CN114565241B - Electric-gas comprehensive energy system scheduling method and device considering carbon cost - Google Patents
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Abstract
The invention provides an electric-gas comprehensive energy system dispatching method and device considering carbon cost, comprising the following steps: establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; determining a target fitness function based on an objective function of an energy scheduling model, wherein the objective function of the energy scheduling model is built based on the minimum total energy supply cost and total carbon emission of the electric-gas comprehensive energy system as targets; and carrying out iterative solution on the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient. The method can effectively reduce the energy supply cost of the electric-gas comprehensive energy system, reduce carbon emission and improve the reliability and low carbon property of energy scheduling.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an electric-gas comprehensive energy system scheduling method and device considering carbon cost.
Background
In order to relieve the environmental pressure caused by excessive carbon emission and improve the capacity of renewable energy sources, the development of an electric-gas comprehensive energy system is important. According to the low-carbon concept, the supply side of the electric-gas comprehensive energy system needs to balance the relation among carbon emission, energy demand and supply cost in a manner of coexistence of traditional energy and clean energy, namely an effective dispatching method of the electric-gas comprehensive energy system for taking carbon cost into account is needed.
However, in the prior art, algorithms involved in the electric-gas comprehensive energy system scheduling method, such as a particle swarm algorithm, generally have the defects of low convergence speed and easy sinking into a local optimal solution, so that the quality of the obtained scheduling scheme is not high, and the reliability and low carbon performance of the electric-gas comprehensive energy system scheduling are affected.
Therefore, how to better implement the scheduling of the electric-gas integrated energy system considering the carbon cost has become a problem to be solved in the industry.
Disclosure of Invention
The invention provides a scheduling method and device of an electric-gas comprehensive energy system considering carbon cost, which are used for better realizing the scheduling of the electric-gas comprehensive energy system considering carbon cost.
The invention provides an electricity-gas comprehensive energy system scheduling method considering carbon cost, which comprises the following steps:
establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system;
determining a target fitness function based on an objective function of the energy scheduling model, wherein the objective function of the energy scheduling model is built based on the objective that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum;
and carrying out iterative solution on the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient.
According to the method for scheduling the electric-gas comprehensive energy system considering the carbon cost, which is provided by the embodiment of the invention, the target fitness function is iteratively solved by taking the minimum target fitness function as a target, and a target energy distribution coefficient corresponding to the minimum fitness value is determined, and the method comprises the following steps:
Initializing a ground wolf optimization algorithm based on a floating point number coding method to obtain a plurality of ground wolf populations and individual ground wolf individuals in each ground wolf population;
based on the target fitness function and the target operator, performing operation of a wolf optimization algorithm on each wolf individual in each wolf population, and obtaining a target wolf individual under the condition that a preset iteration termination condition is met;
determining the target energy distribution coefficient based on the target soil wolf individual;
the target operator comprises at least one of a chaos operator and a clone operator, wherein the chaos operator is used for determining the initial social state of each of the individual wolf in the current iteration process, and the clone operator is used for updating each individual wolf in each wolf population to the individual wolf with the smallest fitness value in each wolf population.
According to the method for scheduling the electric-gas comprehensive energy system considering the carbon cost provided by the embodiment of the invention, based on the target fitness function and the target operator, each of the individual wolves in each wolf population is subjected to operation of a wolf optimization algorithm, and under the condition that a preset iteration termination condition is met, the method for scheduling the electric-gas comprehensive energy system comprises the following steps:
Determining the initial social state of each of the individual wolf in each wolf population based on the chaos operator;
and carrying out the operation of an earth wolf optimization algorithm on each earth wolf individual in each earth wolf population based on the target fitness function, the initial social state of each earth wolf individual in each earth wolf population and the cloning operator, and obtaining a target earth wolf individual under the condition that a preset iteration termination condition is met, wherein the target operator comprises the chaos operator and the cloning operator.
According to the electric-gas comprehensive energy system scheduling method considering carbon cost provided by the embodiment of the invention, the operation of a ground wolf optimization algorithm is performed on each ground wolf individual in each ground wolf population based on the target fitness function, the initial social state of each ground wolf individual in each ground wolf population and the cloning operator, and the target ground wolf individual is obtained under the condition that the preset iteration termination condition is met, and the method comprises the following steps:
based on the target fitness function and the initial social state of each of the wolf individuals in each of the wolf populations, performing operations of a growing stage and an evolving stage in a wolf optimization algorithm on each of the wolf individuals in each of the wolf populations, and generating alpha wolf individuals in each of the wolf populations, wherein the alpha wolf individuals are the wolf individuals with the minimum fitness in each of the wolf populations;
Based on the cloning operators, replacing each of the wolf individuals in each of the wolf populations with alpha wolf individuals in each of the wolf populations to obtain a cloning population corresponding to each of the wolf populations, and carrying out mutation treatment on each cloning population according to the target mutation probability to obtain each mutation cloning population;
determining the optimal wolf individuals with the smallest fitness value in all the cloned wolf individuals based on the target fitness function and each cloned wolf individual in each variant clone population;
and under the condition that the current iteration times meet the preset iteration termination condition, determining the target individual wolf according to the optimal individual wolf.
According to the electric-gas comprehensive energy system scheduling method considering carbon cost provided by the embodiment of the invention, the initial social state of each of the wolf individuals in each wolf population is determined based on the chaos operator, and the method comprises the following steps:
through the formula of the chaos operator:
I z+1 ＝bI z (1-I z ) Z=0, 1, …, dim, determining the initial social status of individual wolf individuals in each of said wolf populations;
wherein ,Iz Represents a floating point number position in the initial social state of the individual Tulang, dim represents the dimension of the solution search space, I 0 Represents [0,1 ]]A random number in between, b=4, representing the chaotic algorithmThe sub-generated sequence of positions is in a chaotic state.
According to the method for scheduling the electric-gas comprehensive energy system considering the carbon cost, which is provided by the embodiment of the invention, the energy scheduling model of the electric-gas comprehensive energy system is established based on the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system, and the method comprises the following steps:
determining the total energy supply cost and the total carbon emission of the electric-gas integrated energy system based on the energy demand of the external demand side of the electric-gas integrated energy system, the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas integrated energy system;
constructing an objective function by taking the minimum total energy supply cost and total carbon emission of the electric-gas comprehensive energy system as optimization targets;
and establishing an energy scheduling model of the electric-electric comprehensive energy system based on the objective function.
The embodiment of the invention provides an electricity-gas comprehensive energy system scheduling device considering carbon cost, which comprises:
the modeling module is used for establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system;
The processing module is used for determining a target fitness function based on the target function of the energy scheduling model, wherein the target function of the energy scheduling model is built based on the target that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum;
and the scheduling module is used for iteratively solving the target fitness function by taking the minimum target fitness function as a target, determining a target energy distribution coefficient corresponding to the minimum fitness value, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
The embodiment of the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the electric-gas comprehensive energy system scheduling method considering the carbon cost when executing the program.
Embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of an electricity-gas integrated energy system scheduling method according to any one of the above, accounting for carbon costs.
