Industrial park integrated energy system optimization method and system

Abstract

The invention discloses an industrial park integrated energy system optimization method and system, and belongs to the technical field of building and energy system design optimizing.By determining constraint conditions of energy balance constraint, equipment capacity constraint and equipment operation constraint, load data in an industrial park are used as initial conditions, design parameters and meteorological data are input, and the optimal energy balance constraint, equipment capacity constraint and equipment operation constraint are determined; taking the hydrogen doping ratio as a decision variable, constructing a comprehensive energy system optimization model and determining a target function; solving the objective function by taking minimization of annual total cost, carbon emission and energy consumption of the integrated energy system as an objective, and outputting a Pareto solution set; and performing multi-target sorting on the Pareto solution set, screening out an optimal solution corresponding to the decision variable, and obtaining a real-time power condition of each device corresponding to the optimal solution. According to the comprehensive energy system optimization model constructed by the method, the use proportion of hydrogen energy can be adjusted under different operation conditions, so that the optimal energy efficiency and environmental protection effect are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of building and energy system design optimization, and more specifically to an industrial park comprehensive energy system optimization method and system. Background Art

[0002] With the steady advancement of my country's "dual carbon" goals and the background of the energy system's transformation to a low-carbon one, how to use renewable energy to meet the needs of multiple loads such as cooling, heating, electricity, and gas has become an important research topic.

[0003] The integrated energy system based on the natural gas hydrogen blending industrial park can make the most of renewable energy such as solar energy, and combine the hydrogen produced by electrolyzing water in the electrolyzer, and after mixing with natural gas, it can reduce the system's dependence on pure natural gas and reduce the system's carbon emissions. This system can integrate different energy equipment and technologies to achieve multi-type load services.

[0004] In order to improve the accuracy and reliability of the design optimization of the multi-energy complementary integrated energy system in industrial parks, the traditional integrated energy system usually combines CCHP with natural gas cogeneration to form a CCHP system. Photovoltaic energy is converted into electricity and then into hydrogen. Hydrogen can be mixed into the natural gas pipeline network to form hydrogen-blended natural gas. This integrated energy utilization method can not only reduce the demand for natural gas at the enterprise or user end, but also effectively reduce carbon dioxide emissions.

[0005] However, the existing comprehensive energy system optimization method for natural gas hydrogen-blended industrial parks does not take into account hydrogen produced by renewable energy such as solar energy in the hydrogen-blended natural gas formed, and lacks consideration of the hydrogen blending ratio when hydrogen is produced by renewable energy such as solar energy and blended with natural gas. It cannot effectively solve the uneconomical and environmentally unfriendly problems of the operation of the multi-energy complementary comprehensive energy system in the industrial park, and the energy utilization rate is low, resulting in high operating costs. Summary of the invention

[0006] In response to the problems existing in the above-mentioned fields, the present invention proposes an optimization method and system for the comprehensive energy system of an industrial park. The constructed comprehensive energy system optimization model takes the local meteorological data, design parameters and load data of the industrial park as input variables, and the hydrogen blending ratio as a decision variable, so as to adjust the proportion of hydrogen energy use under different operating conditions, thereby achieving the best energy efficiency and environmental protection effect.

[0007] In order to solve the above technical problems, the present invention discloses an industrial park comprehensive energy system optimization method, comprising the following steps:

[0008] Obtain design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park;

[0009] Establish mathematical models of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints;

[0010] According to the constraints, the load data in the industrial park is used as the initial condition, the design parameters and meteorological data are input, and the hydrogen blending ratio is used as the decision variable to build an integrated energy system optimization model and determine the objective function; with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, the objective function is solved and the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is output;

[0011] The Pareto solution set is sorted by multiple objectives, the optimal solution corresponding to the decision variables is screened out, and the real-time power status of each device corresponding to the optimal solution is obtained.

[0012] Preferably, the establishment of a mathematical model of each device in the integrated energy system specifically includes:

[0013] A mathematical model is established for various devices in the combined cooling, heating and power microgrid, and the output power of the photovoltaic module is:

[0014] P pv =P pv,s ×G×[1+k pv (T pv -T pv,s )] / G s

[0015] Where P pv is the power output of photovoltaic power generation in period t, P pv,s is the maximum photovoltaic power under standard conditions, G is the actual solar radiation intensity during the period t, k pv is the power temperature coefficient of the photovoltaic module, T pv is the actual temperature of the PV module during the period t, T pv,s Indicates the ambient temperature under standard conditions, G s Indicates the solar radiation intensity under standard conditions;

[0016] The mathematical model of hydrogen production and storage system includes the mathematical model of hydrogen production by electrolysis and the mathematical model of hydrogen storage tank;

[0017] The constructed mathematical model of hydrogen production by electrolysis is:

[0018]

