Multi-target collaborative optimization scheduling method for integrated energy system

Abstract

The invention relates to a multi-objective collaborative optimization scheduling method for an integrated energy system, and aims at a multi-energy unit optimization problem, namely a multi-parameter optimization problem, in an optimization model, an optimization model parameter initialization method based on improved chaotic mapping is adopted. An initial parameter with a high random average distribution probability is generated to improve the convergence speed of a system optimization process and reduce the probability of falling into a local optimal solution. Meanwhile, aiming at a multi-objective optimization problem, an improved grey wolf algorithm based on a Pareto optimal solution theory is adopted to realize optimal scheduling of the system for an economical efficiency optimal objective and a carbon emission minimum objective. According to the system, a comprehensive energy system architecture including water electrolysis hydrogen production using photovoltaic and wind power generation and hydrogen energy storage, power generation, heating and refrigeration is comprehensively considered, comprehensive utilization and control of various new energy power generation and utilization units are achieved, and carbon emission can be effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy technology, and in particular to a multi-objective collaborative optimization scheduling method for an integrated energy system. Background Art

[0002] With the changes in my country's energy structure, the new power system with new energy as the main body will become an important direction for the transformation and development of my country's energy system in the future. In recent years, in areas rich in new energy resources, new energy such as wind and light have become the main power sources in the local area. However, due to the large-scale access of new energy such as wind power and solar energy to the power system, the stability and safety issues brought about have become the key bottleneck restricting the further development of high-proportion new energy power grids.

[0003] Therefore, it is urgent to conduct relevant research on the optimized operation of the integrated energy system composed of energy units such as electricity, heat, cooling and gas, and strive to ensure the reliability and stability of user energy under the new electricity utilization situation. The integrated energy system is an important research topic in the world's energy, including energy storage in multiple energy forms and sharing energy through power grids and heating and cooling networks. It has the advantages of multiple energy complementarity, a large proportion of renewable energy, low total cost, good carbon emission reduction effect, and the ability to adapt to climate change and operating environments with large temperature differences.

[0004] However, the optimization and scheduling of integrated energy systems faces the characteristics of multiple optimized energy objects, complex and diverse optimization objectives, and strong randomness of system changes. It is a typical multi-parameter and multi-objective optimization problem. Traditional system optimization methods targeting single objectives and parameters cannot achieve the ideal system optimization and scheduling effect. There is an urgent need for research and technical application of multi-parameter and multi-objective collaborative optimization and scheduling methods for integrated energy systems. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art, propose a multi-objective collaborative optimization scheduling method for an integrated energy system, and establish a nonlinear optimization model that considers economy and low carbon emissions. For the multi-energy unit optimization problem in the optimization model, that is, the multi-parameter optimization problem, an optimization model parameter initialization method based on improved chaotic mapping is adopted to generate initial parameters with high random average distribution probability to improve the convergence speed of the system optimization process and reduce the probability of falling into the local optimal solution. At the same time, for multi-objective optimization problems, an improved gray wolf algorithm based on the Pareto optimal solution theory is adopted to achieve the system's optimal scheduling for the optimal economic goal and the minimum carbon emission goal.

[0006] The present invention solves the technical problem by adopting the following technical solutions:

[0007] A multi-objective collaborative optimization scheduling method for an integrated energy system comprises the following steps:

[0008] Step 1: Based on the topological characteristics of the integrated energy system, a mathematical model and constraint conditions considering each energy generating unit and energy consuming unit are established;

[0009] The topology of the integrated energy system includes the energy supply side, energy conversion and storage side, and energy load side. The energy supply side includes: traditional thermal power generation, photovoltaic power generation, and wind power generation; the energy conversion and storage side includes: energy storage batteries, electrolyzers, hydrogen storage tanks, hydrogen fuel cells, electric gas equipment, gas storage tanks, electric boilers, heat storage tanks, and electric refrigeration equipment; the energy load side includes: electric load, hydrogen load, gas load, heat load, and cold load;

[0010] The mathematical model includes: photovoltaic power generation system model and wind power generation system model, where the constraints include: wind power and photovoltaic power constraints, battery power and battery state of charge constraints, battery power and battery state of charge constraints, electrolyzer operating power constraints, hydrogen storage tank health status constraints and overall system balance constraints;