Embodiments of the present invention also provide a computer program product comprising a computer program which, when executed by a processor, implements the steps of an electricity-gas integrated energy system scheduling method according to any one of the above, accounting for carbon costs.
The method and the device for scheduling the electric-gas comprehensive energy system, which are provided by the embodiment of the invention, are used for establishing an energy scheduling model of the electric-gas comprehensive energy system by comprehensively considering the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; and the target fitness function is determined by the target function of the energy scheduling model, the minimum target fitness function is used as an optimization target, an algorithm for improving the solving speed and the solving precision is adopted for carrying out iterative solving, and effective analysis on the energy scheduling model of the electric-gas comprehensive energy system is completed, so that a target energy distribution coefficient corresponding to the minimum fitness value is obtained, the energy supply quantity of each subsystem is determined according to the target energy distribution coefficient, effective scheduling on the electric-gas comprehensive energy system is realized, the energy supply cost of the electric-gas comprehensive energy system can be effectively reduced, the carbon emission generated in the energy supply process is reduced, and the scheduling reliability and the low carbon property of the electric-gas comprehensive energy system are improved.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an electricity-gas integrated energy system scheduling method accounting for carbon costs provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of an overall flow of an optimization algorithm in an electrical-gas integrated energy system scheduling method accounting for carbon costs according to an embodiment of the present invention;
FIG. 3 is a graph showing the comparison of the optimization effect in the scheduling method of the electrical-gas integrated energy system, which is provided by the embodiment of the invention and accounts for the carbon cost;
FIG. 4 is a schematic diagram of a scheduling apparatus for an electrical-gas integrated energy system, which accounts for carbon costs, according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of the invention relates to a method and a device for scheduling an electric-gas integrated energy system considering carbon cost, which are described below with reference to fig. 1-5.
The electric-gas integrated energy system is an emerging energy system which interconnects some renewable energy sources such as photovoltaic, natural gas, hydroenergy and wind energy through a power grid and a gas grid, and realizes conversion and cascade utilization between different energy sources. However, at present, the main source of domestic electricity-gas energy is traditional fossil energy such as petroleum, coal gas, coal and the like, and the fossil energy can bring about carbon emission problem in the conversion process, thereby causing environmental problems such as greenhouse effect and the like. Therefore, the balance of the relationship between the traditional fossil energy and the new clean energy has very important significance for the supply side, the demand side and the environment of the electric-gas comprehensive energy system.
Therefore, the invention provides an electric-gas comprehensive energy system scheduling method considering carbon cost, which is used for better solving the problems.
Fig. 1 is a schematic flow chart of an electric-gas integrated energy system scheduling method for accounting for carbon cost according to an embodiment of the present invention, as shown in fig. 1, including: step 101, step 102 and step 103.
specifically, each subsystem in the electric-gas integrated energy system described in the embodiment of the invention refers to various electric energy subsystems and various gas energy subsystems. In this embodiment, the electric energy subsystem may include at least one of a thermal power generation subsystem, a wind power generation subsystem, a hydroelectric power generation subsystem, a photovoltaic power generation subsystem, a nuclear power subsystem, and the like; the gas energy subsystem may include at least one of a liquefied gas, a natural gas, an electrotransfer hydrogen, and an electrotransfer methane subsystem.
The unit cost of energy supply described in the embodiment of the invention refers to the supply cost of 1 kilowatt-hour electric energy and the supply cost of 1 cubic meter of gas energy in the electric-gas integrated energy system.
The unit carbon emissions described in the embodiments of the present invention refer to the carbon emissions generated by the production of 1 kilowatt-hour electric energy in the electric-gas integrated energy system, and the carbon emissions generated by the supply of 1 cubic meter of gas energy.
The energy distribution coefficient described in the embodiment of the invention refers to the energy supply coefficient distributed by each subsystem in the electric-gas integrated energy system;
For example, when the electric energy demand on the external demand side of the electric-gas integrated energy system is 200 kw and the gas energy demand is 200 cubic meters, the energy distribution coefficient of the thermal power generation subsystem is 0.2, the thermal power generation subsystem is scheduled to supply 40 kw of electric energy to the external demand side, and the energy distribution coefficient of the liquefied gas subsystem is 0.3, and the liquefied gas subsystem is scheduled to supply 60 cubic meters of liquefied gas to the external demand side.
It can be understood that the sum of the energy distribution coefficients of all the electric energy subsystems is 1, and the sum of the energy distribution coefficients of all the gas energy subsystems is 1.
In some embodiments, building an energy scheduling model of the integrated electric-gas energy system based on unit cost, unit carbon emissions, and energy distribution coefficients of energy supply of each subsystem in the integrated electric-gas energy system includes:
determining the total energy supply cost and the total carbon emission of the electric-gas integrated energy system based on the energy demand of the external demand side of the electric-gas integrated energy system, the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas integrated energy system;
constructing an objective function by taking the minimum total energy supply cost and total carbon emission of the electric-gas comprehensive energy system as optimization targets;
And establishing an energy scheduling model of the electric-gas comprehensive energy system based on the objective function.
In this embodiment, the total energy supply cost described in the embodiment of the present invention refers to the sum of the energy supply costs of the respective subsystems in the electric-gas integrated energy system.
The total carbon emissions described in the embodiments of the present invention refer to the sum of carbon emissions generated during the power supply of each subsystem in the integrated electric-gas power system.
Further, in the present embodiment, a supply cost matrix and a carbon emission amount matrix of each subsystem energy supply are established according to the energy demand amount on the external demand side of the electric-gas integrated energy system, the unit cost, the unit carbon emission amount, and the energy distribution coefficient of each subsystem energy supply in the electric-gas integrated energy system, and the total energy supply cost and the total carbon emission amount of the electric-gas integrated energy system are calculated.
In the present embodiment, the formula of the calculated total energy supply cost and total carbon emission amount of the electric-gas integrated energy system can be expressed as:
F＝D·(C+E)·X；
wherein F represents the total energy supply cost and total carbon emission matrix of the electric-gas integrated energy system, D represents the demand matrix of the external demand side of the electric-gas integrated energy system, E represents the unit carbon emission matrix of the energy supply of each subsystem, C represents the unit cost matrix of the energy supply of each subsystem, and X represents the energy distribution coefficient matrix of the energy supply of each subsystem.
In an embodiment of the present invention, C may be expressed as:
wherein, C11-C1N represents the unit cost of energy supply of the N-type electric energy subsystem, and C21-C2N represents the unit cost of energy supply of the N-type gas energy subsystem;
e may be expressed as:
wherein E11-E1N represent the unit carbon emission of the N-type electric energy subsystem energy supply, and E21-E2N represent the unit carbon emission of the N-type gas energy subsystem energy supply.
In one specific embodiment, the electric energy subsystem comprises five subsystems of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear power; the gas energy subsystem comprises five subsystems of liquefied gas, coal gas, natural gas, electric hydrogen conversion and electric methane conversion, and can be set
Namely, the unit cost of five sub-system energy supplies of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear power is 0.1, 0.2, 0.3 and 0.3 in sequence, and the unit carbon emission is 0.4, 0.1 and 0.2 respectively; the unit cost of energy supply of the five sub-systems of gas, coal gas, natural gas, electric hydrogen and electric methane is 0.3, 0.1, 0.2, 0.3 and 0.4 in turn, and the unit carbon emission is 0.3, 0.4, 0.2, 0.1 and 0.2 respectively.
