CN113392513A - Multi-objective optimization method, device and terminal for combined cooling, heating and power system - Google Patents

Abstract

The invention is suitable for the technical field of power systems, and particularly relates to a multi-objective optimization method, a multi-objective optimization device and a multi-objective optimization terminal for a combined cooling heating and power system, wherein the method comprises the following steps: constructing a multi-objective optimization function and constraint conditions thereof; performing iterative computation on the multi-objective optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, improving a gull algorithm to calculate each objective function value corresponding to different gull individuals, selecting a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updating the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions; and selecting an optimal solution from a plurality of non-dominant solutions based on a good-bad solution distance method to optimize the target combined cooling heating and power system. The invention can optimize and solve the combined cooling heating and power system and improve the comprehensive performance of the system.

Description

Multi-objective optimization method, device and terminal for combined cooling, heating and power system

technical field

The invention belongs to the technical field of power systems, and in particular relates to a multi-objective optimization method, device and terminal for a combined cooling, heating and power supply system.

Background technique

With the rapid growth of the world economy, the energy consumption of countries around the world is increasing. In order to find a clean and efficient energy supply method, the combined cooling, heating and power supply, which uses the waste heat generated during the power generation process of the generator to provide cooling and heating for the system The system is gradually gaining attention.

As a multi-generation energy supply system, the combined cooling, heating and power system has a flexible and diverse structure, and the selection of equipment type and capacity has a great impact on the overall performance of the system. At present, in the design scheme of the combined cooling, heating and power system, the capacity of each equipment is still set based on the load peak, which makes the capacity of the existing combined cooling, heating and power system equipment cannot be fully utilized, increases the investment cost and is not economical. The combined cooling, heating and power system needs to be optimized for operation.

However, the inventor of the present application found that with the development of distributed power generation technology, wind power, photovoltaic and other equipment are gradually being introduced into the combined cooling, heating and power system, which makes the system structure more complex. Moreover, in order to suppress the influence of the output fluctuation of the renewable energy power generation device on the system, an energy storage device is usually introduced into the system, which further increases the difficulty of the optimization solution. Considering that there are many types of equipment involved in the combined cooling, heating and power system, and there is a strong coupling relationship between the three energies of cooling, heating and power, how to optimize the cooling, heating and power system, and improve the overall performance of the system. Urgent problem to be solved.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present invention provide a multi-objective optimization method, device and terminal for a combined cooling, heating and power system, so as to optimize and solve the combined cooling, heating and power system and improve the comprehensive performance of the system.

A first aspect of the embodiments of the present invention provides a multi-objective optimization method for a combined cooling, heating and power system, including:

A multi-objective optimization function is constructed with the equipment capacity in the target combined cooling, heating and power system as the decision variable, and the minimum operating cost, the minimum energy consumption, and the minimum environmental impact as the goal;

Constraints of the multi-objective optimization function are constructed, and based on the constraints and the improved seagull algorithm, the multi-objective optimization function is iteratively calculated to obtain multiple non-dominated solutions; among them, in each iteration process, the improved seagull algorithm calculates different individual seagulls corresponding to each objective function value, and select non-dominated solutions from the corresponding solutions of each seagull individual according to the objective function value for storage, and update the position of the individual seagull according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions;

The optimal solution is selected from multiple non-dominated solutions based on the distance method between superior and inferior solutions, and the target CCHP system is optimized according to the optimal solution.

A second aspect of the embodiments of the present invention provides a multi-objective optimization device for a combined cooling, heating and power system, including:

The first processing module is used to construct a multi-objective optimization function with the equipment capacity in the target combined cooling, heating and power system as a decision variable, and with the minimum operating cost, minimum energy consumption and minimum environmental impact as the goal;

The second processing module is used to construct the constraints of the multi-objective optimization function;

The third processing module is used to iteratively calculate the multi-objective optimization function based on the constraints and the improved seagull algorithm to obtain multiple non-dominated solutions; wherein, in each iteration process, the improved seagull algorithm calculates the corresponding The objective function value is selected, and the non-dominated solution is selected from the corresponding solutions of each individual seagull according to the objective function value for storage, and the position of the individual seagull is updated according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions;

The optimization module is used to select the optimal solution from multiple non-dominated solutions based on the distance method between superior and inferior solutions, and optimize the target combined cooling, heating and power system according to the optimal solution.

A third aspect of the embodiments of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above-mentioned combined cooling, heating and power system is implemented Steps of a multi-objective optimization method.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned multi-objective optimization method for a combined cooling, heating and power system are implemented .

The beneficial effects that the embodiment of the present invention has compared with the prior art are:

The invention establishes a multi-objective optimization function and its constraint conditions aiming at the minimum operating cost, the minimum energy consumption and the minimum environmental impact, and calculates the three objectives of the combined cooling, heating and power system through the improved seagull algorithm and the distance method between the superior and inferior solutions. The optimal solution under the function is beneficial to reduce the operating cost and fuel consumption of the combined cooling, heating and power system, and reduce the emission of greenhouse gases. Specifically, the constrained non-dominated sorting and external file mechanism are introduced into the traditional seagull algorithm, that is, the improved seagull algorithm calculates each objective function value corresponding to different seagull individuals, and selects the non-dominated solution from each solution according to the objective function value for storage. Thereby, a series of solutions that satisfy the constraints and have a strong domination relationship are obtained; and according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions, the position of the individual seagull is updated, so that the obtained solutions are evenly distributed in the solution space. The improved seagull algorithm can optimize multiple objectives at the same time. Compared with the existing optimization algorithm, it can effectively reduce the random clustering of the obtained solutions and obtain a uniformly distributed solution set. Further, the optimal solution is selected from multiple non-dominated solutions based on the distance method between superior and inferior solutions, and the target CCHP system is optimized according to the optimal solution. The invention can optimize and solve the combined cooling, heating and power supply system and improve the comprehensive performance of the system.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only for the present invention. In some embodiments, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without any creative effort.