[0011] Step 2: According to the mathematical model and constraints constructed in step 1, set the multi-objective function of the integrated energy system operation;

[0012] Step 3: According to the multi-objective function in step 2, the electric power of the adjustable electric energy unit in the integrated energy system is used as the adjustment parameter, and the chaotic mapping is initialized to generate the corresponding initial wolf pack position and the external initial population position;

[0013] Encode the working status of the energy units that need to be adjusted within a certain period of time in the comprehensive energy system. The energy units include energy storage batteries, water electrolysis hydrogen production equipment, electric gas production equipment, electric heating equipment and electric refrigeration equipment. , , The positions of the three alpha wolves in the external population are encoded and the chaotic mapping is initialized;

[0014] Step 4: Calculate the fitness value of each individual in the population, and perform non-dominated sorting and crowding calculation based on the fitness value;

[0015] Step 5: Select the three wolves in the pack based on fitness ranking , , location and store it in an external population collection;

[0016] Step 6: Update the positions and optimization parameters of other individual wolves in the wolf pack;

[0017] Step 7: Check whether the global optimum and the maximum number of iterations have been reached. If not, go to step 5; otherwise, go to step 8.

[0018] Step 8: Output the final scheduling result.

[0019] Moreover, the specific implementation method of step 4 is: calculate the two objective function values ​​corresponding to the i-th individual in the m-th iteration , , Secondly, for each individual i in the population, calculate its dominance number n i , which controls the number of solutions of individual i and S i The set of solutions dominated by individual i, initialize an empty set A, and put the solutions with domination number 0 on the first non-dominated level into A. For each solution in set A, add its dominated solution set S i The domination number of each individual j in set A is reduced by 1; if the domination number of j is reduced to 0, j is also put into set A until no new solutions are added to set A. At this time, set A contains solutions belonging to the second non-dominated level. Repeat the above operation for the solutions in set A to find all non-dominated level solutions layer by layer.

[0020] The crowding distance represents the distribution density of individuals in their neighborhoods, which can obtain a spatially uniform solution, facilitating the subsequent processing of the solution. The calculation formula for crowding is as follows:

[0021] ;

[0022] Where D(i) is the crowding degree of the i-th individual; and They are respectively the maximum and minimum values ​​of the individuals in this layer after being sorted in ascending order according to the objective function.

[0023] Moreover, the specific implementation method of step 5 is: calculate the performance J1, J2 of each individual in carbon emissions and costs according to the specified objective function, perform non-dominated sorting on the population, find the minimum value of each individual in carbon emissions and costs, and sort the individuals according to these optimal values.

[0024] Moreover, the specific implementation method of step 7 is: check whether the number of algorithm iterations m reaches the maximum number of iterations m max , if m <m max Go to step 4; if m≥m max , go to step 8.

[0025] Moreover, the specific implementation method of step 8 is: from the objective function corresponding to the final iterative population and In the above example, the optimal value of each objective function is selected, and the corresponding objective function in the population is calculated. and The Euclidean distance between the optimal value and the objective function with the minimum Euclidean distance and It will be considered as the optimal solution of the Pareto global objective, the final global Pareto global optimal solution will be output, and its corresponding individual position will be used as the operating power of each electric energy unit in the integrated energy system.

[0026] The advantages and positive effects of the present invention are:

[0027] 1. A nonlinear optimization model considering economy and low carbon emissions is established. For the multi-energy unit optimization problem in the optimization model, that is, the multi-parameter optimization problem, the optimization model parameter initialization method based on improved chaotic mapping is adopted to generate initial parameters with high random average distribution probability to improve the convergence speed of the system optimization process and reduce the probability of falling into the local optimal solution. At the same time, for multi-objective optimization problems, the improved gray wolf algorithm based on Pareto optimal solution theory is adopted to achieve the optimal scheduling of the system for the optimal economic goal and the minimum carbon emission goal. The present invention comprehensively considers the comprehensive energy system architecture including the electrolysis of water to produce hydrogen using photovoltaic and wind power generation, as well as hydrogen energy storage, power generation, heating, and refrigeration, and realizes the comprehensive utilization and control of various new energy power generation and power consumption units, which can effectively reduce carbon emissions.

