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

Abstract

The invention provides a method, device, electronic device and storage medium for determining an energy investment strategy. The method relates to the technical field of comprehensive energy, and includes: acquiring target data; the target data includes the construction costs of multiple energy sources corresponding to multiple energy sites respectively Information and carbon emissions information that can be generated by each energy source; based on the construction cost information and carbon emissions information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each energy site, and the elite strategy is used to select the top M in the elite pool The first target mayfly individual with the lowest fitness value is replaced with the second target mayfly individual with the highest fitness value in the initial mayfly population of the next iteration of the mayfly algorithm; based on the target energy type, the energy investment strategy of each energy site is determined . 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, device, electronic device and storage medium for determining energy investment strategy

technical field

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

Background technique

The integrated energy system is a new type of integrated energy system. Compared with the traditional energy supply method, it not only provides users with a single energy source, but also integrates water energy, electric power, Thermal energy, natural gas and other energy sources, realize multi-energy coupling, synergy and complementarity, meet the diversified and abundant energy demand of current users, realize the cascade utilization of energy, and improve the utilization efficiency of various energy sources. According to the reform strategy of my country's energy supply side structure, promoting the change of energy production and consumption patterns, and building a clean, low-carbon, safe and effective modern energy system is an important role in energy innovation. Under the ambitious goal of "carbon peaking and carbon neutrality", the carbon reduction potential in the market environment is huge.

At present, the methods of comprehensive energy optimal low-carbon investment in the market background mainly include: genetic algorithm, particle swarm algorithm and other classical meta-heuristic algorithms, using genetic algorithm and particle swarm algorithm to solve an investment strategy that meets the requirements. For example, the capacity optimization method of integrated energy system equipment based on the improved particle swarm optimization algorithm, which uses the improved dynamic multi-swarm velocity-free particle swarm algorithm, combined with the objective function and constraint conditions of the integrated energy system model, completes the configuration of the equipment capacity of the integrated energy system. to obtain the optimal capacity allocation strategy. However, genetic algorithm and particle swarm algorithm have convergence instability, which leads to low accuracy of the determined energy investment strategy.

SUMMARY OF THE INVENTION

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

The present invention provides a method for determining an energy investment strategy, comprising:

Obtaining target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information on carbon emissions that each of the energy sources can generate;

Based on the construction cost information and the carbon emission information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each of the energy sites. The elite strategy is used to select the top G lowest fitness values in the elite pool The first target mayfly individual is replaced with the first G second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm;

Based on the target energy type, an energy investment strategy for each of the energy sites is determined.

According to a method for determining an energy investment strategy provided by the present invention, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each energy site based on the construction cost information and the carbon emission information, including:

Step A: Based on the construction cost information and the carbon emission information, encode multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm, and determine multiple first coding sequences; the first coding sequence Used to represent the initial energy type invested in each of the energy sites; the initial mayfly population includes a plurality of male mayfly individuals and a plurality of female mayfly individuals;

Step B: Calculate the fitness value of each individual male mayfly and each individual female mayfly based on each of the first coding sequences, the cost information and the carbon emission amount information; the fitness value is calculated as: represents the input evaluation index corresponding to each of the energy sites; the input evaluation index represents the carbon emissions generated by the user when using the energy of the energy site;

Step C: Based on the fitness value, determine the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively;

Step D: Based on the target position, each of the male mayfly individuals and each of the female mayfly individuals move to the target position, respectively, to obtain second coding sequences corresponding to the male mayfly individual and each of the female mayfly individuals respectively ;

Step E: Based on the second coding sequence, each of the male mayfly individuals and each of the female mayfly individuals are paired respectively, and crossed to generate a plurality of progeny mayfly individuals; the positions of each of the progeny mayfly individuals correspond to the third code respectively sequence;

Step F: when the number of iterations does not meet the preset number of iterations, update the mayfly individuals in the initial mayfly population based on the second coding sequence, the third coding sequence and the elite strategy;

Step G: take the fourth coding sequence corresponding to the individual mayfly in the updated initial mayfly population as the new first coding sequence, and iteratively execute steps A-step G until the number of iterations meets the preset number of iterations, based on the final updated The first coding sequence corresponding to the individual mayfly in the initial mayfly population determines the target energy type corresponding to each of the energy stations.

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

Based on the second coding sequence and the third coding sequence, determine whether each individual mayfly crosses the boundary;

In the case that each individual mayfly does not cross the boundary, calculate the fitness value of each individual mayfly;

sorting the fitness values of each individual mayfly;

Based on the elite strategy, the first target mayfly individual corresponding to the lowest fitness value is stored in the elite pool, and the elite pool includes a plurality of the first target mayfly individuals; according to the fitness of the plurality of first target mayfly individuals value, update the mayfly individuals in the initial mayfly population.

According to a method for determining an energy investment strategy provided by the present invention, updating the mayfly individuals in the initial mayfly population according to the fitness values of the plurality of first target mayfly individuals includes:

Replace the top M first target mayfly individuals with the lowest fitness value among the plurality of first target mayfly individuals in the elite pool with the top M second target mayfly individuals with the highest fitness value in the current mayfly population; M is a positive integer;

The mayfly individuals in the initial mayfly population are updated with the replaced current mayfly population.

According to a method for determining an energy investment strategy provided by the present invention, determining the target energy type corresponding to each energy site based on the first coding sequence corresponding to the individual mayfly in the final updated initial mayfly population, including:

The first coding sequence of the target mayfly individual corresponding to the lowest fitness value among the mayfly individuals in the final updated initial mayfly population is determined as the target energy type.

According to a method for determining an energy investment strategy provided by the present invention, determining the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals based on the fitness value, including:

Ranking the fitness values corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively, and determining the fitness value ranking of each of the male mayfly individuals and each of the female mayfly individuals in the initial mayfly population;

Based on the ranking of the fitness values, target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively are determined.

According to a method for determining an energy investment strategy provided by the present invention, determining the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals based on the fitness value ranking, including:

For each of the male mayfly individuals, the first coding sequence corresponding to the highest-ranked male mayfly individual in the fitness value ranking is the target position;

For each of the female mayfly individuals, the first coding sequence corresponding to the male mayfly individual with the same fitness value ranking of each of the female mayfly individuals is the target position.

The present invention also provides a device for determining an energy investment strategy, comprising:

an acquisition module, configured to acquire target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information on carbon emissions that can be generated by each of the energy sources;

The first determination module is used for determining the target energy type corresponding to each of the energy sites based on the construction cost information and the carbon emission information, using the elite strategy and the mayfly algorithm, and the elite strategy is used to put the elite pool before The M first target mayfly individuals with the lowest fitness values are replaced with the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm; the M is a positive integer;

The second determination module is configured to determine, based on the target energy type, an energy investment strategy for each of the energy sites.