Fig. 1 is the realization flow schematic diagram of the multi-objective optimization method of the combined cooling, heating and power system provided by the embodiment of the present invention;

2 is a schematic diagram of a detailed optimization process provided by an embodiment of the present invention;

Fig. 3 is the ambient temperature and solar radiation intensity data graph that the embodiment of the present invention provides;

4 is a user load demand data diagram provided by an embodiment of the present invention;

5 is a schematic diagram of a non-dominated solution output by the improved seagull algorithm provided by an embodiment of the present invention;

6 is a schematic diagram of a system power balance provided by an embodiment of the present invention;

7 is a schematic diagram of a system cold energy and heat energy balance provided by an embodiment of the present invention;

8 is a schematic diagram of a multi-objective optimization device for a combined cooling, heating and power system provided by an embodiment of the present invention;

FIG. 9 is a schematic diagram of a terminal provided by an embodiment of the present invention.

Detailed ways

In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are set forth in order to provide a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

In order to illustrate the technical solutions of the present invention, the following specific embodiments are used for description.

With the rapid growth of the world economy, the energy consumption of countries around the world is increasing. In order to find a clean and efficient energy supply method, the combined cooling, heating and power supply, which uses the waste heat generated during the power generation process of the generator to provide cooling and heating for the system The system is gradually gaining attention. By setting up a power generation unit near the user side, the combined cooling, heating and power system can not only reduce the power loss on the line during the transmission process, but also comprehensively utilize the waste heat in the power generation process through a variety of equipment to provide heating and cooling to the user to meet the diverse needs of the user. Energy demand, improve energy efficiency, alleviate the energy crisis to a certain extent, and reduce greenhouse gas emissions. However, as a multi-generation energy supply system, the combined cooling, heating and power system has flexible and diverse structures, and the choice of equipment type and capacity has a great impact on the overall performance of the system. At present, the design scheme of the CCHP system still sets the capacity of each equipment based on the load peak, which makes the capacity of the existing CCHP system cannot be fully utilized, increases the investment cost and is not economical. For this reason, it is necessary to optimize the operation of the combined cooling, heating and power system.

An embodiment of the present invention provides a multi-objective optimization method for a combined cooling, heating and power system. As shown in FIG. 1 , the method includes the following steps:

Step S101 , building a multi-objective optimization function with the equipment capacity in the target combined cooling, heating and power system as the decision variable, and with the minimum operating cost, minimum energy consumption, and minimum environmental impact as the goal.

In the embodiment of the present invention, the specific construction process of the multi-objective optimization function is as follows.

(1) Establish the mathematical model of each equipment in the target CCHP system

The combined cooling, heating and power system usually includes photovoltaic cells, micro gas turbines, waste heat recovery devices, heat storage tanks, batteries, adsorption refrigerators, electric refrigerators and gas boilers.

The mathematical model of the photovoltaic cell can be:

In the formula, P _PV is the output power of the photovoltaic cell, N _PV is the installed capacity of the photovoltaic cell, G _PV and T _PV represent the radiation intensity and surface temperature received by the photovoltaic panel, and G _STC and T _STC represent the photovoltaic panel received under standard test conditions. Radiation intensity and ambient temperature, α represents the temperature coefficient.

The mathematical model of the micro gas turbine and waste heat recovery unit can be:

P _mt =F _mt ·η _p,mt

H _mt =F _mt ·(1-η _p,mt )

H _hr =H _mt ·η _hr

In the formula, P _mt represents the output electric power of the micro gas turbine, F _mt represents the fuel consumption of the micro gas turbine, η _p,mt represents the power conversion efficiency, η _hr represents the heat energy recovery efficiency of the waste heat recovery device, H _mt and H _hr represent the micro gas turbine and waste heat The recovery device outputs thermal energy.

The mathematical model of the heat storage tank can be:

和

表示蓄热罐在t时刻和t-1时刻储存的热量，

和

分别表示蓄热罐在t时刻吸收和释放的热量，η_hst,loss、η_hst,in和η_hst,out分别表示蓄热罐的热损失率、吸热效率和放热效率。In the formula,

and

represents the heat stored in the heat storage tank at time t and time t-1,

and

represent the heat absorbed and released by the heat storage tank at time t, respectively, and η _hst,loss , η _hst,in and η _hst,out represent the heat loss rate, heat absorption efficiency and heat release efficiency of the heat storage tank, respectively.

The mathematical model of the battery can be:

和

分别表示蓄电池在t时刻和t-1时刻储存的电量，

和

分别表示蓄电池在t时刻吸收电能和释放电能，η_bat,loss、η_bat,in和η_bat,out分别表示蓄电池电损率、充电效率和放电效率。In the formula,

and

Represent the power stored by the battery at time t and time t-1, respectively,

and

Respectively represent the battery absorbs electrical energy and releases electrical energy at time t, η _bat,loss , η _bat,in and η _bat,out represent the battery power loss rate, charging efficiency and discharging efficiency, respectively.