[0028] 2. The present invention adopts an improved gray wolf optimization method. On the basis of comprehensive consideration of operation economy and low-carbon emission targets, it retains the advantages of the traditional gray wolf optimization method, such as simple mathematical modeling, easy programming implementation, fast algorithm convergence speed, and high calculation accuracy. It also solves the problems of the traditional gray wolf algorithm in solving multi-dimensional dynamic function optimization problems, such as lack of population richness, lack of early exploration ability, slow convergence in the later stage, low algorithm accuracy, and possible local search. At the same time, the present invention achieves the purpose of power regulation and optimal scheduling of the integrated energy system. It has the advantages of being able to quickly obtain the optimal value of multi-objective optimization problems, not easy to fall into the local optimum, and simple to implement. DETAILED DESCRIPTION

[0029] Step 1: Based on the topological characteristics of the integrated energy system, a mathematical model and constraints considering each energy generation and consumption unit are established;

[0030] Among them, the comprehensive energy system topology includes: energy supply side: traditional thermal power generation (grid), photovoltaic power generation and wind power generation; energy conversion and storage side: energy storage batteries, electrolyzers, hydrogen storage tanks, hydrogen fuel cells, electric gas equipment, gas storage tanks, electric boilers, heat storage tanks and electric refrigeration equipment; load side: electric load, hydrogen load, gas load, heat load and cold load;

[0031] The mathematical model includes establishing a photovoltaic power generation system model as follows:

[0032] (1)

[0033] in:

[0034]

[0035]

[0036] (4)

[0037]

[0038] (6)

[0039] In formulas (1) to (6), I SC is the short-circuit current; is the change in light intensity; R pv is the internal resistance of the photovoltaic cell; T C is the light intensity of the photovoltaic cell; V OC is the open circuit voltage; I m is the maximum power point current; V m is the maximum power point voltage; and are the temperature coefficient of current change and the temperature coefficient of voltage change under reference conditions respectively; is the series resistance of the solar cell; solar radiation intensity , battery temperature ; Rs is the radiation intensity, T is the temperature. When a photovoltaic power generation failure occurs, the values ​​of Rs and Tc are set to 0.

[0040] The wind power generation system model is established as:

[0041]

[0042] In formula (7), P Wind (t) is the output power of the fan; is the air density; v is the wind speed; Rp is the blade radius of the fan; C p is the wind energy utilization coefficient; is the fault state simulation coefficient of the wind turbine at time t, and its value is 0 or 1. 0 indicates that the wind turbine fails at time t, and 1 indicates that the wind turbine works normally at time t.

[0043] The wind power and photovoltaic power constraints are:

[0044] (8)

[0045] In formula (8), P WT(t) is the output power of the wind power generation system at the current time t, P PV (t) is the output power of the photovoltaic power generation system at the current time t; corresponding to P WT_rated and P PV_rated are the rated powers of wind turbines and photovoltaic units respectively;

[0046] The battery model is established as:

[0047] (9)

[0048] In formula (9), SOC(t) is the state of charge of the battery at time t; is the rated capacity of the battery; P bess,ch (t), P bess,dis (t) are the charging and discharging power of the battery at time t; , are the charging and discharging efficiency of the battery respectively; It is the battery fault state simulation coefficient at time t, and its value is 0 or 1. 0 indicates that the battery fails at time t, and 1 indicates that the battery is charging and discharging normally at time t.

[0049] Battery power and battery state of charge (SOC) constraints:

[0050] (10)

[0051] In formula (10), SOC min The minimum state of charge of the battery, SOC max is the maximum value of the battery state of charge, is the charging power of the battery at time t; represents the discharge power of the battery at time t, is the battery charging efficiency, is the battery discharge efficiency, The maximum power for charging and discharging the battery.

[0052] An electrolytic cell is built to use direct current to electrolyze water to produce hydrogen. The relationship between the amount of hydrogen produced and the power consumption is:

[0053] (11)

[0054] In formula (11), is the volume of hydrogen produced by the alkaline electrolyzer; is the efficiency of the electrolyzer, is the amount of electricity consumed by the electrolyzer to produce hydrogen at time t; is the electrolyzer fault state simulation coefficient at time t, and its value is 0 or 1. 0 indicates that the electrolyzer fails at time t, and 1 indicates that the electrolyzer is operating normally to produce hydrogen by electrolyzing water at time t.

