CN114565241A - Carbon cost-considering electricity-gas comprehensive energy system scheduling method and device - Google Patents

Carbon cost-considering electricity-gas comprehensive energy system scheduling method and device Download PDF

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CN114565241A
CN114565241A CN202210137889.2A CN202210137889A CN114565241A CN 114565241 A CN114565241 A CN 114565241A CN 202210137889 A CN202210137889 A CN 202210137889A CN 114565241 A CN114565241 A CN 114565241A
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周杰
刘阳
李志刚
朱锐
苏革
黎劲松
戴建国
黄超
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Abstract

The invention provides a method and a device for dispatching an electricity-gas comprehensive energy system considering carbon cost, which comprises the following steps: establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system; determining a target fitness function based on a target function of the energy scheduling model, wherein the target function of the energy scheduling model is constructed based on the lowest energy supply total cost and total carbon emission of the electricity-gas integrated energy system as a target; and (4) with the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, 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 method can effectively reduce the energy supply cost of the electricity-gas comprehensive energy system, reduce carbon emission and improve the reliability and low carbon of energy scheduling.

Description

Carbon cost-considering electricity-gas comprehensive energy system scheduling method and device
Technical Field
The invention relates to the technical field of information processing, in particular to a method and a device for scheduling an electricity-gas comprehensive energy system considering carbon cost.
Background
The development of an electricity-gas integrated energy system is of great importance in order to relieve the environmental stress caused by excessive carbon emissions and to improve the absorption capacity of renewable energy. According to the low-carbon concept, the relation among carbon emission, energy demand and supply cost needs to be balanced by adopting a mode of coexistence of traditional energy and clean energy on the supply side of the electricity-gas integrated energy system, namely an effective carbon cost-considering electricity-gas integrated energy system scheduling method is needed.
However, in the prior art, algorithms, such as particle swarm algorithm, involved in the scheduling method of the electric-gas integrated energy system generally have the disadvantages of slow convergence rate and easy falling into a local optimal solution, so that the quality of the obtained scheduling scheme is not high, and the reliability and low carbon of the scheduling of the electric-gas integrated energy system are affected.
Therefore, how to better realize the scheduling of the electric-gas integrated energy system considering the carbon cost has become an urgent problem to be solved in the industry.
Disclosure of Invention
The invention provides a method and a device for dispatching an electricity-gas integrated energy system considering carbon cost, which are used for better realizing the dispatching of the electricity-gas integrated energy system considering carbon cost.
The invention provides a carbon cost-considered electricity-gas comprehensive energy system scheduling method, which comprises the following steps:
establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system;
determining a target fitness function based on a target function of the energy scheduling model, wherein the target function of the energy scheduling model is constructed based on the lowest energy supply total cost and total carbon emission of the electric-gas integrated energy system as a target;
and taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
According to the carbon cost-related scheduling method for the electricity-gas integrated energy system provided by the embodiment of the invention, the step of iteratively solving the target fitness function by taking the minimum target fitness function as a target to determine the target energy distribution coefficient corresponding to the minimum fitness value comprises the following steps:
initializing a wolf optimization algorithm based on a floating point number coding method to obtain a plurality of wolf populations and each wolf individual in each wolf population;
based on the target fitness function and the target operator, performing a wolf optimization algorithm operation 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 individual wolfsbane;
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 wolf individual in the current iteration process, and the clone operator is used for updating each wolf individual in each wolf population into the wolf individual with the minimum fitness value in each wolf population.
According to the carbon cost-considered electric-gas integrated energy system scheduling method provided by the embodiment of the invention, the operation of the wolf optimization algorithm is performed on each wolf individual in each wolf population based on the target fitness function and the target operator, and the target wolf individual is obtained under the condition that a preset iteration termination condition is met, wherein the method comprises the following steps:
determining the initial social state of each wolf individual in each wolf population based on the chaotic operator;
and performing a wolf optimization algorithm operation on each wolf individual in each wolf population based on the target fitness function, the initial social state of each wolf individual in each wolf population and the clone operator, and obtaining a target wolf individual under the condition of meeting a preset iteration termination condition, wherein the target operator comprises the chaos operator and the clone operator.
According to the carbon cost-considered scheduling method for the electricity-gas integrated energy system, the operation of the wolf optimization algorithm is carried out on each wolf individual in each wolf population on the basis of the target fitness function, the initial social state of each wolf individual in each wolf population and the clone operator, and the target wolf individual is obtained under the condition that a 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 wolf individual in each wolf population, carrying out operation of a growing stage and an evolution stage in a wolf optimization algorithm on each wolf individual in each wolf population to generate an alpha wolf individual in each wolf population, wherein the alpha wolf individual is the wolf individual with the minimum fitness in each wolf population;
replacing each wolf individual in each wolf population with an alpha wolf individual in each wolf population based on the cloning operator to obtain a cloning population corresponding to each wolf population, and performing mutation processing on each cloning population according to a target mutation probability to obtain each variant cloning population;
determining the optimal wolf individual with the minimum fitness value in all the clonal wolf individuals based on the target fitness function and each clonal wolf individual in each variant clonal population;
and under the condition that the current iteration times meet a preset iteration termination condition, determining the target wolf individual according to the optimal wolf individual.
According to the carbon cost-considered electric-gas integrated energy system scheduling method provided by the embodiment of the invention, the determining of the initial social state of each wolf individual in each wolf population based on the chaotic operator comprises the following steps:
through the formula of the chaos operator:
Iz+1＝bIz(1-Iz) Z-0, 1, …, dim, determining the initial social status of individual geopenaeus individuals in each of said geopenaeus populations;
wherein ,IzRepresenting a floating point number position in the initial social state of the wolf individual, dim representing the dimension of the solution search space, I0Represents [0,1 ]]B-4, which indicates that the position sequence generated by the chaos operator is in a chaos state.