In this embodiment, with the total energy supply cost and the total carbon emission of the electric-gas integrated energy system being the minimum as the optimization targets, an objective function minF is constructed, and based on the objective function, an energy scheduling model of the electric-gas integrated energy system is established, and the energy scheduling model can be expressed as:
minF＝D·(C+E)·X；
according to the method provided by the embodiment of the invention, the energy supply cost and the carbon emission of various electric energy sources and air energy sources in the electric-air comprehensive energy system are comprehensively considered, and the energy scheduling model of the electric-air comprehensive energy system is established by taking the total energy supply cost and the total carbon emission of the electric-air comprehensive energy system as the optimization targets, so that the accuracy of model construction is ensured, and the optimal energy scheduling scheme can be effectively obtained by optimizing and analyzing the model.
102, determining a target fitness function based on an objective function of an energy scheduling model, wherein the objective function of the energy scheduling model is built based on the minimum total energy supply cost and total carbon emission of an electric-gas comprehensive energy system;
the objective function described in the embodiments of the present invention is constructed based on the objective of minimizing the total energy supply cost and the total carbon emission of the electric-gas integrated energy system, where the total energy supply cost and the total carbon emission can be calculated from the energy demand on the external demand side of the electric-gas integrated energy system, the unit cost of energy supply of each subsystem in the electric-gas integrated energy system, the unit carbon emission, and the energy distribution coefficient, as described above.
Alternatively, in embodiments of the present invention, the energy scheduling model may be analyzed using a wolf optimization algorithm (Coyote Optimization Algorithm, COA) or a modified COA algorithm, based on which the fitness function in the COA algorithm first needs to be determined.
The target fitness function described by the embodiment of the invention can be a fitness function in a COA algorithm, is used for describing main indexes of individual performances of the wolves in the algorithm, and carries out 'superior and inferior elimination' on the individual wolves according to the size of the individual fitness of the wolves to obtain an optimal solution.
In this embodiment, the objective function of the energy scheduling model may be directly used as the objective fitness function, the total energy supply cost and the total carbon emission matrix F of the electric-gas integrated energy system are expressed as fitness functions of the individual wolf, and the energy distribution coefficient matrix X is expressed as social states of the individual wolf, so as to determine the objective fitness function in the COA algorithm, that is, the objective fitness function is:
wherein ,
indicating the fitness value of individual wolves, < ->
The social state of the individual wolf is represented.
In an embodiment of the present invention, the social status of an individual wolf may be represented by the following matrix:
wherein ,p1 ～p 5 G, respectively representing energy distribution coefficients of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear electronic systems 1 ～g 5 Respectively represent the energy distribution coefficients of liquefied gas, coal gas, natural gas, electric hydrogen conversion and electric methane conversion subsystems.
In an embodiment of the present invention, one possible coding sequence is:
in the above coding, assuming that the demand on the external demand side of the electric-gas integrated energy system is 500 kw-hours of electric energy and 300 cu-meters of gas energy, soc means that the electric energy of 500 kw-hours on the external demand side of the electric-gas integrated energy system is respectively from the thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear electronic systems in the electric-gas integrated energy system, wherein 500×0.3=150 kw-hours are from the thermal power generation subsystem, 500×0.1=50 kw-hours are from the wind power generation subsystem, 500×0.2=100 kw-hours are from the hydroelectric power generation subsystem, 500×0.1=50 kw-hours are from the photovoltaic power generation subsystem, and 500×0.3=150 kw-hours are from the nuclear electronic system; the 300 cubic meters of gas energy demand at the external electricity demand side is derived from liquefied gas, coal gas, natural gas, electric hydrogen conversion and electric methane conversion subsystems in the electric-gas comprehensive energy system respectively, and the specific calculation mode of each gas energy source is consistent with the calculation mode of the electric energy source, which is not described herein.
And 103, iteratively solving the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient.
Specifically, the target energy distribution coefficient described in the embodiments of the present invention refers to a scheduling parameter obtained by the scheduling method of the electric-gas integrated energy system of the present invention, which is used to determine a supply coefficient of energy supply of each subsystem in the electric-gas integrated energy system, so as to schedule the energy supply of each subsystem based on the supply coefficient of energy supply of each subsystem and the demand of the external demand side, thereby achieving the purposes of effectively reducing the energy supply cost and reducing the carbon emission generated in the energy supply process.
Further, the minimum of the target fitness function is used as a target, the target fitness function is subjected to iterative solution, and a target energy distribution coefficient corresponding to the minimum fitness value is determined. In this embodiment, the target fitness function may be solved by employing the COA algorithm or the modified COA algorithm.
The COA algorithm described by the embodiment of the invention belongs to a meta-heuristic optimization algorithm, the inspiration comes from the process of the adaptation of the soil wolf group to the environment and the organization structure between the soil wolf group, the COA algorithm has good optimizing effect, the searching space is allowed to be widely explored in the global optimizing process, and the high-quality solution is output.
In the step, initial social states of the individual wolves and the individual wolves can be randomly initialized through a COA algorithm, iterative optimization solution is carried out on the target fitness function based on the initialized individual data, and a minimum fitness value is obtained, wherein the minimum fitness value carries target energy distribution coefficients of all subsystems in the dispatching electric-gas comprehensive energy system.
The improved COA algorithm described by the invention realizes the effects of increasing the diversity of the soil wolf population and improving the solving precision and the convergence speed of the algorithm by introducing operators of a chaotic strategy and/or a cloning strategy into the COA algorithm, thereby effectively accelerating the convergence capacity and the global optimizing capacity of the algorithm.
Preferably, in this embodiment, by performing analysis and iterative solution on the target fitness function of the energy scheduling model based on the electric-gas integrated energy system through an improved COA algorithm, a global optimal solution can be efficiently solved, a higher-quality energy scheduling scheme is obtained, and according to the energy scheduling scheme, the energy supply of each subsystem in the electric-gas integrated energy system can be scheduled, so that the electric-gas integrated energy system can achieve the purposes of low cost and low emission in the energy supply process.
According to the method, the energy scheduling model of the electric-gas comprehensive energy system is established by comprehensively considering the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; and the target fitness function is determined by the target function of the energy scheduling model, the minimum target fitness function is used as an optimization target, an algorithm for improving the solving speed and the solving precision is adopted for carrying out iterative solving, and effective analysis on the energy scheduling model of the electric-gas comprehensive energy system is completed, so that a target energy distribution coefficient corresponding to the minimum fitness value is obtained, the energy supply quantity of each subsystem is determined according to the target energy distribution coefficient, effective scheduling on the electric-gas comprehensive energy system is realized, the energy supply cost of the electric-gas comprehensive energy system can be effectively reduced, the carbon emission generated in the energy supply process is reduced, and the scheduling reliability and the low carbon property of the electric-gas comprehensive energy system are improved.
