CN115187409A

CN115187409A - Method and device for determining energy investment strategy, electronic equipment and storage medium

Info

Publication number: CN115187409A
Application number: CN202211107479.XA
Authority: CN
Inventors: 卢毅; 周杰; 张明月; 俎云霄; 黄明; 苏革; 张瑶; 刘长征
Original assignee: Xinjiang Tianfu Energy Co ltd; Xidian University; Beijing University of Posts and Telecommunications; Shihezi University
Current assignee: Xinjiang Tianfu Energy Co ltd; Xidian University; Beijing University of Posts and Telecommunications; Shihezi University
Priority date: 2022-09-13
Filing date: 2022-09-13
Publication date: 2022-10-14
Anticipated expiration: 2042-09-13
Also published as: CN115187409B

Abstract

The invention provides a method and a device for determining an energy investment strategy, electronic equipment and a storage medium, wherein the method relates to the technical field of comprehensive energy, and comprises the following steps: acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to the energy source sites and carbon emission information generated by the energy sources; based on the construction cost information and the carbon emission information, the target energy types corresponding to each energy site are determined using the elite strategy for replacing first target mayflies having the lowest fitness value of the first M in the elite pool with second target mayflies having the highest fitness value of the first M in the population of the next iteration of the mayflies; and determining an energy investment strategy of each energy site based on the target energy type. The method provided by the invention can improve the convergence speed of the mayfly algorithm, thereby improving the precision of the energy investment strategy and reducing the construction cost and carbon emission of each energy site.

Description

Method and device for determining energy investment strategy, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of comprehensive energy, in particular to a method and a device for determining an energy investment strategy, electronic equipment and a storage medium.

Background

The comprehensive energy system is a novel integrated energy system, is compared with the traditional energy supply mode, not only provides single energy for the user, but also integrates multiple energy sources such as hydroenergy, electric energy, heat energy, natural gas and the like in the region on the basis of the information technology, the internet and the big data technology, realizes multi-energy coupling and cooperative complementation, meets the requirement of the current user on multi-element and rich energy utilization, realizes the cascade utilization of the energy sources, and thereby improves the utilization efficiency of various energy sources. According to the innovation strategy of the energy supply side structure in China, the change of energy yield and consumption modes is promoted, and a clean, low-carbon, safe and effective modern energy system is built, so that the method plays an important role in energy innovation. Under the ambitious goal of 'carbon peak reaching and carbon neutralization', the carbon reduction potential in the market environment is huge.

At present, the method for optimal low-carbon investment of comprehensive energy applied to the market background mainly comprises the following steps: and classical meta-heuristic algorithms such as a genetic algorithm, a particle swarm algorithm and the like are used for solving an investment strategy meeting requirements. For example, the method for optimizing the capacity of the integrated energy system equipment based on the improved particle swarm optimization utilizes the improved dynamic multi-swarm no-speed-term particle swarm optimization to complete the solution of the capacity configuration of the integrated energy system equipment by combining the objective function and the constraint condition of the integrated energy system model, so as to obtain the optimal capacity configuration strategy. However, the convergence of the genetic algorithm and the particle swarm algorithm is unstable, so that the accuracy of the determined energy investment strategy is low.

Disclosure of Invention

The invention provides a method and a device for determining an energy investment strategy, electronic equipment and a storage medium, which are used for solving the defect of low precision of the determined energy investment strategy in the prior art and realizing the determined energy investment strategy with higher precision.

The invention provides a method for determining an energy investment strategy, which comprises the following steps:

acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source;

based on said construction cost information and said carbon emission information, determining the target energy type corresponding to each of said energy sites using an elite strategy for replacing first target mayflies having the lowest fitness value of the front G in the elite pool with second target mayflies having the highest fitness value of the front G in the initial group of mayflies of the next iteration of the algorithm and an elite strategy for replacing the first target mayflies having the highest fitness value of the front G in the initial group of mayflies of the next iteration of the algorithm;

and determining an energy investment strategy of each energy site based on the target energy type.

According to a method for determining an energy investment strategy in accordance with the present invention, said determining the target energy type for each of said energy sites using elite strategies and mayflies based on said construction cost information and said carbon emission information, comprising:

step A: (iii) encoding the plurality of first coding sequences of the initial mayflies in the mayflies involved in the algorithms based on said construction cost information and said carbon emission information; the first coding sequence is used for representing initial energy types invested for the energy sites respectively; the initial mayflies population comprises a plurality of male mayflies and a plurality of female mayflies;

and B, step B: calculating fitness values for each of said male dayflies and each of said female dayflies, respectively, based on each of said first coding sequences, said cost information, and said carbon emission information; the fitness value is used for representing an input evaluation index corresponding to each energy station point; the input evaluation index represents carbon emission generated when a user uses energy of the energy site;

step C: determining target positions corresponding to each said male dayflies and each said female dayflies, respectively, based on said fitness values;

step D: each of said male dayflies and each of said female dayflies move respectively to said target positions based on said target positions, yielding second coding sequences corresponding respectively to said male dayflies and each of said female dayflies;

and E, step E: each said male mayfly and each said female mayfly are individually paired, based on said second coding sequence, to cross-generate a plurality of offspring mayflies; each said progeny mayfly individual is in a position corresponding to a third coding sequence;

step F: updating the dayflies in said initial mayfly population based on said second coding sequence, said third coding sequence and said elite strategy in the event that the number of iterations does not satisfy a preset number of iterations;

step G: the fourth coding sequence corresponding to the mayflies in the updated initial mayflies population is taken as the new first coding sequence and steps a-G are iteratively performed until the number of iterations satisfies the preset number of iterations, the target energy type corresponding to each said energy site is determined based on the first coding sequences corresponding to the mayflies in the final updated initial mayflies population.

According to a method for determining an energy investment strategy provided by the present invention, said updating mayflies in said initial mayflies population based on said second coding sequence, said third coding sequence and said elite strategy, comprising:

judging whether each mayfly individual is out of range based on the second coding sequence and the third coding sequence;

calculating the fitness value of each mayflies in the absence of out-of-range for each mayfly;

sorting said fitness values of each said mayflies;

storing a first target mayflies corresponding to the lowest fitness value based on an elite strategy into an elite pool which includes a plurality of said first target mayflies; the mayflies in the initial population are updated according to the fitness values of the first plurality of first target mayflies.

A method of determining an energy investment strategy according to the invention that updates the mayflies in the initial mayflies in accordance with the fitness values of said plurality of first target mayflies includes:

the first M first target mayflies having the lowest fitness values in the plurality of said first target mayflies in the elite pool replacing the first M second mayflies having the highest fitness values in the current mayflies; m is a positive integer;

the mayflies in the initial mayflies population are updated with the replaced current mayflies population.