[0019] 0≤P EL ≤P EL,max

[0020] |P EL (t)-P EL(t-1)|≤P EL,up

[0021] In the formula, is the amount of hydrogen produced at time t, P EL,up is the upper limit of the electrolytic cell’s climbing capacity, P EL,max is the upper limit of the power of the electrolyzer;

[0022] The mathematical model of the hydrogen storage tank is constructed as follows:

[0023]

[0024] E c,min ≤E h (t)≤E c,max

[0025]

[0026] In the formula, E t is the storage capacity of the hydrogen storage tank at time t, E c,min and E c,max are the minimum and maximum capacities of the hydrogen storage tank, and are the amount of hydrogen charged and discharged at the current moment, and are the hydrogen charging and discharging efficiency, and are the upper limits of hydrogen charging and discharging, is the hydrogen production of the electrolyzer at time t, V GT is the amount of gas consumed in period t;

[0027] The mathematical model of gas turbine flue gas waste heat is constructed as follows:

[0028] Q GT =V GT η GT ×Q LV ×(1-η GT -μ) / η GT

[0029] In the formula, Q GT is the flue gas waste heat generated by the gas turbine during period t, V GT is the amount of gas consumed in period t, η GT is the gas turbine efficiency, μ is the heat loss coefficient, Q LV It is the calorific value of combustion, which will change with the change of hydrogen blending ratio;

[0030] Among them, the total combustion calorific value of hydrogen-blended natural gas is:

[0031] Q LV =α×Q Hmin +(1-α)×Qgasmin

[0032] In the formula, Q Hmin is the lower calorific value of hydrogen, Q gasmin is the lower calorific value of natural gas;

[0033] The mathematical model of the constructed absorption refrigerator is:

[0034] Q AC =Q GT ×μ cool ×C COP,AC

[0035] In the formula, Q AC is the cooling power of the absorption refrigerator in time period t, μ cool is the proportion of waste heat output used for cooling, C COP,AC is the coefficient of performance of the absorption refrigeration unit, Q GT is the residual heat of flue gas;

[0036] The mathematical model of the constructed heat exchanger is:

[0037] Q HE =Q GT ×μ heat ×η HE

[0038] In the formula, Q HE is the cooling power of the heat exchanger in time period t, μ heat is the proportion of waste heat output power used for heating, η HE is the heat transfer efficiency of the heat exchanger;

[0039] The mathematical model of the constructed electric refrigerator is:

[0040] Q EC =W EC ×COP EC

[0041] In the formula, Q EC is the cooling power in period t, W EC is the input power of the electric refrigerator, COP EC is the refrigeration coefficient of the electric refrigerator;

[0042] The mathematical model of the gas boiler constructed is:

[0043] Q GB =Q in ×η GB

[0044] In the formula, Q GB is the heating power of the boiler during period t, Q inThe input energy provided by the gas boiler fuel, η GB It is the heating efficiency of the gas boiler.

[0045] Preferably, the constraint conditions of the mathematical model for determining the energy balance constraint, the equipment capacity constraint, and the equipment operation constraint specifically include:

[0046] Energy balance constraints include power balance constraints, cooling balance constraints, and heat balance constraints, where:

[0047] The power balance constraint equation is:

[0048] P GT +P pv -P EL -P ch +P dis +P grid =L ele ;

[0049] Where P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, P grid Indicates the amount of electricity purchased from the power grid, P GT represents the power generation of the gas turbine;

[0050] The cold equilibrium constraint equation is:

[0051] Q AC +Q EC =L cool

[0052] In the formula, Q AC and Q EC Represent the cooling power of absorption refrigerator and electric refrigerator respectively, L cool Indicates the cooling load of the system;

[0053] The thermal balance constraint equation is:

[0054] Q GB +Q HE =L hot

[0055] Among them, Q GB and Q HE are the heating power of central heating and heat exchanger, L hot Indicates the heat load of the system;

[0056] The output constraint equation of the device is:

[0057] P i,t,min ≤P i,t ≤P i,t,max

[0058] P f,t,min ≤P f,t ≤P f,t,max

[0059] Where P i,t and P f,t represents the current output of non-schedulable equipment and schedulable equipment, P i,t,min and P i,t,max Represent the maximum and minimum output values ​​of non-schedulable equipment, P f,t,min and P f,t,max They respectively represent the scheduling interval of the effective output of the scheduling equipment;

[0060] The rise constraint equation for the device is:

[0061]

[0062] Where P i,t and P i,t-1 Respectively represent the power of the device at the current time and the previous time point. and They represent the maximum power change allowed to increase or decrease per unit time of the device respectively;

[0063] The battery constraint equation for the energy storage system is:

[0064]

[0065] O EES.ch.min U EES.ch (t)≤P EES.ch (t)≤P EES.ch.max U EES.ch (t)

[0066] P EES . dis.min U EES.dis (t)≤P EES.dis (t)≤P EES.dis.max U EES.dis (t)

[0067] Where P EES.ch.min and P EES.ch.max represent the minimum and maximum power levels for charging the storage battery, respectively, while P EES.dis.min and P EES.dis.max They represent the minimum and maximum power levels of the storage battery discharge, E represents the maximum capacity of the storage battery, and η ech and η e.ds Respectively represent the charging and discharging coefficients of the storage battery, U EES.ch (t) and U EES.dis(t) represent the charging and discharging status flags of the storage battery, respectively. When the value is 0, it means off, and when the value is 1, it means on.