In some embodiments, with the minimum of the target fitness function as a target, performing iterative solution on the target fitness function, and determining a target energy allocation coefficient corresponding to the minimum fitness value includes:
Initializing a ground wolf optimization algorithm based on a floating point number coding method to obtain a plurality of ground wolf populations and individual ground wolf individuals in each ground wolf population;
based on the target fitness function and the target operator, performing the operation of a wolf optimization algorithm on each wolf individual in each wolf population, and obtaining a target wolf individual under the condition that a preset iteration termination condition is met;
determining a target energy distribution coefficient based on the target soil wolf individuals;
the target operator comprises at least one of a chaos operator and a clone operator, the chaos operator is used for determining the initial social state of each of the individual wolf in the current iteration process, and the clone operator is used for updating each individual wolf in each wolf population to the one with the smallest fitness value in each wolf population.
In this embodiment, the target wolf individuals described in the embodiment of the present invention refer to those with the smallest fitness value among all the wolf individuals obtained after the algorithm is finished, that is, the optimal solution.
The chaotic operator described by the invention refers to an operator based on a chaotic strategy, is particularly used for determining the initial social state of each of the wolf individuals in the current iteration process, can increase the population diversity of the wolf group, and is beneficial to improving the solving precision.
In this embodiment, the cloning operator refers to an operator based on a cloning strategy, and is used to update each of the wolf individuals in each wolf population to the optimal wolf individual with the smallest fitness value in each wolf population, so that the process of searching the optimal solution is more directional, and the convergence rate of the algorithm can be effectively improved.
The object operator described in the embodiments of the present invention includes at least one of a chaotic operator and a clone operator, that is, in this embodiment, the COA algorithm may be improved by introducing one of the chaotic operator and the clone operator, and preferably, two operators may be introduced simultaneously to improve the COA algorithm, so as to obtain a solution with higher quality.
The preset iteration termination condition described in the embodiment of the present invention may refer to a preset iteration number threshold, that is, the number of times of iterative computation of the algorithm reaches the preset iteration number threshold, and then the algorithm iteration is stopped, so as to obtain a final solution result.
Further, based on a floating point number coding method, performing random initialization of a COA algorithm to obtain a plurality of wolf populations and each wolf individual in each wolf population;
in this embodiment, the individual social states in the COA algorithm are encoded in the form of floating point number encoding, to represent a feasible comprehensive energy demand response scheme, and parameters of the COA algorithm are initialized, including the maximum iteration number MaxLoop, the current iteration number loop, the number Np of the wolf population, the number Nc of the wolf individuals in each wolf population, and the social state of the wolf individuals 
Wherein p=1, 2, …, np, c=1, 2, …, nc, a demand matrix D on the external demand side of the electric-gas integrated energy system, a unit carbon emission matrix E for energy supply of each subsystem, a unit cost matrix C for energy supply of each subsystem, an upper limit of a position value in the social state of the wolf individual being 1, and a lower limit of a position value being 0.
Further, in the embodiment, based on the target fitness function and at least one of the chaotic operator and the cloning operator in the target operator, performing the operation of the COA algorithm on each individual of each group of the Tulang until the number of iterations of the algorithm satisfies the preset iteration termination condition, obtaining the target individual of the Tulang,
in a specific embodiment of the invention, in the operation of performing a COA algorithm on each of the individual wolves in each wolf population, the chaos operator is adopted to initialize each individual wolf to obtain an initial social state of each individual wolf, so as to increase population diversity, obtain a more comprehensive search space, and obtain a target individual wolf by continuously and iteratively calculating fitness values of the individual wolves.
In another embodiment of the invention, in the operation of performing the COA algorithm on each of the individual wolves in each wolf population, a cloning operator can be adopted, and the individual wolves in each wolf population are updated to be the optimal individual wolves with the smallest fitness value in each wolf population, so that the population is subjected to large-scale variation, the algorithm convergence speed and the solving precision are improved, the fitness value of the individual wolves is calculated continuously and iteratively, and the target individual wolves are obtained under the condition that the preset iteration termination condition is met.
Preferably, in still another specific embodiment of the present invention, in the operation of performing the COA algorithm on each individual wolf in each wolf population, a chaos operator and a cloning operator may be introduced simultaneously, so as to more efficiently and accurately solve the objective fitness function minimization iteration, and obtain the objective wolf individual.
After the target individual wolves are determined, determining target energy distribution coefficients according to the social state of the target individual wolves, so as to complete energy scheduling of the electric-gas comprehensive energy system according to the target energy distribution coefficients.
According to the method provided by the embodiment of the invention, the target fitness function of the energy scheduling model based on the electric-gas comprehensive energy system is solved and analyzed through the COA algorithm, the chaos operator is introduced to expand population diversity, a more comprehensive search space is realized, the cloning operator is introduced to further accelerate the convergence speed of the algorithm, so that the global optimizing capability of the algorithm is improved, and a high-quality energy scheduling scheme is obtained.
In some embodiments, based on the target fitness function and the target operator, performing an operation of a wolf optimization algorithm on each wolf individual in each wolf population, and obtaining the target wolf individual if a preset iteration termination condition is satisfied, including:
Based on the chaos operator, determining the initial social state of each individual one of the groups of the wolves;
based on the target fitness function, the initial social state and the cloning operator of each of the individual wolves in each wolf population, carrying out operation of a wolf optimization algorithm on each of the individual wolves in each wolf population, and obtaining the target wolf individuals under the condition that the preset iteration termination condition is met, wherein the target operator comprises a chaos operator and a cloning operator.
Specifically, in the embodiment of the invention, the target operator comprises a chaotic operator and a cloning operator, namely, the COA algorithm is improved by introducing the chaotic operator and the cloning operator simultaneously, so as to carry out minimized iterative solution on the target fitness function.
In some embodiments, determining the initial social state of individual wolf individuals in each wolf population based on the chaos operator comprises:
the formula by chaos operator:
I z+1 ＝bI z (1-I z ) Z=0, 1, …, dim, determining the initial social status of individual wolf individuals in each wolf population;
wherein ,Iz Represents a floating point number position in the initial social state of the individual Tulang, dim represents the dimension of the solution search space, I 0 Represents [0,1 ] ]And a random number, b=4, which indicates that the sequence of positions generated by the chaotic operator is in a chaotic state.
In this embodiment, the formula of the chaotic operator is a Logistic chaotic mapping formula, when b=4, the mapping is full shot, the generated sequence is in a chaotic state, that is, the sequence generated under the Logistic mapping effect is aperiodic and non-convergent, the (0, 1) has ergodic property, and any other b value does not enable the whole sequence to be in a chaotic state.
In the present embodiment, in a specific calculation process, for example, I 0 =0.2, then I 1 Generated according to the chaotic mapping formula is I 1 The generation of the rest positions is analogized by 4 x 0.2 (1-0.2) =0.64, and the generated data sequence is normalized, so that the sum of the energy distribution coefficients of the indicated electric energy source and the air energy source is ensured to be 1, and the initial social state of each wolf individual in each wolf group is determined.
