Equipment type selection method of integrated energy system and computer device

Abstract

The invention belongs to the technical field of integrated energy, and particularly relates to an equipment type selection method of an integrated energy system and a computer device. The method comprises the following steps: S1, constructing an integrated energy system equipment type selection planning model; the decision variable of the equipment type selection planning model comprises the number of installed equipment of different types and different models in the integrated energy system; the target function of the equipment type selection planning model comprises a target function constructed by taking the lowest total cost of the integrated energy system in the whole life cycle as a target; the total cost in the full life cycle comprises the replacement cost of easily-worn equipment; s2, solving the equipment type selection planning model to obtain an optimal decision variable; and S3, carrying out integrated energy system equipment type selection planning according to the optimal decision variable. According to the invention, the technical problem of unreasonable planning of equipment in an integrated energy system in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy, and in particular relates to an equipment selection method and a computer device for an integrated energy system. Background Art

[0002] In the context of the energy revolution, the energy system is developing towards a model of coordinated supply of cold, heat, electricity and gas. The integrated energy system has the characteristics of coordinated supply of multiple energy sources, which can achieve the coordinated supply of cold, heat, electricity and gas in a certain area, reduce costs and reduce carbon emissions, which is of great significance. There are many types of equipment in the integrated energy system, complex energy coupling forms, and diverse energy structure compositions; there are differences in cost and efficiency between equipment models of different brands, which has caused complex problems in the selection of equipment for the integrated energy system.

[0003] The particle swarm optimization algorithm (PSO) was jointly proposed by American social psychologist Kennedy and electrical engineer Eberhart in 1995. It is an emerging random optimization algorithm based on swarm intelligence. Its advantages are that it is easy to implement and has good global search capabilities. This algorithm originates from the simulation of the foraging behavior of bird flocks. The search area of ​​the bird flock corresponds to the range of variation of the design variables, the food corresponds to the optimal solution (optimal fitness) of the objective function, and each bird, that is, each particle, corresponds to a feasible solution in the design space. Applying the particle swarm optimization algorithm to the solution of the comprehensive energy equipment selection planning problem is conducive to quickly and efficiently providing the optimal solution for the comprehensive energy equipment selection.

[0004] The published text of the Chinese invention patent application with the existing application publication number CN114154744A and the application publication date of 2022.03.08 discloses a method for capacity expansion planning of an integrated energy system. The method aims to minimize the annual total cost of the integrated energy system during its life cycle after expansion, and uses a particle swarm algorithm to solve the number of various equipment in the integrated energy system, and plans according to the solution results, wherein the cost during the life cycle mainly includes the annual investment cost, annual operating cost, annual maintenance cost and annual carbon emission cost of the equipment. However, during the implementation process, the service life of various types of equipment in the integrated energy system is often different. For various types of consumable equipment, including those with a short service life, regular replacement is required to maintain the normal operation of the system, and the above technical solution does not plan and solve the cost generated by equipment replacement, making the planning of equipment in the integrated energy system unreasonable. Summary of the invention

[0005] The purpose of the present invention is to provide a method for selecting equipment for an integrated energy system and a computer device to solve the technical problem of unreasonable planning of equipment in the integrated energy system in the prior art.

[0006] In order to solve the above technical problems, the present invention provides a technical solution of a method for selecting equipment for an integrated energy system: a method for selecting equipment for an integrated energy system, the method comprising:

[0007] S1. Construct a comprehensive energy system equipment selection and planning model;

[0008] The decision variables of the equipment selection planning model include the number of installed units of different types and models of equipment in the integrated energy system;

[0009] The objective function of the equipment selection planning model includes an objective function constructed with the goal of minimizing the total cost of the integrated energy system over the entire life cycle; the total cost over the entire life cycle includes the replacement cost of consumable equipment;

[0010] S2, solving the equipment selection planning model to obtain the optimal decision variables;

[0011] S3. Carry out equipment selection planning for the integrated energy system according to the optimal decision variables.

[0012] The beneficial effect of the above technical solution is that the technical solution of the equipment selection method for an integrated energy system of the present invention belongs to an improved invention. On the basis of the prior art, the present invention improves the total cost objective function, puts the replacement cost of consumable equipment into the total cost, and comprehensively considers the total cost of the entire life cycle including the replacement cost of consumable equipment, which is helpful for the reasonable planning of the integrated energy system. The present invention solves the technical problem of unreasonable equipment planning in the integrated energy system in the prior art.