According to the carbon cost-based scheduling method for the electricity-gas integrated energy system provided by the embodiment of the invention, the energy scheduling model of the electricity-gas integrated energy system is established based on the unit cost, the unit carbon emission and the energy distribution coefficient of the energy supply of each subsystem in the electricity-gas integrated 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 of energy supply of each subsystem in the electric-gas integrated energy system, the unit carbon emission and the energy distribution coefficient;
constructing an objective function by taking the minimum energy supply total cost and the total carbon emission of the electricity-gas integrated energy system as optimization targets;
and establishing an energy scheduling model of the electricity-gas integrated 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 electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated 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 constructed based on the lowest aims of the total energy supply cost and the total carbon emission of the electricity-gas integrated energy system;
and the scheduling module is used for carrying out iterative solution on 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.
An embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for scheduling an electricity-gas integrated energy system considering 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, implements the steps of the method for scheduling an electric-gas integrated energy system taking into account carbon costs as described in any of the above.
Embodiments of the present invention further provide a computer program product, which includes a computer program, and the computer program, when executed by a processor, implements the steps of any of the above methods for scheduling an electric-gas integrated energy system considering carbon cost.
According to the carbon cost-related electricity-gas integrated energy system scheduling method and device, an energy scheduling model of the electricity-gas integrated 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 electricity-gas integrated energy system; and determining a target fitness function through a target function of the energy scheduling model, taking the minimum target fitness function as an optimization target, performing iterative solution by adopting an algorithm for improving the solution speed and precision, and completing effective analysis on the energy scheduling model of the electricity-gas integrated energy system, so as to obtain a target energy distribution coefficient corresponding to the minimum fitness value, determine the energy supply quantity of each subsystem according to the target energy distribution coefficient, realize effective scheduling on the electricity-gas integrated energy system, effectively reduce the energy supply cost of the electricity-gas integrated energy system, reduce carbon emission generated in the energy supply process, and improve the scheduling reliability and low carbon property of the electricity-gas integrated energy system.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for scheduling an electric-gas integrated energy system with consideration of carbon costs according to an embodiment of the present invention;
FIG. 2 is a schematic overall flow chart of an optimization algorithm in the method for scheduling an electric-gas integrated energy system considering carbon cost according to the embodiment of the present invention;
FIG. 3 is a graph comparing optimization effects in the method for scheduling an electric-gas integrated energy system considering carbon cost according to the embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an electric-gas integrated energy system dispatching device for accounting carbon cost according to an embodiment of the present invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An electric-gas integrated energy system scheduling method and apparatus considering carbon cost according to an embodiment of the present invention is described below with reference to fig. 1 to 5.
An electricity-gas integrated energy system is a new energy system which interconnects some renewable energy sources, such as photovoltaic, natural gas, water energy and wind energy, through a power grid and a gas grid, so that conversion among different energy sources and cascade utilization are realized. However, at present, the main sources of domestic electricity-gas energy are traditional fossil energy such as petroleum, coal gas and coal, and the fossil energy can bring about the problem of carbon emission in the conversion process, thereby causing environmental problems such as greenhouse effect and the like. Therefore, balancing the relationship between the conventional fossil energy and the new clean energy is of great importance to the supply side, the demand side and the environment of the electric-gas integrated energy system.
Therefore, the invention provides an electric-gas integrated energy system scheduling method considering carbon cost, which is used for better solving the existing problems.
Fig. 1 is a schematic flow chart of an electric-gas integrated energy system scheduling method considering carbon cost according to an embodiment of the present invention, as shown in fig. 1, including: step 101, step 102 and step 103.
101, establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system;
specifically, each subsystem in the electricity-gas integrated energy system described in the embodiment of the present invention refers to various electric energy subsystems and various gas energy subsystems. In the embodiment, the electric energy subsystem may include at least one of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation, nuclear power generation and the like; the gas energy subsystem may include at least one of liquefied gas, coal gas, natural gas, electricity to hydrogen, electricity to methane, and the like.
The unit cost of energy supply described in the embodiments of the present invention refers to the supply cost of 1 kilowatt-hour of electric energy and the supply cost of 1 cubic meter of gas energy in the electric-gas integrated energy system.
The unit carbon emission described in the embodiments of the present invention refers to the carbon emission generated when 1 kwh of electric energy is produced in an electricity-gas integrated energy system, and the carbon emission generated when 1 cubic meter of gas energy is supplied.
The energy distribution coefficient described in the embodiment of the invention refers to the energy supply coefficient distributed by each subsystem in the electricity-gas integrated energy system;
for example, when the electric energy demand of the external demand side of the electricity-gas integrated energy system is 200 kilowatts, 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 kilowatt-hour electric energy to the external demand side, and the energy distribution coefficient of the liquefied gas subsystem is 0.3, the liquefied gas subsystem is scheduled to supply 60 cubic meters of liquefied gas to the external demand side.
It is understood that the sum of the energy distribution coefficients of the various types of electric energy subsystems is 1, and the sum of the energy distribution coefficients of the various types of gas energy subsystems is 1.
In some embodiments, establishing an energy scheduling model of the electric-gas integrated energy system based on the unit cost of energy supply, the unit carbon emission and the energy distribution coefficient of each subsystem in the electric-gas integrated energy system comprises:
determining the total energy supply cost and the total carbon emission of the electricity-gas integrated energy system based on the energy demand of the external demand side of the electricity-gas integrated energy system, the unit cost of energy supply of each subsystem in the electricity-gas integrated energy system, the unit carbon emission and the energy distribution coefficient;
constructing an objective function by taking the minimum energy supply total cost and the total carbon emission of the electricity-gas integrated energy system as optimization targets;
and establishing an energy scheduling model of the electricity-gas integrated energy system based on the objective function.
In this embodiment, the total cost of energy supply described in the embodiment of the present invention refers to the sum of the energy supply costs of the individual subsystems in the electric-gas integrated energy system.
The total carbon emission described in the embodiments of the present invention refers to the sum of the carbon emissions generated during the energy supply of the individual subsystems in the electric-gas integrated energy system.
Further, in the present embodiment, a supply cost matrix and a carbon emission matrix of the energy supply of each subsystem are established based on the energy demand on the external demand side of the electric-gas integrated energy system, the unit cost of the energy supply of each subsystem in the electric-gas integrated energy system, the unit carbon emission, and the energy distribution coefficient, and the total energy supply cost and the total carbon emission 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 of the electric-gas integrated energy system may be expressed as:
F＝D·(C+E)·X；
the energy distribution coefficient matrix of the energy supply of each subsystem is represented by X, C and D.
