CN114565238A

CN114565238A - Comprehensive energy low-carbon scheduling method and device

Info

Publication number: CN114565238A
Application number: CN202210137882.0A
Authority: CN
Inventors: 周杰; 李超群; 朱锐; 戴建国; 黄超; 黎劲松; 苏革; 常泳
Original assignee: Shihezi University
Current assignee: Shihezi University
Priority date: 2022-02-15
Filing date: 2022-02-15
Publication date: 2022-05-31
Anticipated expiration: 2042-02-15
Also published as: CN114565238B

Abstract

The invention provides a comprehensive energy low-carbon scheduling method and a device, and the method comprises the following steps: constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data; solving the comprehensive energy scheduling model based on a carnivorous plant algorithm and an objective function to obtain an objective scheduling scheme; scheduling the electric power energy in the target area based on the target scheduling scheme; wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: each power supplier provides a management and control risk index, an energy delivery convenience index and an energy utilization rate corresponding to each type of electric energy. The comprehensive energy low-carbon scheduling method and device provided by the invention can realize more efficient and more accurate comprehensive energy low-carbon scheduling, can improve the reliability of comprehensive energy scheduling to the maximum extent, and has stronger robustness of comprehensive energy scheduling.

Description

Comprehensive energy low-carbon scheduling method and device

Technical Field

The invention relates to the technical field of energy scheduling, in particular to a comprehensive energy low-carbon scheduling method and device

Background

With the rapid development of economy and the continuous progress of society, energy and environmental problems are increasingly highlighted. By vigorously developing clean energy power generation modes such as wind power generation, photovoltaic power generation, hydroelectric power generation, nuclear power generation and hydrogen power generation, more advanced power generation and power consumption modes are explored and developed, and the method becomes a new direction for the development of the power industry.

Comprehensive energy scheduling means reasonably scheduling various electric energy sources in a coupling area through various electric energy sources in the coupling area, so that dynamic cooperation and advantage complementation of the electric energy sources in the coupling area are realized. In the research of comprehensive energy scheduling, how to utilize the characteristic advantages of various electric energy sources to carry out multi-energy optimization coordination scheduling, realize the advantage complementation of various different electric energy sources, and enable the supply reliability of the electric energy sources to be the highest as possible is a main research target of the comprehensive energy scheduling. The reasonable comprehensive energy scheduling scheme can enable various electric energy sources in the region to have more flexible dynamic allocation complementary characteristics, higher safety and distribution precision, and ensure the safety and reliability of power supply.

With the vigorous development of low-carbon energy, the traditional artificial comprehensive energy scheduling method cannot meet the scheduling requirement of the existing huge comprehensive energy system, and the comprehensive energy scheduling efficiency and the scheduling precision are low. Therefore, how to perform comprehensive energy low-carbon scheduling more efficiently and accurately is a problem to be solved in the field.

Disclosure of Invention

The invention provides a comprehensive energy low-carbon scheduling method and device, which are used for overcoming the defects of low comprehensive energy scheduling efficiency and poor scheduling precision in the prior art and realizing more efficient and accurate comprehensive energy low-carbon scheduling.

The invention provides a comprehensive energy low-carbon scheduling method, which comprises the following steps:

acquiring first target data and second target data;

constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data;

solving the comprehensive energy scheduling model based on a carnivorous plant algorithm and the target function to obtain a target scheduling scheme;

scheduling the electric power energy in the target area based on the target scheduling scheme;

wherein the first target data comprises: the number of power suppliers and the type of power source in the target area; the second target data includes: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supplier for providing the electric energy, and the energy delivery convenience index is used for describing the convenience degree of the corresponding power supplier for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supplier for providing the electric energy.

According to the low-carbon scheduling method of the comprehensive energy, provided by the invention, the comprehensive energy scheduling model is solved based on the carnivorous plant algorithm and the objective function to obtain an objective scheduling scheme, and the method specifically comprises the following steps:

step S1, acquiring a plurality of initial scheduling schemes based on the comprehensive energy scheduling model;

step S2, initializing control parameters of the carnivorous plant algorithm to obtain individual populations corresponding to first iteration, wherein each individual in the individual populations corresponding to the first iteration corresponds to each initial scheduling scheme;

step S3, based on the carnivorous plant algorithm, carrying out population evolution on the individual population corresponding to the mth iteration to obtain the individual population corresponding to the mth iteration after the population evolution; wherein m is more than or equal to 1 and less than or equal to T, and T represents the preset maximum iteration times;

step S4, obtaining an objective function value of each individual in the individual population corresponding to the mth iteration after the population evolution, and taking the individual with the maximum objective function value as an alternative individual corresponding to the mth iteration;

step S5, cloning and recombining the individual population corresponding to the mth iteration after population evolution based on the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution to obtain the individual population corresponding to the (m + 1) th iteration, and repeatedly executing the step S3 and the step S5 until m is larger than T;

step S6, determining a target individual in each of the candidate individuals based on the obtained objective function value corresponding to each of the candidate individuals, and using a scheduling scheme corresponding to the target individual as the target scheduling scheme.

According to the low-carbon scheduling method for the comprehensive energy, provided by the invention, the formula of the objective function is as follows:

result_e，d＝X_i，m×(1-MS_e，d)×MC_e，d×UR_e，d

wherein i is an identifier of any individual in the individual population corresponding to the mth iteration, and i is 1,2, …, M, and M represents the total number of individuals in the individual population; x_i，mRepresenting the ith individual in the individual population corresponding to the mth iteration; f. of_i，mRepresents X_i，mThe objective function value of (1); e denotes the total number of types of electrical energy sources in the target area, E is the identification of the type, E is 1,2, …, E; d represents the number of power suppliers in the target area, D is the identification of the power suppliers, and D is 1,2, …, D; MS (Mass Spectrometry)_e，dIndicating the tone corresponding to the ith individualUnder the condition that the degree scheme schedules the electric energy in the target area, the d-th power supplier provides the control risk index of the e-th type of electric energy; MC (monomer casting)_e，dThe energy source transmission convenience index of the e type of electric energy source provided by the d power supplier under the condition that the electric energy source in the target area is scheduled based on the scheduling scheme corresponding to the i individual; UR_e，dThe energy utilization rate of the e-th type of electric energy provided by the d-th power supplier is represented in the case where the electric energy in the target area is scheduled based on the scheduling plan corresponding to the i-th individual.