The present invention also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implements any one of the energy investment strategies described above when the processor executes the program method of determination.

The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method for determining an energy investment strategy as described above.

The method, device, electronic device and storage medium for determining an energy investment strategy provided by the present invention obtain target data; the target data includes construction cost information of various energy sources corresponding to a plurality of energy sites and the amount of energy generated by each energy source. Carbon emission information; based on the construction cost information and carbon emission information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each energy site. The elite strategy is used to select the first target with the lowest fitness value in the top M in the elite pool. The mayfly individuals are replaced with the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm; M is a positive integer; based on the target energy type, the energy investment strategy of each energy site is determined. The method provided by the invention replaces the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next generation iteration in the mayfly algorithm through the elite strategy, thereby realizing the energy investment strategy of each energy site and improving the mayfly population. The convergence speed of the algorithm improves the accuracy of the energy investment strategy, reduces the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

Description of drawings

In order to explain the present invention or the technical solutions in the prior art more clearly, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are the For some embodiments of the invention, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is one of the schematic flow charts of the determination method of the energy investment strategy provided by the present invention;

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

Fig. 3 is the comparison result schematic diagram of the input evaluation index provided by the present invention;

4 is a schematic structural diagram of a device for determining an energy investment strategy provided by the present invention;

FIG. 5 is a schematic structural diagram of an electronic device provided by the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention. , not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The method for determining an energy investment strategy provided by the present invention will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios thereof.

The present invention provides a method for determining an energy investment strategy. The method for determining an energy investment strategy is applicable to the determination scenario of a comprehensive energy investment strategy in an industrial park by acquiring target data; The construction cost information of each energy source and the carbon emission information that can be produced by each energy source; based on the construction cost information and the carbon emission amount information, the elite strategy and the mayfly algorithm are used to determine the target energy corresponding to each energy site. Type, the elite strategy is used to combine the first M first target mayfly individuals with the lowest fitness values in the elite pool with the first M second target mayflies with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm The individual is replaced; the M is a positive integer; based on the target energy type, the energy investment strategy of each of the energy sites is determined. The method provided by the invention replaces the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next generation iteration in the mayfly algorithm through the elite strategy, thereby realizing the energy investment strategy of each energy site and improving the mayfly population. The convergence speed of the algorithm improves the accuracy of the energy investment strategy, reduces the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

The method for determining the energy investment strategy of the present invention will be described below with reference to FIGS. 1 to 3 .

FIG. 1 is one of the schematic flowcharts of the method for determining an energy investment strategy provided by the present invention. As shown in FIG. 1 , the method includes steps 101 to 105, wherein:

Step 101: Obtain target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information on carbon emissions 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 present invention can be applied to the determination scenario of the comprehensive energy investment strategy of the industrial park. The execution subject of the method may be a device for determining an energy investment strategy, such as an electronic device, or a control module for executing the method for determining an energy investment strategy in the device for determining an energy investment strategy.

Specifically, the target data can be obtained by evaluating the influencing factors of the comprehensive energy investment strategy portfolio under the market environment, that is, the construction cost required by each energy form and the carbon emissions brought by each energy form. The data includes the construction cost information of multiple energy sources corresponding to multiple energy sites and the carbon emission information that each energy source can produce.

In practice, a comprehensive energy station construction investment model can be established according to the influencing factors of the comprehensive energy investment strategy combination in the market environment, and the comprehensive energy station construction investment model can use the method provided by the present invention to realize the energy investment strategy of each energy station.

It should be noted that the number of energy stations can be set according to actual needs, for example, the number of energy stations is 9; the number of energy can also be set according to actual needs, such as water, electricity, heat, gas, etc. 6 energy.

Step 102, based on the construction cost information and the carbon emission information, adopt the elite strategy and the mayfly algorithm to determine the target energy type corresponding to each of the energy sites, and the elite strategy is used to determine the top M fitness in the elite pool. The first target mayfly individual with the lowest value is replaced with the first M second target mayfly individuals with the highest fitness value in the initial mayfly population of the next iteration of the mayfly algorithm; the M is a positive integer.

Specifically, according to the obtained construction cost information of multiple energy sources corresponding to multiple energy sites and the carbon emission information that each energy source can generate, the elite strategy and the mayfly algorithm can be used to determine the target energy type corresponding to each energy site; wherein , the elite strategy is used to replace the first M first target mayfly individuals with the lowest fitness values in the elite pool with the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm , there are multiple first target mayfly individuals stored in the elite pool.

Step 103: Determine an energy investment strategy for each of the energy sites based on the target energy type.

Specifically, according to the target energy type, the optimal low-carbon investment strategy of each energy site is determined, so that 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 allocation of comprehensive energy, the specific energy site construction location has been determined, and the question that needs to be considered is which energy site is to be constructed with what type of energy site. In the market environment, the optimal low-carbon investment strategy for integrated hydropower, heat and gas energy mainly involves two factors, namely the construction costs of different types of energy sites and the carbon emissions caused by users for different energy sources. The target energy type corresponding to the site determines the energy investment strategy of each energy site.

The method for determining an energy investment strategy provided by the present invention obtains target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites respectively and information on carbon emissions that can be generated by each of the energy sources; based on the construction cost Information and carbon emission information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each energy site, and the elite strategy is used to compare the top M first target mayfly individuals with the lowest fitness value in the elite pool with the mayfly algorithm. The first M second target mayfly individuals with the highest fitness value in the initial mayfly population of a generation iteration are replaced; M is a positive integer; based on the target energy type, the energy investment strategy of each energy site is determined. In the method provided by the present invention, the first M second target mayfly individuals with the highest fitness value are replaced in the initial mayfly population of the next generation iteration in the mayfly algorithm through the elite strategy, so as to realize the energy investment strategy of each energy site, and can improve the The convergence speed of the mayfly algorithm improves the accuracy of the energy investment strategy, reduces the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

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

Step A: Based on the construction cost information and the carbon emission information, encode multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm, and determine multiple first coding sequences; the first coding sequence It is used to represent the initial energy type invested in each of the energy stations; the initial mayfly population includes a plurality of male mayfly individuals and a plurality of female mayfly individuals.

Specifically, according to the construction cost information of multiple energy sources and the carbon emissions information that each energy source can generate, encode multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm, and determine multiple mayfly individuals. The first encoding sequence; wherein, the encoding mode may adopt real number encoding, and may also adopt other encoding modes, such as binary encoding.