A method of determining an energy investment strategy in accordance with the invention, the determination of the target energy type corresponding to each of the energy sites based on the first coded sequence of correspondence of mayflies in the final updated initial mayflies includes:

the first coding sequence of the target mayflies corresponding to the lowest fitness value among the mayflies in the initial mayflies population after the final updating is determined as said target energy type.

A method of determining an energy investment strategy in accordance with the invention, said determining the target positions to which each said male mayflies and each said female mayflies respectively correspond based on said fitness values, including:

sorting the fitness values corresponding respectively to each said male mayflies and each said female mayflies, determining the fitness value ranking of each said male mayflies and each said female mayflies in the initial mayflies;

based on said fitness value ranking, the target positions corresponding respectively to each said male mayflies and each said female mayflies are determined.

According to a method for determining an energy investment strategy, said determining the target positions corresponding respectively to each said male mayflies and each said female mayflies based on said fitness value ranking, comprising:

for each of said male mayflies, the first coding sequence corresponding to the highest ranked male mayflies in the fitness value ranking is the target position;

the first coding sequence corresponding to each said female mayflies is the target position with the same fitness value ranking as each said female mayflies.

The invention also provides a device for determining the energy investment strategy, which comprises:

the acquisition module is used for acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source;

a first determining module for determining the target energy type corresponding to each of said energy sites on the basis of said construction cost information and said carbon emission information using an elite strategy for replacing first target mayflies of the first M fitness values in the elite pool with second target mayflies of the first M fitness values in the next iteration of the mayflies and the first M fitness values in the initial group of mayflies of the next iteration of the algorithm; m is a positive integer;

and the second determining module is used for determining the energy investment strategy of each energy site based on the target energy type.

The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor executes the program to implement the method for determining the energy investment strategy as described in any one of the above.

The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of determining an energy investment strategy as described in any one of the above.

The method, the device, the electronic equipment and the storage medium for determining the energy investment strategy provided by the invention acquire target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source; the target energy types corresponding to each energy site are determined based on the construction cost information and carbon emission information using the elite strategies for replacing first target mayflies in the elite pool with the first target mayflies with the first M fitness values being the lowest in the mayflies and second target mayflies with the first M fitness values being the highest in the next iteration of the mayflies; m is a positive integer; and determining an energy investment strategy of each energy site based on the target energy type. The method provided by the invention replaces the first M second target dayflies with the highest fitness value in the next iteration of the initial mayflies in the algorithm of the next generation by the elite strategy, realizes the energy investment strategy for each energy site, can enhance the convergence rate of the mayflies, thereby enhancing the precision of the energy investment strategy, and reducing the construction cost of each energy site and the carbon emission that can be brought by the user using energy.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for determining an energy investment strategy according to the present invention;

FIG. 2 is a second schematic flow chart of the method for determining the energy investment strategy provided by the present invention;

FIG. 3 is a schematic diagram showing a comparison result of the input evaluation indexes provided by the present invention;

FIG. 4 is a schematic diagram illustrating the structure of the device for determining the energy investment strategy provided by the present invention;

fig. 5 is a schematic structural diagram of an electronic device provided in 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 method for determining the energy investment strategy provided by the invention is explained in detail by some embodiments and application scenarios thereof in the following with reference to the attached drawings.

The invention provides a method for determining an energy investment strategy, which is suitable for determining scenes of comprehensive energy investment strategies of industrial parks by acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information generated by each energy source; determining the target energy type corresponding to each of said energy sites on the basis of said construction cost information and said carbon emission information using an elite strategy for replacing first target mayflies of the first M lowest fitness values in the elite pool with second target mayflies of the first M highest fitness values in the initial population of the next iteration of said mayflies; m is a positive integer; and determining an energy investment strategy of each energy station based on the target energy type. The method provided by the invention replaces the first M second target dayflies with the highest fitness value in the next iteration of the initial mayflies in the algorithm of the next generation by the elite strategy, realizes the energy investment strategy for each energy site, can enhance the convergence rate of the mayflies, thereby enhancing the precision of the energy investment strategy, and reducing the construction cost of each energy site and the carbon emission that can be brought by the user using energy.

The method for determining the energy investment strategy according to the present invention will be described with reference to fig. 1 to 3.

Fig. 1 is a schematic flow chart of a method for determining an energy investment strategy provided by the present invention, and as shown in fig. 1, the method includes steps 101-105, wherein:

step 101, acquiring target data; the target data includes construction cost information of a plurality of energy sources corresponding to a plurality of energy source sites, respectively, and carbon emission amount information that can be generated by each of the energy sources.

It should be noted that the method for determining the energy investment strategy provided by the invention can be applied to the determination scene of the comprehensive energy investment strategy of the industrial park. The implementation subject of the method may be the determining means of the energy investment strategy, such as the electronic device, or a control module of the determining means of the energy investment strategy for implementing the determining method of the energy investment strategy.

Specifically, the target data can be obtained by evaluating the influence factors of the comprehensive energy investment strategy combination in the market environment, that is, the construction cost required by each energy form and the carbon emission brought by each energy form, wherein the target data includes the construction cost information of multiple energy sources and the carbon emission information which can be generated by each energy source and corresponds to multiple energy source sites respectively.

In practice, a comprehensive energy station-building investment model can be established according to the influence factors of the comprehensive energy investment strategy combination under the market environment, and the comprehensive energy station-building investment model can realize the energy investment strategy of each energy station by using the method provided by the invention.

It should be noted that the number of energy resource sites may be set according to actual needs, for example, the number of energy resource sites is 9; the amount of energy sources can also be set according to actual requirements, for example, 6 energy sources such as water, electricity, heat, gas and the like.

A step 102 of determining the target energy types corresponding to each of the energy sites based on the construction cost information and the carbon emission information using the elite strategies for replacing first target mayflies in the elite pool with the first M lower fitness values in the mayflies and second target mayflies in the next generation iterations of the mayflies that have the highest fitness values; and M is a positive integer.

Specifically, the target energy types corresponding to the energy sites can be determined by adopting the elite strategy and the mayfly algorithm according to the acquired construction cost information of the various energy sources corresponding to the energy sites and the carbon emission amount information which can be generated by each energy source; wherein the elite strategy is used to replace the first target dayflies of the lowest fitness value of the first M in the elite pool, which have stored therein a plurality of first target dayflies, with the second target dayflies of the highest fitness value of the first M in the initial population of the next iteration of the said algorithm.

And 103, determining an energy investment strategy of each energy site based on the target energy type.

Specifically, the optimal low-carbon investment strategy of each energy site is determined according to the target energy type, so that the comprehensive energy sites can be deployed according to the optimal low-carbon investment strategy.

