Optimization method and system for electric thermal hydrogen system considering the differences of multiple types of electrolyzers

Abstract

The present invention discloses an electric thermal hydrogen system optimization method and system that considers the differentiation of multiple types of electrolyzers. The electric thermal hydrogen system optimization method adopted by the present invention includes: first, analyzing the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility and economy, and establishing a unified general mathematical model of multiple types; second, establishing an electric thermal hydrogen system model to determine the design parameters of the electric thermal hydrogen system; then, considering the differentiated characteristics of different types of electrolyzers, establishing a two-layer optimization design model with the economy, environmental protection and reliability of the electric thermal hydrogen system as optimization goals; finally, combining the particle swarm algorithm and the branch and bound method to solve the two-layer optimization design model. The present invention can comprehensively utilize the differentiated characteristics of multiple types of electrolyzers to design a distributed electric thermal hydrogen system capacity configuration plan that can more flexibly absorb the wind and solar power output with strong volatility, and comprehensively improve the economy, environmental protection and reliability of the distributed electric thermal hydrogen system.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrothermal hydrogen system optimization, and in particular to an electrothermal hydrogen system optimization method and system that takes into account the differentiated characteristics of multiple types of electrolyzers. Background Art

[0002] Currently, electrothermal hydrogen systems are widely used in many fields, such as energy storage, fuel cells, and the chemical industry. These systems efficiently couple electricity, hydrogen, and thermal energy. They convert electricity into hydrogen and thermal energy through water electrolysis, and then convert hydrogen back into electricity and thermal energy through fuel cells, achieving efficient energy conversion and storage. This system offers advantages such as high energy efficiency, low carbon emissions, and the promotion of renewable energy utilization, and is considered a key development direction in the future energy sector.

[0003] However, achieving efficient operation of electrothermal hydrogen systems requires optimization. The electrolyzer is one of the core devices in electrothermal hydrogen systems, utilizing surplus renewable energy to electrolyze water into hydrogen and oxygen. Different types of electrolyzers differ in structure, materials, and operating principles, resulting in varying performance and characteristics. Most current research simplifies the electrolyzer model to a simple conversion factor, while few consider the refined operational constraints of the electrolyzer.

[0004] On the other hand, different types of electrolyzers have different advantages and scopes of application. For example, traditional alkaline electrolyzers have the advantages of low cost and high electrolysis efficiency; proton exchange membrane electrolyzers have high operational flexibility; and high-temperature solid oxide electrolyzers have high electrolysis efficiency. Currently, most studies only consider the application of alkaline electrolyzers, and few studies comprehensively consider the differentiated characteristics of alkaline electrolyzers. Research on the optimization of the differentiated characteristics of multiple types of electrolyzers is relatively insufficient. Therefore, different types of electrolyzers need to be optimized based on their differentiated characteristics to give full play to their respective performance advantages. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an electric thermal hydrogen system optimization method and system that takes into account the differentiated characteristics of multiple types of electrolyzers. The purpose is to achieve coordinated control and differentiated utilization of multiple types of electrolyzers through feature analysis and optimization design for different types of electrolyzers, improve the environmental protection, safety and economy of the electric thermal hydrogen system, and further promote the application of electric thermal hydrogen systems in the energy field.

[0006] To this end, a technical solution adopted by the present invention is: an electric thermal hydrogen system optimization method considering the differentiated characteristics of multiple types of electrolyzers, which includes the following steps:

[0007] 1) Analyze the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economy, and establish a unified universal mathematical model for multiple types;

[0008] 2) Establish an electric thermal hydrogen system model and determine the design parameters of the electric thermal hydrogen system;

[0009] 3) Considering the differentiated characteristics of different types of electrolyzers, a two-layer optimization design model is established with the economic, environmental, and reliability of the electrothermal hydrogen system as the optimization objectives;

[0010] 4) Combine particle swarm optimization and branch and bound method to solve the two-layer optimization design model.

[0011] The electric thermal hydrogen system optimization method of the present invention takes into account multiple types of electrolyzers and comprehensively utilizes the differentiated characteristics of multiple types of electrolyzers to better absorb renewable energy with fluctuations.

[0012] Another technical solution adopted by the present invention is: an electrothermal hydrogen system optimization system that takes into account the differentiated characteristics of multiple types of electrolyzers, which includes:

[0013] Multi-type electrolyzer modeling unit: Analyzes the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economics, and establishes a unified general mathematical model for alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers;

[0014] Electric thermal hydrogen system modeling unit: establishes an electric thermal hydrogen system model including wind turbines, photovoltaics, alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, hydrogen fuel cells, electric boilers, batteries, hydrogen storage tanks, and heat storage tanks, and determines the design parameters of the electric thermal hydrogen system;

[0015] Optimization model building unit: Considering the differentiated characteristics of different types of electrolyzers, a two-layer optimization design model is established with the economic, environmental and reliability of the electric thermal hydrogen system as the optimization objectives;

[0016] Optimization solution solving unit: uses particle swarm optimization algorithm to solve the upper-level equipment capacity design model, and uses branch and bound method to solve the lower-level system optimization operation model.

[0017] The electric thermal hydrogen system optimization system of the present invention takes into account the differentiated characteristics of various types of electrolyzers, designs a distributed electric thermal hydrogen system capacity configuration plan that can more flexibly absorb the highly volatile wind and solar power output, and comprehensively improves the economy, environmental protection and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some implementation cases of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 Schematic diagram of the structure of the electric thermal hydrogen system of the present invention;

[0020] Figure 2 This is a flow chart of the electrothermal hydrogen system optimization method of the present invention;

[0021] Figure 3 This is a graph showing the convergence process of the particle swarm algorithm for the electrothermal hydrogen system of the present invention;

[0022] Figure 4 This is a diagram showing the capacity optimization design results of the electric thermal hydrogen system of the present invention;

[0023] Figure 5 This is a comparison chart of the characteristics of different capacity design schemes of the electric thermal hydrogen system of the present invention;