[0055] Electrolyzer operating power constraints:

[0056] (12)

[0057] In formula (12), P EC (t) is the operating power of the electrolyzer at the current time t, is the electrolytic cell operating power P EC (t) minimum value; is the electrolytic cell operating power P EC (t) rated value.

[0058] The model of building a hydrogen fuel cell to convert hydrogen energy into electrical energy is as follows:

[0059] (13)

[0060] In formula (13), P FC (t) is the output power of the fuel cell at time t, is the volume of hydrogen consumed by the fuel cell at time t, and l is the energy conversion coefficient of the fuel cell; is the hydrogen fuel cell fault state simulation coefficient at time t, and its value is 0 or 1. 0 indicates that the hydrogen fuel cell fails at time t, and 1 indicates that the hydrogen fuel cell works normally at time t.

[0061] (14)

[0062] In formula (14), P FC_rated is the rated power of the hydrogen fuel cell.

[0063] Hydrogen tank health status (SOH) constraints:

[0064] (15)

[0065] In formula (15), SOH(t) is the health status of the hydrogen storage tank at the current time t. is the minimum hydrogen storage value of the hydrogen storage tank; It is the maximum value of hydrogen storage capacity of the hydrogen storage tank.

[0066] The electric gas equipment model is established as follows:

[0067] (16)

[0068] In formula (16), G P2G,t P is the natural gas power output by the electric gas generator at time t; P2G,t is the electric power input to the electric gas generator at time t; It is the conversion efficiency of the electric gas generation equipment.

[0069] Establish a dynamic model of the gas storage device:

[0070] (17)

[0071] In formula (17), E G，t and E G，t+1 are the ratios of the gas volume stored in the natural gas tank to the total volume at time t and t+1 respectively; G chaG,t and G disG,t represents the storage and release power of the natural gas tank at time t; and E is the gas storage and release efficiency of the natural gas tank; capG is the capacity of the gas tank.

[0072] Build an electric boiler model:

[0073] (18)

[0074] In formula (18), H eb (t) is the heat output of the electric boiler at time t, The energy conversion efficiency of electric boilers, is the electric power consumed by the electric boiler at time t; It is the fault state simulation coefficient of the electric boiler at time t, and its value is 0 or 1. 0 means that the electric boiler fails at time t, and 1 means that the electric boiler is heating normally at time t.

[0075] The heat storage tank model is established as follows:

[0076] (19)

[0077] In formula (19), H hs (t) represents the heat storage capacity during period t; μ hs Indicates the heat loss rate; , Q hs,dis (t) represent the heat absorption and release power in the period t respectively. , They respectively represent the heat absorption and release efficiency in time period t.

[0078] Build an electric refrigeration equipment model:

[0079] (20)

[0080] In formula (20), C EC,t is the cooling capacity of the electric refrigeration equipment at time t, P EC2,t is the operating power of the electric refrigeration equipment at time t, COP EC is the cooling coefficient of the electric refrigeration equipment.

[0081] The overall system balance constraints include:

[0082] (1) The operating constraints of the energy conversion unit are as follows:

[0083] (twenty one)

[0084] In formula (21), P EC，t is the output power of the electric refrigeration equipment at time t, P EB，t is the electric power consumed by the electric boiler at time t, P P2G,t P is the electric power input to the electric gas generator at time t; ECmax is the maximum power consumed by the electric refrigeration equipment at time t, P EBmax is the maximum input power of the electric boiler at time t, P P2Gmax It is the maximum electric power input to the electric gas generator at time t.

[0085] (2) Energy storage equipment operation constraints

[0086] Energy storage battery operation constraints:

[0087] (twenty two)

[0088] In formula (22) is the charging power of the energy storage battery at time t, is the rated capacity of the energy storage battery, is the discharge power of the energy storage battery at time t, is the battery capacity ratio at time t, is the percentage of rated capacity of energy storage battery; and is the upper limit coefficient of energy storage battery charge and discharge per unit time; α chaB and α disB It is a binary state variable of the energy storage battery charging and discharging; and It is the upper limit coefficient and lower limit coefficient of the energy storage battery storage capacity.