It should be noted that, before the optimal low-carbon investment strategy distribution of the comprehensive energy is performed, the specific energy site construction location is determined, and the problem to be considered is to construct what energy type site at which energy site. The optimal low-carbon investment strategy for hydropower and hot gas comprehensive energy in a market environment mainly relates to two factors, namely the construction cost of different types of energy sites and the carbon emission of users to different energy sources, so that the energy investment strategy of each energy site can be determined according to the target energy type corresponding to each energy site.

The method for determining the energy investment strategy provided by the invention comprises the steps of obtaining target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source; based on the construction cost information and carbon emission information, the target energy types corresponding to each energy site are determined using the elite strategy for replacing first target mayflies having the lowest fitness value of the first M in the elite pool with second target mayflies having the highest fitness value of the first M in the initial population of mayflies for the next iteration of the algorithm; m is a positive integer; and determining an energy investment strategy of each energy site based on the target energy type. The method provided by the invention replaces the first M second target dayflies with the highest fitness value in the next iteration of the initial mayflies in the algorithm of the next generation by the elite strategy, realizes the energy investment strategy for each energy site, can promote the convergence rate of the mayflies, thereby promoting the precision of the energy investment strategy, and reducing the construction cost of each energy site and the carbon emission amount that can be brought by the user using energy.

Optionally, a specific implementation manner of the step 102 includes the following steps:

step A: on the basis of said construction cost information and said carbon emission information, encoding a plurality of dayflies in the initial population of mayflies involved in said algorithms, determining a plurality of first coding sequences; the first coding sequence is used for representing initial energy types invested for the energy sites respectively; the initial mayflies include a plurality of male mayflies and a plurality of female mayflies.

Specifically, a plurality of first coding sequences are determined on the plurality of mayflies in the initial mayflies involved in the mayflies based on the information of the construction costs of the various energies and the information of the carbon emissions that can be generated by each energy corresponding respectively to the plurality of energy sites; the encoding mode may be real number encoding, or may also be other encoding modes, for example, binary encoding.

It should be noted that the first coding sequence is used to represent the initial energy types invested in each energy site, that is, the first coding sequence can represent a comprehensive energy investment strategy in a feasible market environment; the initial mayflies population comprises a plurality of male mayflies and a plurality of female mayflies.

For example, the energy sources to be allocated by the investment strategy in the market environment are 4 comprehensive energy sources such as water energy, electric energy, heat energy, gas energy, and the like, wherein the number 1 represents the water energy, the number 2 represents the electric energy, the number 3 represents the heat energy, and the number 4 represents the gas energy; a feasible first code sequence for mayflies in the mayflies is [2,1,3,4,1, 2,3,2] indicating that the target energy type for the first energy site is electric energy, i.e. an electric energy site investment is to be made in the first energy site when the number of energy sites to be built is 9 in divided area sites; the target energy type corresponding to the second energy site is hydroenergy, namely the hydroenergy energy site construction investment is planned for the second energy site; the target energy type corresponding to the third energy site is heat energy, namely, the heat energy station building investment is planned for the third energy site; the target energy type corresponding to the fourth energy site is gas energy, namely, gas energy station building investment is planned for the fourth energy site; and so on.

Further, a construction cost proportion matrix and a carbon emission proportion matrix can be determined according to the construction cost information and the carbon emission information, wherein the construction cost proportion matrix represents the proportion of the cost investment required by each energy source for building a station at a certain energy source site to the total cost investment required by all energy sources for building the station at the energy source site, and the carbon emission proportion matrix represents the proportion of the carbon emission brought by each energy source for building the station at the energy source site to the carbon emission brought by all energy sources.

For example, in the market environment, there are 9 energy sites to be built, and the involved energy sources are 4 kinds of comprehensive energy sources such as hydraulic energy, electric energy, thermal energy, and gas energy, and then a construction cost proportion matrix W can be determined according to construction cost information of building each energy source at each energy site, where the construction cost proportion matrix W is expressed as:

wherein, the rows of the construction cost weight matrix W represent 4 comprehensive energy sources such as hydraulic energy, electric energy, thermal energy, and gas energy, the columns of the construction cost weight matrix W represent 9 energy sites, for example, the first row and the first column represent the construction cost of constructing water energy source at the first energy site.

According to the carbon emission amount information generated by each energy source, a carbon emission amount weight matrix P can be determined, wherein the carbon emission amount weight matrix P is as follows:

the rows of the carbon emission weight matrix P represent 4 comprehensive energy sources such as water energy, electric energy, heat energy, and gas energy, the columns of the carbon emission weight matrix P represent 9 energy sites, and for example, the first row and the first column represent the carbon emission generated by the first energy site in the construction of water energy.

Then, initializing the initial mayfly population in the mayfly algorithm and setting the initialization parameters, wherein the parameters comprise the size C of the initial mayfly population, the position L (i.e. the first coding sequence) of each mayfly individual in the mayfly population, the number of energy sites built in the integrated energy system N, the maximum number of iterations MaxIteraction, the number of current iterations Iteraction, the initial speed of each mayfly individual; for example, the number of mayflies in the initial population is 60, the position dimension of each mayfly is 4, the maximum number of iterations is 100, the current number of iterations is 0, the initial speed of each mayfly is 0.

And B: calculating fitness values for each of said male dayflies and each of said female dayflies, respectively, based on each of said first coding sequences, said construction cost information, and said carbon emission information; the fitness value is used for representing an input evaluation index corresponding to each energy station point; the input evaluation index indicates a carbon emission amount generated when the user uses the energy of the energy site.

Specifically, from the construction cost information for the construction of the individual energy sources at the individual energy sites, a construction cost gravity matrix W can be determined, and from the information on the carbon emissions that can be generated by the individual energy sources, a carbon emissions gravity matrix P can be determined, and the fitness values of the individual mayflies can be calculated using the following formula (1), in which formula (1) is expressed as:

wherein, the fitness represents a fitness value, W represents a construction cost proportion matrix of each energy site corresponding to each first coding sequence, and P represents a carbon emission proportion matrix generated by each energy corresponding to each first coding sequence.

For example, the initial mayflies in the algorithm comprise 3 mayflies in which the first coding sequence of a mayfly 1 is [2,3,1, 2,3,4,1,2], the first coding sequence of mayflies 2 is [4,3,1, 2,3,4, 2], the first coding sequence of mayflies 3 is [1,2,3,2, 4]. The first coding sequence of mayflies 1 then represents the initial energy types invested in each energy site individually as: the first coding sequence of each mayflies individual 2 represents the initial type of energy invested in each energy site respectively as follows: the first coding sequence of mayflies 3 represents the initial types of energy which are invested in each energy site individually: water energy, electric energy, heat energy, electric energy, gas energy, and gas energy.