It should be noted that the first coding sequence is used to represent the initial energy type for investment in each energy site, that is, the first coding sequence can represent a comprehensive energy investment strategy in a feasible market environment; the initial mayfly population includes multiple males Mayfly individual and multiple female mayfly individuals.

For example, the energy that needs to be allocated in the investment strategy in the market environment are 4 kinds of comprehensive energy, such as water energy, electric energy, thermal energy, and gas energy. Represents gas energy; the number of energy stations to be constructed in the divided area is 9, then a feasible first coding sequence corresponding to the mayfly individual of the initial mayfly population is [2, 1, 3, 4, 1, 1, 2 , 3, 2], the first coding sequence indicates that the target energy type corresponding to the first energy site is electric energy, that is, the first energy site is to be invested in electric energy energy station construction; the target energy type corresponding to the second energy site is Water energy, that is, the investment in the construction of hydro energy energy for the second energy site; the target energy type corresponding to the third energy site is thermal energy, that is, the investment in the construction of thermal energy for the third energy site; the fourth energy site The corresponding target energy type is gas energy, that is, the investment in the construction of a gas energy energy station is planned for the fourth energy site; and so on.

Further, according to the construction cost information and carbon emission information, the construction cost proportion matrix and the carbon emission proportion matrix can be determined, wherein, the construction cost proportion matrix indicates that the cost input of each energy source required to build a station at an energy site accounts for the proportion of all energy in the energy site. The proportion of the total cost input required for the construction of the energy site, and the carbon emission proportion matrix represents the proportion of the carbon emissions brought by the construction of each energy site at an energy site to the carbon emissions brought by the construction of all energy sites at the energy site.

For example, in the market environment, there are 9 energy stations to be built, and the energy involved are 4 kinds of integrated energy, such as water energy, electric energy, heat energy, and gas energy. The construction cost proportion matrix W can be determined, wherein the construction cost proportion matrix W is expressed as:

Among them, the row of the construction cost proportion matrix W represents 4 comprehensive energy sources such as water energy, electric energy, heat energy, and gas energy, and the column of the construction cost proportion matrix W represents 9 energy stations. For example, the first row and the first column represent the first The construction cost of water energy for energy site construction.

According to the carbon emission information that each energy source can generate, the carbon emission proportion matrix P can be determined, wherein the carbon emission proportion matrix P is:

Among them, the rows of the carbon emission proportion matrix P represent four comprehensive energy sources such as water energy, electric energy, heat energy, and gas energy, and the columns of the carbon emissions proportion matrix P represent 9 energy stations. For example, the first row and the first column represent the first The amount of carbon emissions that can be produced by building water energy at an energy site.

Next, initialize the initial mayfly population in the mayfly algorithm, and set the initialization parameters, wherein the parameters include the size C of the initial mayfly population, the position L of each individual mayfly in the mayfly population (ie the first coding sequence), and the integrated energy system. The number N of energy stations constructed, the maximum iteration times MaxIteration, the current iteration times Iteration, and the initial speed of each individual mayfly; for example, the number of mayflies in the initial mayfly population is 60, the position dimension of each mayfly is 4, and the maximum number of iterations is 100 times, the current iteration number is 0, and the initial speed of each individual mayfly is 0.

Step B: Calculate the fitness value of each individual male mayfly and each individual female mayfly based on each of the first coding sequences, the construction cost information and the carbon emission information; the fitness value It is used to indicate the input evaluation index corresponding to each of the energy stations; the input evaluation index indicates the carbon emissions generated when the user uses the energy of the energy station.

Specifically, the construction cost proportion matrix W can be determined according to the construction cost information of each energy source at each energy site, and the carbon emission proportion matrix P can be determined according to the information on the carbon emissions that can be generated by each energy source, using the following formula (1) The fitness value of each individual mayfly can be calculated, where formula (1) is expressed as:

Among them, fitness represents the fitness value, W represents the construction cost proportion matrix of each energy site corresponding to each first coding sequence, and P represents the proportion matrix of carbon emissions that can be generated by each energy source corresponding to each first coding sequence.

For example, the initial mayfly population involved in the mayfly algorithm includes 3 mayfly individuals, wherein the first coding sequence of mayfly individual 1 is [2, 3, 1, 1, 2, 3, 4, 1, 2], The first coding sequence of 2 is [4, 3, 1, 1, 2, 3, 3, 4, 2], and the first coding sequence of mayfly individual 3 is [1, 2, 3, 2, 4, 3, 2 , 4, 4]. Then the first coding sequence of the mayfly individual 1 indicates that the initial energy types invested in each energy station are: electric energy, thermal energy, water energy, water energy, electric energy, thermal energy, gas energy, water energy, electric energy, and the first coding sequence of the mayfly individual 2 The coding sequence indicates that the initial energy types invested in each energy station are: gas energy, thermal energy, water energy, water energy, electric energy, thermal energy, thermal energy, gas energy, and electric energy. The initial energy types invested separately are: water energy, electrical energy, thermal energy, electrical energy, gas energy, thermal energy, electrical energy, gas energy, and gas energy.

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

According to the above formula (1), the fitness value of individual mayfly 1 can be calculated as fitness1=0.4484, the fitness value of individual mayfly 2 is fitness2=0.5526, and the fitness value of individual mayfly 3 is fitness3=0.6861.

Step C: Based on the fitness value, determine the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively.

Specifically, according to the calculated fitness value of each individual mayfly, the target positions corresponding to each individual male mayfly and each individual may be determined respectively.

Step D: Based on the target position, each of the male mayfly individuals and each of the female mayfly individuals move to the target position, respectively, to obtain second coding sequences corresponding to the male mayfly individual and each of the female mayfly individuals respectively .

Specifically, based on the target positions corresponding to each male mayfly individual and each female mayfly individual, respectively, each male mayfly individual and each female mayfly individual move to the target position, respectively, to obtain the second coding sequences corresponding to the male mayfly individual and each female mayfly individual respectively .

In practice, for male mayfly individuals, the position update of male mayfly individuals adopts the following formula (2), where:

表示第t+1次迭代时第i个 雄性蜉蝣个体对应的第二编码序列，

表示第t次迭代时i个雄性蜉蝣个体对应的第一编码 序列，

表示第t+1次迭代时i个雄性蜉蝣个体对应的飞行速度。 Among them, i represents the i-th male mayfly individual, t represents the t-th iteration,

represents the second coding sequence corresponding to the i-th male mayfly individual at the t+1-th iteration,

represents the first coding sequence corresponding to i male mayfly individuals at the t-th iteration,

Represents the flight speed corresponding to i male mayfly individuals at the t+1th iteration.