In an embodiment of the present invention, C may be represented as:
Figure BDA0003505692310000081
wherein C11-C1N represent the unit cost of the energy supply of the N types of electric energy subsystems, and C21-C2N represent the unit cost of the energy supply of the N types of gas energy subsystems;
e can be expressed as:
Figure BDA0003505692310000082
wherein E11-E1N represent the unit carbon emission of the energy supply of the N-type electric energy subsystem, and E21-E2N represent the unit carbon emission of the energy supply of the N-type gas energy subsystem.
In a 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 generation; the gas energy subsystem comprises five seed systems of liquefied gas, coal gas, natural gas, electricity-to-hydrogen and electricity-to-methane, and can be set
Figure BDA0003505692310000083
Figure BDA0003505692310000084
The unit cost of energy supply of five seed systems of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear power generation 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 the energy supply of the five seed systems of gasification gas, coal gas, natural gas, electricity-to-hydrogen gas and electricity-to-methane is 0.3, 0.1, 0.2, 0.3 and 0.4 in sequence, and the unit carbon emission is 0.3, 0.4, 0.2, 0.1 and 0.2 respectively.
In this embodiment, an objective function minF is constructed with the minimum energy supply total cost and the minimum total carbon emission of the electric-gas integrated energy system as optimization objectives, and an energy scheduling model of the electric-gas integrated energy system is established based on the objective function, that is, 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 gas energy sources in the electricity-gas integrated energy system are comprehensively considered, the energy supply total cost and the total carbon emission of the electricity-gas integrated energy system are the lowest optimization targets, the energy scheduling model of the electricity-gas integrated energy system is established, the accuracy of model construction is favorably ensured, and the optimal energy scheduling scheme can be effectively obtained by performing optimization analysis on the model.
102, determining a target fitness function based on a target function of an energy scheduling model, wherein the target function of the energy scheduling model is constructed with the aim of minimizing the total energy supply cost and the total carbon emission of the electricity-gas integrated energy system;
the objective function described in the embodiment of the present invention is constructed based on the lowest total energy supply cost and total carbon emission of the electric-gas integrated energy system, wherein the total energy supply cost and the total carbon emission can be calculated by 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 the embodiment of the present invention, a wolf Optimization Algorithm (COA) or a modified COA Algorithm may be adopted to analyze the energy scheduling model, based on which a fitness function in the COA Algorithm needs to be determined first.
The target fitness function described in the embodiment of the invention can be a fitness function in a COA algorithm, is used for describing main indexes of the individual performance of the wolf in the algorithm, and performs 'win-loss' on the wolf individual according to the size of the fitness of the wolf individual in the optimized solution, thereby obtaining an optimal solution.
In this embodiment, the target function of the energy scheduling model may be directly used as the target fitness function, the energy supply total cost and the total carbon emission matrix F of the electricity-gas integrated energy system are expressed as the fitness function of the wolf individual, the energy distribution coefficient matrix X is expressed as the social state of the wolf individual, and the target fitness function in the COA algorithm is determined accordingly, that is, the target fitness function is:
Figure BDA0003505692310000091
wherein ,
Figure BDA0003505692310000101
represents the fitness value of a single wolf individual,
Figure BDA0003505692310000102
representing the social status of the wolf individual.
In an embodiment of the present invention, the social status of an individual geowolf can be represented by the following matrix:
Figure BDA0003505692310000103
wherein ,p1～p5Respectively represents the energy distribution coefficients of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear electronic systems, g1～g5Respectively representing the energy distribution coefficients of the liquefied gas, the coal gas, the natural gas, the electricity-to-hydrogen and the electricity-to-methane subsystems.
In an embodiment of the present invention, one possible encoding sequence is:
Figure BDA0003505692310000104
in the above code, assuming that the demand on the external demand side of the electrical-pneumatic integrated energy system is 500 kwh of the electrical energy source, and the pneumatic energy source is 300 cubic meters, the soc represents that the 500 kwh of the external demand side of the electrical-pneumatic integrated energy system is respectively from the thermal power generation, the wind power generation, the hydraulic power generation, the photovoltaic power generation and the nuclear electronic system in the electrical-pneumatic integrated energy system, wherein 500 × 0.3 × 150 kwh from the thermal power generation subsystem, 500 × 0.1 × 50 kwh from the wind power generation subsystem, 500 × 0.2 × 100 kwh from the hydraulic power generation subsystem, 500 × 0.1 × 50 kwh from the photovoltaic power generation subsystem, and 500 × 0.3 × 150 kwh from the nuclear electronic system; the 300 cubic meter gas energy source demand of the electricity external demand side is respectively from liquefied gas, coal gas, natural gas, electricity-to-hydrogen and electricity-to-methane subsystems in the electricity-gas comprehensive energy system, and the specific calculation mode of each gas energy source is consistent with that of the electric energy source, which is not described herein again.
And 103, taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
Specifically, the target energy distribution coefficient described in the embodiment of the present invention refers to the scheduling parameter obtained by the scheduling method of the electric-gas integrated energy system of the present invention, which is used to determine the supply coefficient of the energy supply of each subsystem in the electric-gas integrated energy system, so as to schedule the energy supply amount of each subsystem based on the supply coefficient of the energy supply of each subsystem and the demand amount of the external demand side, thereby achieving the purpose of effectively reducing the energy supply cost and reducing the carbon emission amount generated in the energy supply process.
And further, with the minimum target fitness function as a target, carrying out iterative solution on the target fitness function to determine a target energy distribution coefficient corresponding to the minimum fitness value. In this embodiment, the target fitness function may be solved by using a COA algorithm or a modified COA algorithm.
The COA algorithm described in the embodiment of the invention belongs to a meta-heuristic optimization algorithm, is inspirational from the process of adapting to the environment by the wolf group and the organization structure among the wolf groups, has a good optimization effect, allows wide exploration on a search space in the global optimization process, and outputs a high-quality solution.
