Method, system and storage medium for determining optimal operation plan of integrated energy system

Abstract

The present invention discloses a method, system and storage medium for determining the optimal operation scheme of an integrated energy system, and belongs to the technical field of integrated energy systems. The present invention applies the slime mold algorithm to solve the problem of optimal operation of the integrated energy system, and can quickly and accurately determine the optimal operation scheme and the input and output of each energy device in the optimal operation scheme from various operation schemes, so as to obtain better operation results, better meet the user's four load requirements of electricity, gas, cold and heat, and make the convergence speed of the optimized operation scheduling faster, the economic cost lower, and the carbon emissions smaller, so that the economy and environmental friendliness of the integrated energy system are better. Thereby solving the technical problems of slow convergence speed, high economic cost and high carbon emissions in the existing integrated energy system optimization scheduling method.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and more specifically, to a method, system and storage medium for determining an optimal operation plan of an integrated energy system. Background Art

[0002] With the acceleration of global industrialization, greenhouse effect and environmental problems are becoming increasingly prominent. Against this background, the call for low-carbon emission reduction is getting louder and louder. In particular, China has stated that it will strive to achieve carbon peak by 2030 and carbon neutrality by 2060. Traditional energy supply mainly relies on fossil fuels. As energy demand grows, carbon emissions continue to increase. Therefore, it is necessary to integrate renewable energy, change energy utilization, improve energy efficiency, and reduce carbon emissions.

[0003] Integrated Energy System (IES) can realize the coupling conversion between multiple energy sources, and plays a great role in solving environmental problems and improving the consumption of renewable energy. In addition, IES can also meet the needs of multiple types of loads at the same time. With the advancement of IES research, the situation of single supply and mutual separation of traditional energy systems is changing, and energy waste will be greatly reduced, thus achieving better economic and environmental benefits. Due to the intricate coupling relationship within IES, its mathematical model has the characteristics of multi-variable, nonlinear and multi-constrained. How to obtain the optimal operation plan of IES under complex multi-variable, nonlinear and multi-constrained conditions has gradually become the focus of attention around the world.

[0004] The invention application CN111463836A discloses a method for optimizing and scheduling an integrated energy system, including: obtaining daily predicted values ​​of cold, hot, electric loads and photovoltaic output; generating an error scenario set S according to the predicted error probability distribution of cold, hot, electric loads and photovoltaic output; reducing the error scenario set to obtain typical error scenarios; superimposing the typical error scenarios with the predicted values ​​of cold, hot, electric loads and photovoltaic output to obtain typical scenarios of cold, hot, electric loads and photovoltaic output; constructing a mathematical model of equipment for optimizing and scheduling an integrated energy system; constructing an optimized scheduling model for an integrated energy system according to the mathematical model of equipment for optimizing and scheduling an integrated energy system; inputting typical scenarios into the optimized scheduling model for an integrated energy system, and solving them using the NSGA-II multi-objective algorithm to obtain a Pareto optimal solution set; selecting the best solution from the Pareto optimal solution set to obtain the best operation plan.

[0005] However, traditional integrated energy system optimization and scheduling methods have the disadvantages of slow convergence, high economic cost and high carbon emissions. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method, system and storage medium for determining an optimal operation plan for an integrated energy system.

[0007] According to one aspect of the present invention, a method for determining an optimal operation plan of an integrated energy system is provided, comprising:

[0008] Step 1: Determine the objective function of the integrated energy system;

[0009] Step 2: Set the variable range, constraints and total number of iterations of the objective function;

[0010] Step 3: Initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual;

[0011] Step 4: Based on the variable range of the objective function, constraints, total number of iterations and specific positions of each individual, calculate the objective function value of each individual at the current iteration number;

[0012] Step 5: Determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, update the specific position of each individual based on a preset first algorithm; if the current random number is not less than the preset given value, update the specific position of each individual based on a preset second algorithm;

[0013] Step 6: Based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update, calculate the objective function value of each individual at the next iteration number;

[0014] Step 7: Determine whether the maximum number of iterations has been reached; if so, go to step 8, otherwise go to step 4;

[0015] Step 8: Based on the objective function value of each individual and the specific position of each individual, determine the optimal operation plan from various operation plans and the input and output of each energy device in the optimal operation plan.