According to the low-carbon scheduling method for the comprehensive energy, provided by the invention, based on the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution, the individual population corresponding to the mth iteration after population evolution is cloned and recombined to obtain the individual population corresponding to the (m + 1) th iteration, and the method specifically comprises the following steps:

sequencing the individuals according to the sequence of the objective function value of each individual in the individual population corresponding to the mth iteration after the population is evolved from large to small;

cloning each individual w before ranking for s parts to obtain a clone individual corresponding to each individual;

updating each individual clone based on a preset variation probability threshold, and taking each updated individual clone as each individual in the individual population corresponding to the (m + 1) th iteration;

wherein w is a positive integer no greater than M; and s is M/w.

According to the low-carbon scheduling method for the comprehensive energy, provided by the invention, the variation probability threshold is between 0.5 and 0.6.

According to the low-carbon scheduling method for the comprehensive energy, the individual population comprises carnivorous plant individuals and prey individuals, and the number of the prey individuals is integral multiple of the number of the carnivorous plant individuals.

The invention also provides a comprehensive energy low-carbon scheduling device, which comprises:

the data acquisition module is used for acquiring first target data and second target data;

the model building module is used for building a comprehensive energy scheduling model based on the first target data and building a target function corresponding to the comprehensive energy scheduling model based on the second target data;

the algorithm solving module is used for solving the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain an objective scheduling scheme;

the energy scheduling module is used for scheduling the electric energy in the target area based on the target scheduling scheme;

wherein the first target data comprises: the number of power suppliers and the type of power source in the target area; the second target data includes: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supplier for providing the electric energy, and the energy delivery convenience index is used for describing the convenience degree of the corresponding power supplier for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the power energy provided by the corresponding power supply party.

The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of any one of the comprehensive energy low-carbon scheduling methods.

The invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the integrated energy low carbon scheduling method according to any one of the above.

The invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of any one of the methods for integrated energy low-carbon scheduling described above.

The comprehensive energy low-carbon scheduling method and device provided by the invention construct a comprehensive energy scheduling model based on the number of power supply parties in a target area and the type of electric energy, construct an objective function corresponding to the comprehensive energy scheduling model based on the control risk index, the energy delivery convenience index and the energy utilization rate corresponding to each power supply party, solve the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain a target scheduling scheme, have the highest scheduling reliability of the electric energy in the target area based on the target scheduling scheme, can realize more efficient and accurate comprehensive energy low-carbon scheduling, can maximally improve the scheduling reliability of the comprehensive energy, have stronger robustness of the comprehensive energy scheduling, and can solve the optimal and accurate control scheduling problem of the comprehensive energy under the condition that the energy scheduling environment and the scheduling mode are constantly changed, the economic benefit of comprehensive energy dispatching can be improved, and the method has a better popularization prospect.

Drawings

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

FIG. 1 is a schematic flow chart of a comprehensive energy low-carbon scheduling method provided by the invention;

FIG. 2 is a second schematic flow chart of the comprehensive energy low-carbon scheduling method provided by the present invention;

FIG. 3 is a schematic diagram comparing simulation results of the comprehensive energy low-carbon scheduling method provided by the invention and the existing scheduling method;

FIG. 4 is a schematic structural diagram of the comprehensive energy low-carbon scheduling device provided by the invention;

fig. 5 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

It should be noted that the carnivorous plant algorithm is a new heuristic intelligent algorithm, and is applied to the aspects of a combination optimization problem, high-dimensional design variables, the existence of various constraints, a search space with many local optimal solutions, and the like. The carnivorous plant algorithm can comprise the steps of an algorithm parameter initialization process, a population classification and grouping process, a growth exploration process, a propagation development process, a cloning and recombination process and the like, and a solution space of a problem can be well searched based on the steps.

According to the comprehensive energy low-carbon scheduling method, the carnivorous plant algorithm is applied to the aspect of solving the accurate scheduling of comprehensive energy, the scheduling scheme with the highest reliability can be acquired more accurately and efficiently, the power supply reliability of the comprehensive energy scheduling can be improved to the maximum extent by scheduling the power energy in the target area based on the scheduling scheme, and the optimal accurate control scheduling problem of the comprehensive energy can be solved under the condition that the energy scheduling environment and the scheduling mode are constantly changed.

Fig. 1 is one of the flow diagrams of the comprehensive energy low-carbon scheduling method provided by the present invention. The comprehensive energy low-carbon scheduling method of the invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: step 101, acquiring first target data and second target data; wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: the management and control risk index, the energy transmission convenience index and the energy utilization rate corresponding to each power supply party; the management and control risk index is used for describing the safety degree of the corresponding power supply party for providing the electric energy, and the energy transmission convenience index is used for describing the convenience degree of the corresponding power supply party for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of each type of electric energy provided by the corresponding power supplier.

Specifically, the target area is a scheduling target of the comprehensive energy low-carbon scheduling method provided by the invention, and the electric energy in the target area can be scheduled based on the comprehensive energy low-carbon scheduling method provided by the invention.