[0068] Preferably, the step of constructing an integrated energy system optimization model and determining an objective function specifically includes:

[0069] The total annual cost equation is:

[0070] F1=minATC=C in +C grid +C gas +C om +C EES +C h

[0071] In the formula, G in represents the initial investment cost, C gird , C gas and C om They represent the system’s electricity purchase cost, gas purchase cost, and operation and maintenance cost, respectively. EES and C h denote the costs of electricity storage and hydrogen storage, respectively;

[0072] Among them, the system's electricity purchase cost equation is:

[0073]

[0074] P grid =L ele -P GT -P pv +P EL +P ch -P dis

[0075] Where P grid Indicates the power purchased by the system from the grid in real time, L ele represents the total electrical load of other equipment in the electrolyzer system, α buy represents the cost of the power purchased by the system from the grid in real time, P EL is the real-time power consumption of the electrolytic cell, P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, P GT represents the power generation of the gas turbine;

[0076] The cost equation for natural gas is:

[0077]

[0078] Where P GI represents the real-time operating power of the system gas turbine, βGI represents the power coefficient of the gas turbine, β buy represents the price of natural gas, U GI Indicates the start / stop status flag of the gas turbine. When the value is 0, it means it is off, and when the value is 1, it means it is on.

[0079] The system operation and maintenance cost equation is:

[0080]

[0081] In the formula, C i represents the operation and maintenance cost of each device in the system, P i Indicates the real-time operating power of each device in the system;

[0082] The cost equation for producing hydrogen is:

[0083]

[0084] In the formula, C h represents the cost of producing hydrogen, γ EL represents the cost of producing hydrogen in the electrolytic cell, η EL is the electrolysis efficiency of the electrolytic cell;

[0085] The carbon emission equation of the system is:

[0086] F2=minCDE=G gas +G grid

[0087] G gas =(1-α)×Q fuel ×EF gas

[0088] In the formula, CDE represents the total carbon emissions of the system, G gas represents the carbon emissions caused by natural gas consumption in the system, Q fuel is the total fuel demand, EF gas is the carbon emission coefficient of natural gas, α is the hydrogen blending ratio, G grid Indicates the carbon emissions caused by electricity purchased from the grid;

[0089] The energy consumption of the system is the amount of natural gas consumed, and the equation for the amount of natural gas consumed is:

[0090] F3=minFEC=M CCHP +M burn

[0091] M burn =(1-α)×Q fuel

[0092] Where FEC is the total natural gas consumption, M CCHP represents the amount of natural gas consumed by the combined heating and power system, M burn It indicates the actual natural gas consumption used in the industrial combustion process after taking into account the hydrogen blending ratio.

[0093] Preferably, the output satisfies the Pareto solution set of minimum annual total cost, minimum carbon emissions and minimum energy consumption, specifically including:

[0094] The load data in the industrial park is used as the initial condition, and the solar radiation amplitude, ambient temperature, photovoltaic installed capacity, battery capacity and hydrogen storage tank capacity are input. The produced and stored hydrogen is mixed with natural gas in a certain proportion into the natural gas pipeline network to form hydrogen-blended natural gas for industrial combustion.

[0095] By judging whether the power of photovoltaic and battery for producing and storing hydrogen is sufficient, if it is sufficient, it is necessary to judge whether the cooling and heating loads of the system for burning hydrogen-blended natural gas meet the load demand. If it is satisfied, the hydrogen blending ratio is used as the decision variable, and the Pareto solution set that meets the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is found through a multi-objective pigeon flock optimization algorithm based on fuzzy approximation ideal solution.

[0096] Preferably, the multi-objective pigeon flock optimization algorithm based on fuzzy approximation ideal solution comprises the following steps:

[0097] By initializing the parameters of the multi-objective optimization pigeon swarm algorithm, the initial pigeon swarm is randomly generated, and a group of individual pigeons are randomly generated in the solution space. Each pigeon represents a solution, and the dimension of the solution is the same as the hydrogen doping ratio of the decision variable.