Therefore, each of the wolf populations is initialized through a formula of the chaos operator, the initial social state of each wolf individual in each wolf population is obtained, and the diversity of the wolf populations can be increased.
According to the method provided by the embodiment of the invention, the chaos operator is introduced, each wolf population is initialized based on the characteristic that the chaos operator generates the position sequence in a chaos state, the initial social state of each wolf individual is determined, the diversity of the wolf population is increased, so that the diversity of initial solutions is increased, and the solving precision of an algorithm is improved.
In some embodiments, based on the target fitness function, the initial social state and the cloning operator of each individual of the wolf population, performing an operation of a wolf optimization algorithm on each individual of the wolf population, and obtaining the target wolf individual if a preset iteration termination condition is satisfied, including:
based on the target fitness function and the initial social state of each of the wolf individuals in each of the wolf populations, performing operations of a growing stage and an evolving stage in a wolf optimization algorithm on each of the wolf individuals in each of the wolf populations, and generating alpha wolf individuals in each of the wolf populations, wherein the alpha wolf individuals are the wolf individuals with the minimum fitness in each of the wolf populations;
based on cloning operators, replacing each of the individual wolves in each wolf population with an alpha wolf individual in each wolf population to obtain a clone population corresponding to each wolf population, and carrying out mutation treatment on each clone population according to the target mutation probability to obtain each mutation clone population;
determining the optimal wolf individuals with the minimum fitness value in all cloned wolf individuals based on the target fitness function and each cloned wolf individual in each variant clone population;
And under the condition that the current iteration times meet the preset iteration termination condition, determining a target individual of the wolf according to the optimal individual of the wolf.
Specifically, the clone population described in the embodiment of the invention refers to a population in which clone processing is performed on each of the wolf populations based on a clone operator, which is obtained by replacing each of the wolf individuals in each of the wolf populations with an alpha wolf individual in each of the wolf populations.
The target mutation probability described in the embodiment of the invention refers to a preset population mutation probability, which can be represented by MP, and the specific value of MP is set according to the actual calculation requirement, for example, the mutation probability of each clone population is 0.4, that is, the mutation probability of each clone population is 0.4, and if each clone population has 10 wolf individuals, 4 wolf individuals will be mutated, that is, the social state of the 4 wolf individuals will be changed, and the fitness value of the wolf individuals will be also changed.
The mutation mode is to randomly generate a group of floating point number sequences conforming to a target scheduling rule, wherein the target scheduling rule comprises: the value of each position in the social state of a wolf individual is between 0 and 1; the sum of each row in the social state of a wolf individual is 1; and each position in the social status of a wolf individual corresponds to an energy distribution coefficient of an energy supply.
Further, based on the target fitness function and the initial social state of each of the wolf individuals in each of the wolf populations, performing operations of a growing stage and an evolving stage in a wolf optimization algorithm on each of the wolf individuals in each of the wolf populations to generate alpha wolf individuals in each of the wolf populations, wherein the specific implementation mode of the alpha wolf individuals being the wolf individuals with the smallest fitness in each of the wolf populations includes steps S1 to S6.
Step S1, determining initial alpha wolf individuals in each wolf population for Np wolf populations based on a target fitness function and initial social states of the individual wolf individuals in each wolf population, wherein the formula is as follows:
wherein loop represents the current iteration number of the algorithm, p represents the p-th wolf population, nc represents the number of wolf individuals in each wolf population,
representing the social state that the c-th individual in the p-th wolf population is in the loop iteration process;
in one embodiment, there are 4 of the following individual wolves in one wolf population, including individual wolves 1, individual wolves 2, individual wolves 3 and individual wolves 4, and their social status coding sequences are:
According to the target fitness function calculation formula, the fitness values of the 4 soil wolf individuals can be obtained as follows:
in the process of designing the target fitness function of the COA algorithm, the invention multiplies the energy distribution coefficient matrix in the electric-gas comprehensive energy system carried by each individual wolf by the unit carbon emission matrix and the unit cost matrix, and according to the principle that the lower the values of the two are, the better the value of the two is, the smaller the fitness value of the individual wolf is, and the higher the obtained solution quality is indicated.
Therefore, as can be found from the calculation results of the fitness values of the 4 individual wolves, the fitness value 320.8 of the individual wolves 1 is the smallest, so that the solution carried by the individual wolves is optimal among the 4 individual wolves;
the energy distribution coefficient matrix in the carried electric-gas comprehensive energy system can be obtained, the electric energy supplied to the demand side by the electric-gas comprehensive energy system is supplied by 500 x 0.25=125 kilowatt-hours from the thermal power generation subsystem, 500 x 0.23=115 kilowatt-hours from the wind power generation subsystem, 500 x 0.36=180 kilowatt-hours from the hydraulic power generation subsystem, 500 x 0.05=25 kilowatt-hours from the photovoltaic power generation subsystem, and 500 x 0.11=55 kilowatt-hours from the nuclear power subsystem;
The gas energy provided to the demand side by the electric-gas integrated energy system is 300 x 0.03=9 cubic meters from the liquefied gas subsystem, 300 x 0.23=69 cubic meters from the gas subsystem, 300 x 0.37=111 cubic meters from the natural gas subsystem, 300 x 0.31=93 cubic meters from the electric-rotary hydrogen subsystem, and 300 x 0.06=18 cubic meters from the electric-rotary methane subsystem.
Of the above 4 individual wolves given in the present embodiment, individual wolves 1 were selected as the initial alpha individual wolves.
Step S2, calculating cultural tendencies of each wolf population, wherein the formula is as follows:
wherein ,
represents the ordered social state of the p-th wolf population in the loop iteration process, j is in the range [1, dim ]]Where dim is the dimension of the problem search space.
In the embodiment of the present invention, nc is 10, which is an even number, and should be calculated by other wise.
Step S3, updating social state and calculating fitness value of each individual of the wolf population, and evaluating new social state, wherein the calculation formula is as follows:
wherein ,δ1 and δ2 Respectively represent alpha wolf influence and population influence, r 1 and r2 Weight factors respectively representing alpha wolf influence and population influence, and initial r 1 and r2 Is defined as [0,1 ]]Random value in between, wherein delta 1 and δ2 The calculation formulas of (a) are respectively as follows:
wherein ,cr1 Represents cultural differences, cr, from random to alpha wolves in a population 2 Cultural differences representing cultural tendencies from random wolves to wolves populations;
step S4, carrying out birth and death condition updating on the individual wolves in each wolf population, wherein the formula is as follows:
wherein ,r1 and r2 Representing random individual, j, of the p-th wolf population 1 and j2 Representing two random dimensions, P, in an electro-pneumatic integrated energy scheduling problem s Representing a dispersion probability, P a Representing joint probabilities, R j A random number representing the upper and lower limits of a decision variable in the jth dimension, rnd j Represents one [0,1 ]]Random numbers in between;
step S5, individual migration is carried out among the individual groups of the wolves, and the ages of the individual wolves are updated, wherein the formula is as follows:
wherein ,Pe Indicating the probability of a sirius individual being expelled from the population, it should be noted that to prevent this probability from being greater than 1, N is specified c Not exceeding 14;
in one embodiment of the present invention, 
Namely, the soil wolf individuals are taken from the populationThe probability of eviction is 50%.