The mathematical models of adsorption refrigerators and electric refrigerators can be:

C _ac = H _ac ·COP _ac

C _ec =P _ec ·COP _ec

In the formula, C _ac and C _ec represent the cooling capacity of the adsorption refrigerator and the electric refrigerator, respectively, H _ac represents the thermal energy consumed by the adsorption refrigerator, P _ec represents the electric energy consumed by the electric refrigerator, and COP _ac and COP _ec respectively adsorb energy efficiency coefficients of refrigerators and electric refrigerators.

The mathematical model of the gas boiler can be:

H _gb =F _gb ·η _gb

In the formula, H _gb represents the heat production of the gas boiler, F _gb represents the fuel consumption of the gas boiler, and η _gb represents the heat production efficiency of the gas boiler.

(2) Determine decision variables

In order to optimize the CCHP system, the capacity of the equipment is selected as the decision variable:

X=[N _PV , N _MT , N _grid , N _bat , N _hst , N _gb ]

In the formula, N _PV , N _MT , N _bat , N _hst and N _gb represent the installed capacity of photovoltaic cells, micro gas turbines, batteries, heat storage tanks and gas boilers, respectively, and N _grid represents the upper limit of the system to purchase electricity from the grid.

(3) Establish a multi-objective optimization function of the combined cooling, heating and power system

In order to optimize the overall performance of the combined cooling, heating and power system, according to the above equipment model and decision variables, objective functions are established in terms of system operating cost, energy consumption and environmental impact, and the three objective functions are optimized at the same time to reduce for operating costs, energy consumption and pollutant emissions.

The cost objective function can be:

表示系统发电不足时从电网购电量，i表示利率，n表示设备的使用年限，C_k表示设备投资成本，C_e表示电网购电价格，

表示设备消耗燃料，C_F表示燃料价格，

和

表示运行过程中系统浪费的能量，λ_p和λ_h表示能量浪费惩罚系数。In the formula, Cost _CCHP represents the operating cost of the system, N _k represents the installed capacity of the equipment,

Indicates the electricity purchased from the grid when the power generation of the system is insufficient, i represents the interest rate, n represents the service life of the equipment, C _k represents the investment cost of the equipment, C _e represents the electricity purchase price of the grid,

represents the fuel consumption of the equipment, _CF represents the fuel price,

and

represents the energy wasted by the system during operation, and _λp and _λh represent the energy waste penalty coefficient.

The energy objective function can be:

和

分别表示系统中微型燃气轮机、燃气锅炉和电网的能源消耗，o表示系统的运行时间。In the formula,

and

Respectively represent the energy consumption of the micro gas turbine, gas boiler and grid in the system, and o represents the operating time of the system.

The environmental objective function can be:

和

表示电网和冷热电联供系统的等效排放系数。where CDE _CCHP represents the _CO2 emissions during system operation,

and

Indicates the equivalent emission factor of the grid and the CHP system.

Step S102: Constraints of the multi-objective optimization function are constructed.

In this embodiment of the present invention, the constraints mainly include energy supply and demand balance equation constraints, equipment ramp rate constraints, and equipment capacity constraints. Among them, the energy supply and demand balance equation constraints include electric balance constraints, heat balance constraints and cold balance constraints.

The electrical balance constraint can be:

和

分别表示t时刻电能的缺额和浪费，

表示t时刻用户的电能需求。In the formula,

and

represent the shortage and waste of electric energy at time t, respectively,

Represents the power demand of the user at time t.

The thermal balance constraint can be:

和

分别表示t时刻热能的缺额和浪费，

表示t时刻用户的热能需求。In the formula,

and

represent the shortage and waste of thermal energy at time t, respectively,

Represents the thermal energy demand of the user at time t.

The cold equilibrium constraint can be:

表示用户的冷负荷需求。In the formula,

Indicates the cooling load demand of the user.

The ramp rate constraints for the device can be:

和

分别表示蓄热罐和蓄电池的斜坡率上限。In the formula,

and

Indicate the upper limit of the ramp rate of the heat storage tank and the battery, respectively.

The equipment capacity constraints are shown in Table 1:

Table 1 Equipment Capacity Constraints

Step S103 , based on the constraints and the improved seagull algorithm, iteratively calculate the multi-objective optimization function to obtain multiple non-dominated solutions; wherein, in each iteration process, the improved seagull algorithm calculates each objective function value corresponding to different seagull individuals, According to the objective function value, the non-dominated solution is selected from the corresponding solutions of each individual seagull for storage, and the position of the individual seagull is updated according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions.

In the embodiment of the present invention, in order to solve the multi-objective optimization function, the traditional seagull algorithm is improved, and a constrained non-dominated sorting and an external file mechanism are introduced into the seagull algorithm, that is, the improved seagull algorithm calculates each objective function corresponding to different seagull individuals According to the value of the objective function, non-dominated solutions are selected from each solution for storage, so as to obtain a series of solutions that satisfy the constraints and have a strong dominance relationship; The positions of individual seagulls so that the resulting solutions are uniformly distributed in the solution space. The improved seagull algorithm can optimize multiple objectives at the same time. Compared with the existing optimization algorithm, it can effectively reduce the random clustering of the obtained solutions and obtain a uniformly distributed solution set.

Step S104 , selecting an optimal solution from a plurality of non-dominated solutions based on the distance method between superior and inferior solutions, and optimizing the target combined cooling, heating and power system according to the optimal solution.