[0016] Optionally, the objective function is expressed as follows:

[0017] ;

[0018] ;

[0019] ;

[0020] In the formula, F is the objective function;f buy The purchase cost of electricity and natural gas for the integrated energy system; f op Energy equipment maintenance costs for integrated energy systems; P e ( t )express t Purchase electricity at all times; P g ( t )express t Purchase natural gas at any time; c e ( t )express t The electricity price at the time; c g express t The natural gas price at the time; c pv , , , , , , and Respectively represent the unit maintenance costs of photovoltaic, wind power, gas-fired devices, gas boilers, electric refrigerators, power-to-hydrogen devices, power-to-gas devices and hydrogen fuel cells; P PV ( t )express t The photovoltaic power output at the moment; P WT ( t )express t The power output of wind power at the moment; P GFe ( t )express t The heat output of the gas-fired device at the time; P GB ( t )express t The heat output of the gas boiler at the moment; P EC,in ( t )express t The electrical energy input of the electric refrigerator at the time; P P2H,in ( t )express t The power input of the power-to-hydrogen device at the time; P P2G,in ( t )express t The hydrogen input to the power-to-gas device at the time; PHFC,in ( t )express t The amount of hydrogen input to the hydrogen fuel cell at a given moment.

[0021] Optionally, the specific position of each individual of the slime mold algorithm is determined by the following formula:

[0022] ;

[0023] In the formula, X i Represents an individual i The specific location of lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; r is the current random number between 0 and 1.

[0024] Optionally, updating the specific location of each individual based on a preset first algorithm includes:

[0025] Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula:

[0026] ;

[0027] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; z Indicates a preset given value.

[0028] Optionally, updating the specific location of each individual based on a preset second algorithm includes:

[0029] Calculate the weight of each operation plan based on the objective function value of each individual based on the current random number and the current number of iterations W、 Positional parameters a and individual location evaluation parameters p ;

[0030] Determine the current random number r Is it less than the individual position evaluation parameter?p ;

[0031] If the previous random number r Less than the individual location evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0032] ;

[0033] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; X b n Indicates the current position of the optimal solution; vb is a range in -a arrive a A random number between W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z Indicates a preset given value; p represents the individual position evaluation parameter;

[0034] If the previous random number r Not less than the individual position evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0035] ;

[0036] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n Represents the specific position of individual i in the iteration number n; p Represents the individual location evaluation parameters.

[0037] According to another aspect of the present invention, a system for determining an optimal operation plan of an integrated energy system is provided, comprising:

[0038] An objective function determination module is used to determine the objective function of the integrated energy system;

[0039] The setting module is used to set the variable range, constraints and total number of iterations of the objective function;

[0040] The initialization module is used to initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual;

[0041] A first calculation module is used to calculate the objective function value of each individual at the current iteration number based on the variable range of the objective function, the constraint conditions, the total number of iterations and the specific position of each individual;

[0042] The individual position update module is used to determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, the specific position of each individual is updated based on a preset first algorithm; if the current random number is not less than the preset given value, the specific position of each individual is updated based on a preset second algorithm;

[0043] A second calculation module is used to calculate the objective function value of each individual at the next iteration number based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update;

[0044] A judgment module is used to judge whether the maximum number of iterations has been reached; if the maximum number of iterations has been reached, the program is transferred to the solution determination module, otherwise, the program is transferred to the first calculation module;

[0045] The scheme determination module is used to determine the optimal operation scheme and the input and output of each energy device in the optimal operation scheme from various operation schemes based on the objective function value of each individual and the specific position of each individual.

[0046] Optionally, the objective function is expressed as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] In the formula, F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; f op Energy equipment maintenance costs for integrated energy systems; P e (t )express t Purchase electricity at all times; P g ( t )express t Purchase natural gas at any time; c e ( t )express t The electricity price at the time; c g express t The natural gas price at the time; c pv , , , , , , and Respectively represent the unit maintenance costs of photovoltaic, wind power, gas-fired devices, gas boilers, electric refrigerators, power-to-hydrogen devices, power-to-gas devices and hydrogen fuel cells; P PV ( t )express t The photovoltaic power output at the moment; P WT ( t )express t The power output of wind power at the moment; P GFe ( t )express t The heat output of the gas-fired device at the time; P GB ( t )express t The heat output of the gas boiler at the moment; P EC,in ( t )express t The electrical energy input of the electric refrigerator at the time; P P2H,in ( t )express t The power input of the power-to-hydrogen device at the time; P P2G,in ( t )express t The hydrogen input to the power-to-gas device at the time; P HFC,in ( t )express t The amount of hydrogen input to the hydrogen fuel cell at a given moment.

[0051] Optionally, the specific position of each individual of the slime mold algorithm is determined by the following formula:

[0052] ;

[0053] In the formula, X i Represents an individual i The specific location of lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; r is the current random number between 0 and 1.

[0054] Optionally, updating the specific location of each individual based on a preset first algorithm includes:

[0055] Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula:

[0056] ;

[0057] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; z Indicates a preset given value.