The target area includes a plurality of power suppliers therein. For any power provider, the power provider may provide electrical energy to the target area, and the power provider may provide one or more types of electrical energy. Types of electrical energy sources may include, but are not limited to, coal power generation, gas power generation, wind power generation, photovoltaic power generation, hydroelectric power generation, nuclear power generation, and hydrogen power generation.

The number of power suppliers and the type of power energy sources in the target area may be acquired as the first target data based on a priori knowledge.

For any power supplier in the target area, the management and control risk index, the energy conveying convenience index and the energy utilization rate corresponding to the power supplier can be acquired based on the real-time monitoring of the power supplier.

It should be noted that the management risk index corresponding to any power supplier may include a management risk index corresponding to each type of electric energy provided by the power supplier. The power supplier provides a management and control risk index corresponding to any type of electric energy, and the management and control risk index can be used for describing the safety degree of the power supplier providing the type of electric energy. If the higher the management and control risk index corresponding to any type of electric energy provided by the power supplier is, the lower the safety degree of the electric energy provided by the power supplier is; conversely, it can be said that the higher the degree of safety of the power supplier for supplying each type of electric power source.

The energy transmission convenience index corresponding to any power supplier may include an energy transmission convenience index corresponding to each type of electric energy provided by the power supplier, and the energy transmission convenience index corresponding to any type of electric energy provided by the power supplier may be used to describe the convenience degree of the power supplier for providing the type of electric energy. If the higher the degree of convenience of energy transmission corresponding to any type of electric energy provided by the power supplier is, it can be said that the more convenient the power supplier provides the type of electric energy; conversely, it may be said that the more inconvenient it is for the power supplier to provide each type of electrical energy. Whether or not a power supplier supplies a certain type of electric energy is convenient can be evaluated based on the construction cost required to be invested by the power supplier to supply the certain type of electric energy, the distance between the power supplier and the power receiver, the transmission loss, and the like.

The energy utilization rate corresponding to any power supplier may include the utilization rate of each type of electrical energy provided by the power supplier. The utilization rate of any type of electrical energy provided by the power supplier may be used to describe the actual consumption of that type of electrical energy provided by the power supplier. If the utilization rate of any type of electric energy provided by the power supplier is higher, it can be said that the actual consumption of the type of electric energy provided by the power supplier is higher; conversely, it can be said that the actual consumption of each type of electrical energy provided by the power supplier is lower. The utilization rate of any type of electrical energy source provided by the power supplier may be determined based on the actual amount of electricity of that type of electrical energy source provided by the power supplier and the actual amount of consumption of that type of electrical energy source.

And 102, constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data.

And 103, solving the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain an objective scheduling scheme.

Specifically, based on the carnivorous plant algorithm and the objective function, the comprehensive energy scheduling model can be solved through a numerical calculation method, and an objective scheduling scheme is obtained.

It should be noted that the target scheduling scheme may include a distribution ratio of each power supplier providing each type of electric energy in the target area.

And step 104, scheduling the electric energy in the target area based on the target scheduling scheme.

Specifically, after the target scheduling scheme is obtained, the electric energy sources in the target area may be scheduled based on the distribution ratio of each type of electric energy source provided by each power supplier in the target scheduling scheme.

Compared with other scheduling schemes, the target scheduling scheme has the highest reliability. The target scheduling scheme has the highest reliability, which means that the power supply reliability of the target area is the highest when the power energy in the target area is scheduled based on the target scheduling scheme.

It should be noted that the reliability of power supply, which refers to the capability of the power supply system to continuously supply power, is an important index for assessing the quality of the power supply system, reflects the degree of satisfaction of the power industry to the national economic power demand, and can be evaluated based on the indexes of power supply reliability, the average power failure time of the power receiver, the average power failure times of the power receiver, the equivalent hours of system power failure, and the like.

The embodiment of the invention constructs the comprehensive energy scheduling model based on the number of power supply parties in a target area and the type of the electric energy, constructs the objective function corresponding to the comprehensive energy scheduling model based on the control risk index, the energy delivery convenience index and the energy utilization rate corresponding to each power supply party, solves the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain a target scheduling scheme, schedules the electric energy in the target area based on the target scheduling scheme, can realize more efficient and accurate comprehensive energy low-carbon scheduling, can maximally improve the reliability of the comprehensive energy scheduling, has stronger robustness of the comprehensive energy scheduling, can solve the optimal and accurate control scheduling problem of the comprehensive energy under the condition that the energy scheduling environment and the scheduling mode are continuously changed, and can improve the economic benefit of the comprehensive energy scheduling, has better popularization prospect.

Based on the content of each embodiment, the comprehensive energy scheduling model is solved based on the carnivorous plant algorithm and the objective function, and the objective scheduling scheme is obtained, which specifically comprises the following steps: and step S1, acquiring a plurality of initial scheduling schemes based on the comprehensive energy scheduling model.

Specifically, based on the comprehensive energy scheduling model, a plurality of initial scheduling schemes can be randomly generated; based on the comprehensive energy scheduling model, a plurality of initial scheduling schemes can be generated according to preset rules. Wherein the preset rule may be obtained based on a priori knowledge. For example: the preset rule may be obtained based on a historical scheduling scheme.

It should be noted that each initial scheduling scheme may include a different allocation ratio for each power supplier to provide each type of electrical energy in the target area.

Step S2, initializing control parameters of the carnivorous plant algorithm, and obtaining individual populations corresponding to the first iteration, wherein each individual in the individual populations corresponding to the first iteration corresponds to each initial scheduling scheme.

Specifically, the carnivorous plant algorithm is initialized, and control parameters of the carnivorous plant algorithm can be initialized to obtain an individual population corresponding to the first iteration. Each individual in the population of individuals corresponds to each initial scheduling scheme.