The invention establishes a multi-objective optimization function and its constraint conditions aiming at the minimum operating cost, the minimum energy consumption and the minimum environmental impact, and calculates the three objectives of the combined cooling, heating and power system through the improved seagull algorithm and the distance method between the superior and inferior solutions. The optimal solution under the function is beneficial to reduce the operating cost and fuel consumption of the combined cooling, heating and power system, and reduce the emission of greenhouse gases. Specifically, the constrained non-dominated sorting and external file mechanism are introduced into the traditional seagull algorithm, that is, the improved seagull algorithm calculates each objective function value corresponding to different seagull individuals, and selects the non-dominated solution from each solution according to the objective function value for storage. Thereby, a series of solutions that satisfy the constraints and have a strong domination relationship are obtained; and according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions, the position of the individual seagull is updated, so that the obtained solutions are evenly distributed in the solution space. The improved seagull algorithm can optimize multiple objectives at the same time. Compared with the existing optimization algorithm, it can effectively reduce the random clustering of the obtained solutions and obtain a uniformly distributed solution set. Further, the optimal solution is selected from multiple non-dominated solutions based on the distance method between superior and inferior solutions, and the target CCHP system is optimized according to the optimal solution. The invention can optimize and solve the combined cooling, heating and power supply system and improve the comprehensive performance of the system.

Optionally, as a possible implementation manner, in step S103, based on the constraints and the improved seagull algorithm, iteratively calculates the multi-objective optimization function to obtain multiple non-dominated solutions, which can be described in detail as:

Step S1031, setting the parameters of the improved seagull algorithm, and initializing the position of each individual seagull; wherein, the position of each individual seagull is the equipment capacity, that is, a solution of the multi-objective optimization function;

Step S1032, according to the constraints, determine whether the position of the individual seagull is out of bounds, if not, then calculate each objective function value corresponding to the individual seagull;

Step S1033, according to the objective function value, select a non-dominated solution from the solutions corresponding to each individual seagull for storage;

Step S1034, selecting the non-dominated solution with the lowest crowding degree from the stored non-dominated solutions as the prey position, and updating the position of the individual seagull according to the prey position;

Step S1035: Steps S1032-S1034 are repeatedly executed until the preset maximum number of iterations is reached, and a plurality of stored non-dominated solutions are obtained.

In the embodiment of the present invention, the population position of the improved seagull algorithm is initialized, and the population size N=100, the maximum number of iterations Max _iter =500, the maximum storage number of files is set to 100, and the individual movement behavior control parameter f _e of the seagull is set to 2. In each iteration process, according to the equipment capacity constraints shown in Table 1, it is judged whether the individual seagull is out of bounds, the value of each objective function corresponding to the individual seagull is calculated, and the non-dominated solution is selected from each solution according to the value of the objective function for storage. , at the same time, the non-dominated solution with the lowest crowding degree in the file is selected as the prey position, and the individual seagull performs migration behavior and aggressive behavior to approach the prey position to complete the position update process. If the maximum number of iterations is reached, the termination condition is satisfied, and the stored non-dominated solution is regarded as the compromise solution of the CCHP system under the three objectives of cost, energy and environment. Among them, the selection of the prey position of the improved seagull optimization algorithm can be realized by the roulette method. The calculation method of the input probability is P _i =m/S _i , where m is a constant greater than 1, and Si is the _i -th non-dominated solution neighborhood The number of solutions. When using the roulette method to select the non-dominated solution to be deleted, the input probability is the reciprocal of _Pi .

Specifically, individual seagulls avoid collision with other individuals during their migration behavior, and update their individual positions according to the optimal position. The position update formula of individual seagulls during migration is:

表示在第t次迭代过程中不会与其他海鸥个体碰撞的新位置；

表示最佳位置所在方向；

表示海鸥个体与最优个体之间的距离；

表示当前个体的位置；

表示当前最优海鸥个体位置(猎物位置)，每次迭代计算中最优海鸥个体通过轮盘赌方法选出外部档案中拥挤度低的非支配解；在冷热电联供系统多目标优化问题中，海鸥个体的位置参数表示该问题的一个备选解，每个备选解的维数为6，而

表示当该问题采用改进海鸥优化算法求解时各类设备在第t次迭代中的设备容量。In the formula, A represents the movement behavior of individual seagulls in the search space, f _e can control the frequency of use of variable A; t represents the current number of iterations; Max _iter represents the maximum number of iterations;

Represents a new position that will not collide with other seagull individuals during the t-th iteration;

Indicates the direction of the best position;

represents the distance between the individual seagull and the optimal individual;

Indicates the current position of the individual;

Represents the current optimal individual seagull position (prey position). In each iteration calculation, the optimal seagull individual selects the non-dominated solution with low crowding degree in the external file through the roulette method; in the multi-objective optimization problem of the combined cooling, heating and power system , the position parameter of the individual seagull represents an alternative solution to the problem, and the dimension of each alternative solution is 6, and

It represents the equipment capacity of various equipment in the t-th iteration when the problem is solved by the improved seagull optimization algorithm.

Specifically, in the aggressive behavior, individual seagulls make spiral movements in the air by constantly changing the angle and speed. When the individual seagull performs aggressive behavior, the position update formula is:

In the formula, u′, v′, and w′ represent the movement behavior of individual seagulls in three-dimensional space when they attack their prey, r represents the radius of each turn of the helix, α represents the flight angle of the seagull when it performs helical motion, and h and k define the shape of the helix. The constant of , e is the base of the natural logarithm, and α is a random number in the range [0, 2π].