In the step, the initial social states of the wolf individual and the wolf individual can be initialized randomly 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 a target energy distribution coefficient of each subsystem in the dispatching electric-gas integrated energy system.
The improved COA algorithm described by the invention realizes the effects of increasing the diversity of the wolfsbane population and improving the solving precision and the convergence speed of the algorithm by introducing operators of a chaos strategy and/or a cloning strategy into the COA algorithm, thereby effectively accelerating the convergence capability and the global optimization capability of the algorithm.
Preferably, in this embodiment, through an improved COA algorithm, the target fitness function of the energy scheduling model based on the electric-gas integrated energy system is analyzed and iteratively solved, so that a global optimal solution can be efficiently solved, and an energy scheduling scheme with higher quality can be obtained.
According to the method, the unit cost, the unit carbon emission and the energy distribution coefficient of the energy supply of each subsystem in the electricity-gas integrated energy system are comprehensively considered, and an energy scheduling model of the electricity-gas integrated energy system is established; and determining a target fitness function through a target function of the energy scheduling model, taking the minimum target fitness function as an optimization target, performing iterative solution by adopting an algorithm for improving the solution speed and precision, and completing effective analysis on the energy scheduling model of the electricity-gas integrated energy system, so as to obtain a target energy distribution coefficient corresponding to the minimum fitness value, determine the energy supply quantity of each subsystem according to the target energy distribution coefficient, realize effective scheduling on the electricity-gas integrated energy system, effectively reduce the energy supply cost of the electricity-gas integrated energy system, reduce carbon emission generated in the energy supply process, and improve the scheduling reliability and low carbon property of the electricity-gas integrated energy system.
In some embodiments, the determining the target energy distribution coefficient corresponding to the minimum fitness value by iteratively solving the target fitness function with the target fitness function minimum as a target includes:
initializing a wolf optimization algorithm based on a floating point number coding method to obtain a plurality of wolf populations and each wolf individual in each wolf population;
based on a target fitness function and a target operator, performing a wolf optimization algorithm operation 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 wolf individual;
the target operator comprises at least one of a chaos operator and a cloning operator, the chaos operator is used for determining the initial social state of each wolf individual in the current iteration process, and the cloning operator is used for updating each wolf individual in each wolf population into the wolf individual with the minimum fitness value in each wolf population.
In this embodiment, the target sirius individuals described in the embodiments of the present invention refer to the sirius individual with the minimum fitness value among all the sirius individuals obtained after the algorithm is finished, which is the optimal solution.
The chaos operator described by the invention refers to an operator based on a chaos strategy, and is specifically used for determining the initial social state of each wolf individual in the current iteration process, so that the population diversity of the wolf group can be increased, and the solution precision can be improved.
In this embodiment, the cloning operator refers to an operator based on a cloning strategy, and is used for updating each sirius individual in each sirius population to an optimal sirius individual with the minimum fitness value in each sirius population, so that the process of finding an optimal solution is more directional, and the convergence rate of the algorithm can be effectively improved.
The target operator described in the embodiment of the present invention includes at least one of a chaos operator and a clone operator, that is, in this embodiment, the COA algorithm may be improved by introducing one of the chaos operator and the clone operator, and preferably, the COA algorithm may also be improved by introducing both operators at the same time, 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 threshold, that is, if the number of times of iterative computation of the algorithm reaches the preset iteration threshold, the iteration of the algorithm is stopped, and a final solution result is obtained.
Further, based on a floating point number coding method, conducting COA algorithm random initialization to obtain a plurality of wolf populations and each wolf individual in each wolf population;
in this embodiment, the floating-point number coding is adopted to code the social state of the individual in the COA algorithm 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 wolfsbane populations, the number Nc of wolfsbane individuals in each wolfsbane population, and the social state of the wolfsbane individual
Figure BDA0003505692310000131
The system comprises a power supply system, a power-gas integrated energy system, a power supply system and a control system, wherein p is 1,2, …, Np, C is 1,2, …, Nc, a demand matrix D on the external demand side of the power-gas integrated energy system, a unit carbon emission matrix E of energy supply of each subsystem, a unit cost matrix C of energy supply of each subsystem, an upper limit of a position value in the social state of the wolf individual is 1, and a lower limit of the position value is 0.
Further, in this embodiment, based on the target fitness function and at least one of the chaos operator and the clone operator in the target operator, the COA algorithm is performed on each sirius individual in each sirius population until the iteration number of the algorithm meets the preset iteration termination condition, the target sirius individual is obtained,
in a specific embodiment of the invention, in the operation of performing the COA algorithm on each wolf individual in each wolf population, a chaos operator is adopted to initialize each wolf individual to obtain an initial social state of each wolf individual, so as to increase population diversity and obtain a more comprehensive search space, and the target wolf individual is obtained by continuously and iteratively calculating the fitness value of the wolf individual.
In another embodiment of the invention, in the operation of performing the COA algorithm on each sirius individual in each sirius population, a clone operator can be used, and each sirius individual in each sirius population is updated to the optimal sirius individual with the minimum fitness value in each sirius population, so that the population is subjected to large-scale variation, the algorithm convergence speed and solution accuracy are improved, the fitness value of the sirius individual is continuously calculated in an iterative manner, and the target sirius individual is obtained under the condition that a preset iteration termination condition is met.
Preferably, in another specific embodiment of the present invention, in the operation of performing the COA algorithm on each sirius individual in each sirius population, a chaos operator and a clone operator may also be introduced at the same time to more efficiently and accurately perform the minimum iterative solution on the target fitness function to obtain the target sirius individual.
After the target wolf individual is determined, the target energy distribution coefficient is determined according to the social state of the target wolf individual, and energy dispatching of the electricity-gas comprehensive energy system is completed according to the target energy distribution coefficient.