[0024] Figure 6 This is a graph showing the operating results of multiple types of electrolyzers in the electrothermal hydrogen system of the present invention on a typical summer day;

[0025] Figure 7 This is a diagram showing the operation results of multiple types of energy storage for the electrothermal hydrogen system of the present invention on a typical summer day;

[0026] Figure 8 This is a diagram showing the HFC operation results of the electrothermal hydrogen system of the present invention on a typical summer day. DETAILED DESCRIPTION

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0028] Example 1

[0029] This embodiment provides an electrothermal hydrogen system optimization method that takes into account the differentiated characteristics of multiple types of electrolyzers. The specific contents are as follows:

[0030] 1) Analyze the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economy, and establish a unified universal mathematical model for multiple types;

[0031] 2) Establish an electric thermal hydrogen system model and determine the design parameters of the electric thermal hydrogen system;

[0032] 3) Considering the differentiated characteristics of different types of electrolyzers, a two-layer optimization design model is established with the economic, environmental, and reliability of the electrothermal hydrogen system as the optimization objectives;

[0033] 4) The particle swarm optimization algorithm and branch-and-bound method are combined to solve the two-level optimization design model, and the economy, environmental protection and reliability of the optimized electric thermal hydrogen system are evaluated through case simulation.

[0034] In the step 1), the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility and economy are as follows: In the electric thermal hydrogen system, hydrogen production by electrolysis of water is the most important link. Currently, there are three relatively mature electrolyzers, namely alkaline electrolyzers, proton exchange membrane electrolyzers and solid oxide electrolyzers. Among them, alkaline electrolyzers are the most mature technology and the lowest cost, but have a small workload range, slow response speed, and an electrolysis efficiency of 54% to 66%. In comparison, proton exchange membrane electrolyzers are slightly more expensive, but have a wider load range, faster response speed, and a certain improvement in electrolysis efficiency, but their rated hydrogen production efficiency is less than 70%. In comparison, the electrolysis efficiency of solid oxide electrolyzers can reach more than 90%. However, since solid oxide electrolyzers need to work in a high-temperature environment, their investment cost and response speed still lag behind proton exchange membrane electrolyzers. The unified general mathematical model is as follows:

[0035] In the following text, the superscript M in all symbols represents different electrolytic cells, M{A,P,S}, where A represents alkaline electrolytic cell, P represents proton exchange membrane electrolytic cell, and S represents solid oxide electrolytic cell; the subscript k is the electrolytic cell number; the subscript t represents the unit operating time period, and T is the total operating time period, where 1≤t≤T;

[0036] Start-Stop Model:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] in, Indicates the switch status of the electrolytic cell, which is 0 or 1; Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; α M Indicates the startup delay; Indicates the switch state of the electrolytic cell at time t-1; represents t-α M The moment the electrolytic cell starts to start, They represent the upper limit of the number of startup and shutdown times of M electrolytic cell per day;

[0043] Output model:

[0044]

[0045]

[0046]

[0047] in, The working efficiency of the electrolyzer; is the working power of the electrolytic cell; It represents the energy corresponding to the chemical energy in hydrogen; represents the mass of hydrogen produced, in kg; γ is the calorific value equivalent coefficient of one kilogram of hydrogen; is the thermal power generated by the electrolytic cell; Δt represents unit time;

[0048] Power Constraints:

[0049]

[0050]

[0051]

[0052] Among them, P M，min 、P M，max They represent the upper and lower limits of the working power of M electrolytic cell when it is turned on; P M,boot It represents the electric power consumed during the startup of the electrolytic cell; τ represents the time measurement during the startup of the electrolytic cell; Indicates the start-up action of the electrolytic cell at time t-τ, ΔP M，max Indicates the maximum ramp power per unit time period of M electrolyzer when it is on;

[0053] Temperature model:

[0054]

[0055]

[0056]

[0057] in, and Represent the electrolytic cell temperature at time t and time t+1 respectively; Ta is the ambient temperature; C e is the lumped heat capacity of the electrolytic cell; R e is the lumped thermal resistance; is the lost heat power; is the thermal power output outside the system; T M,max and T M,min Respectively represent the upper and lower temperature limits of the electrolytic cell.

[0058] In step 2), the electric heating hydrogen system model is as follows: Figure 1 As shown, it includes four parts: energy supply equipment, energy conversion equipment, energy storage equipment and load. Energy conversion is carried out through electric-thermal-hydrogen coupling to meet the load supply of different energy sources. Among them, the energy supply equipment includes wind turbines, photovoltaics and upper-level power grids, the energy conversion equipment includes electrolyzers, hydrogen fuel cells and electric boilers, among which the electrolyzers include alkaline electrolyzers, proton exchange membrane electrolyzers and solid oxide electrolyzers, and the energy storage equipment includes batteries, hydrogen storage tanks and heat storage tanks. The design parameters of the electric thermal hydrogen system include the capacity of multiple types of electrolyzers, hydrogen fuel cell capacity, battery capacity, hydrogen storage tank capacity, heat storage tank capacity and electric boiler capacity.

[0059] The specific device models are as follows:

[0060] Hydrogen fuel cell mathematical model:

[0061]

[0062]

[0063]

[0064] in: is the HFC power generation efficiency; P t HFC is the equivalent power of the hydrogen calorific value of the input HFC; Producing electrical power for fuel cells; is the mass of hydrogen consumed by the fuel cell, in kg; is the HFC heat generation efficiency, is the thermal power generated by HFC.