Optionally, as a possible implementation manner, the method for determining the non-dominated solution may be:

Calculate the constraint violation amount corresponding to each solution, and judge the type of each solution according to the constraint violation amount; the types include feasible solutions and infeasible solutions. The solution with the constraint violation amount of 0 is the feasible solution, and the solution with the constraint violation amount greater than 0 is infeasible solution;

则认为x支配y；f_i(x)为可行解x对应的第i个目标函数值，f_i(y)为可行解y对应的第i个目标函数值；Determine the dominance relationship between each feasible solution; among them, for any two feasible solutions x, y, if the

Then it is considered that x dominates y; f _i (x) is the i-th objective function value corresponding to the feasible solution x, and f _i (y) is the i-th objective function value corresponding to the feasible solution y;

Based on the amount of constraint violation, the type of each solution, and the dominance relationship between each feasible solution, the non-dominated solution in each solution is determined.

Optionally, as a possible implementation, the constraint violation corresponding to each solution can be calculated by the following formula:

In the formula, p is the number of inequality constraints in the constraints, <g _i (x)> represents the constraint violation of the ith inequality constraint, if g _i (x)≤0, then <g _i (x)>=0, If g _i (x)>0, then <g _i (x)>=|g _i (x)|, m is the number of equation constraints in the constraints, and |h _j (x)| Constraint violation amount, where the constraint violation amount is the difference between each solution and the constraint condition.

Optionally, as a possible implementation manner, the non-dominated solution in each solution is determined based on the amount of constraint violation, the type of each solution, and the dominance relationship between each feasible solution, which can be described in detail as:

If each solution is an infeasible solution, the infeasible solution with the smallest constraint violation is determined as a non-dominated solution;

If a feasible solution exists in each solution, the feasible solution that is not dominated by any other feasible solution is determined as a non-dominated solution.

Optionally, as a possible implementation manner, storing the non-dominated solution can be described in detail as:

Determine the dominance relationship between the newly obtained non-dominated solution and the stored non-dominated solution;

If there is no domination relationship between the newly obtained non-dominated solution and the stored non-dominated solutions, store the newly obtained non-dominated solution;

If the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, the newly obtained non-dominated solution is ignored;

If the newly obtained non-dominated solution dominates at least one stored non-dominated solution, the newly obtained non-dominated solution is stored and all stored non-dominated solutions dominated by it are deleted.

In the embodiment of the present invention, the constrained non-dominated sorting and the external file mechanism are introduced into the traditional seagull algorithm, and the constrained non-dominated sorting combines the constraint violation amount with the non-dominated sorting process to obtain a series of constraints that satisfy the constraints and have a strong dominance relationship. In each iteration, the non-dominated solutions obtained by constrained non-dominated sorting are stored in the external file, and the solutions in the file are continuously updated according to the update rules, so that the obtained solutions are evenly distributed in the solution space. The improved seagull algorithm can optimize multiple objectives at the same time. Compared with the existing optimization algorithm, it can effectively reduce the random clustering of the obtained solutions and obtain a uniformly distributed solution set.

In addition, when the number of non-dominated solutions stored in the archive exceeds the limit, the congestion degree of each non-dominated solution in the archive can be checked, and the non-dominated solutions with high congestion degree can be deleted.

Optionally, as a possible implementation manner, the optimal solution is selected from multiple non-dominated solutions based on the distance method between superior and inferior solutions, which can be described in detail as:

From multiple non-dominated solutions, the minimum objective function value of each objective function is selected as the ideal point, and the maximum objective function value of each objective function is selected as the negative ideal point;

According to the objective function value of each objective function corresponding to each non-dominated solution, calculate the Euclidean distance between each non-dominated solution and ideal point and negative ideal point respectively;

并选取理想程度最大的非支配解作为最优解；其中，

为第i个非支配解与理想点之间的欧氏距离，

为第i个非支配解与负理想点之间的欧氏距离。Calculate the degree of ideality of each non-dominated solution

And select the non-dominated solution with the greatest degree of ideality as the optimal solution; among them,

is the Euclidean distance between the i-th non-dominated solution and the ideal point,

is the Euclidean distance between the ith non-dominated solution and the negative ideal point.

In this embodiment of the present invention, the ideal point and the negative ideal point can be determined according to the following formula:

Further, the Euclidean distance between each non-dominated solution and the ideal point can be calculated according to:

Finally, the degree of ideality of each non-dominated solution can be calculated according to:

和

为对所有非支配解取最大值、最小值的函数，

和

表示理想点和负理想点，

和

表示非支配解与理想点和负理想点之间的距离，Z_i表示第i个非支配解的理想程度，其中Z_i值最大的非支配解即为冷热电联供系统多目标优化问题的最优解。where ri _,j represents the jth objective function value of the ith non-dominated solution,

and

is a function that takes the maximum and minimum values for all non-dominated solutions,

and

represents the ideal point and the negative ideal point,

and

Represents the distance between the non-dominated solution and the ideal point and the negative ideal point, Z _i represents the ideal degree of the i-th non-dominated solution, and the non-dominated solution with the largest Z _i value is the multi-objective optimization problem of the combined cooling, heating and power system the optimal solution.

In the embodiment of the present invention, after the optimal solution is obtained, the sub-supply system powered by the grid, the heating by the gas boiler, and the cooling by the electric refrigerator is used as the comparison system, and the cost saving rate (CSR) and the primary energy saving rate (PESR) are selected. ), carbon dioxide emission reduction rate (CDERR) and system energy efficiency (η _CCHP ) are used as evaluation indicators to evaluate system performance, and the corresponding calculation formulas are as follows:

In the formula, Cost _SP and Cost _CCHP represent the operating costs of the sub-supply system and the CCHP system, respectively, Fuel _SP and Fuel _CCHP represent the fuel consumption of the sub-supply system and the CCHP system, respectively, CDE _SP and CDE _CCHP Respectively represent the carbon dioxide emissions of the sub-supply system and the combined cooling, heating and power system.