[0098] Generate solutions corresponding to the objective function values ​​of annual total cost, carbon emissions and energy consumption according to the range of hydrogen blending ratio;

[0099] Calculate the fitness of each pigeon and conduct fitness evaluation;

[0100] Through fuzzy logic, various factors in the decision-making problem are converted into fuzzy sets, and by calculating and analyzing the membership of these fuzzy sets, the influence of various factors is comprehensively considered to obtain an optimal or near-optimal decision-making plan for the hydrogen blending ratio;

[0101] Iterative optimization is performed through the pigeon swarm optimization algorithm to gradually improve the performance of individuals in the population until a Pareto solution set that satisfies the minimum annual total cost, minimum carbon emissions, and minimum energy consumption is found.

[0102] Preferably, the multi-objective sorting of the Pareto solution set comprises the following steps:

[0103] Multi-objective sorting is performed through fuzzy approximation to ideal solution, including:

[0104] Standardized decision matrix: Non-dimensionalize the values ​​of multiple objectives and compare objectives in different dimensions:

[0105]

[0106] In the formula, and denote the ideal solution and anti-ideal solution of the i-th objective respectively;

[0107] Calculate the S and R values:

[0108]

[0109] In the formula, S j To solve x j The sum of deviations from all targets, R j To solve x j Deviation from the worst target, ω i is the weight of each goal;

[0110] Calculate the comprehensive index Q value:

[0111]

[0112] In the formula, S * and S - Respectively represent the best and worst values ​​of S among all solutions, R * and R - They represent the best and worst values ​​of R among all solutions respectively, and v is the adjustment coefficient, which represents the overall deviation of the trade-off solution and the worst deviation of a single objective.

[0113] Preferably, obtaining the real-time power status of each device corresponding to the optimal solution specifically includes:

[0114] When the number of iterations reaches the maximum or the Pareto solution set does not change significantly in multiple iterations, the iteration is stopped, and the Pareto solution set is sorted by the fuzzy approximation ideal solution to obtain a solution with the highest priority as the final optimal solution;

[0115] Through the fuzzy decision-making method, according to the ranking results of the fuzzy approximation ideal solution and the decision maker's preference, one solution or multiple optimal solutions are selected as the final decision;

[0116] Based on the selected one or more optimal solutions as the final decision, the real-time power status and hydrogen blending ratio of each device corresponding to the optimal solution are output.

[0117] Preferably, the acquisition of design parameters of the industrial park integrated energy system equipment, meteorological data, and load data within the industrial park includes:

[0118] The integrated energy system equipment includes: photovoltaic power generation system, gas turbine, battery, heat exchanger, boiler, absorption chiller, electric chiller, electrolyzer and hydrogen storage tank;

[0119] The design parameters of the equipment include: installed capacity of photovoltaic panels, installed capacity of hydrogen storage tanks, and installed capacity of storage batteries;

[0120] Meteorological data include: ambient temperature and light radiation intensity;

[0121] The load data in the industrial park include typical daily load data in transition season, winter and summer;

[0122] The typical daily load data is used as the initial condition, the installed capacity of photovoltaic panels, the installed capacity of hydrogen storage tanks, and the installed capacity of storage batteries are input, and the hydrogen blending ratio is used as the decision variable to construct an integrated energy system optimization model.

[0123] Preferably, it also includes an industrial park comprehensive energy system optimization system, including:

[0124] Data acquisition module, used to obtain the design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park;

[0125] The system optimization model building module is used to establish the mathematical model of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints;

[0126] The objective function solving module is used to construct an integrated energy system optimization model and determine the objective function based on the constraints, taking the load data in the industrial park as the initial conditions, inputting the design parameters and meteorological data, and taking the hydrogen blending ratio as the decision variable; solving the objective function with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, and outputting the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption;

[0127] The optimization result output module is used to perform multi-objective sorting on the Pareto solution set, screen out the optimal solution corresponding to the decision variables, and obtain the real-time power status of each device corresponding to the optimal solution.

[0128] Compared with the prior art, the present invention has the following beneficial effects:

[0129] The integrated energy system optimization method for industrial parks proposed in the present invention constructs an integrated energy system optimization model, which uses the local meteorological data, design parameters and load data of the industrial park as input variables, and uses the hydrogen blending ratio as a decision variable, so as to adjust the use ratio of hydrogen energy under different operating conditions, thereby achieving the best energy efficiency and environmental protection effect, and ensuring that the system design can adapt to the specific environment and needs. The optimization model is set to minimize the annual total cost, annual carbon emissions and energy consumption, and performs decision optimization, outputs the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption, and performs multi-objective sorting on the Pareto solution set, and screens out the optimal solution corresponding to the decision variable, that is, obtains a solution with the highest priority as the final optimal solution, obtains the real-time power status of each device corresponding to the optimal solution, and enables the system to adapt to environmental changes to achieve the optimization of the system's annual total cost, annual carbon emissions and energy consumption, ensuring that while achieving economic benefits, it can also meet environmental protection requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0130] Figure 1 A schematic diagram of the process flow of the industrial park integrated energy system optimization method proposed by the present invention;