[0058] Optionally, updating the specific location of each individual based on a preset second algorithm includes:

[0059] Calculate the weight of each operation plan based on the objective function value of each individual based on the current random number and the current number of iterations W、 Positional parameters a and individual location evaluation parameters p ;

[0060] Determine the current random number r Is it less than the individual position evaluation parameter? p ;

[0061] If the previous random number r Less than the individual location evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0062] ;

[0063] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; X b n Indicates the current position of the optimal solution; vb is a range in -a arrive a A random number between W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z Indicates a preset given value; p represents the individual position evaluation parameter;

[0064] If the previous random number r Not less than the individual position evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0065] ;

[0066] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n Represents the specific position of individual i in the iteration number n; p Represents the individual location evaluation parameters.

[0067] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method described in any one of the above aspects of the present invention.

[0068] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing executable instructions of the processor; the processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method described in any one of the above aspects of the present invention.

[0069] The present invention applies the slime mold algorithm to solve the problem of optimizing the operation of the integrated energy system, and can quickly and accurately determine the optimal operation scheme from various operation schemes and the input and output of each energy device in the optimal operation scheme, so as to obtain a better operation result, better meet the user's four load requirements of electricity, gas, cooling and heat, and make the convergence speed of the optimized operation scheduling faster, the economic cost lower, and the carbon emission smaller, so that the economy and environmental friendliness of the integrated energy system are better. Thereby solving the technical problems of slow convergence speed, high economic cost and high carbon emission in the existing integrated energy system optimization scheduling method. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0071] Figure 1 It is a flow chart of a method for determining an optimal operation plan of an integrated energy system provided by an exemplary embodiment of the present invention;

[0072] Figure 2 A structural diagram of an electric-gas-cold-heat integrated energy system provided by an embodiment of the present invention;

[0073] Figure 3 An overall flow chart of a flow chart of a method for determining an optimal operation plan of an integrated energy system provided by an embodiment of the present invention;

[0074] Figure 4 A structure of an electronic device is provided as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0075] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described here.

[0076] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0077] Those skilled in the art can understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0078] It should also be understood that, in the embodiments of the present invention, “plurality” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0079] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0080] In addition, the term "and / or" in the present invention is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects before and after are in an "or" relationship.

[0081] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced to each other, and for the sake of brevity, they will not be described one by one.

[0082] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0083] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0084] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0085] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0086] Embodiments of the present invention can be applied to electronic devices such as communication terminals, computer systems, servers, etc., which can operate with many other general or special computing system environments or configurations. Examples of well-known communication terminals, computing systems, environments and / or configurations suitable for use with electronic devices such as communication terminals, computer systems, servers, etc. include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, large computer systems, and distributed cloud computing technology environments including any of the above systems, etc.

[0087] Electronic devices such as communication terminals, computer systems, and servers can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. In general, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media including storage devices.

[0088] The present invention proposes a method, system and storage medium for determining an optimal operation plan of a comprehensive energy system. Figure 1 FIG. 1 is a flow chart of a method for determining an optimal operation plan of an integrated energy system provided by an exemplary embodiment of the present invention. Figure 1 As shown in FIG. 1 , the method for determining the optimal operation plan of the integrated energy system includes:

[0089] Step 1: Determine the objective function of the integrated energy system;

[0090] Optionally, the objective function is expressed as follows:

[0091] ;

[0092] ;

[0093] ;

[0094] In the formula, F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; f op Energy equipment maintenance costs for integrated energy systems; P e ( t )expresst Purchase electricity at all times; P g ( t )express t Purchase natural gas at any time; c e ( t )express t The electricity price at the time; c g express t The natural gas price at the time; c pv , , , , , , and Respectively represent the unit maintenance costs of photovoltaic, wind power, gas-fired devices, gas boilers, electric refrigerators, power-to-hydrogen devices, power-to-gas devices and hydrogen fuel cells; P PV ( t )express t The photovoltaic power output at the moment; P WT ( t )express t The power output of wind power at the moment; P GFe ( t )express t The heat output of the gas-fired device at the time; P GB ( t )express t The heat output of the gas boiler at the moment; P EC,in ( t )express t The electrical energy input of the electric refrigerator at the time; P P2H,in ( t )express t The power input of the power-to-hydrogen device at the time; P P2G,in ( t )express t The hydrogen input to the power-to-gas device at the time; P HFC,in ( t )express t The amount of hydrogen input to the hydrogen fuel cell at a given moment.

[0095] In the embodiment of the present invention, the integrated energy system includes four different energy forms, namely electricity, gas, cooling and heating, as well as various load types and energy supplies. The corresponding system structure is as follows: Figure 2 As shown. In order to clearly describe the composition and structural characteristics of the system, the equipment is divided into power source, gas source, heat source and cold source. Power sources include photovoltaic (PV), wind power (WT), hydrogen fuel cell (HFC) and gas unit (GFU). Gas sources include power-to-gas (P2G) devices that absorb hydrogen from power-to-hydrogen (P2H) devices. Heat sources include hydrogen fuel cells (HFC), gas boilers (GB) and waste heat recovery units (WHRU). The cold source is only an electric chiller (EC). In addition, insufficient electricity and natural gas can be purchased from the power grid and natural gas stations, thereby meeting the needs of users in the system for four types of loads: electricity load, gas load, heat load and cold load.