The construction cost specific gravity matrix W1 for mayflies 1 is: [0.32,0.16,0.24,0.22,0.14,0.21,0.08,0.26,0.25] the carbon emission specific gravity matrix P1 of the mayflies 1 is: [0.37,0.26,0.14,0.22,0.14,0.21,0.17,0.16,0.35]; the construction cost specific gravity sequence W2 of mayflies 2 is: [0.18,0.16,0.24,0.22,0.14,0.21,0.32,0.30,0.25] the carbon emission specific gravity sequence P2 of the mayflies 2 is: [0.29,0.26,0.14,0.22,0.14,0.21,0.33,0.40,0.35]; the construction cost specific gravity sequence W3 of mayflies 3 is: [0.35,0.27,0.35,0.28,0.32,0.21,0.20,0.30,0.18] the carbon emission specific gravity sequence P3 of the mayflies 3 is: [0.15,0.26,0.35,0.28,0.30,0.21,0.26,0.40,0.28].

The fitness value for mayflies 1 is fitness1=0.4484, the fitness value for dayflies 2 is fitness2=0.5526 and the fitness value for mayflies 3 is fitness3=0.6861, respectively, can be calculated according to formula (1) above.

And C: based on said fitness values, the target positions corresponding respectively to each said male mayfly and each said female mayfly are determined.

In particular, the target positions to which each male mayfly and each female mayfly can be respectively determined from the calculated fitness values of each mayfly.

Step D: based on the target positions, each male mayflies and each female mayflies move respectively to the target positions, yielding second coding sequences corresponding respectively to the male and female mayflies.

In particular, based on the target positions of each male mayfly and each female mayfly respectively, the second coding sequences corresponding to each male and each female mayfly are obtained by moving each male and each female mayfly respectively to the target positions.

In practice, the position updates of mayflies are carried out on male mayflies using the following formula (2), where:

wherein, i tableShows the ith male mayflies, t denotes the t-th iteration,

a second coding sequence representing the correspondence of the ith male mayflies at the t +1 iteration,

represents the first coding sequence corresponding to i male dayflies at the t iteration,

represents the flight speeds corresponding to i male mayflies at iteration t + 1.

Considering that male mayflies always dance in places near the water surface, the flight speeds of male mayflies are not very fast, as expressed by the following formula (3):

wherein,

represents the flight speed corresponding to the jth dimension of the ith male mayflies at the t +1 th iteration,

represents the flight speed corresponding to the jth dimension of the ith male mayfly individual at the t-th iteration,

a first coding sequence representing the jth dimension of the ith male mayfly individual at the t-th iteration,

and

indicating a positive attraction constant, respectivelyIn order to measure the contribution of cognitive components and social components, the influence degree of the current individual and the optimal individual on the current flying speed is colloquially said,

represents the historical optimal positions of the ith male mayflies,

represents the position of the optimal male mayflies,

represents the visibility coefficient of mayflies, controlling the visible range,

to represent

And

the cartesian distance between them is determined by the distance,

to represent

And

a cartesian distance therebetween.

It is noted that the flight speeds of male mayflies with the lowest fitness for the current dances (the least fitness male mayflies) are represented by the following formula (4) in which:

wherein,

the dancing coefficient of mayflies,

is [ -1,1 [ ]]The random value of the (c) bit of the (c),

represents the flight speed corresponding to the jth dimension of the ith male mayflies at iteration t +1,

represents the flight speed for the jth dimension of the ith male mayfly individual at the t-th iteration.

The position update of female dayflies employs the following formula (5) for female dayflies, wherein:

wherein,

represents the second coding sequence of the ith female mayfly at iteration t +1,

the first coding sequence of the ith female dayflies at the t iteration,

represents the corresponding flight speeds of i female mayflies at iteration t + 1.

Since the target position of a female mayflies is the position of a male mayflies ranked in the same fitness value as that of the mayflies, the flight speed of the female mayflies is expressed by the following water supply (6), wherein:

wherein,

represents the flight speed corresponding to the jth dimension of the ith female mayflies at iteration t +1,

represents the flight speed corresponding to the jth dimension of the ith female mayflies at the tth iteration,

representing a positive attraction constant, for measuring the contribution of social components,

represents the visibility coefficient of mayflies, controls the visible range,

represents the distance between the ith mayflies and the male mayflies having the same fitness value ranking as the same,

represents a constant, generally takes the value of 0.1,

is [ -1,1 [ ]]The random value of the (c) bit of the (c),

represents the fitness value of the ith individual male mayflies,

the fitness value of the ith female mayflies is shown.

As can be seen from the above formula (6), when the fitness value of the ith female mayflies ranked the same in fitness value is less than that of the ith male mayflies, the female mayflies move to the male mayflies at a speed update of the upper mode in formula (6); when the fitness value of the ith mayfly having the same rank is greater than that of the ith male mayfly, the mayfly searches for the target position by itself in a speed update manner of the following formula in formula (6).

Step E: (iii) mating each said male mayflies and each said female mayflies individually, on said second coding sequence, cross-producing a plurality of descendant mayflies; the positions of each said progeny mayfly individual correspond to the third coding sequence, respectively.

Specifically, each male mayfly individual and each female mayfly individual are mated respectively according to the second coding sequence corresponding to each male mayfly individual and each female mayfly individual, alternately generating a plurality of daughter mayflies using the following formula (7); wherein, formula (7) is expressed as:

wherein,

and

represents two sub-mayflies which are cross-produced by a pair of male and female mayflies, L represents a random number, and has a value in the range of [0,1]The male denotes the parent male mayflies, the female denotes the mother female mayflies.

In each iteration, N sub-mayflies are selected which are cross-generated from N pairs of male and female mayflies, in the course of each iteration, and the daughter mayflies are either female or male, generated randomly; the positions of the offspring mayflies each correspond to a third coding sequence, which can be calculated by equation (7) above.

Step F: in the event that the number of iterations does not satisfy a preset number of iterations, updating the mayflies in the initial mayflies based on the second coding sequence, the third coding sequence and the elite strategy.

In particular, in the event that the number of iterations does not satisfy the preset number of iterations, i.e., the current number of iterations is less than the preset number of iterations, the mayflies in the initial mayfly population in the current number of iterations can be updated according to the corresponding second coding sequence for each female and each male mayfly, the third coding sequence for descendant mayflies, and the elite strategy.

Specifically, the fourth coding sequence comprises the second and third coding sequences, the fourth coding sequence corresponding to the mayflies in the updated initial mayfly population is taken as the new first coding sequence, and the above-mentioned steps A-G are iteratively repeated until the number of iterations is greater than the preset number of iterations, at which time the target energy types corresponding to the various energy sites are determined according to the first coding sequence corresponding to the mayflies in the finally updated initial mayfly population.