Considering that male mayflies always dance near the water surface, the flight speed of male mayflies is not very fast, which is expressed by the following formula (3), where:

表示第t+1次迭代时第i个雄性蜉蝣个体第j维度所对应的飞行速度，

表示第t次迭代时第i个雄性蜉蝣个体第j维度所对应的飞行速度，

表示第t次迭代时第i 个雄性蜉蝣个体第j维度的第一编码序列，

和

表示正吸引常数，分别用于衡量认知成分 和社会成分的贡献，通俗来讲就是当前个体及最优个体对当前飞行速度的影响程度，

表示第i个雄性蜉蝣个体的历史最优位置，

表示最优雄性蜉蝣个体的位置，

表示蜉蝣的能见度系数，控制着可见范围，

表示

与

之间的笛卡尔距离，

表 示

与

之间的笛卡尔距离。 in,

represents the flight speed corresponding to the jth dimension of the i-th male mayfly individual at the t+1-th iteration,

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

represents the first coding sequence of the j-th dimension of the i-th male mayfly individual at the t-th iteration,

and

Represents the positive attraction constant, which is used to measure the contribution of the cognitive component and the social component respectively. Generally speaking, it is the degree of influence of the current individual and the optimal individual on the current flight speed.

represents the historical optimal position of the i-th male mayfly individual,

represents the position of the optimal male mayfly individual,

Indicates the mayfly's visibility coefficient, which controls the visible range,

express

and

the Cartesian distance between,

express

and

Cartesian distance between.

It should be noted that, for the current dance king (male mayfly individual with the lowest fitness value), the flying speed of the male mayfly individual with the lowest fitness value is expressed by the following formula (4), where:

为雄性蜉蝣个体的舞技系数，

为[-1,1]内的随机值，

表示第t+1次迭 代时第i个雄性蜉蝣个体第j维度所对应的飞行速度，

表示第t次迭代时第i个雄性蜉蝣个 体第j维度所对应的飞行速度。 in,

is the dance skill coefficient of the individual male mayfly,

is a random value in [-1,1],

represents the flight speed corresponding to the jth dimension of the i-th male mayfly individual at the t+1-th iteration,

Represents the flight speed corresponding to the jth dimension of the i-th male mayfly individual at the t-th iteration.

For individual female mayflies, the following formula (5) is used to update the position of individual female mayflies, where:

表示第t+1次迭代时第i个雌性蜉蝣个体的第二编码序列，

第t次迭代 时第i个雌性蜉蝣个体的第一编码序列，

表示第t+1次迭代时i个雌性蜉蝣个体对应的 飞行速度。 in,

represents the second coding sequence of the i-th female mayfly individual at the t+1-th iteration,

The first coding sequence of the i-th female mayfly individual at the t-th iteration,

Indicates the flight speed corresponding to i female mayfly individuals at the t+1th iteration.

Since the target position of the female mayfly individual is the position of the male mayfly individual whose fitness value ranks the same, the flight speed of the female mayfly individual is represented by the following water supply (6), where:

表示第t+1次迭代时第i个雌性蜉蝣个体第j维度所对应的飞行速度，

表示第t次迭代时第i个雌性蜉蝣个体第j维度所对应的飞行速度，

表示正吸引常数，用于 衡量社会成分的贡献，

表示蜉蝣的能见度系数，控制着可见范围，

表示第i个雌性蜉蝣 个体与该雌性蜉蝣个体适应度值排名相同的雄性蜉蝣个体之间的距离，

表示常量，一般 取值为0.1，

为[-1,1]内的随机值，

表示第i个雄性蜉蝣个体的适应度值，

表示第i 个雌性蜉蝣个体的适应度值。 in,

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

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

represents the positive attraction constant, which measures the contribution of social components,

Represents the mayfly's visibility coefficient, which controls the visible range,

represents the distance between the i-th female mayfly individual and the male mayfly individual whose fitness value ranks the same as the female mayfly individual,

Represents a constant, generally the value is 0.1,

is a random value in [-1,1],

represents the fitness value of the i-th male mayfly individual,

Represents the fitness value of the i-th female mayfly individual.

According to the above formula (6), it can be seen that when the fitness value of the i-th female mayfly individual with the same fitness value ranking is smaller than the fitness value of the i-th male mayfly individual, the female mayfly individual will use the above value in formula (6). When the fitness value of the i-th female mayfly individual with the same fitness value ranking is greater than the fitness value of the i-th male mayfly individual, the female mayfly individual uses formula (6) The speed update method in the following formula searches for the target position by itself.

Step E: Based on the second coding sequence, each of the male mayfly individuals and each of the female mayfly individuals are paired respectively, and crossed to generate a plurality of progeny mayfly individuals; the positions of each of the progeny mayfly individuals correspond to the third code respectively sequence.

Specifically, according to the respective second coding sequences corresponding to each male mayfly individual and each female mayfly individual, each male mayfly individual and each female mayfly individual are paired respectively, and the following formula (7) is used to generate multiple offspring mayfly individuals by crossover; , formula (7) is expressed as:

和

表示一对雄性蜉蝣个体和雌性蜉蝣个体交叉产生的 两个子代蜉蝣个体，L表示随机数，取值范围是[0,1]，male表示父代雄性蜉蝣个体，female 表示母代雌性蜉蝣个体。 in,

and

Represents the two offspring mayfly individuals generated by the crossover of a pair of male and female mayfly individuals, L represents a random number, the value range is [0,1], male represents the male mayfly individual of the parent generation, and female represents the female mayfly individual of the mother generation.

It should be noted that, in the process of each iteration, two N progeny mayfly individuals generated by crossing N pairs of male mayfly individuals and female mayfly individuals are selected, and N progeny mayfly individuals are selected, and the progeny mayfly individuals are female or male. , is randomly generated; the positions of the individual progeny mayflies correspond to the third coding sequence, which can be calculated by the above formula (7).

Step F: If the number of iterations does not meet the preset number of iterations, update mayfly individuals in the initial mayfly population based on the second coding sequence, the third coding sequence and the elite strategy.

Specifically, when the number of iterations does not meet the preset number of iterations, that is, when the current number of iterations is less than the preset number of iterations, according to the corresponding second coding sequence, sub-coding sequence and sub-coding sequence corresponding to each female mayfly individual and each male mayfly individual The third coding sequence corresponding to the individual mayfly and the elite strategy can update the individual mayfly in the initial mayfly population in the current iteration number.