[0096] Furthermore, the input and output relationships of different energy sources are as follows:

[0097] (1) Relationship between P2H power input and hydrogen output:

[0098] ;

[0099] In the formula, P P2H,in ( t ), Q P2H ( t ) represent time t The power input and hydrogen output of P2H at ; η P2H is the energy conversion rate of P2H.

[0100] (2) Relationship between hydrogen input and natural gas output of P2G:

[0101] ;

[0102] In the formula, P P2G,in ( t ), Q P2G ( t ) represent time t The hydrogen input and natural gas output of P2G at that time; η P2G is the energy conversion rate of P2G.

[0103] (3) The relationship between HFC’s hydrogen input, electrical energy output, and thermal energy output:

[0104] ;

[0105] In the formula, P HFC,in ( t ), P HFCe ( t )and P HFCh ( t ) respectively represent time t The hydrogen input, electrical energy and thermal energy output of HFC; η HFCe and η HFCh They are the gas-to-electricity conversion rate and gas-to-heat conversion rate of HFC respectively.

[0106] (4) The relationship between the gas input, electrical energy output and thermal energy output of GFU:

[0107] ;

[0108] In the formula, P GF,in ( t ), P GFe ( t )and P GFh ( t ) respectively represent t The gas input, electrical energy and heat output of the GFU at each moment; η GFe is the gas-to-electricity conversion rate of GFU, η GFh is the combined heat and power rate of GFU; η WHRU is the heat recovery rate of WHRU.

[0109] (5) Relationship between natural gas input and heat output of GB:

[0110] ;

[0111] In the formula, P GB,in ( t )and P GB ( t ) respectively represent t Natural gas input and heat output of GB at time; η GB is the energy conversion rate of GB.

[0112] (6) Relationship between electrical energy input and cooling energy output of EC:

[0113] ;

[0114] In the formula, P EC,in ( t )and P EC ( t ) respectively represent t The amount of electrical energy input and cooling energy output of the EC at the moment; η EC is the energy conversion rate of EC.

[0115] The four energies of electricity, gas, cold and heat are continuously coupled and transformed through the work of the above six energy devices, so that different energy sources can be further consumed, greatly reducing energy waste. At the same time, because of the coupling relationship between energy sources, the integrated energy system can have a richer and more environmentally friendly way to meet the user's requirements for different loads. These are all important reasons why the integrated energy system can reduce the system's carbon emissions while meeting the user's load requirements.

[0116] Furthermore, the comprehensive performance of the electricity-gas-cooling-heating integrated energy system is mainly measured by two indicators, namely the purchase cost of electricity and natural gas ( f buy ) and equipment maintenance costs ( f op The purchase cost is related to the amount of energy purchased; the equipment maintenance cost is related to the input or output of the above equipment. The objective function of the optimal operation of the electricity-gas-cooling-heat integrated energy system is to minimize the total cost.

[0117] Therefore, the objective function is expressed as follows:

[0118] ;

[0119] ;

[0120] ;

[0121] In the formula, F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; f op Energy equipment maintenance costs for integrated energy systems; P e ( t )express t Purchase electricity at all times; P g ( t )express t Purchase natural gas at any time;c e ( t )express t The electricity price at the time; c g express t The natural gas price at the time; c pv , , , , , , and Respectively represent the unit maintenance costs of photovoltaic, wind power, gas-fired devices, gas boilers, electric refrigerators, power-to-hydrogen devices, power-to-gas devices and hydrogen fuel cells; P PV ( t )express t The photovoltaic power output at the moment; P WT ( t )express t The power output of wind power at the moment; P GFe ( t )express t The heat output of the gas-fired device at the time; P GB ( t )express t The heat output of the gas boiler at the moment; P EC,in ( t )express t The electrical energy input of the electric refrigerator at the time; P P2H,in ( t )express t The power input of the power-to-hydrogen device at the time; P P2G,in ( t )express t The hydrogen input to the power-to-gas device at the time; P HFC,in ( t )express t The amount of hydrogen input to the hydrogen fuel cell at a given moment.

[0122] Step 2: Set the variable range, constraints and total number of iterations of the objective function;

[0123] In the embodiment of the present invention, the equation constraints of the electricity-gas-cooling-heat integrated energy system mainly involve the balance of electricity demand, the balance of natural gas demand, the balance of heating demand, the balance of cooling demand and the balance of dynamic hydrogen flow, as shown in the following formula:

[0124]

[0125] in, P el ( t ), P gl ( t ), P hl ( t )and P cl ( t ) are time t The electrical load, gas load, heating load and cooling load; Q g ( t ) indicates time t The meanings of other parameters are the same as above.