[0013] Furthermore, the replacement cost of the consumable equipment is: the single replacement cost of the consumable equipment is converted into the replacement cost of the consumable equipment per unit time C gh , C gh According to the following formula:

[0014]

[0015] Where n is the operating life of the integrated energy system, s is the operating life of the consumable equipment, g is the number of times the consumable equipment needs to be replaced, y is the number of years the consumable equipment is converted to net present value, r is the base discount rate, and d is the annual average ... r The cost of replacing a consumable device once.

[0016] Furthermore, the total cost over the entire life cycle also includes the initial investment cost of the equipment, the fuel usage cost during the operation phase, and the operation and maintenance cost.

[0017] Furthermore, the constraints of the equipment selection planning model include area capacity constraints:

[0018] 0≤x i.j A i,j ≤M a,i

[0019] Among them, x i，j A is the number of installed units of type j equipment in the i-th type of equipment, i，j M is the floor space occupied by a single j-type device in the i-th category of equipment. a，i It is the estimated maximum installation area of ​​the i-th category equipment.

[0020] Furthermore, the integrated energy system includes a ground source heat pump, and the constraint conditions of the equipment selection planning model include resource capacity constraints of the ground source heat pump:

[0021]

[0022] Among them, P d,c and P d,h are the cooling power and heating power of the ground source heat pump, N bp is the buried pipe depth of the ground source heat pump, H s is the heat exchange capacity per meter of well depth under the ground source heat pump heat exhaust condition, H w are the heat exchange capacity per meter of well depth under the ground source heat pump heating condition, D e For a limited buried pipe depth, COP c The operating efficiency of the ground source heat pump during cooling, COP h It is the operating efficiency of ground source heat pump during cooling.

[0023] Furthermore, the integrated energy system includes photovoltaic power generation equipment, and the constraints for the photovoltaic power generation equipment in the equipment selection planning model include:

[0024]

[0025] Among them, P pv is the actual output power of the photovoltaic power generation equipment, P N is the rated output power of the photovoltaic power generation equipment, G is the real-time solar radiation intensity, G N is the solar radiation intensity under standard test conditions, is the temperature coefficient of the photovoltaic module, T c is the temperature of the photovoltaic module, T STC is the temperature of the photovoltaic module under standard test conditions.

[0026] Furthermore, the nonparametric kernel density estimation method is used to simulate the solar radiation intensity.

[0027] Furthermore, the integrated energy system includes wind power generation equipment, and the constraints for the wind power generation equipment in the equipment selection planning model include:

[0028]

[0029] Among them, P w is the actual output power of the wind power generation equipment, P w is the rated operating power of the wind power generation equipment, v is the real-time wind speed, v ci is the cut-in wind speed, v co is the cut-out wind speed, v r is the rated wind speed.

[0030] Furthermore, in S2, a particle swarm algorithm is used to solve the equipment selection planning model.

[0031] The present invention also provides a technical solution for a computer device: a computer device, comprising a processor, wherein the processor is used to execute a computer program to implement the steps of the equipment selection method for the integrated energy system as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A method flow chart in an embodiment of the method of the present invention;

[0033] Figure 2 The figure is a flow chart of the particle swarm algorithm solution in the embodiment of the method of the present invention. DETAILED DESCRIPTION

[0034] Based on the prior art, the present invention improves the total cost objective function, places the replacement cost of consumable equipment in the total cost, and comprehensively considers the total cost of the entire life cycle including the replacement cost of consumable equipment, which helps to reasonably plan the integrated energy system. The present invention solves the technical problem of unreasonable equipment planning in the integrated energy system in the prior art.

[0035] Embodiment of equipment selection method for integrated energy system:

[0036] A method for selecting equipment for an integrated energy system, such as Figure 1 As shown, the following steps are included:

[0037] S1. Construct an equipment selection planning model for an integrated energy system. The equipment selection planning model uses the number of installed units of different types and models of equipment in the integrated energy system as a decision variable.