In addition, in the embodiment of the present invention, considering that the traditional strategy of determining electricity by heat is given priority to satisfy the thermal load in the system, the traditional strategy of determining heat by electricity is given priority to satisfy the electrical load in the system, but there is energy in both traditional strategies. waste. Therefore, in order to reduce the energy waste in the system, based on the traditional strategy of determining electricity by heat and determining heat by electricity, a hybrid strategy based on the conversion of the energy storage state of the heat storage tank between the two strategies is proposed. When there is sufficient thermal energy stored in the thermal tank, the strategy of determining electricity based on heat is implemented, and the thermal load in the system is given priority, and the thermal energy in the thermal storage tank is preferentially used, and the excess electrical energy during operation will be absorbed by the battery; When the heat stored in the tank is lower than the minimum limit, the strategy of determining heat by electricity is implemented, and the electric load in the system is given priority. The excess heat generated by the system during operation will be absorbed by the heat storage tank until the stored heat energy reaches the upper limit. Heat constant electricity strategy; during operation, the battery is charged and discharged according to the system electric energy balance. In this way, the combined cooling, heating and power system switches between two traditional strategies according to the energy storage state of the heat storage tank to reduce the waste of energy during operation.

Based on the above steps, the present invention provides a more detailed schematic diagram of the optimization process, as shown in FIG. 2 .

Exemplarily, a Matlab program is compiled according to the above multi-objective optimization method of the combined cooling, heating and power system, and the feasibility of the method is verified.

Specifically, according to the input data of the ambient temperature and solar radiation intensity data graph shown in FIG. 3 and the user load demand data graph shown in FIG. 4 , the output of the improved seagull optimization algorithm is determined by electricity, electricity by heat, and mixed strategies. The compromise solution (non-dominated solution) under the cost, energy and environment of CCHP system is shown in Figure 5. It can be seen that the output compromise solution of the improved _seagull optimization algorithm is evenly distributed in the solution space, and the hybrid strategy proposed by the present invention is closer to the coordinate origin in Fig. It also proves that the improved seagull optimization algorithm is very effective for the optimization of the combined cooling, heating and power system. At the same time, the power balance diagram of the combined cooling, heating and power system in Figure 6 and Figure 7 under the hybrid strategy and the output of each equipment in the cooling and heating energy balance diagram can reliably meet the cooling and heating power load of the equipment, which proves that the constraint is not dominant. The sorting mechanism effectively eliminates the solutions that do not meet the constraints, so that any solution output by the improved seagull optimization algorithm can be used as a design scheme for the combined cooling, heating and power system.

The solution marked by the five-pointed star in Figure 5 is the optimal solution determined by the distance method between the pros and cons of the solution. Multiple evaluation indicators are used to evaluate the system performance under the optimal solution, and the statistical index values are shown in Table 2.

Table 2 Statistical table of system performance evaluation indicators

It can be seen from Table 2 that the CSR of the CCHP system under the hybrid strategy reaches -9.7965, which is greater than the two traditional strategies of determining heat by electricity and determining electricity by heat; Equivalent energy storage devices can make full use of the redundant energy generated during the operation of the system, so the PESR of the system under the hybrid strategy is 31.10%; in terms of CDERR, the hybrid strategy achieves the discount of the two traditional strategies of determining heat by electricity and determining electricity by heat. In terms of η _CCHP , the energy storage device in the hybrid strategy collects and utilizes the excess electrical energy and thermal energy generated during the operation of the system, so the energy efficiency of the system under the hybrid strategy is 55.60%. Hybrid strategies are more advantageous in terms of cost savings, lower fuel consumption and improved system energy efficiency. It is further explained that the present invention is very effective for optimizing the combined cooling, heating and power system in the face of complex constraints and multi-objective optimization models.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

The embodiment of the present invention also provides a multi-objective optimization device for a combined cooling, heating and power system. As shown in FIG. 8 , the device 80 includes:

The first processing module 81 is used for constructing a multi-objective optimization function with the equipment capacity in the target combined cooling, heating and power system as the decision variable, and with the minimum operating cost, minimum energy consumption and minimum environmental impact as the goal.

The second processing module 82 is used for constructing the constraints of the multi-objective optimization function.

The third processing module 83 is used to iteratively calculate the multi-objective optimization function based on the constraints and the improved seagull algorithm to obtain multiple non-dominated solutions; wherein, in each iteration process, the improved seagull algorithm calculates the corresponding Each objective function value is selected, and a non-dominated solution is selected from the corresponding solutions of each individual seagull according to the objective function value for storage, and the position of the individual seagull is updated according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions.

The optimization module 84 is configured to select an optimal solution from a plurality of non-dominated solutions based on the distance method between superior and inferior solutions, and optimize the target combined cooling, heating and power system according to the optimal solution.

Optionally, as a possible implementation manner, the third processing module 83 is configured to execute steps S1031-S1035 in the above-mentioned multi-objective optimization method for a combined cooling, heating and power system.