[0126] In addition, the inequality constraints including power limit and gas flow limit are described as follows:

[0127] ;

[0128] ;

[0129] in, P i ( t ) indicates power-consuming equipment i exist t Power at the moment; Q i ( t ) indicates gas consuming equipment j exist t Gas flow rate at the moment; P i max Indicates power-consuming devices i The maximum power, Q j max Indicates gas consuming equipment j Maximum gas flow rate.

[0130] Step 3: Initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual;

[0131] Optionally, the specific position of each individual of the slime mold algorithm is determined by the following formula:

[0132] ;

[0133] In the formula, X i Represents an individual i The specific location of lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; r is the current random number between 0 and 1.

[0134] In the embodiment of the present invention, it is found that the slime mold algorithm, namely the Slime Mould (SM) algorithm, is a new meta-heuristic algorithm proposed by Li Shimin and other scholars in 2020, which takes into account the movement behavior and feeding strategy of organisms. Compared with other mentioned intelligent algorithms, the main advantages of the SM algorithm are (i) SM uses adaptive weights to perform unique mathematical modeling of optimization problems. (ii) SM requires a small number of populations and iterations. (iii) SM has the ability to handle multi-variable, nonlinear and multi-constrained systems. Due to the above advantages, the SM algorithm has shown excellent performance in terms of faster convergence speed and better ability to obtain global optimal solutions.

[0135] Therefore, the present invention proposes for the first time to apply the SM algorithm to solve the optimal operation problem of the electricity-gas-cold-heat integrated energy system, obtain better operating results, better meet the user's demands for the four loads, and make the economy and environmental friendliness of the integrated energy system better.

[0136] The present invention requires the objective function, variables and parameters of the integrated energy system. Variables are the quantities in the integrated energy system that are directly linked to the optimization results and can be changed (here, the input and output of each energy device): such as purchased electricity, gas output from power-to-gas, heat output from gas boilers, etc. Parameters are quantities that will not change during the optimization process and need to be specified separately: such as the optimization cycle (for example, set to 24h), the optimization interval (for example, set to 1h), and the fixed values ​​in the SM algorithm.

[0137] Furthermore, the variable range (here the variable range is the maximum and minimum values ​​of the input and output of each energy device), constraints and the number of iterations are set.

[0138] Then, if Figure 3As shown, the population size M and the specific position X of each individual are initialized. The population size here refers to the number of slime molds in the slime mold algorithm, that is, the number of operation plans during the optimization process. A single slime mold is called an individual, and an individual represents an operation plan. The M population size represents M operation plans. The specific position X here represents the specific value of each variable in the specific plan, that is, the input and output of each device, and then the objective function value is calculated by the algorithm and compared. The intelligent algorithm is to obtain the final best result, that is, the best operation plan, by constantly comparing different individuals.

[0139] Among them, the calculation formula for the specific position of each individual in the slime mold algorithm is as follows:

[0140] ;

[0141] In the formula, X i Represents an individual i The specific location of lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; r is the current random number between 0 and 1.

[0142] Step 4: Based on the variable range of the objective function, constraints, total number of iterations and specific positions of each individual, calculate the objective function value of each individual at the current iteration number;

[0143] Step 5: Determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, update the specific position of each individual based on a preset first algorithm; if the current random number is not less than the preset given value, update the specific position of each individual based on a preset second algorithm;

[0144] Optionally, updating the specific location of each individual based on a preset first algorithm includes:

[0145] Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula:

[0146] ;

[0147] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; ris the current random number between 0 and 1; lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; z Indicates a preset given value.

[0148] Optionally, the updating of the specific position of each individual based on the preset second algorithm includes: calculating the weight of each operation scheme based on the objective function value of each individual at the current random number and the current iteration number. W、 Positional parameters a and individual location evaluation parameters p ; Determine the current random number r Is it less than the individual position evaluation parameter? p ; If the previous random number r Less than the individual location evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0149] ;

[0150] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; X b n Indicates the current position of the optimal solution; vb is a range in -a arrive a A random number between W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z Indicates a preset given value; p represents the individual position evaluation parameter;

[0151] If the previous random number r Not less than the individual position evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0152] ;

[0153] In the formula, X in+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n Represents the specific position of individual i in the iteration number n; p Represents the individual location evaluation parameters.

[0154] In the embodiment of the present invention, the weight of each operation scheme is calculated by the following formula: W、 Positional parameters a :

[0155] ;

[0156] ;

[0157] ;

[0158] In the formula, condition means that the objective function value of the individual ranks in the first half of the population; F and wxya are the best and worst objective function values ​​in the current iteration respectively; SortIndex is the sorting sequence of the objective function values; n and N are the current number of iterations and the maximum number of iterations respectively; S i Represents an individual i others indicates that the objective function value of the individual ranks in the second half of the population.