[0038] The comprehensive energy system to be planned in this embodiment includes various energy forms such as wind energy, solar energy, natural gas, thermal energy, and electrical energy. The equipment types include wind power generation equipment, photovoltaic power generation equipment, ground source heat pumps, micro internal combustion engines that consume natural gas to generate electricity, energy storage batteries, and electric boilers.

[0039] First, it is necessary to build processing models for various types of equipment as constraints in the solution process.

[0040] Establish a wind power output model: The wind turbine converts wind energy into electrical energy through the turbine, and the output power of the wind turbine changes with the wind speed within a certain range. The key parameters that need to be considered in the selection planning are the cut-in wind speed, cut-out wind speed, rated wind speed, and rated capacity. This embodiment uses a cubic function to characterize the wind turbine output, and the wind turbine output model is shown as follows:

[0041]

[0042] Among them, P w is the actual output power of the wind power generation equipment, P w is the rated operating power of the wind power generation equipment, v is the real-time wind speed, m / s; v ci is the cut-in wind speed, v co is the cut-out wind speed, v r is the cut-out wind speed. When the wind speed is less than the cut-in wind speed v ci Or greater than the cut-out wind speed v co To ensure the safety of the wind turbine, no power generation is performed.

[0043] Establishing photovoltaic power generation output model: Photovoltaic power generation has uncertainty. This paper uses non-parametric kernel density estimation method to simulate solar radiation intensity. The non-parametric kernel density estimation model of solar radiation intensity is shown as follows:

[0044]

[0045] Among them, G s is the solar radiation intensity, W / m 2 ; n is the number of samples; h is the bandwidth; G s，i is the i-th sample value of solar radiation intensity, W / m 2 .

[0046] The output model of photovoltaic power generation equipment is shown as follows:

[0047]

[0048] Among them, P pv is the actual output power of the photovoltaic power generation equipment, MW; P N is the rated output power of the photovoltaic power generation equipment, MW; G is the real-time solar radiation intensity, G N is the solar radiation intensity under standard test conditions, generally 1000W / m 2 ; is the temperature coefficient of the photovoltaic module, which is -0.35 / ℃ in this embodiment; T c is the temperature of the photovoltaic module; T STCis the temperature of the photovoltaic module under standard test conditions, which is 25° C. in this embodiment.

[0049] For standard test conditions, the IEC 61853-1 "Photovoltaic module performance testing and energy efficiency evaluation Part 1: Irradiance and temperature performance measurement and power evaluation" international standard issued by the International Electrotechnical Commission (IEC) stipulates the requirements for measuring the performance of photovoltaic modules by power evaluation within a certain range of irradiance and temperature, so as to provide a full set of performance parameters of photovoltaic modules under different irradiance and temperature conditions. Among them, IEC 61853-1 sets the standard test condition (Standard Test Condition, STC) for power evaluation, which is an irradiance of 1000W / m2 (the above G N ), battery temperature 25℃ (the above T STC ) and spectrum AM1.5.

[0050] Establish a heat pump output model: The heat pump obtains low-grade heat energy from external resources such as soil and groundwater, and converts it into high-grade heat energy after compressing it using electrical energy to provide heating in winter and cooling in summer. The output model of the ground source heat pump is as follows:

[0051] P dhp,c =P dhp COP dhp,c T dhp

[0052] P dhp,h =P dhp COP dhp,h (1-T dhp )

[0053] Among them, P dhp,c and P dhp,h are the cooling power and heating power of the ground source heat pump respectively; P dhp The power consumption of the ground source heat pump during operation; COP dhp,c and COP dhp,h are the energy efficiency ratio curves of ground source heat pump cooling and heating respectively; T dhp Indicates the operating condition of the ground source heat pump, T dhp =1 means the ground source heat pump is in cooling condition, T dhp =1 indicates that the ground source heat pump is in heating condition.

[0054] Establishing a micro internal combustion engine output model: The micro internal combustion engine generates electricity by consuming natural gas. The micro internal combustion engine output model is as follows:

[0055] P cchp,e (t)PV ng H r h cchp,e (t)

[0056] Among them, P cchp,e (t) is the power generation of the micro internal combustion engine at time t, MW; V ng is the volume of natural gas consumed by the micro internal combustion engine per unit time, m 3 / h; H r is the calorific value of natural gas; η cchp,e (t) is the power generation efficiency of the micro internal combustion engine at time t.