[0065] Electric boiler model:

[0066]

[0067] Among them, η EB Represents the electric-to-heat conversion efficiency of electric boilers; P EB,max and P EB,min Respectively represent the upper and lower limits of electric boiler output; Q EB,tIndicates the heating power of the electric boiler; P EB,t Indicates the power consumption of electric boiler;

[0068] Hydrogen storage tank model:

[0069]

[0070] in, is the mass of hydrogen in the hydrogen storage tank; and are the hydrogen charging and discharging efficiency respectively; and They are the upper and lower limits of hydrogen storage capacity in the hydrogen storage tank respectively; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; and are the state variables for injecting and releasing hydrogen respectively; and They represent the hydrogen mass in the hydrogen storage tank at the initial and final moments of scheduling respectively; Indicates the upper limit of hydrogen mass filled into the hydrogen storage tank at time t; Indicates the upper limit of the hydrogen mass released by the hydrogen storage tank at time t;

[0071] Thermal storage tank model:

[0072]

[0073] Among them, H t is the heat of the heat storage pool at time t; Q dis,t is the heat release power of the heat storage tank at time t; Q ch,t is the heat storage power of the heat storage pool at time t; They are storage and discharge, thermal efficiency; They are the storage and discharge of the thermal storage tank, the maximum power limit of heat, H max 、H min are the upper and lower limits of the heat storage tank capacity respectively; A ch,t 、A dis,t Respectively represent storage and release, thermal flag; H0 and H T Respectively represent the heat of the heat storage tank at the initial and final moments of scheduling;

[0074] Battery model:

[0075]

[0076] Among them, E t is the battery charge at time t; P dis,t is the discharge power of the battery at time t; P ch,t is the charging power of the battery at time t; ηch ,η dis are charge and discharge efficiency respectively; They are the maximum power limits of battery charge and discharge, E max 、E min are the upper and lower limits of battery power respectively; B ch,t 、B dis,t Respectively represent the charge and discharge flags; E T , E0 represent the battery capacity at the initial and final moments of scheduling respectively;

[0077] In step 3), the two-layer optimization design model with the economic, environmental and reliability of the electric thermal hydrogen system as the optimization objectives is as follows:

[0078] Upper-layer equipment capacity design model objective function:

[0079] The objective function of the upper-level equipment capacity design model is to maximize daily net revenue, where daily net revenue is equal to the daily operating revenue obtained from the optimized operation of the lower-level system optimization operation model minus the system investment cost. By designing the capacity of various equipment in the electric thermal hydrogen system (batteries, heat storage tanks, hydrogen storage tanks, fuel cells, and various types of electrolyzers), the daily net revenue is maximized while the system operates safely and reliably. The specific objective function expression is as follows:

[0080]

[0081]

[0082] Where: Subscript i refers to different equipment, including alkaline electrolyzer, proton exchange membrane electrolyzer, solid oxide electrolyzer, battery, heat storage tank, hydrogen storage tank, hydrogen fuel cell and electric boiler; F up The objective function designed for upper layer optimization; F down The objective function for the lower layer optimization operation; C in is the system investment cost, N is the total number of devices in the integrated energy system, η i is the interest rate, take 5%, k i is the investment cost per unit capacity of each device, t i is the service life of each device, S i Indicates the capacity of each device in the integrated energy system.

[0083] Constraints of the upper-layer equipment capacity design model:

[0084] Taking into account the limitations of factors such as construction scale and construction conditions, the constraints of the upper-level optimization design model include capacity constraints of various types of electrolyzers, hydrogen fuel cells, hydrogen storage tanks, heat storage tanks, batteries, electric boilers, and other equipment. The specific expressions are as follows:

[0085]

[0086] Where: S AEC 、S PEMEC 、S SOEC 、S HFC 、S ESS 、S HST 、S HOT and S EB They are the capacities of alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks and electric boilers; and and These are the upper / lower capacity limits for alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks and electric boilers.

[0087] The objective function of the lower-level system optimization operation model is:

[0088] Optimization scheduling is performed over 24 hours a day, with the goals of economic, reliability, and efficiency of system operation. The economic goal considers equipment start-up and shutdown costs, electricity purchase costs, and hydrogen and heat sales revenues; the environmental goal considers carbon emission penalty costs and wind and solar power abandonment penalty costs; and the reliability goal considers the penalty costs of unmet loads. The objective function expression of the lower-level system optimization operation model is:

[0089] F down =max(F1+F2+F3)(25)

[0090] Economic goals:

[0091] F1=-f2-f2+f3+f4

[0092]

[0093] Among them, f1 represents the transaction fee for power purchase and sale between the microgrid and the upper-level power grid. represents the system electricity purchase price at time t, P buy,t represents the electricity purchased at time t; f2 represents the cost of starting and stopping the electrolyzer, and are the startup and shutdown costs of electrolyzer M, respectively; Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; subscript k is the electrolytic cell number, K is the total number of electrolytic cells; subscript t is the unit operating time period, T is the total operating time period, where 1≤t≤T; f3 is the heat sales revenue, ρ heat Indicates the unit price of selling heat, Qsell,t represents the thermal energy sold by the system at time t; f4 is the revenue from hydrogen sales, Indicates the unit price of hydrogen sold, represents the hydrogen sold by the system at time t;

[0094] Environmental goals:

[0095]

[0096] Among them, α and β represent the penalty coefficients for curtailing solar power and wind power, respectively, and ΔP pv,t and ΔP wt,t are the abandoned solar power and abandoned wind power respectively; K in Represents the electricity-to-carbon conversion coefficient, λ in represents the environmental penalty factor for carbon dioxide emissions;

[0097] Reliability goals:

[0098]

[0099] Among them, λ ele ,λ heat and are the penalty coefficients for loss of load on electricity, heat and hydrogen respectively, and are the electricity, heat and hydrogen load loss powers respectively;

[0100] Constraints of the lower-level system optimization operation model:

[0101] Electric power balance constraints:

[0102]

[0103] Among them, P pv,t and P wt,t Respectively represent the predicted output of photovoltaic and wind power at time t; ΔP pv,t and ΔP wt,t Represent the abandoned solar power and abandoned wind power at time t; P EB,t represents the electric power consumed by the electric boiler at time t; P load,t represents the electrical load at time t; is the working power of the electrolytic cell; P t HFC P is the equivalent power of hydrogen calorific value of input HFC; dis,t is the discharge power of the battery at time t; P ch,t is the charging power of the battery at time t;

[0104] Thermal power balance constraints:

[0105]

[0106] Among them, Q EB,t represents the heat energy generated by the electric boiler at time t; Q ch,t represents the amount of heat at time t; Q dis,t represents the heat release at time t; Q load,t is the heat load at time t; is the heat power output outside the system; The thermal power generated by HFC;

[0107] Hydrogen balance constraints:

[0108]

[0109] in, represents the hydrogen load at time t; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; Represents the mass of hydrogen produced, in kg; Represents the mass of hydrogen consumed by the fuel cell, in kg;

[0110] Purchased power constraints:

[0111] 0≤P buy,t ≤P buy,max (32)

[0112] Among them, P buy,max Represents the maximum amount of electricity purchased.