则认为x支配y；f_i(x)为可行解x对应的第i个目标函数值，f_i(y)为可行解y对应的第i个目标函数值；基于约束违反量、各个解的类型、各个可行解之间的支配关系，确定各个解中的非支配解。Optionally, as a possible implementation manner, the third processing module 83 is used to calculate the constraint violation amount corresponding to each solution, and judge the type of each solution according to the constraint violation amount; wherein, the types include feasible solutions and infeasible solutions. , the solution whose constraint violation amount is 0 is a feasible solution, and the solution whose constraint violation amount is greater than 0 is an infeasible solution; determine the dominance relationship between each feasible solution; among them, for any two feasible solutions x and y, if the

Then it is considered that x dominates y; f _i (x) is the i-th objective function value corresponding to the feasible solution x, and f _i (y) is the i-th objective function value corresponding to the feasible solution y; Type, the domination relationship between each feasible solution, and determine the non-dominated solution in each solution.

Optionally, as a possible implementation manner, the third processing module 83 is configured to calculate the constraint violation amount corresponding to each solution by the following formula:

In the formula, p is the number of inequality constraints in the constraints, <g _i (x)> represents the constraint violation of the ith inequality constraint, if g _i (x)≤0, then <g _i (x)>=0, If g _i (x)>0, then <g _i (x)>=|g _i (x)|, m is the number of equation constraints in the constraints, and |h _j (x)| The amount of constraint violation.

Optionally, as a possible implementation manner, the third processing module 83 is configured to, if each solution is an infeasible solution, determine the infeasible solution with the smallest constraint violation amount as a non-dominated solution; Feasible solutions, the feasible solutions that are not dominated by any other feasible solutions are determined as non-dominated solutions.

Optionally, as a possible implementation manner, the third processing module 83 is used to judge the dominance relationship between the newly obtained non-dominated solution and the stored non-dominated solution; If there is no domination relationship among the non-dominated solutions of , then store the newly obtained non-dominated solution; if the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, then Ignore; if the newly obtained non-dominated solution dominates at least one stored non-dominated solution, store the newly obtained non-dominated solution and delete all stored non-dominated solutions dominated by it.

并选取理想程度最大的非支配解作为最优解；其中，

为第i个非支配解与理想点之间的欧氏距离，

为第i个非支配解与负理想点之间的欧氏距离。Optionally, as a possible implementation manner, the optimization module 84 is used to, from a plurality of non-dominated solutions, select the minimum objective function value of each objective function as the ideal point, and select the maximum objective function value of each objective function as the ideal point. Negative ideal point; according to the objective function value of each objective function corresponding to each non-dominated solution, calculate the Euclidean distance between each non-dominated solution and ideal point and negative ideal point respectively; calculate the ideal degree of each non-dominated solution

And select the non-dominated solution with the greatest degree of ideality as the optimal solution; among them,

is the Euclidean distance between the i-th non-dominated solution and the ideal point,

is the Euclidean distance between the ith non-dominated solution and the negative ideal point.

FIG. 9 is a schematic diagram of a terminal 90 provided by an embodiment of the present invention. As shown in FIG. 9 , the terminal 90 of this embodiment includes a processor 91 , a memory 92 , and a computer program 93 stored in the memory 92 and executable on the processor 91 . When the processor 91 executes the computer program 93, the steps in each of the above embodiments of the multi-objective optimization method for the combined cooling, heating and power system are implemented, for example, steps S101 to S104 shown in FIG. 1 . Alternatively, when the processor 91 executes the computer program 93, the functions of the modules in the above-mentioned apparatus embodiments, for example, the functions of the modules 81 to 84 shown in FIG. 8 are implemented.

Exemplarily, the computer program 93 may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 92 and executed by the processor 91 to accomplish the present invention. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 93 in the terminal 90 . For example, the computer program 93 can be divided into a first processing module 81, a second processing module 82, a third processing module 83, and an optimization module 84 (modules in a virtual device), and the specific functions of each module are as follows:

The first processing module 81 is used for constructing a multi-objective optimization function with the equipment capacity in the target combined cooling, heating and power system as the decision variable, and with the minimum operating cost, minimum energy consumption and minimum environmental impact as the goal.

The second processing module 82 is used for constructing the constraints of the multi-objective optimization function.

The third processing module 83 is used to iteratively calculate the multi-objective optimization function based on the constraints and the improved seagull algorithm to obtain multiple non-dominated solutions; wherein, in each iteration process, the improved seagull algorithm calculates the corresponding Each objective function value is selected, and a non-dominated solution is selected from the corresponding solutions of each individual seagull according to the objective function value for storage, and the position of the individual seagull is updated according to the non-dominated solution with the lowest crowding degree among the stored non-dominated solutions.

The optimization module 84 is configured to select an optimal solution from a plurality of non-dominated solutions based on the distance method between superior and inferior solutions, and optimize the target combined cooling, heating and power system according to the optimal solution.

The terminal 90 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The terminal 90 may include, but is not limited to, a processor 91 and a memory 92 . Those skilled in the art can understand that FIG. 9 is only an example of the terminal 90, and does not constitute a limitation on the terminal 90. It may include more or less components than the one shown, or combine some components, or different components, such as The terminal 90 may also include input and output devices, network access devices, buses, and the like.

The so-called processor 91 may be a central processing unit (Central Processing Unit, CPU), and may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 92 may be an internal storage unit of the terminal 90 , such as a hard disk or a memory of the terminal 90 . The memory 92 can also be an external storage device of the terminal 90, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card), etc. equipped on the terminal 90. . Further, the memory 92 may also include both an internal storage unit of the terminal 90 and an external storage device. The memory 92 is used to store computer programs and other programs and data required by the terminal 90 . The memory 92 may also be used to temporarily store data that has been or will be output.

Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated in one processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit, and the above-mentioned integrated units may adopt hardware. It can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing from each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above-mentioned system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the foregoing embodiments, the description of each embodiment has its own emphasis. For parts that are not described or described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be Incorporation may either be integrated into another system, or some features may be omitted, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can be When executed by the processor, the steps of the foregoing method embodiments may be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate forms, and the like. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in computer-readable media may be appropriately increased or decreased in accordance with the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media does not include Electrical carrier signals and telecommunication signals.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still be used for the foregoing implementations. The technical solutions described in the examples are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the within the protection scope of the present invention.

Claims (10)

1. A multi-objective optimization method for a combined cooling heating and power system is characterized by comprising the following steps:

constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;

constructing a constraint condition of the multi-objective optimization function, and performing iterative computation on the multi-objective optimization function based on the constraint condition and an improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the improved gull algorithm calculates each objective function value corresponding to different gull individuals, selects a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updates the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;

and selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method, and optimizing the target combined cooling heating and power system according to the optimal solution.

2. The combined cooling, heating and power system multi-objective optimization method of claim 1, wherein the iterative computation of the multi-objective optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions comprises:

step S1031, setting improved gull algorithm parameters, and initializing positions of individual gulls; wherein the position of each gull individual is the equipment capacity, namely a solution of the multi-objective optimization function;

step S1032, judging whether the position of the individual gull exceeds the threshold or not according to the constraint condition, and if not, calculating each objective function value corresponding to the individual gull;

step S1033, selecting a non-dominant solution from the solutions corresponding to all gull individuals according to the objective function value, and storing the non-dominant solution;

step S1034, selecting the non-dominated solution with the lowest crowdedness from the stored non-dominated solutions as a prey position, and updating the position of the individual gull according to the prey position;

and S1035, repeatedly executing the steps S1032-S1034 until the preset maximum iteration number is reached, and obtaining a plurality of stored non-dominated solutions.

3. The combined cooling, heating and power system multi-objective optimization method of claim 1, wherein the determination method of the non-dominant solution is as follows:

calculating constraint violation quantities corresponding to the solutions, and judging the type of each solution according to the constraint violation quantities; wherein the type comprises a feasible solution and an infeasible solution, the solution with the constraint violation quantity of 0 is the feasible solution, and the solution with the constraint violation quantity of more than 0 is the infeasible solution;

determining a domination relation among all feasible solutions; wherein, for any two feasible solutions x and y, if satisfied

Then x is considered to dominate y; f. of_i(x) For the ith objective function value, f, corresponding to the feasible solution x_i(y) is the ith objective function value corresponding to the feasible solution y;

and determining non-dominant solutions in the solutions based on the constraint violation quantities, the types of the solutions and the dominant relationship among the feasible solutions.

4. The multi-objective optimization method for the combined cooling heating and power system as claimed in claim 3, wherein the constraint violations corresponding to each solution are calculated by the following formula:

in the formula, p is a constraint conditionThe number of the inequality constraints in the middle,<g_i(x)>a constraint violation quantity representing the ith inequality constraint, if g_i(x) Less than or equal to 0<g_i(x)>0, if g_i(x) If greater than 0<g_i(x)>＝|g_i(x) I, m is the number of equality constraints in the constraint condition, | h_j(x) And | represents the constraint violation of the jth equality constraint.

5. The multi-objective optimization method for the combined cooling heating and power system according to claim 3, wherein the determining of the non-dominant solution in the solutions based on the constraint violation quantity, the type of the solutions and the dominant relationship among the feasible solutions comprises:

if all the solutions are infeasible solutions, determining the infeasible solution with the minimum constraint violation amount as a non-dominated solution;

and if feasible solutions exist in the solutions, determining the feasible solutions which are not dominated by any other feasible solutions as non-dominated solutions.

6. The multi-objective optimization method for a combined cooling, heating and power system according to any one of claims 1 to 5, wherein storing the non-dominant solution comprises:

judging the domination relationship between the newly obtained non-domination solution and the stored non-domination solution;

if the newly obtained non-dominated solution and each stored non-dominated solution have no domination relationship, storing the newly obtained non-dominated solution;

if the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, ignoring the newly obtained non-dominated solution;

and if the newly obtained non-dominated solution dominates at least one stored non-dominated solution, storing the newly obtained non-dominated solution and deleting all the stored non-dominated solutions dominated by the newly obtained non-dominated solution.

7. The multi-objective optimization method for the combined cooling heating and power system according to any one of claims 1 to 5, wherein selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method comprises:

selecting the minimum objective function value of each objective function as an ideal point and selecting the maximum objective function value of each objective function as a negative ideal point from the non-dominated solutions;

respectively calculating Euclidean distances between each non-dominated solution and an ideal point and between each non-dominated solution and a negative ideal point according to the objective function value of each objective function corresponding to each non-dominated solution;

computing the desirability of each non-dominated solution

Selecting a non-dominated solution with the maximum ideal degree as an optimal solution; wherein,

is the euclidean distance between the ith non-dominated solution and the ideal point,

is the euclidean distance between the ith non-dominated solution and the negative ideal point.

8. The utility model provides a combined cooling heating and power system multi-objective optimization device which characterized in that includes:

the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;

the second processing module is used for constructing constraint conditions of the multi-objective optimization function;

the third processing module is used for carrying out iterative computation on the multi-target optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the improved gull algorithm calculates each objective function value corresponding to different gull individuals, selects a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updates the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;

and the optimization module is used for selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method and optimizing the target combined cooling heating and power system according to the optimal solution.

9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

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