[0057] Establish an electric boiler model: Electric boiler (EB) uses semiconductor heating to generate heat, can start quickly, and can be used as an energy supply supplement device. The heat generation power of the electric boiler is shown in the following formula:

[0058] P EB,h (t) = P EB,e (t)h EB

[0059] Among them, P EB，h (t) is the heating power of the electric boiler at time t, MW; P EB，e (t) is the input power of the electric boiler at time t; η EB is the electric heat conversion coefficient of the electric boiler.

[0060] Establishing an energy storage battery model: Energy storage batteries use fast charging and discharging characteristics to smooth out fluctuations in renewable energy. The main physical parameters are rated capacity, state of charge (remaining power), charge and discharge power, and charge and discharge efficiency. The energy storage battery model is as follows:

[0061]

[0062] Among them, SOC(t) and SOC(t0) are the remaining power of the energy storage battery at time t and time t0 respectively; δ is the self-discharge rate of the energy storage battery, % / h; P ch is the charging power of the energy storage battery, MW; P dis is the discharge power of the energy storage battery, MW; η ch is the charging efficiency of the energy storage battery; η dis is the discharge efficiency of the energy storage battery.

[0063] It is also necessary to clarify the objective function of the equipment selection planning model. In this embodiment, the minimum total cost within the user's entire life cycle is used as the objective function of planning optimization. The initial investment cost of the equipment, the fuel cost during the operation phase, the equipment operation and maintenance cost, and the depreciation cost of consumable equipment are mainly considered. The objective function F is expressed as:

[0064] FPmin(C in +C op +C mc +Cgh )

[0065] Among them, C in is the initial investment cost of the system; C op is the annual operating cost of the system during its entire life cycle (i.e., the fuel usage cost during the operation phase), mainly the purchase cost of natural gas; C mc C is the operation and maintenance cost; gh The cost of replacing consumable equipment.

[0066] For the initial investment cost, based on the time value of investment funds, this embodiment uses the annual value of expense method to calculate the initial investment cost, as shown in the following formula:

[0067]

[0068] Among them, T i，j is the purchase cost of a single device of model j in the i-th category of equipment, x i，j is the number of installed units of type j equipment in the i-th category of equipment, r is the base discount rate; n is the planning period of the project.

[0069] For the fuel cost in the operation phase, the status of each device is simulated when selecting and planning the integrated energy system. This paper takes 8760 hours a year as the simulation cycle and the simulation step length is 1 hour. The energy cost generated during operation is mainly the natural gas consumption cost C gas :

[0070]

[0071] Among them, C gs is the unit price of natural gas, p l，j (t) is the operating power of the equipment at time t; COP l，j (t) is the operating efficiency of the equipment; R rz is the calorific value of natural gas; q is the number of gas-consuming equipment.

[0072] As for the replacement cost of consumable equipment, this embodiment mainly considers the replacement cost of energy storage batteries. This embodiment converts the single replacement cost of consumable equipment into the replacement cost of consumable equipment within an annual period, C gh As shown below:

[0073]

[0074] Among them, n is the operating life of the integrated energy system, s is the operating life of the consumable equipment, g is the number of times the consumable equipment needs to be replaced, y is the number of years the consumable equipment is converted to net present value, r is the base discount rate, d is the annual average ... r The cost of replacing a worn-out device once.

[0075] In addition to the output models of the above-mentioned various types of equipment, the constraints of the equipment selection planning model in this embodiment also include:

[0076] (1) Area and capacity constraints

[0077] 0≤x i.j A i,j ≤M a,i

[0078] Among them, x i，j A is the number of installed units of type j equipment in the i-th type of equipment, i，j M is the floor space occupied by a single j-type device in the i-th category of equipment. a，i It is the estimated maximum installation area of ​​the i-th category equipment.

[0079] (2) Resource capacity constraints

[0080] Resource capacity constraints are mainly aimed at ground source heat pump equipment, which refers to the constraints of underground geothermal energy on the installed capacity of ground source heat pumps. Ground source heat pump equipment needs to meet both area capacity constraints and resource capacity constraints.