[0113] In step 4), the solution method and solution steps are as follows:

[0114] The upper-level equipment capacity design model involves capacity design decisions for multiple types of equipment, including multiple types of electrolyzers, multiple types of energy storage equipment, fuel cells, and electric boilers. Therefore, the present invention uses a particle swarm optimization algorithm to solve the upper-level equipment capacity design model and a branch-and-bound method to solve the lower-level system optimization operation model. The solution process of the two-level optimization design model is shown in the attached figure. Figure 2 shown.

[0115] The specific solution steps are as follows:

[0116] 1) Particle swarm initialization:

[0117] Set the particle swarm algorithm parameters, including population size, particle dimension, learning factor, and inertia weight settings, and give the initial values ​​of the speed and position of each particle.

[0118] 2) Calculate the fitness value of the particle:

[0119] The branch and bound method is used to solve the optimal operation model of the lower system, and the daily operating income under a certain capacity configuration result is obtained. The equivalent daily investment cost is deducted as the fitness value, and non-inferiority ranking is performed.

[0120] 3) Update the position and velocity of the particle:

[0121] First, the particle's position and velocity range boundaries are given, and the particle's position and velocity are updated according to the update formula. Boundary detection is performed after each update to determine whether the new particle's speed and position are greater than the boundary value. If so, the boundary value is directly used as the particle's new position and velocity.

[0122] 4) Calculate the fitness of the particle's new position:

[0123] The optimization operation results of the lower-level system optimization operation model are called again to calculate the new fitness value, and the new fitness values ​​are sorted according to the size of the fitness values.

[0124] 5) Update individual optimal values ​​and global optimal values:

[0125] Compare the fitness value of the new population particles with the original optimal fitness value, and update the individual optimal value and the global optimal value.

[0126] 6) Termination condition judgment:

[0127] The algorithm terminates when the fitness of the optimal individual reaches a given threshold, or when the fitness of the optimal individual and the fitness of the group converge, or when the maximum number of iterations is reached.

[0128] In this embodiment, when solving the upper model, the capacity configuration results generated by the particle swarm algorithm are substituted into the typical summer day and the typical winter day to calculate their daily net benefits, and the weighted average of the two is used as the fitness value of the upper particle swarm algorithm. The convergence process of the solution is as follows: Figure 3 As shown, the capacity design results of the upper model are as follows Figure 4 shown.

[0129] In order to compare the economic, environmental and reliability characteristics of the system under different configuration ratios of multiple types of electrolyzers, the capacity of other equipment except the electrolyzer was fixed, and the sum of the capacity of the three electrolyzers was fixed to 60. The ratio of alkaline electrolyzer, proton exchange membrane electrolyzer and solid oxide electrolyzer was changed. 19 configuration schemes were selected. The specific configuration schemes are shown in Table 1. The results are compared. Figure 5 shown.

[0130] Table 1 Different configuration schemes for various types of electrolyzers

[0131]

[0132] The operating results of multiple types of electrolyzers on a typical summer day are as follows Figure 6 , the operation results of various types of energy storage equipment are as follows Figure 7 , the results of hydrogen fuel cell operation are as follows Figure 8 .

[0133] Example 2

[0134] This embodiment provides an electric thermal hydrogen system optimization system that takes into account the differentiated characteristics of multiple types of electrolyzers, which consists of a multiple type of electrolyzer modeling unit, an electric thermal hydrogen system modeling unit, an optimization model establishment unit, and an optimization solution solving unit.

[0135] Multi-type electrolyzer modeling unit: Analyzes the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economy, and establishes a refined unified universal mathematical model for alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers;

[0136] Electric thermal hydrogen system modeling unit: establishes an electric thermal hydrogen system model including wind turbines, photovoltaics, alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, hydrogen fuel cells, electric boilers, batteries, hydrogen storage tanks, and heat storage tanks, and determines the design parameters of the electric thermal hydrogen system;

[0137] Optimization design unit: Considering the differentiated characteristics of different types of electrolyzers, a two-layer optimization design model is established with the economic, environmental, and reliability of the electric thermal hydrogen system as the optimization objectives;

[0138] Optimization design solution solving unit: The particle swarm optimization algorithm is used to solve the upper-level equipment capacity design model, and the branch and bound method is used to solve the lower-level system optimization operation model. The economy, environmental protection and reliability of the optimized electric thermal hydrogen system are evaluated through case simulation.

[0139] Specifically, the unified universal mathematical model of the multi-type electrolytic cells is as follows:

[0140] In the following text, the superscript M in all symbols represents different electrolytic cells, M{A,P,S}, where A represents alkaline electrolytic cell, P represents proton exchange membrane electrolytic cell, and S represents solid oxide electrolytic cell; the subscript k is the electrolytic cell number; the subscript t represents the unit operating time period, and T is the total operating time period, where 1≤t≤T;

[0141] Start-Stop Model:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] Among them: 0-1 variable Indicates the switch status of the electrolytic cell, Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; α M Indicates the startup delay; Indicates the switch state of the electrolytic cell at time t-1; represents t-α M The moment the electrolytic cell starts to start, They represent the upper limit of the number of startup and shutdown times of M electrolytic cell per day;

[0148] Output model:

[0149]

[0150]

[0151]

[0152] in: The working efficiency of the electrolyzer; is the working power of the electrolytic cell; The energy corresponding to the chemical energy in hydrogen is converted; represents the mass of hydrogen produced, in kg; γ is the calorific value equivalent coefficient of one kilogram of hydrogen; is the thermal power generated by the electrolyzer;

[0153] Power Constraints:

[0154]

[0155]

[0156]

[0157] Where: P M，min / P M，max Respectively represent the upper / lower limit of the working power of M electrolyzer in the power-on state; P M,boot It represents the electric power consumed during the startup of the electrolytic cell; τ represents the time measurement during the startup of the electrolytic cell; Indicates the start-up action of the electrolytic cell at time t-τ, ΔP M，max Indicates the maximum ramp power per unit time period when the M electrolyzer is in the on state.