The method for determining the energy investment strategy provided by the invention comprises the following steps: on the basis of the construction cost information and the carbon emission information, a plurality of mayflies in the initial population relating to the mayflies are encoded, determining a plurality of first coding sequences; the first coding sequence is used for representing the initial energy types of respective investment on each energy site; the initial mayflies include a plurality of male mayflies and a plurality of female mayflies; and B: the fitness values of each male mayflies and each female mayflies are calculated respectively on the basis of the respective first coding sequences, the construction cost information and the carbon emission information; the fitness value is used for representing the input evaluation index corresponding to each energy station; the input evaluation index represents the carbon emission amount generated when the user uses the energy of the energy site; step C: determining target positions corresponding to each male mayfly and each female mayfly, respectively, based on the fitness values; step D: on the basis of the target positions, the respective male and female mayflies move respectively to the target positions to obtain second coding sequences to which the male and female mayflies correspond respectively; step E: each male mayflies and each female mayflies are paired individually on the second coding sequence to cross produce a plurality of offspring mayflies; the positions of each offspring mayfly individual correspond to the third coding sequence, respectively; step F: updating the mayflies in the initial mayflies on the basis of the second coding sequence, the third coding sequence and the elite strategy in the case that the number of iterations does not satisfy a preset number of iterations; step G: the fourth coding sequence corresponding to mayflies in the updated initial mayflies is taken as the new first coding sequence and the steps a-G are iteratively executed until the number of iterations meets a preset number of iterations, the target energy types corresponding to the energy sites are determined based on the first coding sequences corresponding to mayflies in the final updated initial mayflies. The method provided in the invention improves the mayflies through the elite strategy that is used to replace the second target mayflies of the first M maximum fitness in the initial mayflies of the next generation iteration in the mayflies, realizing the energy investment strategies of the various energy sites that can enhance the rate of convergence of the mayflies, thereby enhancing the precision of the energy investment strategies, reducing the construction costs of the various energy sites and the carbon emissions that can be brought about by the users using energy.

Optionally, a specific implementation manner of the step F includes the following steps:

step 1) on the basis of the second and third coding sequences, it is judged whether each mayfly individual crosses the border.

In particular, on the basis of the second and said third coding sequences, it is judged whether each mayfly individual is out of range, i.e. whether the value of each dimension in the second and third coding sequences exceeds a target value, for example a target value of 4.

Step 2) calculating the fitness value of each mayflies in the absence of out-of-range of each mayflies.

In particular, in the absence of transgression in each of the mayflies, the fitness values of each mayfly including the mayflies and the daughter mayflies in the initial population are calculated based on the second coding sequence of each mayfly and the third coding sequence of a daughter mayfly using the above formula (1), respectively.

Step 3) sorting the fitness values of each mayfly individual.

Step 4) storing the first target mayflies corresponding to the lowest fitness value, based on the elite strategy, into the elite pool which includes a plurality of said first target mayflies; the mayflies in the initial population are updated in accordance with the fitness values of the first plurality of first target mayflies.

In particular, using the elite strategy, the first target mayflies corresponding to the lowest fitness value in the ordering result are stored into the elite pool, which, in successive iterations, includes a plurality of first target mayflies; the mayflies in the initial population are then updated according to the fitness values of the first plurality of first target mayflies.

The method for determining the strategy of energy investment provided by the invention judges whether each mayflies cross the border according to the second and third coding sequences; the fitness value of each mayflies is calculated in the absence of a cross-border in each mayflies; sorting the fitness values of each mayflies; storing a first target mayfly individual corresponding to the lowest fitness value into an elite pool, comprising a plurality of said first target mayfly individuals, based on an elite strategy; the mayflies in the initial mayflies are updated in accordance with the fitness values of a plurality of first target mayflies, thereby realizing the energy investment strategies of the various energy sites, enabling the convergence speed of the mayflies to be increased, thereby increasing the precision of the energy investment strategy, reducing the construction costs of the various energy sites and the carbon emissions that can be brought about by the user using energy.

Alternatively, said updating the mayflies in said initial mayflies population according to the fitness values of said plurality of said first target mayflies in step 4) above, comprising:

the first M first target mayflies of the plurality of first target mayflies in the elite pool having the lowest fitness value are substituted for the first M second target mayflies of the current mayflies having the highest fitness value; the mayflies in the initial mayflies population are updated with the replaced current mayflies population.

Specifically, the fitness values corresponding to the first plurality of first target mayflies in the elite pool are ranked, the first M first target mayflies with the lowest fitness value are selected, and the M first target mayflies are substituted for the first M second target mayflies with the highest fitness value in the current mayfly population; the mayflies in the current mayflies after replacement are then used as the mayflies in the next generation initial mayflies to update the mayflies in the initial mayflies.

The method for determining the energy investment strategy provided in the present invention replaces the first M first target mayflies of the plurality of first target mayflies in the elite pool having the lowest fitness value with the first M second target mayflies of the current mayfly population having the highest fitness value; the mayflies in the initial mayflies population are updated with the current mayflies population after replacement, the convergence speed of the mayflies algorithm is promoted, the energy investment strategy for each energy site can be realized faster, thereby promoting the precision of the energy investment strategy, reducing the construction cost of each energy site and the carbon emission that can be brought by the user using energy.

Optionally, the determination of the target energy type corresponding to each said energy site based on the first coding sequences corresponding to the mayflies in the final updated initial mayflies in step G above comprises: the first coding sequence of the target mayflies corresponding to the lowest fitness value among the mayflies in the initial mayflies population after the final updating is determined as said target energy type.

Specifically, after updating the current mayflies population after the replacement with the mayflies in the initial mayflies population, it is judged whether or not the current number of iterations is greater than a preset number of iterations; if the current iteration times are not more than the preset iteration times, continuing the process of the next iteration; if the current number of iterations is greater than the preset number of iterations, the algorithm iteration process ends with the initial mayflies in the last iteration as the final updated initial mayflies, and the first coded sequence of the target mayflies corresponding to the lowest fitness values in the final updated initial mayflies as the target investment scenario, the target energy types for the energy sites are determined from the real codes corresponding to the energy sites in the first coded sequence.

The lowest fitness value of mayflies in the initial mayflies population after the final update can also be taken as the global optimum value, i.e. the lowest evaluation index representing the input of each energy site.