Step G: take the fourth coding sequence corresponding to the individual mayfly in the updated initial mayfly population as the new first coding sequence, and iteratively execute steps A-step G until the number of iterations meets the preset number of iterations, based on the final updated The first coding sequence corresponding to the individual mayfly in the initial mayfly population determines the target energy type corresponding to each of the energy stations.

Specifically, the fourth coding sequence includes a second coding sequence and a third coding sequence, and the fourth coding sequence corresponding to the individual mayfly in the updated initial mayfly population is taken as the new first coding sequence, and the above step A is repeatedly and iteratively executed - Step G, until the number of iterations is greater than the preset number of iterations, at this time, the target energy type corresponding to each energy station is determined according to the first coding sequence corresponding to the individual mayfly in the final updated initial mayfly population.

The method for determining an energy investment strategy provided by the present invention includes step A: encoding multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm based on construction cost information and carbon emission information, and determining multiple first coding sequences; The first coding sequence is used to represent the initial energy type invested in each energy site; the initial mayfly population includes multiple male mayfly individuals and multiple female mayfly individuals; step B: based on each first coding sequence, construction cost information and carbon emissions The fitness value of each individual male mayfly and each individual female mayfly is calculated separately; the fitness value is used to represent the input evaluation index corresponding to each energy site; the input evaluation index represents the carbon emission generated by the user when using the energy of the energy site Step C: Based on the fitness value, determine the target positions corresponding to each male mayfly individual and each female mayfly individual respectively; Step D: Based on the target position, each male mayfly individual and each female mayfly individual move to the target position, respectively, to obtain a male The second coding sequence corresponding to the individual mayfly and each individual female mayfly; Step E: Based on the second coding sequence, each male individual mayfly and each individual female mayfly are paired respectively, and a plurality of progeny mayfly individuals are generated by crossover; The positions of , respectively correspond to the third coding sequence; Step F: when the number of iterations does not meet the preset number of iterations, update the mayfly individuals in the initial mayfly population based on the second coding sequence, the third coding sequence and the elite strategy; Step G : take the fourth coding sequence corresponding to the individual mayfly in the updated initial mayfly population as the new first coding sequence, and iteratively execute steps A-step G until the number of iterations meets the preset number of iterations, based on the final updated initial The first coding sequence corresponding to the individual mayfly in the mayfly population determines the target energy type corresponding to each of the energy stations. In the method provided by the invention, the mayfly algorithm is improved through the elite strategy, and the first M second target mayfly individuals with the highest fitness value in the initial mayfly population of the next iteration in the mayfly algorithm are replaced by the elite strategy, and various energy sources are realized. The energy investment strategy of the site can improve the convergence speed of the mayfly algorithm, thereby improving the accuracy of the energy investment strategy, reducing the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

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

Step 1) Based on the second coding sequence and the third coding sequence, determine whether each individual mayfly crosses the boundary.

Specifically, according to the second coding sequence and the third coding sequence, it is determined whether each individual mayfly crosses the boundary, that is, whether the value of each dimension in the second coding sequence and the third coding sequence exceeds the target value, for example, the target value is 4.

Step 2) Calculate the fitness value of each mayfly individual under the condition that each individual mayfly does not cross the boundary.

Specifically, in the case that each individual mayfly does not cross the boundary, according to the second coding sequence of each individual mayfly and the third coding sequence of the individual progeny mayfly, the above formula (1) is used to calculate the fitness value of each individual mayfly. , wherein each mayfly individual includes mayfly individuals and progeny mayfly individuals in the initial mayfly population.

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

Step 4) Based on the elite strategy, store the first target mayfly individual corresponding to the lowest fitness value in the elite pool, where the elite pool includes a plurality of the first target mayfly individuals; according to the plurality of first target mayfly individuals The fitness value of , update the mayfly individuals in the initial mayfly population.

Specifically, using the elite strategy, the first target mayfly individual corresponding to the lowest fitness value in the sorting result is stored in the elite pool. In the continuous iterative process, the elite pool includes multiple first target mayfly individuals; The fitness value of the first target mayfly individual is to update the mayfly individual in the initial mayfly population.

In the method for determining the energy investment strategy provided by the present invention, according to the second coding sequence and the third coding sequence, it is judged whether each individual mayfly crosses the boundary; if the individual mayfly does not cross the boundary, the fitness value of each individual mayfly is calculated; The fitness value of each mayfly individual is sorted; based on the elite strategy, the first target mayfly individual corresponding to the lowest fitness value is stored in the elite pool, and the elite pool includes a plurality of the first target mayfly individuals; The fitness value of the first target mayfly individual is to update the individual mayfly in the initial mayfly population, thereby realizing the energy investment strategy of each energy site, which can improve the convergence speed of the mayfly algorithm, thereby improving the accuracy of the energy investment strategy and reducing the cost of energy investment. The construction cost of energy sites and the carbon emissions that users can bring from using energy.

Optionally, the updating of the mayfly individuals in the initial mayfly population according to the fitness values of the plurality of first target mayfly individuals in the above step 4) includes:

Replace the top M first target mayfly individuals with the lowest fitness value among the plurality of first target mayfly individuals in the elite pool with the top M second target mayfly individuals with the highest fitness value in the current mayfly population; The replaced mayfly population updates the mayfly individuals in the initial mayfly population.

Specifically, the fitness values corresponding to multiple first target mayfly individuals in the elite pool are sorted, the top M first target mayfly individuals with the lowest fitness values are selected, and the M first target mayfly individuals are replaced by the current mayfly population The first M second target mayfly individuals with the highest fitness value in the middle; and then the mayfly individuals in the current mayfly population after replacement are used as mayfly individuals in the next generation of the initial mayfly population, so as to update the mayfly individuals in the initial mayfly population.

The method for determining the energy investment strategy provided by the present invention replaces the top M first target mayfly individuals with the lowest fitness value among the multiple first target mayfly individuals in the elite pool with the highest fitness value in the current mayfly population. A second target mayfly individual; update the mayfly individual in the initial mayfly population with the replaced current mayfly population, which improves the convergence speed of the mayfly algorithm, and can quickly implement the energy investment strategy of each energy site, thereby improving the efficiency of the energy investment strategy. Accuracy reduces the construction cost of each energy site and the carbon emissions that users can bring from using energy.

Optionally, in the above-mentioned step G, based on the first coding sequence corresponding to the individual mayfly in the final updated initial mayfly population, determining the target energy type corresponding to each of the energy sites includes: the final updated initial mayfly population. The first coding sequence of the target mayfly individual corresponding to the lowest fitness value among the mayfly individuals in the mayfly is determined as the target energy type.

