme of the comprehensive low-carbon energy base with the highest economic benefit), and the site selection construction is carried out according to the scheme, so that the optimal benefit can be obtained.
The following details are provided for the parameter setting and simulation results of the software simulation experiment of the embodiment:
in the embodiment of the present invention, there are 5 types of energy bases in total, and 13 target urban areas to be selected. The number of the artificial moths in the elite moth group optimization algorithm is M-45, and the upper limit of the iteration times is GmaxThe position change of the artificial moth is 250, the minimum value is 1, and the maximum value is 5. A cuckoo search algorithm as a comparison algorithm, in which the probability of being found by a host is set to 0.25, the population size M is set to 45, and the upper limit of the number of iterations G is setmax250; simulated annealing algorithm as comparison, upper limit of iteration times GmaxThe initial temperature is set to 100 and the upper limit of the number of searches per temperature is 25, 250.
FIG. 3 is a graph comparing the scheduling benefit values of a cuckoo search algorithm and a simulated annealing algorithm in a prior art method according to the method of the embodiment of the present invention. As can be seen from fig. 3, the lowest red line is a cost index curve obtained by solving the problem by the comprehensive low-carbon energy site selection intelligent optimization method provided by the present invention, the middle pink line is a cost index curve obtained by the cuckoo search algorithm, and the uppermost brown line is a cost index curve obtained by the simulated annealing algorithm. It can be seen from the experimental results of fig. 3 that after the algorithm is run for 250 times, the cost index value obtained by the method according to the embodiment of the present invention is 0.252 lower than that of the cuckoo search algorithm and 0.388 lower than that of the simulated annealing algorithm. The performance of the method of the embodiment is greatly improved compared with the performance of other two algorithms. The comparison results prove that the scheduling scheme obtained by the method can obviously reduce the comprehensive low-carbon energy site selection cost. In particular, it can also be seen from fig. 3 that the performance of the solution problem of the method of the present invention is more stable than that of both the cuckoo search algorithm and the simulated annealing algorithm. In the 250 iteration processes, the overall search solution space capacity of other two algorithms is weak, and the final cost index value is higher than that of the method, so that the method is not beneficial to the site selection of an actual energy base.
In summary, according to the embodiment, the comprehensive low-carbon energy base construction benefit evaluation function is constructed according to the low-carbon energy base construction cost and the electric power transmission cost, the elite moth swarm optimization algorithm is adopted to select the comprehensive low-carbon energy base site selection scheme with the highest economic benefit based on the comprehensive low-carbon energy base construction benefit evaluation function, and the comprehensive low-carbon energy base site selection scheme with the highest economic benefit is adopted to perform the site selection of the comprehensive low-carbon energy base, so that the comprehensive low-carbon energy base site selection intelligent optimization method with the advantages of high solving precision, strong robustness and the like can be provided, and the requirements of high benefit and low cost of the comprehensive low-carbon energy base construction site selection can be met.
Fig. 4 is a schematic diagram of an integrated low-carbon energy base site selection intelligent optimization device according to an embodiment of the invention. Referring to fig. 4, the apparatus includes: the objective function construction unit 401 constructs a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost of each urban area; the optimal scheme calculating unit 402 selects the comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth colony optimization algorithm according to the comprehensive low-carbon energy base construction benefit evaluation function; and the intelligent optimization unit 403 is used for performing comprehensive low-carbon energy base site selection according to the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
Since the device for intelligently optimizing the site selection of the comprehensive low-carbon energy base provided by the embodiment of the invention can be used for executing the method for intelligently optimizing the site selection of the comprehensive low-carbon energy base described in the embodiment, the working principle is similar, so detailed description is omitted here, and specific contents can be referred to the description of the embodiment.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 5: a processor 501, memory 502, communication interface 503, and communication bus 504; the processor 501, the memory 502 and the communication interface 503 complete communication with each other through the communication bus 504.
The processor 501 is configured to call the computer program in the memory 502, and when the processor executes the computer program, the processor implements all the steps of the above-mentioned integrated low-carbon energy base site selection intelligent optimization method, for example, when the processor executes the computer program, the processor implements the following processes: constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost; based on a comprehensive low-carbon energy base construction benefit evaluation function, selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth colony optimization algorithm, wherein the elite moth colony optimization algorithm is a moth colony optimization algorithm improved by replacing artificial moths with the worst fitness by artificial moths with the best fitness of the previous generation during the evolution of the next generation; and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
It will be appreciated that the detailed functions and extended functions that the computer program may perform may be as described with reference to the above embodiments.