Through the operation, the solutions in different wolf populations can be exchanged to obtain a better solution.
S6, recalculating fitness values of all the wolf individuals in each wolf population, and determining alpha wolf individuals in each wolf population;
according to the method provided by the embodiment of the invention, after the alpha wolves in each wolf population are generated through introducing the cloning operator and iterating the COA algorithm, each wolf individual in each wolf population is cloned into the alpha wolves in each population, and then large-scale mutation processing is carried out, so that the convergence speed of the algorithm can be increased, the local searching capability of the algorithm can be increased, the solving precision of the COA algorithm can be improved, and a high-quality solution can be obtained.
Further, based on cloning operators, each of the wolf individuals in each of the wolf populations is replaced with an alpha wolf individual in each of the wolf populations to obtain a cloned population corresponding to each of the wolf populations, and mutation processing is performed on each cloned population according to the target mutation probability to obtain each mutated cloned population, wherein the specific implementation mode of the method comprises the step S7.
Specifically, in step S7, the alpha wolves in each wolf population are cloned in equal proportion, that is, each wolf individual in each wolf population is replaced by its own alpha wolf individual, so as to form a new cloned population, and the total number of wolf individuals is kept unchanged in the process.
Further, according to the target mutation probability MP, carrying out large-scale mutation treatment on each obtained clone population to obtain a mutated clone population. The mutation mode is to randomly generate a set of floating point number sequences conforming to the scheduling rule, and in a specific embodiment of the present invention, the target mutation probability after cloning is mp=0.4.
And S8, calculating the fitness value of each cloned wolf individual based on the target fitness function and each cloned wolf individual in each variant clone population, and obtaining the optimal wolf individual with the minimum fitness value in all cloned wolf individuals.
Step S9, outputting an optimal solution if the current iteration number loop meets a preset iteration termination condition, namely obtaining a target soil wolf individual according to the optimal soil wolf individual so as to determine a target energy distribution coefficient based on the target soil wolf individual;
if the current iteration number loop does not reach the specified maximum iteration number MaxLoop, loop=loop+1 is returned to step S1, and the next iteration of the algorithm is entered.
According to the method provided by the embodiment of the invention, through the improved COA algorithm and the introduction of the chaos operator and the cloning operator, not only is the diversity of the initial solution increased, so that the solution space is more comprehensively searched, but also the algorithm convergence speed and the local optimizing capability can be further accelerated through cloning the alpha-soil wolf individuals, the algorithm solving precision is improved, and a high-quality energy scheduling scheme is obtained efficiently.
Fig. 2 is a schematic overall flow chart of an optimization algorithm in the scheduling method of the electric-gas integrated energy system for accounting for carbon cost according to the embodiment of the invention, and as shown in fig. 2,
step S201, encoding the social state of the individual wolves in the algorithm based on a floating point number encoding method to obtain a plurality of wolves and each individual wolves in each wolves;
step S202, initializing each of the wolf populations by adopting a chaos operator to obtain an initial social state of each wolf individual;
step S203, calculating the fitness value of each of the wolf individuals based on the target fitness function and the initial social state of each of the wolf individuals in each of the wolf populations, and determining the initial alpha wolf individuals in each of the wolf populations for Np wolf populations;
step S204, calculating cultural tendencies of each wolf population;
step S205, updating the social state and calculating the fitness value of each individual of the wolf population, and evaluating the new social state;
step S206, updating the birth and death conditions of the individual wolves in each wolf population;
step S207, individual migration is carried out among the individual wolf populations, and the ages of the individual wolves are updated;
step S208, recalculating fitness values of all the wolf individuals in each wolf population, and determining alpha wolf individuals in each wolf population;
Step S209, determining clone populations corresponding to each of the wolf populations based on clone operators, and carrying out large-scale mutation treatment on each clone population according to target mutation probability to obtain each mutation clone population, thereby obtaining the optimal wolf individuals with the minimum fitness value in all the wolf individuals;
step S210, judging whether the current iteration times loop reach the maximum iteration times MaxLoop, if not, making loop=loop+1, and returning to step S203; otherwise, jumping to step 211, ending the algorithm, and outputting the optimal solution to obtain the target soil wolf individual.
According to the method provided by the embodiment of the invention, the chaotic operator and the cloning operator are introduced into the COA algorithm, so that the convergence speed and the solving precision of the algorithm are effectively improved, and the quality of the obtained electric-gas comprehensive energy system scheduling scheme is improved.
Fig. 3 is a graph showing comparison of optimization effects in the scheduling method of the electric-gas integrated energy system, which is provided by the embodiment of the invention and is used for considering carbon cost, as shown in fig. 3, based on the above embodiment, detailed parameter configuration and simulation results of a simulation experiment are provided in the embodiment.
Specifically, in this embodiment of the present invention, the energy demand amounts on the external demand side of the electric-gas integrated energy system are 500 kwh and 300 cubic meters of the electric energy source, respectively. In the supply side of the electric-gas comprehensive energy system, the sources of electric energy are five modes of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear power, and the sources of gas energy are five modes of liquefied gas, coal gas, natural gas, electric hydrogen conversion and electric methane conversion.
In order to verify the effectiveness of the method provided by the invention, a butterfly optimization algorithm, a hybrid frog-leaping algorithm, a particle swarm optimization algorithm and a simulated annealing algorithm in the prior art are selected for comparison. In terms of parameter setting, the number np=6 of different wolf populations of the improved wolf optimization algorithm provided by the embodiment of the invention, the number nc=10 of the wolf individuals in each population, the initial population of the algorithm is generated by adopting a chaotic operator, and the large-scale variation probability of a clone operator adopted in the operation process of the algorithm is mp=0.4.
In the comparison algorithm, the population size of the butterfly optimization algorithm is 60, the sensory factor c=0.6, and the stimulus intensity i=0.8; the population size in the mixed frog-leaping algorithm is 60, the number of sub-populations is 4, and the transition probability is 0.6; the population size in the particle swarm algorithm is 60, the individual learning factor and the social learning factor are 2, the maximum speed is 3, and the minimum speed is-3; the simulated annealing algorithm had a population size of 60, an initial temperature of 240, and an annealing rate of 0.98.
In this embodiment, the maximum number of iterations of the four algorithms is uniformly set to maxloop=140.
As shown in FIG. 3, the method provided by the embodiment of the invention is compared with the existing four algorithms, namely a butterfly optimization algorithm, a hybrid frog-leaping algorithm, a particle swarm optimization algorithm and a simulated annealing algorithm, and finally the scheduling scheme of the electric-gas comprehensive energy system based on the four comparison algorithms is in premature convergence, so that a solution with higher quality cannot be obtained.