[0131] Figure 2 A flowchart of the algorithm of the industrial park comprehensive energy system optimization method proposed by the present invention;

[0132] Figure 3 The equipment composition of the comprehensive energy system for an industrial park provided in an embodiment of the present invention and a diagram of its electricity, heat, gas and cold transmission system. DETAILED DESCRIPTION

[0133] The following will be combined with the attached embodiment of the present invention Figure 1-Figure 3 , the technical solutions in the embodiments of the present invention are clearly and completely described. It should be understood that the terms described in the present invention are only used to describe specific implementation methods and are not used to limit the present invention.

[0134] Example

[0135] like Figure 1 As shown, the present invention proposes an industrial park comprehensive energy system optimization method, comprising the following steps:

[0136] S1: Obtain the design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park;

[0137] S2: Establish mathematical models of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints;

[0138] S3: Based on the constraints, the load data in the industrial park is used as the initial condition, the design parameters and meteorological data are input, and the hydrogen blending ratio is used as the decision variable to determine the objective function of the annual total cost, carbon emissions and energy consumption of the integrated energy system; with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, the objective function is solved and the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is output;

[0139] S4: Perform multi-objective sorting on the Pareto solution set, screen out the optimal solution corresponding to the decision variables, and obtain the real-time power status of each device corresponding to the optimal solution.

[0140] The comprehensive energy system equipment of the industrial park includes photovoltaic power generation systems, gas turbines, batteries, heat exchangers, boilers, absorption chillers, electric chillers, electrolyzers, hydrogen storage tanks and other equipment; the energy management system is used to achieve coordinated control and energy optimization among equipment.

[0141] Specifically, the meteorological data (photovoltaic data) in step S1 mainly include: ambient temperature, solar radiation amplitude. Equipment design parameters mainly include: installed capacity of photovoltaic panels, installed capacity of hydrogen storage tanks, and installed capacity of storage batteries. Load data in the industrial park includes typical daily load data in transition season, winter and summer.

[0142] like Figure 3 As shown, the present invention divides the components of the comprehensive energy system of the hydrogen-blended natural gas industrial park into four modules, namely the upper energy supply unit, the coupling device unit, the energy storage device unit and the final energy unit. The upper energy supply unit: the national grid, the photovoltaic power generation system, the natural gas supply network; the coupling device unit: the gas turbine, the boiler, the heat exchanger; the energy storage device unit: the battery, the hydrogen storage tank; the final energy unit: the electrolyzer, other users.

[0143] The equipment mentioned in step S2 mainly includes: photovoltaic power generation system, gas turbine, battery, heat exchanger, boiler, absorption chiller, electric chiller, electrolyzer, hydrogen storage tank, etc.

[0144] In a combined cooling, heating and power microgrid, electricity is primarily provided by photovoltaic power generation systems and gas turbines, followed by batteries. At the same time, the microgrid can balance the demand for electricity within the microgrid through the power purchase and sale relationship with the distribution network, thereby realizing the power supply to the electrical load, thus forming a way of electricity flow. Gas turbines generate a large amount of waste heat during operation, which can be recovered and converted by absorption refrigerators and heat exchangers to become usable cold energy and heat energy. When absorption refrigerators and heat exchangers cannot meet the demand for cold energy and heat energy within the micro-cold and micro-heat networks, electric refrigerators and centralized heating can be used to supplement the lack of cold energy and heat energy in the micro-cold and micro-heat networks, thereby forming a flow of cold energy and heat energy. Hydrogen is produced by electrolyzers and mixed in the natural gas pipeline network. Natural gas comes from gas companies, forming the flow of hydrogen-blended natural gas.

[0145] Establish mathematical models of each device in the integrated energy system, including:

[0146] The output power of the photovoltaic module is:

[0147] P pv =P pv,s ×G×[1+k pv (T pv -T pv,s )] / G s

[0148] Where P pv is the power output of photovoltaic power generation in period t, P pv,s is the maximum photovoltaic power under standard conditions, G is the actual solar radiation intensity during the period t, k pv is the power temperature coefficient of the photovoltaic module, T pv is the actual temperature of the PV module during the period t, T pv,s Indicates the ambient temperature under standard conditions, generally 25°C, G s Indicates the solar radiation intensity under standard conditions, taking 1000W / m 2 .

[0149] The mathematical model of hydrogen production by electrolysis is constructed as follows:

[0150]

[0151] 0≤P EL ≤P EL,max

[0152] |P EL (t)-P EL (t-1)|≤P EL,up

[0153] In the formula, is the amount of hydrogen produced at time t, P EL is the real-time power consumption of the electrolytic cell, P EL,up is the upper limit of the electrolytic cell’s climbing capacity, η EL is the electrolysis efficiency of the electrolytic cell, P EL,max is the upper limit of the electrolyzer power.