Based on the same inventive concept, another embodiment of the present invention provides a non-transitory computer-readable storage medium, having a computer program stored thereon, which when executed by a processor implements all the steps of the above-mentioned integrated low carbon energy base site selection intelligent optimization method, for example, when the processor executes the computer program, the processor implements the following processes: constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost; selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm based on a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation during the next generation evolution; and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
It will be appreciated that the detailed functions and extended functions that the computer program may perform may be as described with reference to the above embodiments.
Based on the same inventive concept, another embodiment of the present invention provides a computer program product, which includes a computer program, when being executed by a processor, the computer program implements all the steps of the above-mentioned comprehensive low-carbon energy base site selection intelligent optimization method, for example, when the processor executes the computer program, the processor implements the following processes: constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost; selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth population optimization algorithm based on a comprehensive low-carbon energy base construction benefit evaluation function, wherein the elite moth population optimization algorithm is a moth population optimization algorithm improved by replacing the artificial moth with the worst fitness with the artificial moth with the best fitness of the previous generation during the next generation evolution; and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
It will be appreciated that the detailed functions and extended functions that the computer program may perform may be as described with reference to the above embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the technical solutions mentioned above may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the comprehensive low carbon energy base site selection intelligent optimization method according to each embodiment or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent optimization method for site selection of a comprehensive low-carbon energy base is characterized by comprising the following steps:
constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost;
based on a comprehensive low-carbon energy base construction benefit evaluation function, selecting a comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth colony optimization algorithm, wherein the elite moth colony optimization algorithm is a moth colony optimization algorithm improved by replacing artificial moths with the worst fitness by artificial moths with the best fitness of the previous generation during the evolution of the next generation;
and carrying out comprehensive low-carbon energy base site selection by adopting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit.
2. The integrated low carbon energy base site selection intelligent optimization method of claim 1, wherein the objective function is expressed as:
fi＝EC(x(i,:),i)*TCT
in the formula (f)iThe benefit value of the ith artificial moth is represented, x (i): represents the individual code of the ith artificial moth, EC represents a low-carbon energy base construction cost matrix, and TC represents an electric power transmission cost matrix.
3. The comprehensive low-carbon energy base site selection intelligent optimization method of claim 1, wherein the elite moth colony optimization algorithm comprises the following steps:
initializing basic control parameters and a moth colony population;
calculating a fitness value according to a target evaluation function to start an algorithm iteration process;
judging whether the maximum iteration times is reached, and if so, obtaining an optimal solution; if not, continuing to return to the step of calculating the fitness value according to the target evaluation function and starting the algorithm iteration process.
4. The intelligent optimization method for comprehensive low-carbon energy base site selection according to claim 3, wherein the calculation of the fitness value according to the objective evaluation function starts an algorithm iteration process, comprising: and replacing the artificial moth with the worst fitness with the artificial moth with the best fitness in the next generation of evolution.
5. The intelligent optimization method for comprehensive low-carbon energy base site selection according to claim 4, wherein the input quantity of the elite moth colony optimization algorithm comprises the scale of the moth colony, the iteration times, the space dimension, the upper and lower bounds of single-dimension variables of artificial moths, and the output quantity comprises an optimal comprehensive low-carbon energy base site selection scheme.
6. The method of claim 3, wherein the number of rows of the low-carbon energy base construction cost matrix represents the number of energy base types, the number of columns represents the number of target urban areas to be selected, and the number of columns of the power transmission cost matrix represents the number of target urban areas to be selected.
7. The comprehensive low-carbon energy base site selection intelligent optimization method of claim 3, wherein the manner of generating the moth colony initial solution is as follows:
xi,j＝λ*(UB-LB)+LB
in the formula, xi,jThe initial solution of the moth population is lambda is a random number between 0 and 1, UB is the upper bound of a single dimension variable of the artificial moth, and LB is the humanThe lower bound of the single dimension variable of the worker moth.
8. An intelligent optimization device for site selection of a comprehensive low-carbon energy base is characterized by comprising:
the target function construction unit is used for constructing a comprehensive low-carbon energy base construction benefit evaluation function according to the low-carbon energy base construction cost and the electric power transmission cost of each urban area;
the optimal scheme calculation unit is used for selecting the comprehensive low-carbon energy base site selection scheme with the highest economic benefit by adopting an elite moth group optimization algorithm according to the comprehensive low-carbon energy base construction benefit evaluation function;
and the intelligent optimization unit is used for carrying out site selection on the comprehensive low-carbon energy base according to the site selection scheme of the comprehensive low-carbon energy base with the highest economic benefit.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the integrated low carbon energy base site selection intelligent optimization method of any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the integrated low carbon energy base siting intelligent optimization method according to any one of claims 1 to 7.
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