For example, the first coded sequence [2,3,1, 2,3,4,1,2] of the target mayflies 1 corresponding to the lowest fitness value among the finally updated initial mayflies in the population is taken as the target investment plan, according to the real number codes corresponding to the energy sites in the first coded sequence, that is, the real number code corresponding to the energy site numbered 1 is 2, the real number code corresponding to the energy site numbered 2 is 3, the real number code corresponding to the energy site numbered 3 is 1, the real number code corresponding to the energy site numbered 4 is 1, the real number code corresponding to the energy site numbered 5 is 2, the real number code corresponding to the energy site numbered 6 is 3, the real number code corresponding to the energy site numbered 7 is 4, the real number code corresponding to the energy site numbered 8 is 1, and the real number code corresponding to the energy site numbered 9 is 2, so that the target energy type of the energy site numbered 1 can be determined to be electric energy, that the electric energy source is established in the first area; determining the target energy type of the energy site with the number of 2 as heat energy, namely constructing a heat energy source site in a first established area; determining that the target energy type of the energy station with the number of 3 is water energy, namely building a water energy station in a first set area; determining that the target energy type of the energy station with the number of 4 is water energy, namely building a water energy station in a first set area; determining the target energy type of the energy site with the number 5 as electric energy, namely constructing an electric energy source site in a first established area; determining the target energy type of the energy site with the number of 6 as heat energy, namely constructing a heat energy source site in a first established area; determining the target energy type of the energy site with the number of 7 as gas energy, namely constructing a gas energy site in a first established area; determining that the target energy type of the energy station with the number of 8 is water energy, namely building a water energy station in a first set area; and determining the target energy type of the energy site with the number 9 as electric energy, namely constructing the electric energy source site in the first set area.

After the construction cost of the comprehensive energy sites and the degree of demands of users on various energies are considered, the fitness value of the investment strategy corresponding to the mayflies 1 is 0.4484, which is the global optimum value, and the investment evaluation index of each energy site is the lowest.

Optionally, a specific implementation manner of the step C includes the following steps:

step 1) sorting the fitness values corresponding respectively to each said male dayflies and each said female dayflies, determining the fitness value ranking of each said male and female dayflies in the initial dayfly population.

Specifically, the fitness values of each male mayfly individual are ranked, determining the ranking of the fitness values of each male mayfly individual in the initial mayfly population; the fitness values of each female mayflies are then ranked and the ranking of the fitness values of each female mayflies in the initial mayfly population is determined.

Step 2) determining the target positions to which each said male mayflies and each said female mayflies respectively correspond, based on said fitness value ranking.

In particular, the fitness value rankings of each male mayfly individual and each female mayfly individual in the initial population can determine the target positions to which each male mayfly individual and each female mayfly individual correspond respectively.

Optionally, the specific implementation manner of step 2) includes:

2-1) for each of said male mayflies, the first coding sequence corresponding to the highest ranked male mayfly in the fitness value ranking is the target position.

In particular, the first coding sequence corresponding to the highest-ranked male mayflies in the order of their fitness value ranking is selected as the target position for other male mayflies in accordance with the sequence of their fitness values for each male mayflies, while the first coding sequence corresponding to the male mayflies is the target position for the male mayflies that are the highest ranking and the male mayflies exhibit dance in that target position.

2-2) for each said female mayflies, the first coding sequence corresponding to a male mayflies having the same ranking as the fitness value of each said female mayflies is the target position.

In particular, the first coding sequence corresponding to a male mayflies having the same rank as the fitness value of each female mayflies is taken as the target position of each female mayflies according to the ranking of the fitness values of each male mayflies and according to the ranking of the fitness values of each female mayflies.

The method of determining the energy investment strategy provided by the invention determines the fitness value ranks of the respective male dayflies and female dayflies in the initial mayflies by sorting the fitness values corresponding to the respective male and female dayflies, for each male mayflies, the first coding sequence corresponding to the highest ranked male mayflies in the fitness value ranks is the target position for each male mayflies, for each female mayflies, the first coding sequence corresponding to the male mayflies having the same rank as the fitness value of each female mayflies is the target position, by continually iterating the fitness values of the individual' flies, thereby achieving the energy investment strategy for each energy site, increasing the precision of the energy investment strategy, reducing the construction costs of each energy site and the amount of carbon emission that the user can bring in using energy.

Fig. 2 is a second flow chart of the method for determining the energy investment strategy provided by the present invention, as shown in fig. 2, the method includes steps 201 to 212, wherein:

step 201, acquiring target data; the target data includes construction cost information of a plurality of energy sources corresponding to the plurality of energy source sites, respectively, and carbon emission amount information that can be generated by each energy source.

Step 202 encodes the plurality of mayflies in the initial mayflies in the mayflies. On the basis of the construction cost information and the carbon emission information, a plurality of mayflies in the initial population relating to the mayflies are encoded, determining a plurality of first coding sequences; the first coding sequence is used for representing the initial energy types of investment respectively for each energy site; the initial mayflies population comprises a plurality of male mayflies and a plurality of female mayflies.

In step 203, the parameters of the mayflies are initialized. The size C of the initial mayfly population is initialized, the position L (i.e. the first coding sequence) of each mayfly individual in the mayfly population, the number N of energy sites built in the integrated energy system, the maximum number of iterations MaxIteration, the current number of iterations Iteration, the initial speed of each mayfly individual; for example, the number of mayflies in the initial mayflies is 60, the position dimension of each mayflies is 4, the maximum number of iterations is 100, the current number of iterations is 0, the initial speed of each mayfly is 0.

In step 204, the fitness value of each mayfly individual is calculated. Calculating the fitness values of each male mayflies and each female mayflies, respectively, based on each first coding sequence, the construction cost information and the carbon emission information; the fitness value is used for representing an input evaluation index corresponding to each energy station point; the input evaluation index indicates the amount of carbon emissions generated by the user when using the energy at the energy site.

In step 205, the target positions corresponding to each male mayfly individual and each female mayfly individual are determined. The fitness values corresponding to each male mayflies and each female mayflies are ranked, respectively, to determine the ranking of the fitness values of each male mayflies and each female mayflies in the initial mayfly population; based on the fitness value ranking, for each male daydaydayflies, the first coding sequence corresponding to the highest ranked male dayflies in the fitness value ranking is the target position; the first coding sequence corresponding to a male mayflies having the same rank in fitness value as each female mayflies is the target position.

In step 206, the positions of the respective mayflies are updated. Based on the target positions, each male mayfly and each female mayfly are moved to the target positions, respectively, to obtain the second coding sequences corresponding to each male and each female mayfly.

And step 207, cross-pairing. Each male mayflies and each female mayflies are paired individually, producing in cross a plurality of offspring mayflies; the positions of each offspring mayflies correspond to the third coding sequence respectively.

In step 208, it is judged whether each mayfly individual crosses the border. On the basis of the second coding sequence and said third coding sequence, it is judged whether or not each mayfly individual is out of range. The fitness value of each mayflies was calculated without the occurrence of out-of-bounds on each mayflies. In the case of occurrence of out-of-bounds in each mayflies, the fitness value of each mayfly is calculated after the adjustment of each mayfly.