[0154] The mathematical model of the hydrogen storage tank is constructed as follows:

[0155]

[0156] E c,min ≤E h (t)≤E c,max

[0157]

[0158] In the formula, E t is the storage capacity of the hydrogen storage tank at time t, E c,min and E c,max are the minimum and maximum capacities of the hydrogen storage tank, and are the amount of hydrogen charged and discharged at the current moment, and are the hydrogen charging and discharging efficiency, and are the upper limits of hydrogen charging and discharging, is the hydrogen production of the electrolyzer at time t, V GT is the amount of gas consumed during period t.

[0159] The mathematical model of gas turbine flue gas waste heat is constructed as follows:

[0160] Q GT =V GT η GT ×Q LV ×(1-η GT -μ) / η GT

[0161] In the formula, Q GT is the flue gas waste heat generated by the gas turbine during period t, V GT is the amount of gas consumed in period t, η GT is the gas turbine efficiency, μ is the heat loss coefficient, Q LV It is the calorific value of combustion, which will change with the change of hydrogen blending ratio.

[0162] Among them, the total combustion calorific value of hydrogen-blended natural gas is:

[0163] Q LV =α×Q Hmin +(1-α)×Q gasmin

[0164] In the formula, Q Hmin is the lower calorific value of hydrogen, Q gasmin It is the lower calorific value of natural gas.

[0165] The basic principle of refrigeration by absorbing waste heat is to use the chemical properties of lithium bromide and water. During the refrigeration process, the lithium bromide aqueous solution absorbs heat from the heat source, so that the water vapor in the solution is excited, thereby producing a cooling effect. This process not only improves the efficiency of energy utilization, but also effectively converts low-grade heat sources into useful cold energy.

[0166] The mathematical model of the constructed absorption refrigerator is:

[0167] Q AC =Q GT ×μ cool ×C COP,AC

[0168] In the formula, Q AC is the cooling power of the absorption refrigerator in time period t, μ cool is the proportion of waste heat output used for cooling, C COP,AC is the coefficient of performance of the absorption refrigeration unit, Q GT is the waste heat of flue gas.

[0169] The mathematical model of the constructed heat exchanger is:

[0170] Q HE =Q GT ×μ heat ×η HE

[0171] In the formula, Q HE is the cooling power of the heat exchanger in time period t, μ heat is the proportion of waste heat output power used for heating, η HE is the heat transfer efficiency of the heat exchanger.

[0172] The mathematical model of the constructed electric refrigerator is:

[0173] Q EC =W EC ×COP EC

[0174] In the formula, Q EC is the cooling power in period t, W EC is the input power of the electric refrigerator, COP EC is the cooling coefficient of the electric refrigerator.

[0175] The mathematical model of the gas boiler constructed is:

[0176] Q GB =Q in ×η GB

[0177] In the formula, Q GB is the heating power of the boiler during period t, Q in The input energy provided by the gas boiler fuel, η GB It is the heating efficiency of the gas boiler.

[0178] Determine the constraints of the mathematical model of energy balance constraints, equipment capacity constraints, and equipment operation constraints. The energy balance constraints include power balance constraints, cold balance constraints, and heat balance constraints, among which:

[0179] The power balance constraint equation is:

[0180] P GT +P pv -P EL -P ch +P dis +P grid =L ele ;

[0181] Where P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, L ele Indicates the electrical load of the system except the electrolytic cell, P grid Indicates the amount of electricity purchased from the power grid, P GT Indicates the power generation of the gas turbine.

[0182] The cold equilibrium constraint equation is:

[0183] Q AC +Q EC =L cool

[0184] In the formula, Q AC and Q EC Represent the cooling power of absorption refrigerator and electric refrigerator respectively, L cool Represents the cooling load of the system.

[0185] The thermal balance constraint equation is:

[0186] Q GB +Q HE =L hot

[0187] Among them, Q GB and Q HE are the heating power of central heating and heat exchanger, L hot Represents the heat load of the system.

[0188] The output constraint equation of the device is:

[0189] P i,t,min ≤P i,t ≤P i,t,max

[0190] P f,t,min ≤P f,t ≤P f,t,max

[0191] Where P i,t and P f,t represents the current output of non-schedulable equipment and schedulable equipment, P i,t,min and P i,t,max Represent the maximum and minimum output values ​​of non-schedulable equipment, P f,t,min and P f,t,max They respectively represent the scheduling interval of effective output of the scheduling equipment.

[0192] The rise constraint equation for the device is:

[0193]

[0194] Where P i,t and P i,t-1 Respectively represent the power of the device at the current time and the previous time point. and Respectively represent the maximum power change allowed to increase or decrease per unit time of the device.