[0159] Step 6: Based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update, calculate the objective function value of each individual at the next iteration number;

[0160] Step 7: Determine whether the maximum number of iterations has been reached; if so, go to step 8, otherwise go to step 4;

[0161] Step 8: Based on the objective function value of each individual and the specific position of each individual, determine the optimal operation plan from various operation plans and the input and output of each energy device in the optimal operation plan.

[0162] The present invention compares the optimization results of the slime mold algorithm with the traditional optimization results. Under the premise of the same number of iterations and population size, the optimization speed of the slime mold algorithm is faster. In addition, in terms of daily operating cost, the optimization result of the SM algorithm is 737.195 yuan / day less than the optimization result of the traditional algorithm; in terms of daily carbon emissions, the optimization result of the SM algorithm is 2295.52 kilograms / day less than the optimization result of the traditional algorithm; in terms of daily wind and solar abandonment, the optimization result of the SM algorithm is 1456 kilowatts / day less than the optimization result of the traditional algorithm.

[0163] In summary, the present invention applies the slime mold algorithm to solve the problem of optimal operation of the integrated energy system, and can quickly and accurately determine the optimal operation scheme and the input and output of each energy device in the optimal operation scheme from various operation schemes, thereby obtaining a better operation result, better meeting the user's four load requirements of electricity, gas, cooling and heat, while making the convergence speed of the optimized operation scheduling faster, the economic cost lower, and the carbon emissions smaller, so that the economy and environmental friendliness of the integrated energy system are better. Thereby solving the technical problems of slow convergence speed, high economic cost and high carbon emissions in the existing integrated energy system optimization scheduling method.

[0164] Exemplary Devices

[0165] The present invention also provides a system for determining an optimal operation plan of a comprehensive energy system, comprising:

[0166] An objective function determination module is used to determine the objective function of the integrated energy system;

[0167] The setting module is used to set the variable range, constraints and total number of iterations of the objective function;

[0168] The initialization module is used to initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual;

[0169] A first calculation module is used to calculate the objective function value of each individual at the current iteration number based on the variable range of the objective function, the constraint conditions, the total number of iterations and the specific position of each individual;

[0170] The individual position update module is used to determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, the specific position of each individual is updated based on a preset first algorithm; if the current random number is not less than the preset given value, the specific position of each individual is updated based on a preset second algorithm;

[0171] A second calculation module is used to calculate the objective function value of each individual at the next iteration number based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update;

[0172] A judgment module is used to judge whether the maximum number of iterations has been reached; if the maximum number of iterations has been reached, the program is transferred to the solution determination module, otherwise, the program is transferred to the first calculation module;

[0173] The scheme determination module is used to determine the optimal operation scheme and the input and output of each energy device in the optimal operation scheme from various operation schemes based on the objective function value of each individual and the specific position of each individual.

[0174] Optionally, the objective function is expressed as follows:

[0175] ;

[0176] ;

[0177] ;

[0178] In the formula, F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; f op Energy equipment maintenance costs for integrated energy systems; P e ( t )express t Purchase electricity at all times; P g ( t )express t Purchase natural gas at any time; c e ( t )express t The electricity price at the time; c g express t The natural gas price at the time; c pv ,, , , , , and Respectively represent the unit maintenance costs of photovoltaic, wind power, gas-fired devices, gas boilers, electric refrigerators, power-to-hydrogen devices, power-to-gas devices and hydrogen fuel cells; P PV ( t )expresst The photovoltaic power output at the moment; P WT ( t )express t The power output of wind power at the moment; P GFe ( t )express t The heat output of the gas-fired device at the time; P GB ( t )express t The heat output of the gas boiler at the moment; P EC,in ( t )express t The electrical energy input of the electric refrigerator at the time; P P2H,in ( t )express t The power input of the power-to-hydrogen device at the time; P P2G,in ( t )express t The hydrogen input to the power-to-gas device at the time; P HFC,in ( t )express t The amount of hydrogen input to the hydrogen fuel cell at a given moment.

[0179] Optionally, the specific position of each individual of the slime mold algorithm is determined by the following formula:

[0180] ;

[0181] In the formula, X i Represents an individual i The specific location of lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; r is the current random number between 0 and 1.

[0182] Optionally, updating the specific location of each individual based on a preset first algorithm includes:

[0183] Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula:

[0184] ;

[0185] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; lb i Represents an individual i The minimum power; ub i Represents an individual i Maximum power; z Indicates a preset given value.