According to the method, the COA algorithm is used for solving and analyzing the target fitness function of the energy scheduling model based on the electricity-gas integrated energy system, the chaotic operator is introduced to expand population diversity, a more comprehensive search space is realized, and the clone operator is introduced to further accelerate the convergence speed of the algorithm, so that the global optimization 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 sirius optimization algorithm on each sirius individual in each sirius population, and obtaining the target sirius individual when a preset iteration termination condition is met, including:
determining the initial social state of each wolf individual in each wolf population based on a chaos operator;
and performing a wolf optimization algorithm operation on each wolf individual in each wolf population based on a target fitness function, the initial social state and the clone operator of each wolf individual in each wolf population, and obtaining a target wolf individual under the condition of meeting a preset iteration termination condition, wherein the target operator comprises a chaos operator and a clone operator.
Specifically, in the embodiment of the present invention, the target operator includes a chaos operator and a cloning operator, that is, the COA algorithm is improved by introducing the chaos operator and the cloning operator at the same time, so as to perform a minimum iterative solution on the target fitness function.
In some embodiments, determining the initial social state of each of the individual sirius individuals in each sirius population based on a chaotic operator comprises:
by the formula of the chaos operator:
Iz+1＝bIz(1-Iz) Z ═ 0,1, …, dim, determining the initial social status of individual geopenaeus subjects in each geopenaeus population;
wherein ,IzRepresenting a floating point number position in the initial social state of the wolf individual, dim representing the dimension of the solution search space, I0Represents [0,1 ]]B is 4, which indicates that the position sequence generated by the chaos operator is in a chaos state.
In this embodiment, the formula of the chaotic operator is a Logistic chaotic mapping formula, when b is 4, the mapping is a fill-up, the generated sequence is in a chaotic state, that is, the sequence generated under the effect of the Logistic mapping is aperiodic and non-convergent, and has a traversability in (0,1), and any other b value does not make the entire sequence in the chaotic state.
In this embodiment, in the specific calculation process, for example, I0When equal to 0.2, then I1Generated according to the chaotic mapping formula as I1And 4, 0.2 (1-0.2), and the rest positions are generated by analogy, and the generated data sequence is normalized to ensure that the sum of the energy distribution coefficients of the represented electric energy source and the represented gas energy source is 1, thereby determining the initial social state of each wolf individual in each wolf population.
Therefore, each wolf population is initialized through the 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, each wolf population is initialized by introducing the chaotic operator and generating the characteristic that the position sequence is in the chaotic state based on the chaotic operator, the initial social state of each wolf individual is determined, and the diversity of the wolf population is increased, so that the diversity of the initial solution is increased, and the solution precision of the algorithm is favorably improved.
In some embodiments, based on the target fitness function, the initial social state and the clone operator of each sirius individual in each sirius population, performing a sirius optimization algorithm operation on each sirius individual in each sirius population, and obtaining a target sirius individual under the condition that a preset iteration termination condition is met, including:
based on a target fitness function and the initial social state of each wolf individual in each wolf population, performing an operation of a growing stage and an evolution stage in a wolf optimization algorithm on each wolf individual in each wolf population to generate an alpha wolf individual in each wolf population, wherein the alpha wolf individual is the wolf individual with the lowest fitness in each wolf population;
replacing each wolf individual in each wolf population with an alpha wolf individual in each wolf population based on a clone operator to obtain a clone population corresponding to each wolf population, and performing mutation processing on each clone population according to a target mutation probability to obtain each mutated clone population;
determining the optimal wolf individual with the minimum 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 a preset iteration termination condition, determining the target wolf individual according to the optimal wolf individual.
Specifically, the clone population described in the embodiment of the present invention refers to a population subjected to a cloning process for each sirius population based on a cloning operator, which is obtained by replacing each sirius individual in each sirius population with an alpha sirius individual in each sirius population.
The target variation probability described in the embodiment of the present invention refers to a preset population variation probability, which can be represented by MP, and the specific value of MP is set according to the actual calculation requirement, for example, it can be set to 0.4, that is, the variation probability of each clone population is 0.4, and 10 sirius individuals exist in each clone population, so that 4 sirius individuals are varied, that is, the social status of the 4 sirius individuals is changed, and the fitness value of the sirius individuals is also changed.
The variation mode is that a group of floating point number sequences which accord with a target scheduling rule are randomly generated, and the target scheduling rule comprises the following steps: the value of each position in the social state of an individual satchel is between 0 and 1; the sum of each row in the social state of an individual wolf is 1; and each position in the social state of an individual wolf corresponds to an energy distribution coefficient of energy supply.
Further, based on the target fitness function and the initial social state of each satsuma individual in each satsuma population, the operation of the development stage and the evolution stage in the satsuma optimization algorithm is carried out on each satsuma individual in each satsuma population, and alpha satsuma individuals in each satsuma population are generated, wherein the specific implementation mode that the alpha satsuma individuals are the satsuma individuals with the lowest fitness in each satsuma population comprises the steps of S1 to S6.
Step S1, determining an initial alpha wolf individual in each wolf population for Np wolf populations based on the target fitness function and the initial social status of each wolf individual in each wolf population, the formula is as follows:
Figure BDA0003505692310000161
wherein, loop represents the current iteration times of the algorithm, p represents the p-th wolf population, Nc represents the number of wolf individuals in each wolf population,
Figure BDA0003505692310000162
representing the social state of the c < th > wolf individual in the p < th > wolf population in the iteration process of the loop;
in one embodiment, there are 4 sirius individuals in a sirius population, including sirius individual 1, sirius individual 2, sirius individual 3 and sirius individual 4, and their social status coding sequences are:
Figure BDA0003505692310000163
Figure BDA0003505692310000164
Figure BDA0003505692310000165
Figure BDA0003505692310000166
Figure BDA0003505692310000167
according to the target fitness function calculation formula, the fitness values of the 4 wolf individuals are respectively obtained as follows:
Figure BDA0003505692310000171
Figure BDA0003505692310000172
Figure BDA0003505692310000173
Figure BDA0003505692310000174
it should be noted that, in the process of designing the target fitness function of the COA algorithm, the energy distribution coefficient matrix in the electricity-gas integrated energy system carried by each sirius is multiplied 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 is, the smaller the fitness value of the sirius is, the higher the solution quality obtained is.