[0081]

[0082] Among them, P d,c and P d,h are the cooling power and heating power of the ground source heat pump, N bp is the buried pipe depth of the ground source heat pump, H s is the heat exchange capacity per meter of well depth under the ground source heat pump heat exhaust condition, H w are the heat exchange capacity per meter of well depth under the ground source heat pump heating condition, D e For a limited buried pipe depth, COP c The operating efficiency of the ground source heat pump during cooling, COP h It is the operating efficiency of ground source heat pump during cooling.

[0083] (3) Electric power constraints

[0084] This embodiment does not consider the transmission loss of the power grid. The power balance constraints in the integrated energy system are as follows:

[0085]

[0086] Among them, P grid (t) is the interaction power between the integrated energy system park and the power grid at time t; is the power generation of the gas turbine at time t; P w (t) is the power generation of the wind turbine at time t; P pv (t) is the photovoltaic power generation power at time t; P bess_dis(t) is the discharge power of the energy storage battery at time t; P eload (t) is the basic electric load power in the system at time t; P bess_ch (t) is the charging power of the energy storage battery at time t; P GN (t) is the total power consumption of electricity-to-cooling, electricity-to-gas, electricity-to-heat, etc. at time t.

[0087] (4) Operation constraints of energy supply equipment

[0088]

[0089] in, is the output of the i-th type of equipment at time t; S max Maximum ramp rate of equipment of category i.

[0090] (5) Operation constraints of energy storage equipment

[0091] SOC is used to represent the remaining capacity of the energy storage device. The following constraints need to be met when running the simulation:

[0092]

[0093] Among them, P CN，t is the operating power of energy storage equipment; P CN，max This is the maximum operating power of the energy storage device. To ensure the longest service life of the energy storage device, the charge and discharge depth is set to [0.1-0.9].

[0094] S2. Solve the equipment selection planning model established above to obtain the optimal decision variables.

[0095] Specifically, this embodiment uses a particle swarm algorithm to solve the equipment selection planning model. The flow chart is as follows: Figure 2 shown.

[0096] Before solving, first, particle the integrated energy system selection scheme; based on the actual integrated energy equipment performance, price and other resources, combined with actual engineering requirements, select all available equipment models, and arrange and combine all equipment to obtain an applicable integrated energy system selection scheme, thereby forming the particle swarm (solution space) required by the particle swarm algorithm.

[0097] Then, 0 and 1 are used to indicate whether the integrated energy system selection plan is executed. The state of whether the supporting plan is executed or not is represented by a set of codes consisting of 0 and 1, that is, the state code of the execution of the integrated energy system selection plan is the solution to the entire integrated energy system selection plan problem.

[0098] The particle swarm algorithm of this embodiment includes the following steps:

[0099] Step 1: Initialize the particle swarm size and assign initial values ​​to particle speeds;

[0100] Step 2: Update the velocity and position of the particle;

[0101] Assume that in a d-dimensional solution space, a population is composed of m particles, and the properties of the i-th particle at time t, namely its position and velocity, are:

[0102]

[0103] In the formula, i=1,2,…,m.

[0104] The speed and position update formula of each particle is:

[0105]

[0106] In the formula, p i is the historical optimal position of the i-th particle; p g Iterate the entire particle swarm to the optimal position currently searched; rand() is a random number uniformly distributed in the interval [0,1]; c1 and c2 are learning factors, which control the particles to learn from their own historical experience and the optimal individuals in the group, thereby controlling them to approach the optimal point within the group or the neighborhood, and take c1+c2≤4, usually set to c1+c2 P4.

[0107] Step 3: After the maximum number of iterations is met, the optimal comprehensive energy system selection and planning scheme is output.

[0108] S3. Perform equipment selection planning for the integrated energy system based on the optimal integrated energy system selection planning scheme (i.e., optimal decision variables) obtained in step 3.

[0109] Computer device embodiment:

[0110] A computer device includes a processor, the processor is used to execute a computer program to implement the steps of the device selection method for the integrated energy system as described above. The specific device selection method for the integrated energy system has been introduced in sufficient detail in the embodiment of the device selection method for the integrated energy system as described above, and will not be repeated here.