[0158] Temperature model:

[0159]

[0160]

[0161]

[0162] Where: T a is the ambient temperature; C e is the lumped heat capacity of the electrolytic cell; R e is the lumped thermal resistance; is the lost heat power; is the thermal power output outside the system; Δt represents unit time.

[0163] Specifically, the electrothermal hydrogen system model is as follows: The electrothermal hydrogen system schematic is shown in the attached Figure 1 As shown, the electric thermal hydrogen system includes four parts: energy supply equipment, energy conversion equipment, energy storage equipment and load. Energy conversion is carried out through electricity-heat-hydrogen coupling to meet the load supply of different energy sources. Among them, the energy supply equipment includes wind turbines, photovoltaics and upstream power grids, the energy conversion equipment includes electrolyzers, hydrogen fuel cells and electric boilers, among which the electrolyzers include alkaline electrolyzers, proton exchange membrane electrolyzers and solid oxide electrolyzers, and the energy storage equipment includes batteries, hydrogen storage tanks and heat storage tanks. The design parameters of the electric thermal hydrogen system include the capacity of multiple types of electrolyzers, hydrogen fuel cell capacity, battery capacity, hydrogen storage tank capacity, heat storage tank capacity and electric boiler capacity.

[0164] More specifically, the device models are as follows:

[0165] Hydrogen fuel cell mathematical model:

[0166]

[0167]

[0168]

[0169] in: is the HFC power generation efficiency; P t HFC is the equivalent power of hydrogen calorific value of input HFC; Producing electrical power for fuel cells; is the mass of hydrogen consumed by the fuel cell, in kg; is the HFC heat generation efficiency, is the thermal power generated by HFC.

[0170] Electric boiler model:

[0171]

[0172] Where: ηEB Represents the electric-to-heat conversion efficiency of electric boilers; P EB,max and P EB,min They represent the upper and lower limits of electric boiler output respectively.

[0173] Mathematical model of hydrogen storage tank:

[0174]

[0175] in: is the mass of hydrogen in the hydrogen storage tank; and are the hydrogen charging and discharging efficiency respectively; and among them, is the mass of hydrogen in the hydrogen storage tank; and are the hydrogen charging and discharging efficiency respectively; and They are the upper and lower limits of hydrogen storage capacity in the hydrogen storage tank respectively; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; and are the state variables for injecting and releasing hydrogen respectively; and They represent the hydrogen mass in the hydrogen storage tank at the initial and final moments of scheduling respectively; Indicates the upper limit of hydrogen mass filled into the hydrogen storage tank at time t; Indicates the upper limit of the hydrogen mass released by the hydrogen storage tank at time t.

[0176] Thermal storage tank model:

[0177]

[0178] Among them: H t is the heat of the thermal storage pool at time t; Q dis,t is the heat release power of the heat storage tank at time t; Q ch,t is the thermal storage power of the thermal storage pool at time t; Δt is the time interval; are heat storage and release efficiency respectively; They are the maximum power limit of heat storage and release of the heat storage tank, H max 、H min are the upper and lower limits of the heat storage tank capacity respectively. ch,t 、A dis,t They respectively represent the heat storage and release flags.

[0179] Battery model:

[0180]

[0181] Where: Et is the battery charge at time t; P dis,t is the discharge power of the battery at time t; P ch,t is the charging power of the battery at time t; Δt is the time interval; η ch ,η dis are charge and discharge efficiency respectively; They are the maximum power limits of battery charge and discharge, E max 、E min are the upper and lower limits of battery power respectively; B ch,t 、B dis,t Respectively represent the charge and discharge flags.

[0182] Specifically, the two-layer optimization design model with the economy, environmental protection and reliability of the electric thermal hydrogen system as optimization objectives is as follows:

[0183] Upper-layer equipment capacity design model objective function:

[0184] The upper-level objective function is to maximize daily net revenue, where daily net revenue is equal to the daily operating revenue obtained from the lower-level optimization operation minus the system investment cost. By designing the capacity of various equipment in the electric thermal hydrogen system (batteries, heat storage tanks, hydrogen storage tanks, fuel cells, and multiple types of electrolyzers), the daily net revenue is maximized while the system operates safely and reliably. The specific objective function expression is as follows:

[0185]

[0186]

[0187] Where: Subscript i refers to different equipment, including various types of electrolyzers, batteries, hydrogen storage tanks, heat storage tanks, hydrogen fuel cells and electric boilers; F up The objective function designed for upper layer optimization; F down The objective function for the lower layer optimization operation; C in is the system investment cost, N is the total number of devices in the integrated energy system, η i is the interest rate, take 5%, k i is the investment cost per unit capacity of each device, t i is the service life of each device, S i Indicates the capacity of each device in the integrated energy system.

[0188] Constraints of the upper-layer equipment capacity design model:

[0189] Taking into account the limitations of factors such as construction scale and construction conditions, the constraints of the upper-level optimization design model include capacity constraints of various types of electrolyzers, hydrogen fuel cells, hydrogen storage tanks, heat storage tanks, batteries, electric boilers, and other equipment. The specific expressions are as follows:

[0190]

[0191] Where: S AEC 、S PEMEC 、S SOEC 、S HFC 、S ESS 、S HST 、S HOT and S EB They are the capacities of alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks and electric boilers; and and These are the upper / lower capacity limits for alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks and electric boilers.