[0195] Batteries store electricity and discharge it during peak hours to reduce the amount of electricity purchased by the grid, thereby reducing the total cost of the grid. Although the battery's charging and discharging power is reflected in the power balance equation, the expression for the grid's purchased power needs to be optimized.

[0196] The battery constraint equation for the energy storage system is:

[0197]

[0198] P EES.ch.min U EES.ch (t)≤P EES.ch (t)≤P EES.ch.max U EES.ch (t)

[0199] P EES.dis.min U EES.dis (t)≤P EES.dis (t)≤P EES.dis.max U EES.dis (t)

[0200] Where P EES.ch.min and P EES.ch.maxrepresent the minimum and maximum power levels for charging the storage battery, respectively, while P EES.dis.min and P EES.dis.max They represent the minimum and maximum power levels of the storage battery discharge, E represents the maximum capacity of the storage battery, and η ech and η e.ds Respectively represent the charging and discharging coefficients of the storage battery, U EES.ch (t) and U EES.dis (t) represent the charging and discharging status flags of the storage battery, respectively. When the value is 0, it means off, and when the value is 1, it means on.

[0201] In step S3, the objective function of the integrated energy system is determined, where:

[0202] The first objective function is the annual total cost of the integrated energy system (F1). The annual total cost includes the investment cost of each equipment, the cost of purchasing electricity and gas from the power grid, operation and maintenance costs, and the cost of electricity and hydrogen storage of the system.

[0203] The second objective function is the carbon emissions of the integrated energy system (F2), which is used to monitor and control CO2 emissions. The carbon emissions of the system consist of two parts, one is generated by the consumption of natural gas, and the other is generated by the purchase of electricity from the power grid.

[0204] The third objective function is the energy consumption of the comprehensive energy system (F3), that is, the natural gas consumption. The energy consumption of the system consists of two parts, one is the natural gas consumed by industrial combustion, and the other is the natural gas consumed by the combined heating, cooling and power system.

[0205] In order to optimize effectively, the hydrogen blending ratio is set as a decision variable so that the proportion of hydrogen energy can be adjusted under different operating conditions to achieve the best energy efficiency and environmental protection effect. Based on these inputs and goals, a system design optimization model is established to determine the optimal operation strategy of the system through constraints to ensure that economic benefits are achieved while meeting environmental protection requirements.

[0206] The constraints of the optimization design model of this system mainly include three parts. The first part is the energy balance constraint, including the grid balance constraint, the heat network balance constraint, and the cold network balance constraint; the second part is the equipment capacity constraint, including the maximum output power and the minimum output power of the equipment; the third part is the equipment operation constraint, also called the ramp constraint, which is expressed as the maximum power and minimum power allowed to be output by each device per unit time. At the same time, with the increase of the hydrogen blending ratio, hydrogen-blended natural gas is more likely to explode and burn, while the calorific value of hydrogen-blended natural gas gradually decreases with the increase of the hydrogen blending ratio. In order to ensure the safety of natural gas after hydrogen blending and to ensure that hydrogen-blended natural gas can have a higher calorific value, the hydrogen blending ratio of the system is designed to be between 0 and 20%.

[0207] The total annual cost equation is:

[0208] F1=minATC=C in +C grid +C gas +C om +C EES +C h

[0209] In the formula, C in represents the initial investment cost, C grid , C gas and C om They represent the system’s electricity purchase cost, gas purchase cost, and operation and maintenance cost, respectively. EES and C h denote the costs of electricity storage and hydrogen storage, respectively.

[0210] Among them, the system's electricity purchase cost equation is:

[0211]

[0212] P grid =L ele -P GT -P pv +P EL +P ch -P dis

[0213] Where P grid Indicates the power purchased by the system from the grid in real time, P EL is the real-time power consumption of the electrolytic cell, α buy represents the cost of the power purchased by the system from the grid in real time, P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, P GT Indicates the power generation of the gas turbine.

[0214] The cost equation for natural gas is:

[0215]

[0216] Where P GI represents the real-time operating power of the system gas turbine, β GI represents the power coefficient of the gas turbine, β buy represents the price of natural gas, U GI Indicates the start / stop status flag of the gas turbine. When the value is 0, it means it is off, and when the value is 1, it means it is on.

[0217] The system operation and maintenance cost equation is:

[0218]

[0219] In the formula, C i Indicates the operation and maintenance cost of each device in the system, in US dollars ($). i Indicates the real-time operating power of each device in the system.

[0220] The cost equation for producing hydrogen is:

[0221]

[0222] In the formula, C h represents the cost of producing hydrogen, γ EL Represents the cost of producing hydrogen in an electrolyser.