Specifically, after updating the mayfly individuals in the initial mayfly population with the replaced current mayfly population, determine whether the current number of iterations is greater than the preset number of iterations; if the current number of iterations is not greater than the preset number of iterations, continue the next iteration process; if the current number of iterations is greater than the preset number of iterations, the algorithm iteration process ends, the initial mayfly population in the last iteration process is taken as the final updated initial mayfly population, and the mayfly individuals in the final updated initial mayfly population The first coding sequence of the target mayfly individual corresponding to the lowest fitness value is used as the target investment plan, and the target energy type of each energy site is determined according to the real number codes corresponding to each energy site in the first coding sequence.

It should be noted that the lowest fitness value of the individual mayfly in the final updated initial mayfly population can also be taken as the global optimum value, which means that the input evaluation index of each energy station is the lowest.

For example, the first coding sequence [2, 3, 1, 1, 2, 3, 4, 1, 2] of the target mayfly individual 1 corresponding to the lowest fitness value of the mayfly individuals in the final updated initial mayfly population is used as the target The investment plan, according to the real number codes corresponding to each energy site in the first coding sequence, that is, the real number code corresponding to the energy site number 1 is 2, the real number code corresponding to the energy site number 2 is 3, and the energy site number 3 corresponds to the real number code 3. The corresponding real number code is 1, the real number code corresponding to the energy station number 4 is 1, the real number code corresponding to the energy station number 5 is 2, the real number code corresponding to the energy station number 6 is 3, and the real number code corresponding to the energy station number 7 is 3. The real number code corresponding to the energy site is 4, the real number code corresponding to the energy site number 8 is 1, and the real number code corresponding to the energy site number 9 is 2, so it can be determined that the target energy type of the energy site number 1 is electric energy , that is, to build an electric energy station in the first predetermined area; determine the target energy type of the energy station numbered 2 as thermal energy, that is, to build a thermal energy station in the first predetermined area; determine the target energy type of the energy station numbered 3 It is hydropower, that is, the construction of a water energy station in the first given area; the target energy type of the energy station numbered 4 is determined to be hydropower, that is, the construction of a water energy station in the first predetermined area; the energy station numbered 5 is determined The target energy type is electric energy, that is, the electric energy station is constructed in the first given area; the target energy type of the energy station numbered 6 is determined to be thermal energy, that is, the thermal energy station is constructed in the first predetermined area; The target energy type of the energy station is gas energy, that is, the gas energy station is built in the first predetermined area; the target energy type of the energy station numbered 8 is determined to be water energy, that is, the water energy station is built in the first predetermined area; determine The target energy type of the energy station numbered 9 is electric energy, that is, the construction of an electric energy station in the first given area.

After considering the construction cost of the comprehensive energy site and the user's demand for various energy sources, the fitness value of the investment strategy corresponding to the mayfly individual 1 is 0.4484, which is the global optimal value, and the investment evaluation index of each energy site is the lowest.

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

Step 1) Rank the fitness values corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively, and determine the fitness of each of the male mayfly individuals and each of the female mayfly individuals in the initial mayfly population value ranking.

Specifically, the fitness value of each male mayfly individual is sorted to determine the fitness value ranking of each male mayfly individual in the initial mayfly population; and then the fitness value of each female mayfly individual is sorted to determine the Ranking of fitness values in the initial mayfly population.

Step 2) Based on the ranking of the fitness values, determine the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively.

Specifically, the fitness value ranking of each male mayfly individual and each female mayfly individual in the initial mayfly population can determine the target positions corresponding to each male mayfly individual and each female mayfly individual respectively.

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

2-1) For each male mayfly individual, take the first coding sequence corresponding to the male mayfly individual with the highest ranking in the fitness value ranking as the target position.

Specifically, for each male mayfly individual, according to the ranking order of the fitness value of each male mayfly individual, the first coding sequence corresponding to the male mayfly individual with the highest ranking in the fitness value ranking is used as the target position of other male mayfly individuals, and for The highest ranked male mayfly individual, the first coding sequence corresponding to the male mayfly individual is the target position of the male mayfly individual, and the male mayfly individual displays dance skills at the target position.

2-2) For each of the female mayfly individuals, the first coding sequence corresponding to the male mayfly individual with the same fitness value ranking of each of the female mayfly individuals is the target position.

Specifically, for each female mayfly individual, according to the ranking order of the fitness value of each male mayfly individual and according to the ranking order of the fitness value of each female mayfly individual, the corresponding male mayfly individual with the same fitness value ranking of each female mayfly individual The first coding sequence serves as the target location for each individual female mayfly.

The method for determining the energy investment strategy provided by the present invention determines the fitness value of each male mayfly individual and each female mayfly individual in the initial mayfly population by sorting the corresponding fitness values of each male mayfly individual and each female mayfly individual. Ranking, for each male mayfly individual, take the first coding sequence corresponding to the male mayfly individual with the highest ranking in the fitness value ranking as the target position, and for each female mayfly individual, the male mayfly whose fitness value ranks the same as that of each female mayfly individual The first coding sequence corresponding to the individual is the target position. By continuously optimizing the fitness value of each mayfly individual, the energy investment strategy of each energy site can be realized, thereby improving the accuracy of the energy investment strategy and reducing the construction cost of each energy site. and carbon emissions from energy use by users.

Fig. 2 is the second schematic flowchart of the method for determining an energy investment strategy provided by the present invention. As shown in Fig. 2, the method includes steps 201 to 212, wherein:

In step 201, target data is acquired; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information of carbon emissions that can be generated by each energy source.

Step 202, encoding a plurality of mayfly individuals in the initial mayfly population in the mayfly algorithm. Based on the construction cost information and carbon emission information, encode multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm, and determine multiple first coding sequences; the first coding sequences are used to represent the respective investment in each of the energy sites The initial energy type of ; the initial mayfly population includes multiple male mayfly individuals and multiple female mayfly individuals.

Step 203, initialize the parameters of the mayfly algorithm. Initialize the size C of the initial mayfly population, the position L of each individual mayfly in the mayfly population (ie, the first coding sequence), the number N of energy stations constructed in the integrated energy system, the maximum number of iterations MaxIteration, the current iteration number Iteration, the individual mayfly For example, the number of mayfly individuals in the initial mayfly population is 60, the position dimension of each mayfly is 4, the maximum number of iterations is 100, the current iteration number is 0, and the initial speed of each individual mayfly is 0.

Step 204: Calculate the fitness value of each individual mayfly. Based on each first coding sequence, construction cost information and carbon emission information, the fitness value of each male mayfly individual and each female mayfly individual is calculated respectively; the fitness value is used to represent the input evaluation index corresponding to each of the energy sites; input The evaluation index represents the carbon emissions generated by the user when using the energy of the energy site.