[0192] The objective function of the lower-level system optimization operation model is:

[0193] Optimization scheduling is performed over 24 hours a day, with the goals of economic, reliability, and efficiency of system operation. The economic goal considers equipment start-up and shutdown costs, electricity purchase costs, and hydrogen and heat sales revenues; the environmental goal considers carbon emission penalty costs and wind and solar power curtailment penalty costs; and the reliability goal considers the penalty costs of unmet loads. The objective function expression of the optimization operation model is:

[0194] F down =max(F1+F2+F3) (25)

[0195] Economic goals:

[0196] F1=-f2-f2+f3+f4

[0197]

[0198] Among them, f1 represents the transaction fee for power purchase and sale between the microgrid and the upper-level power grid. represents the system electricity purchase price at time t, P buy,t represents the electricity purchased at time t; f2 represents the cost of starting and stopping the electrolyzer, and are the startup and shutdown costs of electrolyzer M, respectively; Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; subscript k is the electrolytic cell number, K is the total number of electrolytic cells; subscript t is the unit operating time period, T is the total operating time period, where 1≤t≤T; f3 is the heat sales revenue, ρ heatIndicates the unit price of selling heat, Q sell,t represents the thermal energy sold by the system at time t; f4 is the revenue from hydrogen sales, Indicates the unit price of hydrogen sold, represents the hydrogen sold by the system at time t;

[0199] Environmental goals:

[0200]

[0201] Among them, α and β represent the penalty coefficients for curtailing solar power and wind power, respectively, and ΔP pv,t and ΔP wt,t are the abandoned solar power and abandoned wind power respectively; K in Represents the electricity-to-carbon conversion coefficient, λ in represents the environmental penalty factor for carbon dioxide emissions;

[0202] Reliability goals:

[0203]

[0204] Among them, λ ele ,λ heat and are the penalty coefficients for loss of load on electricity, heat and hydrogen respectively, and are the electricity, heat and hydrogen load loss powers respectively;

[0205] Constraints of the lower-level system optimization operation model:

[0206] Electric power balance constraints:

[0207]

[0208] Among them, P pv,t and P wt,t Respectively represent the predicted output of photovoltaic and wind power at time t; ΔP pv,t and ΔP wt,t Respectively represent the abandoned solar power and abandoned wind power at time t; P EB,t represents the electric power consumed by the electric boiler at time t; P load,t represents the electrical load at time t; is the working power of the electrolytic cell; P t HFC P is the equivalent power of hydrogen calorific value of input HFC; dis,t is the discharge power of the battery at time t; P ch,t is the charging power of the battery at time t;

[0209] Thermal power balance constraints:

[0210]

[0211] Among them, Q EB,t represents the heat energy generated by the electric boiler at time t; Q ch,t represents the amount of heat at time t; Q dis,t represents the heat release at time t; Q load,t is the heat load at time t; is the heat power output outside the system; The thermal power generated by HFC;

[0212] Hydrogen balance constraints:

[0213]

[0214] in, represents the hydrogen load at time t; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; Represents the mass of hydrogen produced, in kg; Represents the mass of hydrogen consumed by the fuel cell, in kg;

[0215] Purchased power constraints:

[0216] 0≤P buy,t ≤P buy,max (32)

[0217] Among them, P buy,max Represents the maximum amount of electricity purchased.

[0218] Specifically, in the optimization solution solving unit, a particle swarm algorithm is used to solve the upper-level equipment capacity design model, and a branch and bound method is used to solve the lower-level system optimization operation model.

[0219] The upper-level equipment capacity design model involves capacity design decisions for multiple types of equipment, including multiple types of electrolyzers, multiple types of energy storage equipment, fuel cells, and electric boilers. Therefore, the present invention uses a particle swarm optimization algorithm to solve the upper-level equipment capacity optimization design model, and uses a branch and bound method to solve the lower-level system optimization operation model. The solution process of the system optimization design model is shown in the attached figure. Figure 2 shown.

[0220] The specific solution steps are as follows:

[0221] 1) Particle swarm initialization:

[0222] Set the particle swarm algorithm parameters, including population size, particle dimension, learning factor, and inertia weight settings, and give the initial values ​​of the speed and position of each particle.

[0223] 2) Calculate the fitness value of the particle:

[0224] The branch and bound method is used to solve the optimal operation model of the lower system, and the daily operating income under a certain capacity configuration result is obtained. The equivalent daily investment cost is deducted as the fitness value, and non-inferiority ranking is performed.

[0225] 3) Update the particle's position and velocity:

[0226] First, the particle's position and velocity range boundaries are given, and the particle's position and velocity are updated according to the update formula. Boundary detection is performed after each update to determine whether the new particle's speed and position are greater than the boundary value. If so, the boundary value is directly used as the particle's new position and velocity.

[0227] 4) Calculate the fitness of the particle's new position:

[0228] Call the lower-level optimization run results again and calculate the new fitness value, and sort them according to the size of the fitness value.

[0229] 5) Update individual optimal values ​​and global optimal values:

[0230] Compare the fitness value of the new population particles with the original optimal fitness value, and update the individual optimal value and the global optimal value.

[0231] 6) Termination condition judgment:

[0232] The algorithm terminates when the fitness of the optimal individual reaches a given threshold, or when the fitness of the optimal individual and the fitness of the group converge, or when the maximum number of iterations is reached.