[0186] Optionally, updating the specific location of each individual based on a preset second algorithm includes:

[0187] Calculate the weight of each operation plan based on the objective function value of each individual based on the current random number and the current number of iterations W、 Positional parameters a and individual location evaluation parameters p ;

[0188] Determine the current random number r Is it less than the individual position evaluation parameter? p ;

[0189] If the previous random number r Less than the individual location evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0190] ;

[0191] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; X b n Indicates the current position of the optimal solution; vb is a range in -a arrive a A random number between W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z Indicates a preset given value; p represents the individual position evaluation parameter;

[0192] If the previous random number r Not less than the individual position evaluation parameter p , then the specific position of each individual is updated by the following formula:

[0193] ;

[0194] In the formula, X i n+1 Indicates the specific position of individual i in iteration number n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n Represents the specific position of individual i in the iteration number n; p Represents the individual location evaluation parameters.

[0195] The system for determining the optimal operation plan of the integrated energy system of the embodiment of the present invention corresponds to the method for determining the optimal operation plan of the integrated energy system of another embodiment of the present invention, which will not be described in detail here.

[0196] Exemplary Electronic Devices

[0197] Figure 4 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 4 As shown, the electronic device 40 includes one or more processors 41 and a memory 42 .

[0198] The processor 41 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0199] The memory 42 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 41 may run the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may also include: an input device 43 and an output device 44, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0200] In addition, the input device 43 may also include, for example, a keyboard, a mouse, etc.

[0201] The output device 44 can output various information to the outside, and can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0202] Of course, to simplify, Figure 4 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device may also include any other appropriate components.

[0203] Exemplary computer program products and computer-readable storage media

[0204] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above-mentioned "Exemplary Method" section of this specification.

[0205] The computer program product may be written in any combination of one or more programming languages ​​to write program code for performing the operations of the embodiments of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0206] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enable the processor to execute the steps of the method according to various embodiments of the present invention described in the above “Exemplary Method” section of this specification.

[0207] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0208] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details disclosed above are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0209] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0210] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The word "such as" used here refers to the phrase "such as but not limited to", and can be used interchangeably with it.

[0211] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware or any combination of software, hardware, firmware. The above order of steps for the method is only for illustration, and the steps of the method of the present invention are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0212] It should also be noted that in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in the field to make or use the present invention. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but in accordance with the widest range consistent with the principles and novel features disclosed here.

[0213] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for determining an optimal operation plan for an integrated energy system, characterized in that: include: Step 1: Determine the objective function of the integrated energy system; Step 2: Set the variable range, constraints and total number of iterations of the objective function; Step 3: Initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual; Step 4: Based on the variable range of the objective function, constraints, total number of iterations and specific positions of each individual, calculate the objective function value of each individual at the current iteration number; Step 5: Determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, update the specific position of each individual based on a preset first algorithm; if the current random number is not less than the preset given value, update the specific position of each individual based on a preset second algorithm; Step 6: Based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update, calculate the objective function value of each individual at the next iteration number; Step 7: Determine whether the maximum number of iterations has been reached; if so, go to step 8, otherwise go to step 4; Step 8: Based on the objective function value of each individual and the specific position of each individual, determine the optimal operation plan from various operation plans and the input and output of each energy device in the optimal operation plan; The expression of the objective function is as follows: F=min(f buy +f op ); Where F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; op is the energy equipment maintenance cost of the integrated energy system; e (t) represents the purchased electricity at time t; P g (t) represents the purchased natural gas at time t; c e (t) represents the electricity price at time t; c g represents the natural gas price at time t; c pv 、c WT 、c GF 、c GB 、c EC 、c P2H 、c P2G and c HFC represents the unit maintenance cost of photovoltaic, wind power, gas equipment, gas boiler, electric refrigerator, power-to-hydrogen device, power-to-gas device and hydrogen fuel cell respectively; P PV (t) represents the photovoltaic power output at time t; P WT (t) represents the wind power output at time t; P GFe (t) represents the heat output of the gas device at time t; P GB (t) represents the heat output of the gas boiler at time t; P EC,in (t) represents the electric energy input of the electric refrigerator at time t; P P2H,in (t) represents the power input of the power-to-hydrogen device at time t; P P2G,in (t) represents the hydrogen input of the power-to-gas device at time t; P HFC,in (t) represents the hydrogen input amount of the hydrogen fuel cell at time t; The specific position of each individual in the slime mold algorithm is determined by the following formula: X i =lb i +r×(ub i -lb i ),r∈random[0,1]; Where, X i Indicates the specific location of individual i; lb i represents the minimum power of individual i; ub i represents the maximum power of individual i; r is the current random number between 0 and 1; The updating of the specific location of each individual based on the preset first algorithm includes: Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; lb i represents the minimum power of individual i; ub i represents the maximum power of individual i; z represents the preset given value; The updating of the specific location of each individual based on the preset second algorithm includes: Based on the objective function value of each individual at the current random number and the current number of iterations, the weight W, position parameter a and individual position evaluation parameter p of each operation scheme are calculated; Determine whether the current random number r is less than the individual position evaluation parameter p; If the previous random number r is less than the individual position evaluation parameter p, the specific position of each individual is updated using the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; X b n represents the current position of the optimal solution; vb is a random number ranging from -a to a; W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z represents a preset given value; p represents an individual position evaluation parameter; If the previous random number r is not less than the individual position evaluation parameter p, the specific position of each individual is updated using the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n represents the specific position of individual i in iteration number n; p represents the individual position evaluation parameter.