Specifically, the optimal effect value obtained by the electric-gas comprehensive energy system scheduling method based on the improved wolf optimization algorithm provided by the embodiment of the invention is 261, and the optimal effect values obtained by the butterfly optimization algorithm, the mixed frog-leaping algorithm, the particle swarm optimization algorithm and the simulated annealing algorithm are 282, 310, 323 and 345 respectively. In the process of designing the established fitness function, the energy supply cost and the carbon emission of different subsystems of the electric-gas comprehensive energy system are in direct proportion to the fitness value, so that the lower the final optimization effect value is, the best comprehensive effect of the scheduling scheme of the obtained electric-gas comprehensive energy system is shown, and the optimization effect of the method provided by the invention is superior to that of the other four existing common scheduling methods.
It should be noted that, the reason why the method of the present invention has an effect superior to other methods is mainly that the chaos operator is adopted to generate the initial population, so that the diversity of the initial solution is increased, and the implementation of the subsequent optimization step of the algorithm is facilitated. In addition, the application of the cloning operator accelerates the convergence speed of the algorithm and increases the capability of the algorithm for searching the optimal solution locally, thereby improving the precision of the algorithm solution. Therefore, the scheduling method of the electric-gas comprehensive energy system provided by the invention can well balance the relation among carbon emission, energy demand and supply cost, accords with multiple benefits and is beneficial to practical application.
The following describes the electric-gas integrated energy system scheduling device for accounting for carbon cost, and the electric-gas integrated energy system scheduling device for accounting for carbon cost and the electric-gas integrated energy system scheduling method for accounting for carbon cost described in the following can be correspondingly referred to each other.
Fig. 4 is a schematic structural diagram of an electric-gas integrated energy system scheduling device for accounting for carbon cost according to an embodiment of the present invention, as shown in fig. 4, including:
a modeling module 410, configured to establish an energy scheduling model of the electric-gas integrated energy system based on a unit cost, a unit carbon emission amount, and an energy distribution coefficient of energy supply of each subsystem in the electric-gas integrated energy system;
a processing module 420, configured to determine a target fitness function based on an objective function of an energy scheduling model, where the objective function of the energy scheduling model is constructed based on a minimum total energy supply cost and total carbon emission of the electric-gas integrated energy system;
the scheduling module 430 is configured to iteratively solve the target fitness function with the minimum target fitness function as a target, determine a target energy distribution coefficient corresponding to the minimum fitness value, and determine the energy supply of each subsystem according to the target energy distribution coefficient.
The electric-gas comprehensive energy system scheduling device for accounting for carbon cost according to the embodiment can be used for executing the embodiment of the electric-gas comprehensive energy system scheduling method for accounting for carbon cost, and the principle and the technical effect are similar, and are not repeated here.
According to the device provided by the embodiment of the invention, the energy scheduling model of the electric-gas comprehensive energy system is established by comprehensively considering the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; and the target fitness function is determined by the target function of the energy scheduling model, the minimum target fitness function is used as an optimization target, an algorithm for improving the solving speed and the solving precision is adopted for carrying out iterative solving, and effective analysis on the energy scheduling model of the electric-gas comprehensive energy system is completed, so that a target energy distribution coefficient corresponding to the minimum fitness value is obtained, the energy supply quantity of each subsystem is determined according to the target energy distribution coefficient, effective scheduling on the electric-gas comprehensive energy system is realized, the energy supply cost of the electric-gas comprehensive energy system can be effectively reduced, the carbon emission generated in the energy supply process is reduced, and the scheduling reliability and the low carbon property of the electric-gas comprehensive energy system are improved.
Fig. 5 is a schematic physical structure of an electronic device according to an embodiment of the present invention, as shown in fig. 5, the electronic device may include: processor 510, communication interface (Communications Interface) 520, memory 530, and communication bus 540, wherein processor 510, communication interface 520, memory 530 complete communication with each other through communication bus 540. Processor 510 may invoke logic instructions in memory 530 to perform the electricity-gas integrated energy system scheduling method accounting for carbon costs provided by the methods described above, the method comprising: establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; determining a target fitness function based on an objective function of the energy scheduling model, wherein the objective function of the energy scheduling model is built based on the objective that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum; and carrying out iterative solution on the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient.
Further, the logic instructions in the memory 530 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the method for scheduling an electric-gas integrated energy system accounting for carbon costs provided by the above methods, the method comprising: establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; determining a target fitness function based on an objective function of the energy scheduling model, wherein the objective function of the energy scheduling model is built based on the objective that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum; and carrying out iterative solution on the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for scheduling an electric-gas integrated energy system accounting for carbon costs provided by the above methods, the method comprising: establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system; determining a target fitness function based on an objective function of the energy scheduling model, wherein the objective function of the energy scheduling model is built based on the objective that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum; and carrying out iterative solution on the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. An electricity-gas integrated energy system scheduling method considering carbon cost, which is characterized by comprising the following steps:
establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system;
determining a target fitness function based on an objective function of the energy scheduling model, wherein the objective function of the energy scheduling model is built based on the objective that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum;
iteratively solving the target fitness function by taking the minimum of the target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value so as to determine the energy supply quantity of each subsystem according to the target energy distribution coefficient;
the step of iteratively solving the target fitness function by taking the minimum target fitness function as a target, and determining a target energy distribution coefficient corresponding to the minimum fitness value comprises the following steps:
initializing a ground wolf optimization algorithm based on a floating point number coding method to obtain a plurality of ground wolf populations and individual ground wolf individuals in each ground wolf population;
Based on the target fitness function and the target operator, performing operation of a wolf optimization algorithm on each wolf individual in each wolf population, and obtaining a target wolf individual under the condition that a preset iteration termination condition is met;
determining the target energy distribution coefficient based on the target soil wolf individual;
the target operator comprises at least one of a chaos operator and a clone operator, wherein the chaos operator is used for determining the initial social state of each of the individual wolf in the current iteration process, and the clone operator is used for updating each individual wolf in each wolf population to the individual wolf with the smallest fitness value in each wolf population;
the energy scheduling model is expressed as:
wherein ,
matrix representing total energy supply cost and total carbon emission of the electric-gas integrated energy system,/->
A demand matrix representing the external demand side of an electrical-gas integrated energy system,/for>
Matrix representing the unit carbon emission of the energy supply of the respective subsystem, < >>
A unit cost matrix representing the energy supply of each subsystem,>
an energy distribution coefficient matrix representing energy supply of each subsystem;
the target fitness function is expressed as:
wherein ,
indicating the fitness value of individual wolves, < ->
Representing the social state of the individual wolf; energy supply total cost and total carbon emission matrix of electric-gas integrated energy systemFExpressed as fitness function of individual wolves, energy distribution coefficient matrixXSocial status expressed as individual wolves;
the step of performing an operation of a wolf optimization algorithm on each wolf individual in each wolf population based on the target fitness function and the target operator, and obtaining the target wolf individual under the condition that a preset iteration termination condition is met, includes:
determining the initial social state of each of the individual wolf in each wolf population based on the chaos operator;
based on the target fitness function, the initial social state of each of the wolf individuals in each of the wolf populations and the cloning operator, performing operation of a wolf optimization algorithm on each of the wolf individuals in each of the wolf populations, and obtaining target wolf individuals under the condition that a preset iteration termination condition is met, wherein the target operator comprises the chaotic operator and the cloning operator;
wherein, through the formula of the chaos operator:
in the formula ,
representing a floating point number location in the initial social state of the individual wolf,dimrepresenting the dimension of the solution search space, +.>
Represents [0,1 ]]A random number in between and a random number in between,b=4, which indicates that the sequence of positions generated by the chaotic operator is in a chaotic state;
the method for obtaining the target wolf individuals under the condition that the preset iteration termination condition is met includes the steps of:
based on the target fitness function and the initial social state of each individual wolf in each wolf population, determining the initial alpha wolf individuals in each wolf population according to the following formula:
The cultural tendency of each of the wolf populations was calculated according to the following formula:
wherein ,
is shown in the firstloopIn the second iteration processpThe ordered social state of the individual wolf population,jbelonging to the range [1 ],dim]；
carrying out social state updating, birth and death condition updating, individual migration and individual age updating operation on each of the wolf populations, and recalculating fitness values of each of the wolf populations to determine alpha wolf individuals in each of the wolf populations;
based on the cloning operators, replacing each of the wolf individuals in each of the wolf populations with alpha wolf individuals in each of the wolf populations to obtain a cloning population corresponding to each of the wolf populations, and carrying out mutation treatment on each cloning population according to the target mutation probability to obtain each mutation cloning population;
determining the optimal wolf individuals with the smallest fitness value in all the cloned wolf individuals based on the target fitness function and each cloned wolf individual in each variant clone population;
and under the condition that the current iteration times meet the preset iteration termination condition, determining the target individual wolf according to the optimal individual wolf.