Claims

1. An optimization method for an electrothermal hydrogen system considering the differentiation of multiple types of electrolyzers, characterized in that: Including steps: 1) Analyze the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economics, and establish a unified universal mathematical model for alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers, including a start-stop model, an output model, and a temperature model; 2) Establish an electric thermal hydrogen system model and determine the design parameters of the electric thermal hydrogen system; 3) Considering the differentiated characteristics of different types of electrolyzers, a two-level optimization design model is established with the economic, environmental, and reliability optimization objectives of the electrothermal hydrogen system; The objective function of the upper-level equipment capacity design model is to maximize daily net revenue, which is equal to the daily operating revenue obtained from the lower-level system optimization operation model minus the system investment cost. By designing the capacity of various equipment in the electric thermal hydrogen system, the daily net revenue is maximized while ensuring safe and reliable operation of the system. The objective function of the lower-level system optimization operation model is based on the economy, reliability, and environmental protection of the system operation. The economic objective takes into account equipment startup and shutdown costs, electricity purchase costs, and hydrogen and heat sales revenues. The environmental objective takes into account carbon emission penalty costs and wind and solar power curtailment penalty costs. The reliability objective takes into account the penalty costs of unmet load. 4) Combine the particle swarm optimization algorithm and the branch-and-bound method to solve the two-layer optimization design model. The particle swarm optimization algorithm is used to solve the upper-layer equipment capacity design model, and the branch-and-bound method is used to solve the lower-layer system optimization operation model. In step 1), the unified universal mathematical model is as follows: The superscript M in all the following symbols represents different electrolytic cells, M{A,P,S}, where A represents alkaline electrolytic cell, P represents proton exchange membrane electrolytic cell, and S represents solid oxide electrolytic cell; the subscript k is the electrolytic cell number; the subscript t represents the unit operating time period, and T is the total operating time period, where 1≤t≤T; Start-Stop Model: （1） （2） （3） （4） （5） in, Indicates the switch status of the electrolytic cell, which is 0 or 1; Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; Indicates the startup delay; Indicates the switch state of the electrolytic cell at time t-1; express The moment the electrolytic cell starts to start, 、 They represent the upper limit of the number of startup and shutdown times of M electrolytic cell per day; Output model: （6） （7） （8） in, The working efficiency of the electrolyzer; is the working power of the electrolytic cell; It represents the energy corresponding to the chemical energy in hydrogen; Represents the mass of hydrogen produced, in kg; is the calorific value equivalent coefficient of one kilogram of hydrogen; is the thermal power generated by the electrolyzer; Indicates unit time; Power Constraints: （9） （10） （11） in, 、 They represent the upper and lower limits of the working power of M electrolyzer when it is turned on; Indicates the electric power consumed during the startup of the electrolyzer; Indicates the time measurement during the electrolytic cell startup process; express The moment the electrolytic cell starts to start, Indicates the maximum ramp power per unit time period of M electrolyzer when it is on; Temperature model: （12） （13） （14） in, and Represent the electrolytic cell temperature at time t and time t+1 respectively; is the ambient temperature; is the lumped heat capacity of the electrolytic cell; is the lumped thermal resistance; is the lost heat power; is the heat power output outside the system; and Respectively represent the upper and lower temperature limits of the electrolytic cell.

2. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 1 is characterized in that: In step 2), the electric thermal hydrogen system model is as follows: the electric thermal hydrogen system includes four parts: energy supply equipment, energy conversion equipment, energy storage equipment and load, and energy conversion is performed through electricity-heat-hydrogen coupling to meet the load supply of different energy sources; wherein, the energy supply equipment includes wind turbines, photovoltaics and upstream power grids, the energy conversion equipment includes electrolyzers, hydrogen fuel cells and electric boilers, wherein the electrolyzers include alkaline electrolyzers, proton exchange membrane electrolyzers and solid oxide electrolyzers, and the energy storage equipment includes batteries, hydrogen storage tanks and heat storage tanks; the design parameters of the electric thermal hydrogen system include the capacity of multiple types of electrolyzers, the capacity of hydrogen fuel cells, the capacity of batteries, the capacity of hydrogen storage tanks, the capacity of heat storage tanks and the capacity of electric boilers.

3. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 1 is characterized in that: In step 2), the specific equipment model of the electric thermal hydrogen system includes: Hydrogen fuel cell mathematical model: （15） （16） （17） in, is the HFC power generation efficiency; is the equivalent power of the hydrogen calorific value of the input HFC; Producing electrical power for fuel cells; is the mass of hydrogen consumed by the fuel cell, in kg; is the HFC heat generation efficiency, The thermal power generated by HFC; is the calorific value equivalent coefficient of one kilogram of hydrogen; is the time interval; Electric boiler model: （18） in, Represents the electric-to-heat conversion efficiency of electric boilers; and Respectively represent the upper and lower limits of electric boiler output; Indicates the heating power of the electric boiler; Indicates the power consumption of electric boiler; Mathematical model of hydrogen storage tank: （19） in, is the mass of hydrogen in the hydrogen storage tank; and are the hydrogen charging and discharging efficiencies, respectively; and They are the upper and lower limits of hydrogen storage capacity in the hydrogen storage tank respectively; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; and are the state variables for injecting and releasing hydrogen respectively; and They represent the hydrogen mass in the hydrogen storage tank at the initial and final moments of scheduling respectively; Indicates the upper limit of hydrogen mass filled into the hydrogen storage tank at time t; Indicates the upper limit of the hydrogen mass released by the hydrogen storage tank at time t; Thermal storage tank model: （20） in, for t The heat of the thermal pool at all times; for t The heat release power of the heat storage pool at all times; for t The heat storage capacity of the heat storage pool at all times; 、 They are storage and discharge, thermal efficiency; 、 They are the storage and discharge of the thermal storage tank and the maximum power limit of heat. 、 are the upper and lower limits of the heat storage tank capacity respectively; 、 Respectively represent storage and release, and thermal flags; and Respectively represent the heat of the heat storage tank at the initial and final moments of scheduling; Battery model: （21） in, is the battery charge at time t; is the discharge power of the battery at time t; is the charging power of the battery at time t; 、 are charge and discharge efficiency respectively; 、 They are the maximum power limits for battery charging and discharging, 、 They are the upper and lower limits of the battery power respectively; 、 Respectively represent the charge and discharge flags; 、 They represent the battery power at the initial and final moments of scheduling respectively.

4. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 1 is characterized in that: In step 3), the objective function expression of the upper-layer equipment capacity design model is as follows: (22) (23) Among them, the subscript i Refers to different equipment, including various types of electrolyzers, batteries, hydrogen storage tanks, heat storage tanks, hydrogen fuel cells and electric boilers; The objective function of the upper layer equipment capacity design model; Optimize the objective function of the operation model for the lower-level system; is the system investment cost, is the total number of devices in the integrated energy system, is the interest rate, is the investment cost per unit capacity of each device, is the service life of each device, Indicates the capacity of each device in the integrated energy system.

5. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 4 is characterized in that: The constraints of the upper-level equipment capacity design model include the capacity constraints of multiple types of electrolyzers, hydrogen fuel cells, hydrogen storage tanks, heat storage tanks, batteries, and electric boilers. The specific expressions are as follows: (24) in, 、 、 、 、 、 、 and They are the capacities of alkaline electrolyzer, proton exchange membrane electrolyzer, high-temperature solid oxide electrolyzer, hydrogen fuel cell, battery, hydrogen storage tank, heat storage tank and electric boiler; 、 、 、 、 、 、 、 These are the upper limits of the capacity of alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks, and electric boilers; 、 、 、 、 、 、 、 These are the lower capacity limits of alkaline electrolyzers, proton exchange membrane electrolyzers, high-temperature solid oxide electrolyzers, hydrogen fuel cells, batteries, hydrogen storage tanks, heat storage tanks and electric boilers.

6. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 5 is characterized in that: The objective function expression of the lower-level system optimization operation model is: (25) Economic goals: (26) in, It represents the transaction fee of electricity purchase and sale between microgrid and upper power grid, represent The unit price of electricity purchased by the system at the moment, Represents time Electricity purchased at any given moment; represents the start-up and shutdown cost of the electrolyzer, and are the startup and shutdown costs of electrolyzer M, respectively; Indicates the start-up action of the electrolytic cell. Indicates the start and stop of the electrolytic cell; subscript k is the electrolytic cell number, K represents the total number of electrolytic cells; the subscript t represents the unit operating period, T is the total operating period, where 1≤t≤T; For the sales revenue, Indicates the unit price of selling heat, Representing the system Thermal energy sold at all times; To earn revenue from hydrogen sales, Indicates the unit price of hydrogen sold, Representing the system Hydrogen sold at all times; Environmental goals: (27) in, and are the penalty coefficients for curtailing solar power and wind power, and are the abandoned solar and wind power respectively; represents the electricity-to-carbon conversion coefficient, represents the environmental penalty factor for carbon dioxide emissions; Reliability goals: (28) in, 、 and are the penalty coefficients for loss of load on electricity, heat and hydrogen respectively, 、 and are the electricity, heat and hydrogen load loss powers respectively; Constraints of the lower-level system optimization operation model: Electric power balance constraints: (29) in, and Respectively represent the predicted output of photovoltaic and wind power at time t; and They represent the curtailed solar and wind power at time t respectively; represents the electric power consumed by the electric boiler at time t; represents the electrical load at time t; is the working power of the electrolytic cell; is the equivalent power of the hydrogen calorific value of the input HFC; is the discharge power of the battery at time t; is the charging power of the battery at time t; Thermal power balance constraints: (30) in, represents the heat energy generated by the electric boiler at time t; Represents the amount of heat charged at time t; represents the heat release at time t; is the heat load at time t; is the heat power output outside the system; The thermal power generated by HFC; Hydrogen balance constraint: (31) in, represents the hydrogen load at time t; and They represent the mass of hydrogen filled into or released from the hydrogen storage tank at time t respectively; Represents the mass of hydrogen produced, in kg; Represents the mass of hydrogen consumed by the fuel cell, in kg; Power purchase power constraints: (32) in, Represents the maximum amount of electricity purchased.

7. The electrothermal hydrogen system optimization method considering the differences of multiple types of electrolyzers according to claim 1 is characterized in that: The specific steps of step 4) are as follows: 1) Particle swarm initialization Set the particle swarm algorithm parameters, including population size, particle dimension, learning factor, and inertia weight settings, and give the initial values ​​of the speed and position of each particle; 2) Calculate the fitness value of the particle The branch-and-bound method is used to solve the optimization operation model of the lower-level system, and the daily operating income under a certain capacity configuration result is obtained. The equivalent daily investment cost is subtracted as the fitness value, and non-inferiority ranking is performed. 3) Update the position and velocity of the particle First, the particle's position and velocity range boundaries are given, and the particle's position and velocity are updated according to the update formula. After each update, a boundary check is performed to determine whether the new particle's velocity and position are greater than the boundary value. If so, the boundary value is directly used as the particle's new position and velocity. 4) Calculate the fitness of the particle's new position Call the lower-level system again to optimize the model and run the results, calculate the new fitness value, and sort them according to the size of the fitness value; 5) Update individual optimal values ​​and global optimal values Compare the fitness value of the new population particles with the original fitness optimal value, and update the individual optimal value and the global optimal value; 6) Termination condition judgment The algorithm terminates when the fitness of the optimal individual reaches a given threshold, or when the fitness of the optimal individual and the fitness of the group converge, or when the maximum number of iterations is reached.

8. An electrothermal hydrogen system optimization system considering the differentiation of multiple types of electrolyzers, used to implement the electrothermal hydrogen system optimization method considering the differentiation of multiple types of electrolyzers as described in any one of claims 1 to 7, characterized in that: include: Multi-type electrolyzer modeling unit: Analyzes the differentiated characteristics of different types of electrolyzers in terms of output efficiency, flexibility, and economics, and establishes a unified general mathematical model for alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers; Electric thermal hydrogen system modeling unit: establishes an electric thermal hydrogen system model including wind turbines, photovoltaics, alkaline electrolyzers, proton exchange membrane electrolyzers, solid oxide electrolyzers, hydrogen fuel cells, electric boilers, batteries, hydrogen storage tanks, and heat storage tanks, and determines the design parameters of the electric thermal hydrogen system; Optimization model building unit: Considering the differentiated characteristics of different types of electrolyzers, a two-layer optimization design model is established with the economic, environmental and reliability of the electric thermal hydrogen system as the optimization objectives; Optimization solution solving unit: uses particle swarm optimization algorithm to solve the upper-level equipment capacity design model, and uses branch and bound method to solve the lower-level system optimization operation model.

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