Step 209, sort the fitness values of each mayfly individual. Based on the elite strategy, the first target mayflies corresponding to the lowest fitness value are stored in an elite pool, which comprises a plurality of said first target mayflies.

Step 210, replacing the first M first target mayflies of the plurality of first target mayflies in the elite pool with the lowest fitness value in the current mayflies with the first M second target mayflies of the plurality of first target mayflies of the mayfly population with the highest fitness value; the mayflies in the initial mayflies population are updated according to the fitness values of the first plurality of target mayflies.

In step 211, it is determined whether the iteration count is greater than a preset iteration count (maximum iteration count). Under the condition that the iteration times are not more than the preset iteration times, turning to step 203; in case the number of iterations is greater than the preset number of iterations, go to step 212.

And step 212, determining the energy investment strategy of each energy site. The first coding sequence of the target mayflies corresponding to the lowest fitness value among the mayflies in the initial mayflies population after the final updating is determined as the target energy type. And determining an energy investment strategy of each energy site based on the target energy type, namely investing and constructing the site of the target energy type corresponding to each energy site on each energy site.

The method for determining the energy investment strategy provided by the invention comprises the steps of obtaining target data; the target data comprises construction cost information of various energy sources corresponding to the energy source sites and carbon emission information generated by the energy sources; encoding a plurality of dayflies in the initial dayflies in the mayfly algorithm; initializing parameters of the mayflies algorithm; calculating the fitness value of each mayflies; determining the target positions to which each male mayflies and each female mayflies respectively correspond; updating the positions of each mayflies; each male mayflies and each female mayflies are paired individually, producing in cross a plurality of offspring mayflies; the positions of each offspring mayfly individual correspond to the third coding sequence, respectively; determining whether or not the mayflies cross the border, calculating the fitness value of each mayfly individual in the absence of a cross-border in the case of each mayfly individual, after adjusting each mayfly individual in the case of a cross-border in the case of each mayfly individual, calculating the fitness value of each mayfly individual; sorting the fitness values of each mayflies, storing the first target mayflies corresponding to the lowest fitness value in an elite pool that includes a plurality of the first target mayflies, based on an elite strategy, replacing the first M first target mayflies having the lowest fitness value among the plurality of first target mayflies in the elite pool with the first M first target mayflies having the lowest fitness value in the current mayfly population; updating the dayflies in the initial mayfly population according to the fitness values of the first plurality of first target mayflies, determining whether the number of iterations is greater than a preset number of iterations, and continuing the next iteration if the number of iterations is not greater than the preset number of iterations; the first coding sequence of the target mayflies corresponding to the lowest fitness value in the mayflies in the initial mayflies after the final update is determined as the target energy type in the case that the number of iterations is greater than the preset number of iterations, thereby determining the energy investment strategy for each energy site, enabling the energy investment strategy for each energy site to be realized by continuously iterating through the fitness values of the mayflies, promoting the convergence speed of the mayfly algorithm, thereby promoting the precision of the energy investment strategy, reducing the construction costs of each energy site and the carbon emissions that can be brought about by the user in using energy.

Fig. 3 is a schematic diagram of a comparison result of the investment evaluation index provided by the present invention, and as shown in fig. 3, the comparison is made with the optimal low-carbon investment strategy evaluation index values of comprehensive energy sources such as hydraulic energy, electric energy, heat energy, and gas energy in the market environment after 100 iterations of the simulated annealing algorithm and the particle swarm algorithm in the prior art, respectively, in the diagram, the uppermost curve is the investment evaluation index result obtained by the simulated annealing algorithm, the middle curve is the investment evaluation index result obtained by the particle swarm algorithm, and the lowermost curve is the investment evaluation index result obtained by the method provided by the present invention.

As can be seen from fig. 3, after 100 times of iterative operations, the investment evaluation index value of the comprehensive energy optimal low-carbon investment strategy of hydroenergy, electric energy, heat energy, gas energy and the like in the market environment finally obtained by the method provided by the invention is 0.40, the investment evaluation index value of the optimal low-carbon investment strategy obtained by the particle swarm algorithm is 0.47, and the investment evaluation index value of the optimal low-carbon investment strategy obtained by the simulated annealing algorithm is 0.50, i.e., the investment required by the mayday algorithm is reduced by about 14% and 20% respectively compared with the investment sequence solved by the particle swarm algorithm and the simulated annealing algorithm, which shows that the comprehensive energy investment sequence in the market environment obtained by the method provided by the invention can effectively balance the construction cost of energy sites and the carbon emission of users for different energy sources, and reduce the construction cost and the carbon emission of users for different energy sources. Meanwhile, as can be seen from fig. 3, compared with the particle swarm algorithm and the simulated annealing algorithm, the convergence speed of the method provided by the invention is obviously higher, the particle swarm algorithm and the simulated annealing algorithm are trapped in premature convergence in the iteration process, and cannot jump out of local optimality, so that the obtained investment sequence has lower quality and is not beneficial to practical application.

The following describes the determining apparatus of the energy investment strategy according to the present invention, and the determining apparatus of the energy investment strategy described below and the determining method of the energy investment strategy described above can be referred to each other.

Fig. 4 is a schematic structural diagram of an apparatus for determining an energy investment strategy according to the present invention, and as shown in fig. 4, the apparatus 400 for determining an energy investment strategy includes: an obtaining module 401, a first determining module 402 and a second determining module 404; wherein,

an obtaining module 401, configured to obtain target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source;

a first determination module 402 for determining the target energy types for each of the energy sites using elite strategies for replacing first target mayflies whose fitness values are the lowest in the elite pool with second target mayflies whose fitness values are the highest in the next generation of iterations of the mayflies, and the algorithms based on the construction cost information and the carbon emission information; m is a positive integer;

a second determining module 404, configured to determine an energy investment strategy for each of the energy sites based on the target energy type.

The device for determining the energy investment strategy provided by the invention obtains target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source; based on the construction cost information and carbon emission information, the target energy types corresponding to each energy site are determined using the elite strategy for replacing first target mayflies having the lowest fitness value of the first M in the elite pool with second target mayflies having the highest fitness value of the first M in the initial population of mayflies for the next iteration of the algorithm; m is a positive integer; and determining an energy investment strategy of each energy site based on the target energy type. The device provided in this invention replaces the first M second target mayflies with the highest fitness in the initial mayflies of the next generation iteration in the mayfly algorithm by an elite strategy, realizing the energy investment strategy for various energy sites, and being able to promote the convergence rates of the mayfly algorithms, thereby promoting the precision of the energy investment strategy, reducing the construction costs of the various energy sites and the carbon emissions that users can bring in using energy.