2. The method for scheduling an electric-gas integrated energy system taking into account carbon costs according to claim 1, wherein the establishing an energy scheduling model of the electric-gas integrated energy system based on a unit cost, a unit carbon emission amount, and an energy distribution coefficient of energy supply of each subsystem in the electric-gas integrated energy system comprises:
determining the total energy supply cost and the total carbon emission of the electric-gas integrated energy system based on the energy demand of the external demand side of the electric-gas integrated energy system, the unit cost, the unit carbon emission and the energy distribution coefficient of energy supply of each subsystem in the electric-gas integrated energy system;
constructing an objective function by taking the minimum total energy supply cost and total carbon emission of the electric-gas comprehensive energy system as optimization targets;
and establishing an energy scheduling model of the electric-electric comprehensive energy system based on the objective function.
3. An electricity-gas integrated energy system scheduling device that accounts for carbon costs, comprising:
the modeling module is used for establishing an energy scheduling model of the electric-gas comprehensive energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electric-gas comprehensive energy system;
The processing module is used for determining a target fitness function based on the target function of the energy scheduling model, wherein the target function of the energy scheduling model is built based on the target that the total energy supply cost and the total carbon emission of the electric-gas comprehensive energy system are minimum;
the scheduling module is used for iteratively solving the target fitness function by taking the minimum target fitness function as a target, determining a target energy distribution coefficient corresponding to the minimum fitness value, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient;
the scheduling module is specifically configured to:
initializing a ground wolf optimization algorithm based on a floating point number coding method to obtain a plurality of ground wolf populations and individual ground wolf individuals in each ground wolf population;
based on the target fitness function and the target operator, performing operation of a wolf optimization algorithm on each wolf individual in each wolf population, and obtaining a target wolf individual under the condition that a preset iteration termination condition is met;
determining the target energy distribution coefficient based on the target soil wolf individual;
the target operator comprises at least one of a chaos operator and a clone operator, wherein the chaos operator is used for determining the initial social state of each of the individual wolf in the current iteration process, and the clone operator is used for updating each individual wolf in each wolf population to the individual wolf with the smallest fitness value in each wolf population;
The energy scheduling model is expressed as:
wherein ,
matrix representing total energy supply cost and total carbon emission of the electric-gas integrated energy system,/->
A demand matrix representing the external demand side of an electrical-gas integrated energy system,/for>
Matrix representing the unit carbon emission of the energy supply of the respective subsystem, < >>
A unit cost matrix representing the energy supply of each subsystem,>
an energy distribution coefficient matrix representing energy supply of each subsystem;
the target fitness function is expressed as:
wherein ,
indicating the fitness value of individual wolves, < ->
Representing the social state of the individual wolf; energy supply total cost and total carbon emission matrix of electric-gas integrated energy systemFExpressed as fitness function of individual wolves, energy distribution coefficient matrixXSocial status expressed as individual wolves;
the step of performing an operation of a wolf optimization algorithm on each wolf individual in each wolf population based on the target fitness function and the target operator, and obtaining the target wolf individual under the condition that a preset iteration termination condition is met, includes:
determining the initial social state of each of the individual wolf in each wolf population based on the chaos operator;
Based on the target fitness function, the initial social state of each of the wolf individuals in each of the wolf populations and the cloning operator, performing operation of a wolf optimization algorithm on each of the wolf individuals in each of the wolf populations, and obtaining target wolf individuals under the condition that a preset iteration termination condition is met, wherein the target operator comprises the chaotic operator and the cloning operator;
wherein, through the formula of the chaos operator:
in the formula ,
representing a floating point number location in the initial social state of the individual wolf,dimrepresenting the dimension of the solution search space, +.>
Represents [0,1 ]]A random number in between and a random number in between,b=4, which indicates that the sequence of positions generated by the chaotic operator is in a chaotic state;
the method for obtaining the target wolf individuals under the condition that the preset iteration termination condition is met includes the steps of:
Based on the target fitness function and the initial social state of each individual wolf in each wolf population, determining the initial alpha wolf individuals in each wolf population according to the following formula:
the cultural tendency of each of the wolf populations was calculated according to the following formula:
wherein ,
is shown in the firstloopIn the second iteration processpThe ordered social state of the individual wolf population,jbelonging to the range [1 ],dim], wherein dimIs the dimension of the problem search space;
carrying out social state updating, birth and death condition updating, individual migration and individual age updating operation on each of the wolf populations, and recalculating fitness values of each of the wolf populations to determine alpha wolf individuals in each of the wolf populations;
based on the cloning operators, replacing each of the wolf individuals in each of the wolf populations with alpha wolf individuals in each of the wolf populations to obtain a cloning population corresponding to each of the wolf populations, and carrying out mutation treatment on each cloning population according to the target mutation probability to obtain each mutation cloning population;
Determining the optimal wolf individuals with the smallest fitness value in all the cloned wolf individuals based on the target fitness function and each cloned wolf individual in each variant clone population;
and under the condition that the current iteration times meet the preset iteration termination condition, determining the target individual wolf according to the optimal individual wolf.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method for scheduling an electro-pneumatic integrated energy system that accounts for carbon costs as claimed in any one of claims 1 to 2 when the program is executed.
5. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the electricity-gas integrated energy system scheduling method of any one of claims 1 to 2, accounting for carbon costs.
6. A computer program product comprising a computer program, characterized in that the computer program when executed by a processor implements the steps of the electricity-gas integrated energy system scheduling method of any one of claims 1 to 2, taking into account carbon costs.
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