It should be noted that the population of individuals corresponding to the first iteration does not distinguish between carnivorous plant individuals and prey individuals.

Optionally, m represents the number of iterations, and m is 1,2, …, T represents a preset maximum number of iterations; the total number of individuals in the individual population corresponding to the mth iteration is M, and the individual population corresponding to the mth iteration can be expressed as Pop_m。Pop_mWherein the ith individual can be represented by X_i，mI is 1,2, …, M.

The ith individual X in the individual population corresponding to the mth iteration_i，mCan be expressed as:

wherein E represents the total number of types of the electric energy sources in the target area, E is the identifier of the type, and E is 1,2, …, E; d represents the number of power suppliers in the target area, D is the identifier of the power supplier, and D is 1,2, …, D.

In addition, the formula (1) represents the individual X_i，mThe matrix coding of (2). X_i，mEach element x in (a) may represent a distribution ratio of each power supplier providing each type of electrical energy within the target area, for example:

can represent an individual X_i，mIn the corresponding scheduling scheme, the d-th power supplier provides the proportion of the e-th type of electric energy source. X_i，mThe upper and lower limits of the variation of any one element are [ L_min，L_max]。

Optionally, the preset maximum number of iterations T may be between 100 and 150; lower limit of variation L_minCan be 0, upper limit of variation L_maxCan be 0.5; the total number M of individuals may be between 30 and 50.

Preferably, the preset maximum number of iterations T may be 120; the total number of individuals M may be 40.

In this connection, the individual X_i，mWherein the sum of each element in each row and the sum of each element in each column are 1, representing an individual X_i，mIn the corresponding scheduling scheme, the sum of the distribution proportions of the electric energy sources provided by each power supplier is 1, and the sum of the distribution proportions of each type of electric energy source is 1.

Individual population Pop corresponding to mth iteration_mCan be expressed as:

step S3, performing population evolution on the individual population corresponding to the mth iteration based on a carnivorous plant algorithm, and acquiring the individual population corresponding to the mth iteration after the population evolution; where m is 1,2, …, and T denotes a preset maximum number of iterations.

Specifically, the individual population Pop corresponding to the mth iteration_mOf (i) th individual X_i，mThe corresponding objective function can be expressed by the following formula:

result_e，d＝X_i，m×(1-MS_e，d)×MC_e，d×UR_e，d (4)

wherein i is an identifier of any individual in the individual population corresponding to the mth iteration, and i is 1,2, …, M, and M represents the total number of individuals in the individual population; x_i，mRepresenting the ith individual in the individual population corresponding to the mth iteration; f. of_i，mRepresents X_i，mThe objective function value of (1); e denotes the total number of types of electric energy sources in the target area, E is the identifier of the type, and E is 1,2, …, E; d represents the number of power suppliers in the target area, D is the identifier of the power supplier, and D is 1,2, …, D; MS (Mass Spectrometry)_e，dThe method comprises the steps that a d-th power supplier provides a management and control risk index of the e-th type of electric energy under the condition that the electric energy in a target area is scheduled based on a scheduling scheme corresponding to the i-th individual; MC (monomer casting)_e，dThe energy source transmission convenience index of the e type of electric energy source provided by the d power supplier under the condition that the electric energy source in the target area is scheduled based on the scheduling scheme corresponding to the i individual; UR_e，dThe energy utilization rate of the e-th type of electric energy provided by the d-th power supplier is represented in the case where the electric energy in the target area is scheduled based on the scheduling plan corresponding to the i-th individual.

It should be noted that the matrix coding type of the management and control risk index, the energy transportation convenience index, and the energy utilization rate corresponding to any power supplier is a floating point number.

The individual X can be obtained based on the formula (3) and the formula (4)_i，mTarget function value off_i，m。

In this connection, the individual X_i，mTarget function value f of_i，mCan be used to describe an individual X_i，mReliability of the corresponding scheduling scheme. f. of_i，mThe larger, the individual X_i，mThe higher the reliability of the corresponding scheduling scheme, which can be stated as being based on the individual X_i，mWhen the corresponding scheduling scheme performs comprehensive energy scheduling on the target area, the higher the power supply reliability of the target area is; conversely, it can be said that the lower the power supply reliability of the target area is.

Optionally, the m-th iteration corresponds to an individual population Pop_mThe objective function values of (a) are as follows:

obtaining individual population Pop through numerical calculation_mAfter the objective function value of each individual, the individual population Pop can be adjusted from large to small based on the objective function value_mThe individual of (a) is ordered.

Based on individual population Pop_mThe order of individuals and the predetermined relationship between the number of carnivorous plant individuals CP and the number of Prey individuals, the individual population Pop can be determined_mThe individuals in (1) are divided into two types, carnivorous plant individuals CP and Prey individuals. Individual population Pop_mMiddle top plant solution of N_CPCan be used as individual CP of carnivorous plant, and the rest can be used for dissolving N_PreyCan be used as Prey individual.

It should be noted that, in the embodiment of the present invention, the relationship between the number of carnivorous plant individuals CP and the number of Prey individuals may be that the number of Prey individuals is an integral multiple of the number of carnivorous plant individuals CP.

Preferably, the number of Prey individuals is 3 times the number of CP individuals of the carnivorous plant.

For any individual population, ordering the individuals in the individual population according to the target function value can be expressed as:

the order of the individuals in the population of individuals can be expressed as:

wherein the grouping process needs to simulate the environment of each carnivorous plant and its prey. In the grouping process, the prey with the largest objective function value is assigned to the carnivorous plant ranked first. Similarly, the second and third preys were ranked to the second and third carnivorous plants, respectively. This process is repeated until the Nth_CPGrade prey is assigned to Nth_CPAfter the carnivorous plant is graded, the current round of distribution is completed, the next round of distribution is started, and the Nth round of distribution is carried out_CPGrade +1 game was assigned to grade 1 carnivorous plants. By analogy, a total of k rounds of assignment were performed to divide the individuals in the population of individuals into k groups, each group consisting of only one carnivorous plant individual but at least two prey individuals.