Therefore, from the calculation results of the fitness values of the 4 saturnus orientalis individuals, it can be found that the fitness value 320.8 of the saturnus orientalis individual 1 is the smallest, so that the carried solution is the best among the 4 saturnus orientalis individuals;
the energy distribution coefficient matrix in the carried electric-gas integrated energy system can be obtained, the electric energy source provided by the electric-gas integrated energy system to the demand side is supplied with 500 × 0.25-125 kilowatt hours from the thermal power generation subsystem, 500 × 0.23-115 kilowatt hours from the wind power generation subsystem, 500 × 0.36-180 kilowatt hours from the hydraulic power generation subsystem, 500.05-25 kilowatt hours from the photovoltaic power generation subsystem, and 500 × 0.11-55 kilowatt hours from the nuclear electronic system;
the supply of the gas energy source provided by the electricity-gas integrated energy system to the demand side is 300 × 0.03 ═ 9 cubic meters from the liquefied gas subsystem, 300 × 0.23 ═ 69 cubic meters from the gas subsystem, 300 × 0.37 ═ 111 cubic meters from the natural gas subsystem, 300 × 0.31 ═ 93 cubic meters from the electricity-to-hydrogen subsystem, and 300 ═ 0.06 ═ 18 cubic meters from the electricity-to-methane subsystem.
Among the 4 wolf individuals given in the examples of the present invention, wolf individual 1 was selected as the initial alpha wolf individual.
Step S2, calculating the culture tendency of each wolf population, the formula is as follows:
Figure BDA0003505692310000181
wherein ,
Figure BDA0003505692310000182
representing the ranked social status of the pth Weak population in the next iteration of loop, j belongs to 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 the calculation should be performed by otherwise.
Step S3, updating the social state and calculating the fitness value of the wolf individual of each wolf population, and evaluating the new social state, wherein the calculation formula is as follows:
Figure BDA0003505692310000183
Figure BDA0003505692310000184
Figure BDA0003505692310000185
wherein ,δ1 and δ2Respectively representing alpha Tung influence and population influence, r1 and r2Weight factors representing the alpha Tung influence and the population influence, initial r, respectively1 and r2Is defined as [0,1 ]]A random value of between, wherein δ1 and δ2The calculation formulas of (A) and (B) are respectively as follows:
Figure BDA0003505692310000186
Figure BDA0003505692310000187
wherein ,cr1Representing cultural differences from random wolfs in the population to alpha wolfs, cr2Cultural differences representing cultural trends from random wolfs to wolfs populations;
step S4, updating the birth and death status of the wolf individuals in each wolf population, the formula is as follows:
Figure BDA0003505692310000191
wherein ,r1 and r2Represents random wolf individuals in the p-th wolf population, j1 and j2Representing two random dimensions, P, in an electric-gas integrated energy scheduling problemsRepresents a dispersion probability, PaRepresenting the joint probability, RjRandom number, rnd, representing a decision variable upper and lower bound in the j-th dimensionjRepresents a [0,1 ]]A random number in between;
step S5, performing individual migration between the wolf populations, and updating the ages of the wolf individuals, wherein the formula is as follows:
Figure BDA0003505692310000192
wherein ,PeIndicating the probability of the wolf individual being expelled from the population, it should be noted that in order to prevent the probability from being greater than 1, it is specified that N iscDoes not exceed 14;
in one particular embodiment of an embodiment of the present invention,
Figure BDA0003505692310000193
i.e., the probability of sirius individuals being expelled from the population is 50%.
Through the operation, the solutions in different wolfsbane populations can be exchanged to obtain a better solution.
Step S6, recalculating fitness value of each wolf individual in each wolf population, and determining alpha wolf individual in each wolf population;
according to the method provided by the embodiment of the invention, the cloning operator is introduced, after the alpha wolfs in each wolf population are generated by iteration of the COA algorithm, each wolf individual in each wolf population is cloned into the alpha wolfs in each population, and then large-scale variation processing is carried out, so that the convergence speed of the algorithm can be accelerated, the local search capability of the algorithm can be increased, the solution precision of the COA algorithm can be improved, and a high-quality solution can be obtained.
Further, based on a clone operator, each sirius individual in each sirius population is replaced with an alpha sirius individual in each sirius population to obtain a clone population corresponding to each sirius population, and variation processing is performed on each clone population according to a target variation probability to obtain each variation clone population, wherein the specific implementation mode of the method comprises the step S7.
Specifically, in step S7, the alpha sirius in each sirius population is cloned proportionally, that is, each sirius individual in each sirius population is replaced with its own alpha sirius individual to form a new clone population, and the total number of sirius individuals is kept unchanged in the process.
Further, according to the target mutation probability MP, performing large-scale mutation treatment on each obtained clone population to obtain a variant clone population. In an embodiment of the present invention, the mutation mode is to randomly generate a group of floating point number sequences that meet the scheduling rule, and the cloned target mutation probability is MP 0.4.
And step 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, if the current iteration loop times meet the preset iteration termination condition, outputting an optimal solution, namely obtaining a target wolf individual according to the optimal wolf individual, and determining a target energy distribution coefficient based on the target wolf individual;
if the current iteration number loop does not reach the specified maximum iteration number MaxLoop, the loop is set to loop +1, the process returns to step S1, and the next iteration of the algorithm is performed.
According to the method provided by the embodiment of the invention, through the improved COA algorithm, the chaos operator and the clone operator are introduced at the same time, so that the diversity of the initial solution is increased, the solution space is searched more comprehensively, the convergence speed and the local optimization capability of the algorithm can be further accelerated by cloning the alpha wolf individual, the solution precision of the algorithm is favorably improved, and the 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 considering carbon cost according to the embodiment of the present invention, as shown in fig. 2,
step S201, based on a floating point number coding method, coding the social state of the wolf individuals in the algorithm to obtain a plurality of wolf populations and each wolf individual in each wolf population;
step S202, initializing each wolf population by using a chaotic operator to obtain the initial social state of each wolf individual;
step S203, calculating the fitness value of each wolf individual based on the target fitness function and the initial social state of each wolf individual in each wolf population, and determining the initial alpha wolf individual in each wolf population for Np wolf populations;
step S204, calculating the culture tendency of each wolf population;
step S205, updating the social state and calculating the fitness value of the wolf individual of each wolf population, and evaluating the new social state;
step S206, updating the birth and death conditions of the wolf individuals in each wolf population;
step S207, carrying out individual migration among the wolf populations and updating the ages of the wolf individuals;
step S208, recalculating the fitness value of each wolf individual in each wolf population, and determining an alpha wolf individual in each wolf population;
step S209, determining a clone population corresponding to each wolf population based on a clone operator, and performing large-scale mutation processing on each clone population according to the target mutation probability to obtain each mutated clone population and obtain the optimal wolf individual with the minimum fitness value among all wolf individuals;
step S210, determining whether the current iteration number loop reaches the maximum iteration number MaxLoop, if not, making the loop equal to loop +1, and returning to step S203; otherwise, jumping to step 211, ending the algorithm, and outputting the optimal solution to obtain the target wolf individual.