Step 205: Determine the target positions corresponding to each male mayfly individual and each female mayfly individual respectively. Rank the respective fitness values of each male mayfly individual and each female mayfly individual to determine the fitness value ranking of each male mayfly individual and each female mayfly individual in the initial mayfly population; Individual, take the first coding sequence corresponding to the male mayfly individual with the highest ranking in the fitness value ranking as the target position; for each female mayfly individual, the first coding sequence corresponding to the male mayfly individual whose fitness value is the same as that of each female mayfly individual The sequence is the target position.

Step 206, update the position of each individual mayfly. Based on the target position, each male mayfly individual and each female mayfly individual move to the target position, respectively, to obtain second coding sequences corresponding to the male and female mayfly individuals respectively.

Step 207, cross pairing. Each male mayfly individual and each female mayfly individual are paired respectively, and a plurality of progeny mayfly individuals are generated by crossover; the positions of each progeny mayfly individual correspond to the third coding sequence respectively.

Step 208: Determine whether each individual mayfly crosses the boundary. Based on the second coding sequence and the third coding sequence, determine whether each individual mayfly crosses the boundary. In the case that each individual mayfly does not cross the boundary, the fitness value of each individual mayfly is calculated. In the case that each individual mayfly is out of bounds, after adjusting each individual mayfly, the fitness value of each individual mayfly is calculated.

Step 209: Rank the fitness values of each individual mayfly. Based on the elite strategy, the first target mayfly individual corresponding to the lowest fitness value is stored in the elite pool, and the elite pool includes a plurality of the first target mayfly individuals.

Step 210, replace the top M first target mayfly individuals with the lowest fitness value among the multiple first target mayfly individuals in the elite pool, and replace the top M second target mayfly individuals with the highest fitness value in the current mayfly population; The fitness value of the first target mayfly individual, and update the mayfly individual in the initial mayfly population.

Step 211 , determine whether the number of iterations is greater than the preset number of iterations (maximum number of iterations). If the number of iterations is not greater than the preset number of iterations, go to step 203 ; if the number of iterations is greater than the preset number of iterations, go to step 212 .

Step 212: Determine the energy investment strategy of each energy station. The first coding sequence of the target mayfly individual corresponding to the lowest fitness value of the mayfly individuals in the final updated initial mayfly population is determined as the target energy type. Based on the target energy type, determine the energy investment strategy of each energy site, that is, invest in each energy site to build a site of the target energy type corresponding to each energy site.

The method for determining the energy investment strategy provided by the present invention obtains target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information of carbon emissions that can be generated by each energy source; Encoding multiple mayfly individuals in the initial mayfly population; initializing the parameters of the mayfly algorithm; calculating the fitness value of each mayfly individual; determining the target positions corresponding to each male and female mayfly individuals; The male mayfly individual and each female mayfly individual are paired respectively, and the crossover produces multiple progeny mayfly individuals; the positions of each progeny mayfly individual correspond to the third coding sequence respectively; judge whether it is out of bounds. The fitness value of each individual mayfly, in the case that each individual mayfly is out of bounds, after adjusting each individual mayfly, calculate the fitness value of each individual mayfly; sort the fitness value of each individual mayfly, based on the elite The strategy is to store the first target mayfly individual corresponding to the lowest fitness value in the elite pool, the elite pool includes a plurality of the first target mayfly individuals, and store the first target mayfly individual with the lowest fitness value in the elite pool. The first M first target mayfly individuals replace the first M second target mayfly individuals with the highest fitness values in the current mayfly population; update the mayfly individuals in the initial mayfly population according to the fitness values of multiple first target mayfly individuals, Determine whether the number of iterations is greater than the preset number of iterations, and if the number of iterations is not greater than the number of preset iterations, continue to the next iteration; if the number of iterations is greater than the preset number of iterations, the final updated initial mayfly population The first coding sequence of the target mayfly individual corresponding to the lowest fitness value among the mayfly individuals is determined as the target energy type, so as to determine the energy investment strategy of each energy site. The energy investment strategy of the site improves the convergence speed of the mayfly algorithm, thereby improving the accuracy of the energy investment strategy, reducing the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

Fig. 3 is a schematic diagram of the comparison result of the input evaluation index provided by the present invention. As shown in Fig. 3, the water energy, electric energy, water energy, electric energy, water energy, electric energy, water energy, water energy, electric energy, water energy, water energy, water energy, water energy, electric energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy, water energy and water energy in the market environment after 100 iterations with the simulated annealing algorithm and the particle swarm algorithm in the prior art are respectively shown in Fig. 3. Comparison of the evaluation index values of the optimal low-carbon investment strategies for comprehensive energy such as thermal energy and gas energy. The top part of the figure is the input evaluation index result obtained by the simulated annealing algorithm, the middle curve is the input evaluation index result obtained by the particle swarm algorithm, and the bottom The curve is the input evaluation index result obtained by the method provided by the present invention.

It can be seen from Figure 3 that after 100 iterations, the method provided by the present invention finally obtains the investment evaluation index value of the optimal low-carbon investment strategy for comprehensive energy such as water energy, electric energy, thermal energy, and gas energy under the market environment. is 0.40, the input evaluation index value of the optimal low-carbon investment strategy obtained by the particle swarm algorithm is 0.47, and the input evaluation index value of the optimal low-carbon investment strategy obtained by the simulated annealing algorithm is 0.50, which is the required value of the mayfly algorithm provided by the present invention. Compared with the particle swarm algorithm and simulated annealing algorithm in solving the investment sequence, the input amount is reduced by about 14% and 20% respectively, indicating that the comprehensive energy investment sequence obtained by the method provided by the present invention under the market environment can effectively balance the energy consumption of the site. Construction costs and users' carbon emissions from different energy sources reduce construction costs and users' carbon emissions from different energy sources. At the same time, it can also be seen from FIG. 3 that, compared with the particle swarm algorithm and the simulated annealing algorithm, the convergence speed of the method provided by the present invention is significantly faster, and the particle swarm algorithm and the simulated annealing algorithm fall into premature convergence in the iterative process , and cannot jump out of the local optimum, and the quality of the obtained investment sequence is low, which is not conducive to practical adoption.

The device for determining an energy investment strategy provided by the present invention is described below. The device for determining an energy investment strategy described below and the method for determining an energy investment strategy described above can be referred to each other correspondingly.