The formula for k is as follows:

wherein k is a positive integer, and the value of k is the multiple of the Prey number of Prey individuals to the CP number of carnivorous plant individuals.

From a biomimetic perspective, carnivorous plants attract, capture and digest prey for growth due to poor soil nutrition. Prey is attracted to the carnivorous plant by the odor of the carnivorous plant, and may also intermittently succeed in escaping the carnivorous plant. An attraction rate attract _ rate is therefore introduced for describing the above process.

For any prey individual in any individual population, on the one hand, if the attraction attract _ rate is higher than the random number generated, the carnivore individual will catch and digest the prey individual for growth.

The growth model for the carnivorous plant individual capturing and digesting prey individuals for growth is as follows:

growth＝growth_rate*θ (10)

wherein,

representing a grade r carnivorous plant individual in an individual population;

denotes an individual carnivorous plant after propagation and growth; x_prey(j) Represents randomly selected prey individuals; theta represents [0,1]]The selected random value of (1). The growth rate growth _ rate is constant.

On the other hand, if the attraction rate, attract _ rate, is not higher than the random number generated above, the prey individual can successfully escape the carnivorous plant individual and continue to grow.

The growth model for the prey individuals to successfully escape from the carnivorous plant individuals and continue to grow is as follows:

wherein,

representing prey individuals after reproductive growth; f [ X ]_Prey(j)]Objective function values for randomly selected prey j; λ is [0,1]]A random value in between.

Optionally, the attraction rate attract _ rate may be between [0.7, 0.8 ]; the growth rate growth _ rate may be between 0.8, 0.85.

Preferably, the attraction rate attract _ rate may be 0.82; the growth rate growth _ rate may be 0.75.

From a biomimetic perspective, carnivorous plants capture and digest prey, can absorb nutrients from the prey body, and utilize these nutrients for growth and reproduction. In terms of breeding, only the first carnivorous plant of the individual population, i.e. the most reliable scheduling scheme of the individual population, is allowed to breed. The aim is to ensure that the development of the algorithm only focuses on the best solution. Unnecessary utilization of other solutions can be avoided, thereby saving computational costs. The reproduction update process for the first ranked carnivorous plant is represented as:

wherein,

representing the meat-eating plant individual ranked first in the individual population;

representing propagating the renewed carnivorous plant individuals; reproduction _ rate is a constant; eta is [0,1]]A random value in between; x_CP(a) And X_CP(b) Is a randomly selected carnivorous plant individual.

And after the meat-eating plant individuals ranked first in the individual population are bred and updated, obtaining the meat-eating plant individuals bred and updated. And (4) propagating the updated carnivorous plant individuals, the rest carnivorous plant individuals and the prey individuals to form an individual population after population evolution, and realizing the population evolution of the individual population.

Alternatively, the reproduction _ rate may be between [0.9,0.92 ].

Preferably, the reproduction _ rate may be 0.91.

Step S4, acquiring an objective function value of each individual in the individual population corresponding to the mth iteration after population evolution, and taking the individual with the maximum objective function value as an alternative individual corresponding to the mth iteration;

specifically, for the individual population corresponding to the mth iteration, after the individual population corresponding to the mth iteration after population evolution is obtained, the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution can be obtained.

Based on the objective function value, the individual with the maximum objective function value can be obtained as the candidate individual corresponding to the mth iteration.

And step S5, carrying out cloning recombination on the individual population corresponding to the mth iteration after population evolution based on the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution, obtaining the individual population corresponding to the (m + 1) th iteration, and repeatedly executing the step S3 and the step S5 until m is greater than T.

Specifically, for the individual population corresponding to the mth iteration, after the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution is obtained, under the condition that m is not greater than the preset maximum iteration time T, the individual population corresponding to the mth iteration after population evolution can be varied based on the objective function values, and the individual population corresponding to the mth iteration after population evolution after variation can be used as the individual population corresponding to the m +1 th iteration.

After the individual population corresponding to the (m + 1) th iteration is obtained, the above steps S3 to S5 may be repeated to obtain the candidate individual corresponding to the (m + 1) th iteration.

And step S6, determining target individuals in the alternative individuals based on the obtained objective function values of the alternative individuals, and taking the scheduling scheme corresponding to the target individuals as a target scheduling scheme.

Specifically, for m iterations, in the case where m is greater than a preset maximum number of iterations T, the iterative computation process may be ended, and may be based on a mathematical-physical systemDetermining the candidate individual with the maximum objective function value as the target individual X according to the acquired objective function value of the candidate individual corresponding to each iteration_beSt。

Determination of target individuals X_beStAnd then, acquiring a scheduling scheme corresponding to the target individual as a target scheduling scheme.

And comprehensively scheduling the electric energy sources in the target area based on the occupation ratio of each type of electric energy source provided by each power supplier in the target scheduling scheme.

According to the embodiment of the invention, the comprehensive energy scheduling model is solved based on the objective function corresponding to the carnivorous plant algorithm and the comprehensive energy scheduling model, the objective scheduling method with the highest reliability can be better searched by population evolution of individual population, and the objective scheduling scheme with the highest reliability can be more accurately and efficiently obtained, so that the electric energy in the objective area can be scheduled based on the objective scheduling scheme, and the algorithm has the advantages of simple structure, obvious steps, low complexity and strong robustness.

Based on the content of each embodiment, based on the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution, the individual population corresponding to the mth iteration after population evolution is cloned and recombined to obtain the individual population corresponding to the (m + 1) th iteration, which specifically includes: and sequencing the individuals according to the sequence of the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution from large to small.