According to the method provided by the embodiment of the invention, the chaos operator and the clone 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 comparison graph of optimization effects in the method for scheduling an electric-gas integrated energy system considering carbon costs according to the embodiment of the present invention, and as shown in fig. 3, based on the above embodiment, detailed parameter configurations and simulation results of simulation experiments are given in this embodiment.
Specifically, in this embodiment of the present invention, the energy demand on the external demand side of the electric-gas integrated energy system is 500 kilowatt-hours of electric energy and 300 cubic meters of gas energy, respectively. In the supply side of the electricity-gas comprehensive energy system, the sources of the electric energy source are five modes of thermal power generation, wind power generation, hydroelectric power generation, photovoltaic power generation and nuclear power generation, and the sources of the gas energy source are five modes of liquefied gas, coal gas, natural gas, electricity-to-hydrogen and electricity-to-methane.
In order to verify the effectiveness of the method provided by the invention, a butterfly optimization algorithm, a mixed frog-leaping algorithm, a particle swarm optimization algorithm and a simulated annealing algorithm in the prior art are selected for comparison. In the aspect of parameter setting, the number Np of different populations of the improved sirius is 6, the number of individuals of the sirius in each population is Nc is 10, the initial population of the algorithm is generated by using a chaotic operator, and the large-scale mutation probability of a clone operator used in the operation process of the algorithm is MP is 0.4.
In the comparison algorithm, the population size of the butterfly optimization algorithm is 60, the sensory factor c is 0.6, and the stimulation intensity I is 0.8; the size of the population in the mixed frog-leaping algorithm is 60, the number of the sub-populations is 4, and the transition probability is 0.6; in the particle swarm algorithm, the size of a population 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 population size of the simulated annealing algorithm was 60, the initial temperature was 240, and the annealing rate was 0.98.
In the present 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 compares the existing four algorithms, namely the butterfly optimization algorithm, the mixed frog-leaping algorithm, the particle swarm optimization algorithm and the simulated annealing algorithm, and the electric-gas integrated energy system scheduling scheme based on the four comparison algorithms finally falls into premature convergence and fails to obtain a solution with higher quality.
Specifically, the optimal effect value obtained by the scheduling method of the electric-gas integrated energy system based on the improved sirius optimization algorithm provided by the embodiment of the invention is 261, and the optimal effect values obtained by the scheduling method based on 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 fitness function set by the method, the energy supply cost and the carbon emission of different subsystems of the electric-gas integrated energy system are in direct proportion to the fitness value, so that the lower the final optimization effect value is, the optimal comprehensive effect of the scheduling scheme of the electric-gas integrated energy system is shown, and the optimization effect of the method provided by the invention is superior to that of the other four conventional scheduling methods.
It should be noted that the reason why the effect of the method of the present invention is superior to that of other methods is that the present invention adopts a chaos operator to generate an initial population, thereby increasing the diversity of the initial solution and facilitating the implementation of the subsequent optimization steps of the algorithm. In addition, the application of the clone operator also accelerates the convergence speed of the algorithm and increases the capability of locally searching the optimal solution, thereby improving the accuracy of the algorithm solution. Therefore, the electric-gas comprehensive energy system scheduling method provided by the invention can well balance the relationship among carbon emission, energy demand and supply cost, accords with multi-party benefits and is beneficial to practical application.
The carbon cost-related electric-gas integrated energy system scheduling apparatus according to the present invention is described below, and the carbon cost-related electric-gas integrated energy system scheduling apparatus described below and the carbon cost-related electric-gas integrated energy system scheduling method described above may be referred to in correspondence with each other.
Fig. 4 is a schematic structural diagram of an electric-gas integrated energy system dispatching device considering carbon cost according to an embodiment of the present invention, as shown in fig. 4, including:
the modeling module 410 is used for establishing an energy scheduling model of the electricity-gas integrated energy system based on the unit cost, the unit carbon emission and the energy distribution coefficient of the energy supply of each subsystem in the electricity-gas integrated energy system;
the processing module 420 is configured to determine a target fitness function based on a target function of the energy scheduling model, where the target function of the energy scheduling model is constructed based on the lowest total energy supply cost and total carbon emission of the electric-gas integrated energy system as a target;
and the scheduling module 430 is configured to perform iterative solution on the target fitness function with the target fitness function minimum as a target, determine a target energy distribution coefficient corresponding to the minimum fitness value, and determine the energy supply amount of each subsystem according to the target energy distribution coefficient.
The carbon cost-considered electric-gas integrated energy system scheduling apparatus described in this embodiment may be used in the embodiment of the above-mentioned carbon cost-considered electric-gas integrated energy system scheduling method, and its principle and technical effect are similar, and will not be described herein again.
The device of the embodiment of the invention establishes the energy scheduling model of the electricity-gas integrated energy system by comprehensively considering the unit cost, the unit carbon emission and the energy distribution coefficient of the energy supply of each subsystem in the electricity-gas integrated energy system; and determining a target fitness function through a target function of the energy scheduling model, taking the minimum target fitness function as an optimization target, performing iterative solution by adopting an algorithm for improving the solution speed and precision, and completing effective analysis on the energy scheduling model of the electricity-gas integrated energy system, so as to obtain a target energy distribution coefficient corresponding to the minimum fitness value, determine the energy supply quantity of each subsystem according to the target energy distribution coefficient, realize effective scheduling on the electricity-gas integrated energy system, effectively reduce the energy supply cost of the electricity-gas integrated energy system, reduce carbon emission generated in the energy supply process, and improve the scheduling reliability and low carbon property of the electricity-gas integrated energy system.
Fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device may include: 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. Processor 510 may invoke logic instructions in memory 530 to perform the carbon cost-accounting electric-gas integrated energy system dispatch method provided by the methods described above, the method comprising: establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system; 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 constructed based on the lowest total energy supply cost and total carbon emission of the electricity-gas integrated energy system as a target; and taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
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 or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 also provides a computer program product comprising a computer program, the computer program being stored 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 taking into account carbon costs provided by the above methods, the method comprising: establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system; 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 constructed based on the lowest total energy supply cost and total carbon emission of the electricity-gas integrated energy system as a target; and taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining 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 that when executed by a processor implements an electric-gas integrated energy system scheduling method accounting for carbon costs provided by the above methods, the method comprising: establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system; 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 constructed based on the lowest total energy supply cost and total carbon emission of the electricity-gas integrated energy system as a target; and taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An electricity-gas integrated energy system scheduling method considering carbon cost is characterized by comprising the following steps:
establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated energy system;
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 constructed based on the lowest total energy supply cost and total carbon emission of the electricity-gas integrated energy system as a target;
and taking the minimum target fitness function as a target, carrying out iterative solution on the target fitness function, determining a target energy distribution coefficient corresponding to the minimum fitness function, and determining the energy supply quantity of each subsystem according to the target energy distribution coefficient.
2. The method for scheduling an electric-gas integrated energy system according to claim 1, wherein the step of iteratively solving the target fitness function to determine the target energy distribution coefficient corresponding to the minimum fitness value with the target fitness function being the minimum comprises:
initializing a wolf optimization algorithm based on a floating-point number coding method to obtain a plurality of wolf populations and each wolf individual in each wolf population;
based on the target fitness function and the target operator, performing a wolf optimization algorithm operation 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 individual wolfsbane;
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 wolf individual in the current iteration process, and the clone operator is used for updating each wolf individual in each wolf population into the wolf individual with the minimum fitness value in each wolf population.
3. The carbon cost-based scheduling method for an electric-gas integrated energy system according to claim 2, wherein the step of performing a sirius optimization algorithm on each sirius individual in each sirius population based on the target fitness function and the target operator to obtain a target sirius individual when a preset iteration termination condition is met comprises:
determining the initial social state of each wolf individual in each wolf population based on the chaotic operator;
and performing a wolf optimization algorithm operation on each wolf individual in each wolf population based on the target fitness function, the initial social state of each wolf individual in each wolf population and the clone operator, and obtaining a target wolf individual under the condition of meeting a preset iteration termination condition, wherein the target operator comprises the chaos operator and the clone operator.
4. The carbon-cost-considered scheduling method for an electric-gas integrated energy system according to claim 3, wherein the performing of the sirius optimization algorithm on each sirius individual in each sirius population based on the target fitness function, the initial social status of each sirius individual in each sirius population and the clone operator to obtain a target sirius individual in case of satisfying a preset iteration termination condition comprises:
based on the target fitness function and the initial social state of each wolf individual in each wolf population, performing an operation of a growth stage and an evolution stage in a wolf optimization algorithm on each wolf individual in each wolf population to generate an alpha wolf individual in each wolf population, wherein the alpha wolf individual is the wolf individual with the lowest fitness in each wolf population;
replacing each wolf individual in each wolf population with an alpha wolf individual in each wolf population based on the cloning operator to obtain a cloning population corresponding to each wolf population, and performing mutation processing on each cloning population according to a target mutation probability to obtain each variant cloning population;
determining the optimal wolf individual with the minimum fitness value in all the clonal wolf individuals based on the target fitness function and each clonal wolf individual in each variant clonal population;
and under the condition that the current iteration times meet a preset iteration termination condition, determining the target wolf individual according to the optimal wolf individual.
5. The carbon cost-aware electric-gas integrated energy system scheduling method of claim 3, wherein the determining the initial social status of each of the individual geopenaeus dauriculus individuals in each of the geopenaeus dauriculus populations based on the chaotic operator comprises:
through the formula of the chaos operator:
Iz+1＝bIz(1-Iz) (iii), z ═ 0,1, …, dim, determining the initial social status of individual geopenaeus wolf individuals in each of said geopenaeus populations;
wherein ,IzRepresenting a floating point number position in the initial social state of the wolf individual, dim representing the dimension of the solution search space, I0Represents [0,1 ]]B-4, which indicates that the position sequence generated by the chaos operator is in a chaos state.
6. The method for scheduling an electric-gas integrated energy system taking into account carbon cost according to claim 1, wherein the establishing an energy scheduling model of the electric-gas integrated energy system based on unit cost of energy supply, unit carbon emission and energy distribution coefficient 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 of energy supply of each subsystem in the electric-gas integrated energy system, the unit carbon emission and the energy distribution coefficient;
constructing an objective function by taking the minimum energy supply total cost and the total carbon emission of the electricity-gas integrated energy system as optimization targets;
and establishing an energy scheduling model of the electricity-gas integrated energy system based on the objective function.
7. An electricity-gas integrated energy system dispatching device considering carbon cost is characterized by comprising:
the modeling module is used for establishing an energy scheduling model of the electricity-gas integrated energy system based on unit cost, unit carbon emission and energy distribution coefficient of energy supply of each subsystem in the electricity-gas integrated 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 constructed based on the lowest aims of the total energy supply cost and the total carbon emission of the electricity-gas integrated energy system;
and the scheduling module is used for carrying out iterative solution on 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.
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 when executing the program performs the steps of the method of electric-gas integrated energy system dispatch taking into account carbon costs as claimed in any one of claims 1 to 6.
9. 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 method for scheduling an electric-gas integrated energy system taking into account carbon costs 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 method for electric-gas integrated energy system dispatch taking into account carbon costs according to any of claims 1 to 6.
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