FIG. 4 is a schematic structural diagram of a device for determining an energy investment strategy provided by the present invention. As shown in FIG. 4 , the device 400 for determining an energy investment strategy includes: an acquisition module 401 , a first determination module 402 and a second determination module 404 ; in,

The acquisition module 401 is used for acquiring target data; the target data includes construction cost information of various energy sources corresponding to a plurality of energy sites and carbon emission amount information that each energy source can generate;

The first determination module 402 is configured to determine the target energy type corresponding to each of the energy sites by adopting the elite strategy and the mayfly algorithm based on the construction cost information and the carbon emission information, and the elite strategy is used to select the elite pool into The first M first target mayfly individuals with the lowest fitness values are replaced with the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm; the M is a positive integer;

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

The device for determining an energy investment strategy provided by the present invention obtains target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information on carbon emissions that can be generated by each of the energy sources; based on the construction cost Information and carbon emission information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each energy site, and the elite strategy is used to compare the top M first target mayfly individuals with the lowest fitness value in the elite pool with the mayfly algorithm. The first M second target mayfly individuals with the highest fitness value in the initial mayfly population of a generation iteration are replaced; M is a positive integer; based on the target energy type, the energy investment strategy of each energy site is determined. The device provided by the invention replaces the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next generation iteration in the mayfly algorithm through the elite strategy, thereby realizing the energy investment strategy of each energy site and improving the mayfly The convergence speed of the algorithm improves the accuracy of the energy investment strategy, reduces the construction cost of each energy site and the carbon emissions caused by the use of energy by users.

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

Step A: Based on the construction cost information and the carbon emission information, encode multiple mayfly individuals in the initial mayfly population involved in the mayfly algorithm, and determine multiple first coding sequences; the first coding sequence Used to represent the initial energy type invested in each of the energy sites; the initial mayfly population includes a plurality of male mayfly individuals and a plurality of female mayfly individuals;

Step B: Calculate the fitness value of each individual male mayfly and each individual female mayfly based on each of the first coding sequences, the construction cost information and the carbon emission information; the fitness value Used to represent the input evaluation index corresponding to each of the energy sites; the input evaluation index represents the carbon emissions generated by the user when using the energy of the energy site;

Step C: Based on the fitness value, determine the target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively;

Step D: Based on the target position, each of the male mayfly individuals and each of the female mayfly individuals move to the target position, respectively, to obtain second coding sequences corresponding to the male mayfly individual and each of the female mayfly individuals respectively ;

Step E: Based on the second coding sequence, each of the male mayfly individuals and each of the female mayfly individuals are paired respectively, and crossed to generate a plurality of progeny mayfly individuals; the positions of each of the progeny mayfly individuals correspond to the third code respectively sequence;

Step F: when the number of iterations does not meet the preset number of iterations, update the mayfly individuals in the initial mayfly population based on the second coding sequence, the third coding sequence and the elite strategy;

Step G: take the fourth coding sequence corresponding to the individual mayfly in the updated initial mayfly population as the new first coding sequence, and iteratively execute steps A-step G until the number of iterations meets the preset number of iterations, based on the final updated The first coding sequence corresponding to the individual mayfly in the initial mayfly population determines the target energy type corresponding to each of the energy stations.

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

Based on the second coding sequence and the third coding sequence, determine whether each individual mayfly crosses the boundary;

In the case that each individual mayfly does not cross the boundary, calculate the fitness value of each individual mayfly;

sorting the fitness values of each individual mayfly;

Based on the elite strategy, the first target mayfly individual corresponding to the lowest fitness value is stored in the elite pool, and the elite pool includes a plurality of the first target mayfly individuals; according to the fitness of the plurality of first target mayfly individuals value, update the mayfly individuals in the initial mayfly population.

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

replacing the top M first target mayfly individuals with the lowest fitness values among the plurality of first target mayfly individuals in the elite pool with the top M second target mayfly individuals with the highest fitness values in the current mayfly population;

The mayfly individuals in the initial mayfly population are updated with the replaced current mayfly population.

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

The first coding sequence of the target mayfly individual corresponding to the lowest fitness value among the mayfly individuals in the final updated initial mayfly population is determined as the target energy type.

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

Ranking the fitness values corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively, and determining the fitness value ranking of each of the male mayfly individuals and each of the female mayfly individuals in the initial mayfly population;

Based on the ranking of the fitness values, target positions corresponding to each of the male mayfly individuals and each of the female mayfly individuals respectively are determined.

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

For each of the male mayfly individuals, the first coding sequence corresponding to the highest-ranked male mayfly individual in the fitness value ranking is the target position;

For each of the female mayfly individuals, the first coding sequence corresponding to the male mayfly individual with the same fitness value ranking of each of the female mayfly individuals is the target position.

FIG. 5 is a schematic diagram of the physical structure of an electronic device provided by the present invention. As shown in FIG. 5 , the electronic device 500 may include: a processor (processor) 510, a communications interface (Communications Interface) 520, a memory (memory) 530 and The communication bus 550, wherein the processor 510, the communication interface 520, and the memory 530 complete the communication with each other through the communication bus 550. The processor 510 can call the logic instructions in the memory 530 to execute a method for determining an energy investment strategy, the method includes: acquiring target data; the target data includes construction cost information and each energy source corresponding to a plurality of energy sites respectively. The carbon emission information that the energy can generate; based on the construction cost information and the carbon emission information, the elite strategy and the mayfly algorithm are used to determine the target energy type corresponding to each of the energy sites, and the elite strategy is used for Replace the first M first target mayfly individuals with the lowest fitness values in the elite pool with the first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next iteration of the mayfly algorithm; the M is A positive integer; based on the target energy type, determine the energy investment strategy of each energy site.

In addition, the above-mentioned logic instructions in the memory 530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to execute the method for determining an energy investment strategy provided by the above methods, the The method includes: acquiring target data; the target data includes construction cost information of multiple energy sources corresponding to multiple energy sites and information on carbon emissions that each energy source can produce; based on the construction cost information and the carbon emissions Emission information, using the elite strategy and the mayfly algorithm to determine the target energy type corresponding to each of the energy sites, the elite strategy is used to compare the top M first target mayfly individuals with the lowest fitness values in the elite pool with the mayfly algorithm The first M second target mayfly individuals with the highest fitness values in the initial mayfly population of the next generation iteration are replaced; the M is a positive integer; based on the target energy type, the energy investment strategy of each energy site is determined.

The device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

From the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on this understanding, the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: it can still be Modifications are made to the technical solutions described in the foregoing embodiments, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

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

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;

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: (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.

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:

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 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:

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 population are updated with the replaced current mayflies population.

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:

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.

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:

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;

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

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:

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 has as a target position the same fitness value ranking as the male mayflies.

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

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 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.

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