[0089] Hydrogen storage equipment operation constraints:

[0090] (twenty three)

[0091] In formula (23) is the storage power of hydrogen, is the capacity of the hydrogen storage tank, is the release power of hydrogen, is the hydrogen content stored in the hydrogen storage tank at time t; and is the upper limit coefficient of hydrogen storage and release of the hydrogen storage tank per unit time; and is a binary state variable for hydrogen storage and hydrogen release in the hydrogen storage tank; and It is the upper and lower limit coefficient of the storage capacity of the hydrogen storage tank.

[0092] Gas storage equipment operation constraints:

[0093] (twenty four)

[0094] In formula (24) is the storage power of the natural gas tank at time t, is the release power of the natural gas tank at time t, is the capacity of the gas tank; is the gas storage capacity of the gas storage equipment at time t; and The upper limit coefficient of gas storage and gas release per unit time of the gas storage equipment; and It is a binary state variable of the gas storage device charging and degassing; and It is the upper limit coefficient and lower limit coefficient of the storage capacity of the gas storage equipment.

[0095] Thermal storage equipment operation constraints:

[0096] (25)

[0097] In formula (25) is the storage power of the heat storage tank at time t, is the power of the heat storage tank at time t, is the capacity of the heat storage tank, is the heat content in the heat storage tank at time t, and is the upper limit coefficient of heat storage and heat release of the heat storage tank per unit time; and It is a binary state variable for heat storage and heat release of the heat storage tank; and are the upper and lower limit coefficients of the heat storage tank capacity.

[0098] (3) Energy operation balance constraints

[0099] Power Constraints:

[0100] (26)

[0101] In formula (26), P pv，t is the output power of the photovoltaic power generation system at the current time t, P wt，t is the output power of the wind power generation system at the current time t, P grid,t is the interaction power with the grid at time t, P FCis the output power of the hydrogen fuel cell, the power purchased from the power grid is taken as positive, P L,t is the power load value of the system at time t.

[0102] Hydrogen energy constraints:

[0103] (27)

[0104] In formula (27) is the hydrogen output power of the electrolytic cell equipment at time t, W L,t is the hydrogen load value of the system at time t.

[0105] Natural Gas Constraints:

[0106] (28)

[0107] In formula (28), G L,t is the natural gas load value of the system at time t.

[0108] Thermal Constraints:

[0109] (29)

[0110] In formula (29) is the thermal power output of the electric boiler at time t, Q L,t is the thermal energy load value of the system at time t; is the storage power of the heat storage tank at time t, is the heat release power of the heat storage tank at time t.

[0111] Cold Energy Constraint:

[0112] (30)

[0113] In formula (30), C L,t is the cooling energy load value of the system at time t.

[0114] Step 2: Set the multi-objective function of the integrated energy system operation, and its specific implementation method is as follows;

[0115] Define the economic cost of integrated energy system operation as objective function 1:

[0116] (31)

[0117] In formula (31): The economic cost of operation of the i-th population individual in the m-th iteration of the corresponding integrated energy system; is the system-grid interaction cost corresponding to the i-th population individual in the m-th iteration; is the system operation and maintenance cost corresponding to the i-th population individual in the m-th iteration.

[0118] (32)

[0119] (33)

[0120] In the formula, C sale (t) is the price of electricity sold by the system to the grid at the time corresponding to the t-th individual; C buy (t) is the price of electricity purchased from the grid by the system at the time corresponding to the tth individual; c bess , c EB , c FC , c EC are the operation and maintenance cost coefficients of batteries, electric boilers, electrolyzers and hydrogen fuel cells respectively; Represents the hydrogen fuel cell output power P in the mth algorithm iteration FC ; Represents the energy storage charging and discharging power P in the mth algorithm iteration bess ; =P EC,t Represents the electrolytic cell operating power P in the mth algorithm iteration EC,t ; =P Eb,t The power of the electric boiler P in the mth algorithm iteration Eb,t ; =P grid,t The grid interaction power P in the mth algorithm iteration grid,t ;

[0121] Define the minimum carbon emission as objective function 2:

[0122] (34)

[0123] In formula (34), Refers to the carbon emission objective function corresponding to the i-th individual in the m-th iteration of the integrated energy system; Refers to the carbon emission coefficient of the interaction between the system and the power grid; P grid,t is the power of the system interacting with the grid at time t;

[0124] Step 3: Use the electric power of the adjustable electric energy unit in the integrated energy system as the adjustment parameter, and perform chaos mapping initialization on it to generate the corresponding initial wolf pack position and the external initial population position. The specific implementation method is as follows:

[0125] Encode the working status of energy units that need to be adjusted within a certain period of time in the comprehensive energy system. The energy units include energy storage batteries, water electrolysis hydrogen production equipment, electric gas production equipment, electric heating equipment, and electric refrigeration equipment. , , The external population position codes of the three alpha wolves are expressed as:

[0126] (35)

[0127] For the above population, initialize the initial value of the individual , n={1,…,5}, that is, the initial value of the population at m=0 iterations, which is calculated based on the following chaotic mapping function.

[0128] (36)

[0129] in The chaos factor.

[0130] Step 4: Calculate the fitness value of the individuals in the population, and perform non-dominated sorting and crowding calculation based on the fitness value. The specific implementation method is as follows:

[0131] First, calculate the two objective function values ​​corresponding to the i-th individual in the m-th iteration , . Secondly, for each individual i in the population, calculate its dominance number n i (the number of solutions that dominate individual i) and S i (The set of solutions dominated by individual i). Initialize an empty set A and put the solutions with domination number 0 on the first non-dominated level into A. For each solution in set A, add its dominating solution set S i The dominance number of each individual j in the set is reduced by 1. If the dominance number of j is reduced to 0, j is also added to the set A until no new solutions are added to the set A. At this time, the set A contains the solutions belonging to the second non-dominated level. Repeat the above operation for the solutions in the set A, and all non-dominated level solutions can be found layer by layer.

[0132] The crowding distance represents the distribution density of individuals in their neighborhoods, which can obtain a spatially uniform solution, facilitating the subsequent processing of the solution. The calculation formula for crowding is as follows:

[0133] (37)

[0134] Where D(i) is the crowding degree of the i-th individual; and They are respectively the maximum and minimum values ​​of the individuals in this layer after being sorted in ascending order according to the objective function.

[0135] Step 5: Select the three wolves in the pack based on fitness ranking , , location and store it in an external population collection;

[0136] Step 5: Select the three wolves in the pack based on their fitness. , , Position and store it in an external population, specifically:

[0137] Calculate the performance J1 and J2 of each individual in terms of carbon emissions and costs according to the specified objective function. Perform non-dominated sorting on the population, that is, find the minimum value of each individual in terms of carbon emissions and costs, and sort the individuals according to these optimal values.

[0138] Step 6: Update the positions and optimization parameters of other individual wolves in the wolf pack;

[0139] Step 6 updates the positions and optimization parameters of other individual wolves in the wolf pack, specifically:

[0140] (38)

[0141] (39)

[0142] (40)

[0143] (41)

[0144] in Represents the location of the i-th individual wolf in the gray wolf algorithm, is the set of dominated solutions corresponding to the i-th individual wolf.

[0145] Step 7: Check whether the global optimum and the maximum number of iterations have been reached. If not, go to step 5; otherwise, go to step 8.

[0146] Step 7 is as follows: Check whether the number of algorithm iterations m reaches the maximum number of iterations m max , if m <m max Go to step 5; if , go to step 8.

[0147] Step 8: Output the final scheduling result. The specific implementation method is as follows:

[0148] From the objective function corresponding to the final iterative population and Then, by calculating the corresponding objective function in the population and The Euclidean distance between the optimal value and the objective function with the minimum Euclidean distance and It will be considered as the optimal solution of the Pareto global objective. The final global Pareto global optimal solution is output, and its corresponding individual position is used as the operating power of each electric energy unit in the integrated energy system.