Optionally, the first determining module 402 is specifically configured to:

step A: on the basis of said construction cost information and said carbon emission information, encoding a plurality of dayflies in the initial population of mayflies involved in said algorithms, determining a plurality of first coding sequences; the first coding sequence is used for representing initial energy types invested for each energy site respectively; the initial mayflies population comprises a plurality of male mayflies and a plurality of female mayflies;

and B, step B: calculating fitness values for each said male mayflies and each said female mayflies, respectively, based on each said first coding sequence, said construction cost information and said carbon emission information; the fitness value is used for representing an input evaluation index corresponding to each energy station point; the input evaluation index represents carbon emission generated when a user uses energy of the energy site;

and C: determining target positions corresponding to each said male dayflies and each said female dayflies, respectively, based on said fitness values;

step D: (ii) moving each said male and female dayflies respectively to said target positions on the basis of said target positions to yield second coding sequences corresponding respectively to said male and female dayflies;

and E, step E: (iii) mating each said male mayflies and each said female mayflies individually, on said second coding sequence, cross-producing a plurality of descendant mayflies; each said offspring mayfly individual's position corresponds to a third coding sequence, respectively;

step F: updating the mayflies in the initial mayflies based on the second coding sequence, the third coding sequence and the elite strategy in the case that the number of iterations does not satisfy a preset number of iterations;

g: the fourth coding sequence corresponding to the mayflies in the updated initial mayflies population is taken as the new first coding sequence and steps a-G are iteratively performed until the number of iterations satisfies the preset number of iterations, the target energy type corresponding to each said energy site is determined based on the first coding sequences corresponding to the mayflies in the final updated initial mayflies population.

Optionally, the first determining module 402 is specifically configured to:

calculating the fitness value of each mayflies in the absence of transgression in each said mayflies;

sorting said fitness values of each said mayflies;

Optionally, the first determining module 402 is specifically configured to:

the first M first target mayflies having the lowest fitness values in the plurality of said first target mayflies in the elite pool replacing the first M second mayflies having the highest fitness values in the current mayflies;

the mayflies in the initial mayflies are updated with the current mayflies after replacement.

Optionally, the first determining module 402 is specifically configured to:

sorting the fitness values corresponding respectively to each said male mayflies and each said female mayflies, determining the ranking of the fitness values of each said male mayflies and each said female mayflies in said initial mayfly population;

Optionally, the first determining module 402 is specifically configured to:

the first coding sequence corresponding to each said female mayflies has as a target position the same fitness value ranking as the male mayflies.

Fig. 5 is a schematic physical structure diagram of an electronic device provided in the present invention, and as shown in fig. 5, the electronic device 500 may include: a processor (processor) 510, a communication Interface (Communications Interface) 520, a memory (memory) 530, and a communication bus 550, wherein the processor 510, the communication Interface 520, and the memory 530 communicate with each other via the communication bus 550. The processor 510 may invoke logic instructions in the memory 530 to perform a method of determining an energy investment strategy, the method comprising: acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source; determining the target energy type corresponding to each of said energy sites on the basis of said construction cost information and said carbon emission information using an elite strategy for replacing first target mayflies of the first M lowest fitness values in the elite pool with second target mayflies of the first M highest fitness values in the initial population of the next iteration of said mayflies; m is a positive integer; and determining an energy investment strategy of each energy station based on the target energy type.

In addition, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for determining an energy investment strategy provided by the above methods, the method comprising: acquiring target data; the target data comprises construction cost information of multiple energy sources corresponding to multiple energy source sites respectively and carbon emission information which can be generated by each energy source; determining the target energy type corresponding to each of said energy sites on the basis of said construction cost information and said carbon emission information using an elite strategy for replacing first target mayflies of the first M lowest fitness values in the elite pool with second target mayflies of the first M highest fitness values in the initial population of the next iteration of said mayflies; m is a positive integer; and determining an energy investment strategy of each energy station based on the target energy type.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

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. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments 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 should 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

1. A method for determining an energy investment strategy, comprising:

determining the target energy type corresponding to each of said energy sites on the basis of said construction cost information and said carbon emission information using an elite strategy for replacing first target mayflies of the first M lowest fitness values in the elite pool with second target mayflies of the first M highest fitness values in the initial population of the next iteration of said mayflies; m is a positive integer;

and determining an energy investment strategy of each energy station based on the target energy type.

2. The method for determining an energy investment strategy according to claim 1, wherein said determining target energy types corresponding to each of said energy sites using elite strategies and mayflies algorithms based on said construction cost information and said carbon emission information comprises:

step A: on the basis of said construction cost information and said carbon emission information, encoding a plurality of dayflies in the initial population of mayflies involved in said algorithms, determining a plurality of first coding sequences; the first coding sequence is used for representing initial energy types invested for each energy site respectively; the initial mayflies include a plurality of male mayflies and a plurality of female mayflies;

and B: calculating fitness values for each of said male dayflies and each of said female dayflies, respectively, based on each of said first coding sequences, said construction cost information, and said carbon emission information; the fitness value is used for representing an input evaluation index corresponding to each energy station point; the input evaluation index represents the carbon emission amount generated when the user uses the energy of the energy site;

step C: determining target positions for each said male mayflies and each said female mayflies respectively, based on said fitness values;

3. The method for determining an energy investment strategy according to claim 2, wherein said updating the mayflies in the initial mayflies population based on said second coding sequence, said third coding sequence and said elite strategy comprises:

sorting the fitness values of each of the mayflies;

storing a first target mayfly individual corresponding to the lowest fitness value into an elite pool, comprising a plurality of said first target mayfly individuals, based on an elite strategy; the mayflies in the initial population are updated according to the fitness values of the first plurality of first target mayflies.

4. A method for determining an energy investment strategy according to claim 3, wherein said updating the mayflies in said initial mayflies population according to the fitness values of said first target mayflies of the plurality comprises:

5. The method of determining an energy investment strategy according to claim 4, wherein said determining the target energy type corresponding to each of said energy sites based on the first coded sequences corresponding to the mayflies in the final updated initial mayflies population comprises:

6. A method of determining an energy investment strategy according to claim 2, wherein said determining the target positions to which each of the male mayflies and each of the female mayflies respectively corresponds based on said fitness value includes:

7. The method for determining an energy investment strategy according to claim 6, wherein said determining the target locations corresponding respectively to each of said male dayflies and each of said female dayflies based on said fitness value ranking comprises:

8. An apparatus for determining an energy investment strategy, comprising:

and the second determination module is used for determining the energy investment strategy of each energy station based on the target energy type.

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 executes the program to implement the method for determining an energy investment strategy according to any one of claims 1 to 7.

10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method for determining an energy investment strategy according to any one of claims 1 to 7.