Specifically, for the individual population corresponding to the mth iteration, after the individual population corresponding to the mth iteration after population evolution is obtained, based on the objective function value of each individual in the individual population corresponding to the mth iteration after population evolution, the individuals in the individual population corresponding to the mth iteration after population evolution are sorted according to the sequence of the objective function values from large to small.

Cloning each individual w before ranking for s parts to obtain a clone individual corresponding to each individual; wherein w is a positive integer no greater than M; and s is M/w.

Specifically, based on the sequence of each individual in the individual population corresponding to the mth iteration after population evolution, each individual of w before ranking may be cloned, and each individual may be cloned s times, so as to obtain a cloned individual corresponding to each individual.

W is a positive integer not greater than the total number M of individuals; the number of clones s is the quotient of M and w.

Alternatively, w may be between [3.5 ].

Preferably, w may be 4.

And updating each individual clone based on a preset variation probability threshold, and taking each updated individual clone as each individual in the individual population corresponding to the (m + 1) th iteration.

Specifically, based on a preset mutation probability threshold τ, each individual clone may be updated to obtain each updated individual clone.

After obtaining each updated individual clone, each individual clone can be used as each individual in the individual population corresponding to the (m + 1) th iteration.

Optionally, the variation probability threshold τ is between 0.5 and 0.6.

Preferably, the mutation probability threshold τ is 0.54.

According to the embodiment of the invention, after the individual with the rank w in the individual population after the population evolution corresponding to the iteration is cloned, the cloned individual corresponding to each individual is obtained, each cloned individual is updated based on the preset variation probability threshold, and each updated cloned individual is used as the individual population corresponding to the next iteration, so that the calculation amount of iterative calculation can be reduced, the calculation efficiency of iterative calculation can be improved, and the efficiency of comprehensive energy scheduling can be further improved under the condition of not influencing the calculation accuracy.

In order to facilitate understanding of the comprehensive energy low-carbon scheduling method provided by the invention, the comprehensive energy low-carbon scheduling method provided by the invention is described below by an example. In the above example, the target area includes 7 energy source types including 16 power suppliers.

In order to facilitate algorithm operation, the management and control risk index MS, the energy transmission convenience index CD, and the energy utilization rate UR, which are provided by the power supplier and correspond to each type of electric energy, may be respectively and normatively mapped to floating point numbers between [0,1 ].

For example, the management risk index MS corresponding to each type of electric energy provided by the power supplier may be as follows:

for another example, the energy delivery convenience index CD corresponding to each type of electric energy provided by the power supplier can be as follows:

for another example, the energy utilization rate UR corresponding to each type of electric energy provided by the power supplier can be as follows:

fig. 2 is a second schematic flow chart of the comprehensive energy low-carbon scheduling method provided by the present invention. As shown in fig. 2, after constructing the integrated energy scheduling model and generating a plurality of initial scheduling plans based on the integrated energy scheduling model, the method includes: step S201, initializing carnivorous plant algorithm parameters and individual populations corresponding to the first iteration.

Specifically, the individual population Pop corresponding to the first iteration₁The total number of individuals is M, each individual corresponding to each initial scheduling scheme.

For the individual population Pop corresponding to the first iteration₁Of (i) th individual X_i,1Of individual X_i,1May represent the distribution ratio of the above-mentioned 7 types of electric power sources provided by each power supplier within the target area.

And S202, calculating the objective function value of each individual in the population. Specifically, based on formula (3) and formula (4), the individual population Pop corresponding to the first iteration can be calculated₁The value of the objective function of each individual in (1), individual X_i,1Objective function value f of_i,1＝4.4647。

And step S203, a population classification and grouping process. The method comprises the following specific steps: individual population Pop based on formula (5)₁The individual entities in (a) are sorted and grouped. Wherein the grouping process needs to simulate the environment of each carnivorous plant and its prey. In the grouping process, the prey with the largest objective function value is assigned to the carnivorous plant ranked first. Similarly, the second and third preys were ranked to the second and third carnivorous plants, respectively. This process is repeated until the Nth_CPGrade prey is assigned to Nth_CPAfter the carnivorous plant is graded, the current round of distribution is completed, the next round of distribution is started, and the Nth round of distribution is carried out_CPGrade +1 game was assigned to grade 1 carnivorous plants. By analogy, a total of k rounds of assignment were performed to divide the individuals in the population of individuals into k groups, each group consisting of only one carnivorous plant individual but at least two prey individuals.

And step S204, a population growth exploration process. From a biomimetic perspective, carnivorous plants attract, capture and digest prey for growth due to poor soil nutrition. Prey is attracted to the carnivorous plant by the odor of the carnivorous plant, and may also intermittently succeed in escaping the carnivorous plant. An attraction rate attract _ rate is therefore introduced for describing the above process.

For any prey individual in any individual population, on the one hand, if the attraction attract _ rate is higher than the random number generated, the carnivore individual will catch and digest the prey individual for growth. The individual carnivorous plant after propagation and growth can be obtained based on the formula (9) and the formula (10).

On the other hand, if the attraction rate, attract _ rate, is not higher than the random number generated above, the prey individual can successfully escape the carnivorous plant individual and continue to grow. Prey individuals after reproductive growth can be obtained based on formula (11) and formula (12).