[0149] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A multi-objective collaborative optimization scheduling method for an integrated energy system, characterized by: The following steps are involved: Step 1: Based on the topological characteristics of the integrated energy system, a mathematical model and constraint conditions considering each energy generating unit and energy consuming unit are established; The topology of the integrated energy system includes the energy supply side, energy conversion and storage side, and energy load side. The energy supply side includes: traditional thermal power generation, photovoltaic power generation, and wind power generation; the energy conversion and storage side includes: energy storage batteries, electrolyzers, hydrogen storage tanks, hydrogen fuel cells, electric gas equipment, gas storage tanks, electric boilers, heat storage tanks, and electric refrigeration equipment; the energy load side includes: electric load, hydrogen load, gas load, heat load, and cold load; The mathematical model includes: photovoltaic power generation system model and wind power generation system model, where the constraints include: wind power and photovoltaic power constraints, battery power and battery state of charge constraints, battery power and battery state of charge constraints, electrolyzer operating power constraints, hydrogen storage tank health status constraints and overall system balance constraints; Step 2: According to the mathematical model and constraints constructed in step 1, set the multi-objective function of the integrated energy system operation; Step 3: According to the multi-objective function in step 2, the electric power of the adjustable electric energy unit in the integrated energy system is used as the adjustment parameter, and the chaotic mapping is initialized to generate the corresponding initial wolf pack position and the external initial population position; Encode the working status of the energy units that need to be adjusted within a certain period of time in the comprehensive energy system. The energy units include energy storage batteries, water electrolysis hydrogen production equipment, electric gas production equipment, electric heating equipment and electric refrigeration equipment. , , The positions of the three alpha wolves in the external population are encoded and the chaotic mapping is initialized; Step 4: Calculate the fitness value of each individual in the population, and perform non-dominated sorting and crowding calculation based on the fitness value; Step 5: Select the three wolves in the pack based on fitness ranking , , location and store it in an external population collection; Step 6: Update the positions and optimization parameters of other individual wolves in the wolf pack; Step 7: Check whether the global optimum and the maximum number of iterations have been reached. If not, go to step 5; otherwise, go to step 8. Step 8: Output the final scheduling result.

2. A multi-objective collaborative optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The specific implementation method of step 4 is: calculate the two objective function values ​​corresponding to the i-th individual in the m-th iteration , , Secondly, for each individual i in the population, calculate its dominance number n i , which controls the number of solutions of individual i and S i The set of solutions dominated by individual i, initialize an empty set A, and put the solutions with domination number 0 on the first non-dominated level into A. For each solution in set A, add its dominated solution set S i The dominance number of each individual j in the set is reduced by 1; if the dominance number of j is reduced to 0, j is also put into set A until there are no new solutions added to set A. At this time, set A contains solutions belonging to the second non-dominated level. Repeat the above operation for the solutions in set A to find all non-dominated level solutions layer by layer. The crowding distance represents the distribution density of individuals in their neighborhoods. It can obtain a solution that is evenly distributed in space, which is convenient for the subsequent processing of the solution. The calculation formula for crowding is as follows: ； Where D(i) is the crowding degree of the i-th individual; and They are respectively the maximum and minimum values ​​of the individuals in this layer after being sorted in ascending order according to the objective function.

3. A multi-objective collaborative optimization scheduling method for an integrated energy system according to claim 2, characterized in that: The specific implementation method of step 5 is: calculate the performance J1, J2 of each individual in carbon emissions and costs according to the specified objective function, perform non-dominated sorting on the population, find the minimum value of each individual in carbon emissions and costs, and sort the individuals according to these optimal values.

4. A multi-objective collaborative optimization scheduling method for an integrated energy system according to claim 3, characterized in that: The specific implementation method of step 7 is: check whether the number of algorithm iterations m reaches the maximum number of iterations m max , if m <m max Go to step 5; if m≥m max , go to step 8.

5. A multi-objective collaborative optimization scheduling method for an integrated energy system according to claim 4, characterized in that: The specific implementation method of step 8 is: from the objective function corresponding to the final iterative population and In the above example, the optimal value of each objective function is selected, and the corresponding objective function in the population is calculated. and The Euclidean distance between the optimal value and the objective function with the minimum Euclidean distance and It will be considered as the optimal solution of the Pareto global objective, the final global Pareto global optimal solution will be output, and its corresponding individual position will be used as the operating power of each electric energy unit in the integrated energy system.

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