[0111] The present invention has the following characteristics:

[0112] The present invention provides a technical and economic selection method for integrated energy equipment based on a particle swarm algorithm, which can comprehensively consider the economic efficiency and technical reliability of energy equipment of different brands and models, quickly and efficiently provide the optimal solution for the selection of integrated energy equipment, support the automatic formulation of planning and design solutions for multiple scenarios of integrated energy, and assist business market personnel and designers in the integrated energy field.

[0113] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments without creative work, or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for selecting equipment for a comprehensive energy system, characterized in that: The method includes: S1. Construct a comprehensive energy system equipment selection and planning model; The decision variables of the equipment selection planning model include the number of installed units of different types and models of equipment in the integrated energy system; The objective function of the equipment selection planning model includes an objective function constructed with the goal of minimizing the total cost of the integrated energy system over the entire life cycle; the total cost over the entire life cycle includes the replacement cost of consumable equipment; S2, solving the equipment selection planning model to obtain the optimal decision variables; S3. Carry out equipment selection planning for the integrated energy system according to the optimal decision variables.

2. The equipment selection method for the integrated energy system according to claim 1, characterized in that: The replacement cost of the consumable equipment: the single replacement cost of the consumable equipment converted to the replacement cost of the consumable equipment per unit time C gh , C gh According to the following formula: Where n is the operating life of the integrated energy system, s is the operating life of the consumable equipment, g is the number of times the consumable equipment needs to be replaced, y is the number of years the consumable equipment is converted to net present value, r is the base discount rate, and d is the annual average ... r The cost of replacing a consumable device once.

3. The equipment selection method for the integrated energy system according to claim 1 is characterized in that: The total cost over the entire life cycle also includes the initial investment cost of the equipment, the fuel usage cost during the operation phase, and the operation and maintenance cost.

4. The equipment selection method for the integrated energy system according to claim 1, characterized in that: The constraints of the equipment selection planning model include area capacity constraints: 0≤x i.j A i,j ≤M a,i Among them, x i，j A is the number of installed units of type j equipment in the i-th type of equipment, i，j M is the floor space occupied by a single j-type device in the i-th category of equipment. a，i It is the estimated maximum installation area of ​​the i-th category equipment.

5. The equipment selection method for the integrated energy system according to claim 1, characterized in that: The integrated energy system includes a ground source heat pump, and the constraint conditions of the equipment selection planning model include resource capacity constraints of the ground source heat pump: Among them, P d,c and P d,h are the cooling power and heating power of the ground source heat pump, N bp is the buried pipe depth of the ground source heat pump, H s is the heat exchange capacity per meter of well depth under the ground source heat pump heat exhaust condition, H w are the heat exchange capacity per meter of well depth under the ground source heat pump heating condition, D e For a limited buried pipe depth, COP c The operating efficiency of the ground source heat pump during cooling, COP h It is the operating efficiency of ground source heat pump during cooling.

6. The equipment selection method for the integrated energy system according to claim 1, characterized in that: The integrated energy system includes photovoltaic power generation equipment, and the constraints for the photovoltaic power generation equipment in the equipment selection planning model include: Among them, P pv is the actual output power of the photovoltaic power generation equipment, P N is the rated output power of the photovoltaic power generation equipment, G is the real-time solar radiation intensity, G N is the solar radiation intensity under standard test conditions, is the temperature coefficient of the photovoltaic module, T c is the temperature of the photovoltaic module, T STC is the temperature of the photovoltaic module under standard test conditions.

7. The method for selecting equipment for a comprehensive energy system according to claim 6, characterized in that: The solar radiation intensity is simulated using nonparametric kernel density estimation method.

8. The method for selecting equipment for a comprehensive energy system according to claim 1, characterized in that: The integrated energy system includes wind power generation equipment, and the constraints for the wind power generation equipment in the equipment selection planning model include: Among them, P w is the actual output power of the wind power generation equipment, P w is the rated operating power of the wind power generation equipment, v is the real-time wind speed, v ci is the cut-in wind speed, v co is the cut-out wind speed, v r is the rated wind speed.

9. The method for selecting equipment for a comprehensive energy system according to any one of claims 1 to 8, characterized in that: In S2, a particle swarm algorithm is used to solve the equipment selection planning model.

10. A computer device comprising a processor, characterized in that: The processor is used to execute a computer program to implement the steps of the equipment selection method for the integrated energy system as described in any one of claims 1 to 9.

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