And step S205, population evolution process. Specifically, from a biomimetic perspective, carnivorous plants capture and digest prey, can absorb nutrients from the prey body, and utilize these nutrients for growth and reproduction. In terms of breeding, only the first carnivorous plant of the individual population, i.e. the most reliable scheduling scheme of the individual population, is allowed to breed. The aim is to ensure that the development of the algorithm only focuses on the best solution. Unnecessary utilization of other solutions can be avoided, thereby saving computational costs. The meat plant individual with the first rank in the individual population can be bred and updated based on the formula (13) and the formula (14), and the meat plant individual with the updated breeding can be obtained. And (4) breeding the updated carnivorous plant individuals, the rest carnivorous plant individuals and the prey individuals to form individual populations after population evolution corresponding to the first iteration, and realizing population evolution of the individual populations. And taking the individual with the maximum objective function value as an alternative individual corresponding to the first iteration.

And S206, performing population cloning and recombination. Specifically, based on the sequence of objective function values from large to small, all the individuals in the individual population after population evolution corresponding to the first iteration are sequenced, each individual of w before ranking is cloned, and d parts of each individual are cloned respectively to obtain a cloned individual corresponding to each individual. Based on a preset mutation probability threshold τ, each individual clone may be updated to obtain each updated individual clone. After obtaining each updated individual clone, each individual clone may be used as each individual in the individual population corresponding to the second iteration.

And step S207, judging whether the iteration frequency reaches a preset maximum iteration frequency T. If not, the process returns to the step 202 to the step 206, and the alternative individuals corresponding to each iteration are obtained.

Step S208, if the iteration times reach the preset maximum iteration times, the objective function value is obtained according to the objective function value of the alternative individual corresponding to each iterationDetermining the largest candidate individual as the target individual X_beSt. Determination of target individuals X_beStAnd then, acquiring a scheduling scheme corresponding to the target individual, and outputting the scheduling scheme as a target scheduling scheme.

Comprehensive energy scheduling can be performed based on the target scheduling scheme, and the highest energy supply reliability and the best scheduling economic benefit can be obtained.

The comprehensive energy low-carbon scheduling method provided by the invention is used for carrying out comprehensive energy scheduling simulation, and the simulation parameter setting and simulation results are as follows:

the target area includes 7 types of electric power sources, which are coal power generation, gas power generation, wind power generation, photovoltaic power generation, hydroelectric power generation, nuclear power generation, and hydrogen power generation, respectively. The target area includes 16 power suppliers. The number of individuals in the individual population in the carnivorous plant algorithm is N-40, and the number of prey individuals is 3 times that of carnivorous plant individuals. The mutation probability threshold tau is 0.54; and presetting the upper limit T of the maximum iteration number as 120. The upper and lower limits of variation for any element in an individual are [0,0.5 ].

The ant colony algorithm as a comparison algorithm comprises 40 individuals, the pheromone volatilization factor is set to be 0.89, and the upper limit T of the preset maximum iteration number is 120.

The population of the whale optimization algorithm serving as a comparison algorithm comprises 40 individuals, and the upper limit T of the preset maximum iteration number is 120.

The cuckoo search algorithm as the comparison algorithm includes 40 individuals, and the upper limit T of the preset maximum iteration number is 120.

For the particle swarm optimization for comparison, the number of particles is set to 40, the social learning factor and the individual learning factor are both set to 1.8, and the upper limit T of the preset maximum iteration number is 120.

In the comprehensive energy scheduling simulation, comprehensive energy scheduling can be performed on the target interval based on the comparison algorithm.

Fig. 3 is a schematic diagram showing a comparison of simulation results of the comprehensive energy low-carbon scheduling method provided by the invention and an existing scheduling method. As shown in fig. 3, the uppermost curve in fig. 3 is a reliability curve of the target scheduling scheme obtained based on the comprehensive energy low-carbon scheduling method provided by the present invention. Compared with the scheduling methods based on other four algorithms, the comprehensive energy low-carbon scheduling method provided by the invention has the advantage that the solving precision and convergence are greatly improved.

According to the simulation result, the comprehensive energy low-carbon scheduling method provided by the invention can obviously improve the reliability of the obtained target scheduling scheme.

Fig. 4 is a schematic structural diagram of the comprehensive energy low-carbon scheduling device provided by the invention. The integrated energy low-carbon scheduling device provided by the invention is described below with reference to fig. 4, and the integrated energy low-carbon scheduling device described below and the integrated energy low-carbon scheduling method provided by the invention described above may be referred to correspondingly. As shown in fig. 4, the apparatus includes: a data acquisition module 401, a model construction module 402, an algorithm solving module 403 and an energy scheduling module 404.

A data obtaining module 401, configured to obtain first target data and second target data.

And a model building module 402, configured to build an integrated energy scheduling model based on the first target data, and build an objective function corresponding to the integrated energy scheduling model based on the second target data.

And an algorithm solving module 403, configured to solve the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function, to obtain an objective scheduling scheme.

And an energy scheduling module 404, configured to schedule the electric energy in the target area based on the target scheduling scheme.

Wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supply party for providing the electric energy, and the energy transmission convenience index is used for describing the convenience degree of the corresponding power supply party for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supply party for providing the electric energy.

Specifically, the data acquisition module 401, the model construction module 402, the algorithm solving module 403, and the energy scheduling module 404 are electrically connected.

It should be noted that the comprehensive energy low-carbon scheduling device may be a cloud server.

The embodiment of the invention constructs the comprehensive energy scheduling model based on the number of power supply parties in a target area and the type of the power energy, constructs the objective function corresponding to the comprehensive energy scheduling model based on the control risk index, the energy delivery convenience index and the energy utilization rate corresponding to each power supply party, solves the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain a target scheduling scheme, has the highest scheduling reliability of the power energy in the target area based on the target scheduling scheme, can realize more efficient and accurate low-carbon scheduling of the comprehensive energy, can maximally improve the scheduling reliability of the comprehensive energy, has stronger robustness of the comprehensive energy scheduling, can solve the optimal and accurate control scheduling problem of the comprehensive energy under the condition that the energy scheduling environment and the scheduling mode are constantly changed, and can improve the economic benefit of the comprehensive energy scheduling, has better popularization prospect.

Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)510, a communication Interface (Communications Interface)520, a memory (memory)530 and a communication bus 540, wherein the processor 510, the communication Interface 520 and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call logic instructions in the memory 530 to perform an integrated energy low carbon scheduling method, the method comprising: acquiring first target data and second target data; constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data; solving the comprehensive energy scheduling model based on a carnivorous plant algorithm and an objective function to obtain an objective scheduling scheme; scheduling the electric power energy in the target area based on the target scheduling scheme; wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: each power supply party provides a control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supply party for providing the electric energy, and the energy transmission convenience index is used for describing the convenience degree of the corresponding power supply party for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supply party for providing the electric energy.

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

In another aspect, the present invention further provides a computer program product, where the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, a computer can execute the integrated energy low carbon scheduling method provided by the above methods, and the method includes: acquiring first target data and second target data; constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data; solving the comprehensive energy scheduling model based on a carnivorous plant algorithm and an objective function to obtain an objective scheduling scheme; scheduling the electric power energy in the target area based on the target scheduling scheme; wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supply party for providing the electric energy, and the energy transmission convenience index is used for describing the convenience degree of the corresponding power supply party for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supply party for providing the electric energy.

In still another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for integrated energy low-carbon scheduling provided by the foregoing methods, and the method includes: acquiring first target data and second target data; constructing a comprehensive energy scheduling model based on the first target data, and constructing a target function corresponding to the comprehensive energy scheduling model based on the second target data; solving the comprehensive energy scheduling model based on a carnivorous plant algorithm and an objective function to obtain an objective scheduling scheme; scheduling the electric power energy in the target area based on the target scheduling scheme; wherein, the first target data comprises: the number of power suppliers and the type of power source in the target area; second target data comprising: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supply party for providing the electric energy, and the energy transmission convenience index is used for describing the convenience degree of the corresponding power supply party for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supply party for providing the electric energy.

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

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive energy low-carbon scheduling method is characterized by comprising the following steps:

acquiring first target data and second target data;

wherein the first target data comprises: the number of power suppliers and the type of electrical energy source in the target area; the second target data includes: each power supply party provides a management and control risk index, an energy transmission convenience index and an energy utilization rate corresponding to each type of electric energy; the management and control risk index is used for describing the safety degree of the corresponding power supplier for providing the electric energy, and the energy delivery convenience index is used for describing the convenience degree of the corresponding power supplier for providing the electric energy; the energy utilization rate is used for describing the actual consumption condition of the corresponding power supplier for providing the electric energy.

2. The comprehensive energy low-carbon scheduling method according to claim 1, wherein the solving of the comprehensive energy scheduling model based on the carnivorous plant algorithm and the objective function to obtain an objective scheduling scheme specifically comprises:

step S3, performing population evolution on the individual population corresponding to the mth iteration based on the carnivorous plant algorithm, and acquiring the individual population corresponding to the mth iteration after the population evolution; wherein m is 1,2,3, …, and T represents a preset maximum iteration number;

3. The comprehensive energy low-carbon scheduling method of claim 2, wherein the formula of the objective function is as follows:

result_e,d＝X_i,m×(1-MS_e,d)×MC_e,d×UR_e,d

wherein i is an identifier of any individual in the individual population corresponding to the mth iteration, and i is 1,2, …, M, and M represents the total number of individuals in the individual population; x_i,mRepresenting the ith individual in the individual population corresponding to the mth iteration; f. of_i,mRepresents X_i,mThe objective function value of (1); e denotes the total number of types of electrical energy sources in the target area, E is the identification of the type, E is 1,2, …, E; d represents the number of power suppliers in the target area, D is the identifier of the power supplier, and D is 1,2, …, D; MS (Mass Spectrometry)_e,dThe method comprises the steps that a d-th power supplier provides a management and control risk index of the e-th type of electric energy under the condition that the electric energy in a target area is scheduled based on a scheduling scheme corresponding to the i-th individual; MC (monomer casting)_e,dThe energy source transmission convenience index of the e type of electric energy source provided by the d power supplier under the condition that the electric energy source in the target area is scheduled based on the scheduling scheme corresponding to the i individual; UR_e,dThe energy utilization rate of the e-th type of electric energy provided by the d-th power supplier is represented in the case where the electric energy in the target area is scheduled based on the scheduling plan corresponding to the i-th individual.

4. The comprehensive energy low-carbon scheduling method of claim 3, wherein the cloning and recombination of the individual population corresponding to the mth iteration after the population evolution based on the objective function value of each individual in the individual population corresponding to the mth iteration after the population evolution to obtain the individual population corresponding to the (m + 1) th iteration specifically comprises:

sequencing the individuals according to the sequence of the objective function value of each individual in the individual population corresponding to the mth iteration after the population evolution from large to small;

wherein w is a positive integer no greater than M; and s is M/w.

5. The integrated energy low carbon scheduling method of claim 4, wherein the variation probability threshold is between 0.5 and 0.6.

6. The integrated energy low-carbon dispatching method of any one of claims 2 to 5, wherein the individual population comprises carnivorous plant individuals and prey individuals, and the number of the prey individuals is integral multiple of the number of the carnivorous plant individuals.

7. The utility model provides a comprehensive energy low carbon scheduling device which characterized in that includes:

8. 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 steps of the integrated energy low carbon scheduling method according to any one of claims 1 to 6.

9. A non-transitory computer readable storage medium, having a computer program stored thereon, wherein the computer program, when being executed by a processor, implements the steps of the integrated energy low carbon scheduling method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the integrated energy low carbon scheduling method according to any one of claims 1 to 6.