2. A system for determining the optimal operation plan of a comprehensive energy system, characterized in that: include: An objective function determination module is used to determine the objective function of the integrated energy system; The setting module is used to set the variable range, constraints and total number of iterations of the objective function; The initialization module is used to initialize the population size of the slime mold algorithm and determine the specific location of each individual in the slime mold algorithm; the population size is the total number of individuals in the slime mold algorithm, one individual represents an operation plan, and the specific location represents the input and output of each energy device in the operation plan corresponding to the individual; A first calculation module is used to calculate the objective function value of each individual at the current iteration number based on the variable range of the objective function, the constraint conditions, the total number of iterations and the specific position of each individual; The individual position update module is used to determine whether the current random number is less than a preset given value; if the current random number is less than or equal to the preset given value, the specific position of each individual is updated based on a preset first algorithm; if the current random number is not less than the preset given value, the specific position of each individual is updated based on a preset second algorithm; A second calculation module is used to calculate the objective function value of each individual at the next iteration number based on the current random number, the objective function value of each individual at the current iteration number, and the specific position of each individual after the update; A judgment module is used to judge whether the maximum number of iterations has been reached; If the maximum number of iterations is reached, the program will be transferred to the solution determination module, otherwise, the program will be transferred to the first calculation module; A scheme determination module, used to determine the optimal operation scheme and the input and output of each energy device in the optimal operation scheme from various operation schemes based on the objective function value of each individual and the specific position of each individual; The expression of the objective function is as follows: F=min(f buy +f op ); Where F is the objective function; f buy The purchase cost of electricity and natural gas for the integrated energy system; op is the energy equipment maintenance cost of the integrated energy system; e (t) represents the purchased electricity at time t; P g (t) represents the purchased natural gas at time t; c e (t) represents the electricity price at time t; c g represents the natural gas price at time t; c pv , C WT , C GF , C GB , C EC , C P2H , C P2G and c HFC represents the unit maintenance cost of photovoltaic, wind power, gas equipment, gas boiler, electric refrigerator, power-to-hydrogen device, power-to-gas device and hydrogen fuel cell respectively; P PV (t) represents the photovoltaic power output at time t; P WT (t) represents the wind power output at time t; P GFe (t) represents the heat output of the gas device at time t; P GB (t) represents the heat output of the gas boiler at time t; P EC,in (t) represents the electric energy input of the electric refrigerator at time t; P P2H,in (t) represents the power input of the power-to-hydrogen device at time t; P P2G,in (t) represents the hydrogen input of the power-to-gas device at time t; P HFC,in (t) represents the hydrogen input amount of the hydrogen fuel cell at time t; The specific position of each individual in the slime mold algorithm is determined by the following formula: X i =lb i +r×(ub i -lb i ),r∈random[0,1]; Where, X i Indicates the specific location of individual i; lb i represents the minimum power of individual i; ub i represents the maximum power of individual i; r is the current random number between 0 and 1; The updating of the specific location of each individual based on the preset first algorithm includes: Based on the current random number, the maximum power and minimum power of each energy device in the integrated energy system, the specific position of each individual is updated through the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; lb i represents the minimum power of individual i; ub i represents the maximum power of individual i; z represents the preset given value; The updating of the specific location of each individual based on the preset second algorithm includes: Based on the objective function value of each individual at the current random number and the current number of iterations, the weight W, position parameter a and individual position evaluation parameter p of each operation scheme are calculated; Determine whether the current random number r is less than the individual position evaluation parameter p; If the previous random number r is less than the individual position evaluation parameter p, the specific position of each individual is updated using the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; X b n represents the current position of the optimal solution; vb is a random number ranging from -a to a; W is the weight of each operation plan; X A n and X B n represents two randomly selected individuals; z represents a preset given value; p represents an individual position evaluation parameter; If the previous random number r is not less than the individual position evaluation parameter p, the specific position of each individual is updated using the following formula: Where, X i n+1 represents the specific position of individual i in iteration n+1; r is the current random number between 0 and 1; vc is a number that decreases linearly from 1 to 0; X i n represents the specific position of individual i in iteration number n; p represents the individual position evaluation parameter.

3. A computer-readable storage medium storing a computer program, wherein the computer program is used to execute the method according to claim 1.

4. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is used to read the executable instructions from the memory and execute the executable instructions to implement the method of claim 1.

Images