[0223] The carbon emission equation of the system is:

[0224] F2=minCDE=G gas +G grid

[0225] G gas =(1-α)×Q fuel ×EF gas

[0226] In the formula, CDE represents the total carbon emissions of the system, G gas represents the carbon emissions caused by natural gas consumption in the system, Q fuel is the total fuel demand, EF gas is the carbon emission coefficient of natural gas; G grid Represents the carbon emissions caused by electricity purchased from the grid.

[0227] The energy consumption of the system is the amount of natural gas consumed, and the equation for the amount of natural gas consumed is:

[0228] F3=minFEC=M CCHP +M burn

[0229] M burn =(1-α)×Q fuel

[0230] Where FEC is the total natural gas consumption, M CCHP represents the amount of natural gas consumed by the combined heating and power system, M burn It indicates the actual natural gas consumption used in the industrial combustion process after taking into account the hydrogen blending ratio.

[0231] The present invention adopts a multi-objective pigeon swarm optimization algorithm (MOPIO) based on a fuzzy-VIKOR method to optimize the integrated energy system of a hydrogen-blended natural gas industrial park, and establishes an integrated energy system design optimization model.

[0232] like Figure 2 As shown in the figure, in order to optimize the annual total cost, annual carbon emissions and energy consumption, the multi-objective pigeon flock optimization algorithm (MOPIO) based on the fuzzy approximation ideal solution (Fuzzy-VIKOR) is used to solve. The load data in the industrial park is used as the initial condition, and the solar illumination amplitude, ambient temperature, photovoltaic installed capacity, battery capacity and hydrogen storage tank capacity are input. The produced and stored hydrogen is mixed with the natural gas pipeline network and natural gas in a certain proportion to form hydrogen-blended natural gas for industrial combustion; by judging whether the photovoltaic and battery power of the hydrogen produced and stored is sufficient, if not, it is necessary to purchase electricity from the power grid; if it is sufficient, it is necessary to judge whether the cold and hot loads of the combustion system of the hydrogen-blended natural gas meet the load demand, and if not, it is necessary to start the auxiliary equipment; if it is satisfied, the hydrogen blending ratio is used as the decision variable, and the Pareto solution set that meets the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is found through the multi-objective pigeon flock optimization algorithm (MOPIO) based on the fuzzy approximation ideal solution.

[0233] According to the hourly load data of the multi-energy complementary integrated energy system of the industrial park, representative typical days in winter, summer and transition seasons were selected for analysis. The system introduces a time-of-use electricity price policy to facilitate and guide users to use electricity stably and safely, and play a role in shaving peaks and filling valleys. The system also introduces a carbon trading mechanism. Users can compare the carbon emissions calculated by the system with the carbon trading quota and make a difference, which is a part of the benefits obtained by reducing carbon emissions. By analyzing and processing the load data of these typical days, the capacity configuration model of the multi-energy complementary integrated energy system of the industrial park is solved. Until the number of loop iterations reaches the maximum set value, the Pareto solution set is output, and the final solution is determined by the fuzzy decision method.

[0234] Output the Pareto solution set that satisfies the minimum annual total cost, minimum carbon emissions, and minimum energy consumption, including:

[0235] The Pareto solution set is solved by the Multi-Objective Pigeon Optimization Algorithm (MOPIO) based on the Fuzzy-VIKOR method. The Multi-Objective Pigeon Optimization Algorithm (MOPIO) is an optimization algorithm inspired by the navigation behavior of pigeons in nature. It simulates the navigation mechanism used by pigeons during migration, combines swarm intelligence and multi-objective optimization strategies, and thus finds a set of solutions that can achieve a compromise effect on multiple objectives.

[0236] Specifically, by initializing the parameters of the multi-objective optimization pigeon swarm algorithm, the population size is set to 200; the maximum number of iterations is 1000; the setting range of the decision variable hydrogen doping ratio is 0-20%; for the setting of the hydrogen doping ratio step, its initial value is 1%, and 0.1% step and 0.01% step are used in turn for optimization calculation to find the appropriate hydrogen doping ratio.

[0237] The initial pigeon group is randomly generated, and a group of individual pigeons are randomly generated in the solution space. Each pigeon represents a solution, and the dimension of the solution is the same as the decision variable hydrogen blending ratio; according to the range of hydrogen blending ratio, the solution corresponding to the annual total cost, carbon emissions and energy consumption objective function values ​​is generated. The optimization variables corresponding to the solution mainly include total cost, carbon emissions, and natural gas consumption, which will change according to the change of hydrogen blending ratio.

[0238] To evaluate the fitness, each pigeon position is represented by a solution vector x = [x1, x2, ..., x n ], each solution is evaluated by the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption; the three optimization objectives of annual total cost, annual carbon emissions and energy consumption are comprehensively considered, and the fitness of each pigeon is calculated according to the Pareto solution set advantage of the solution.

[0239] Through fuzzy logic, various factors in the decision-making problem are converted into fuzzy sets, and by calculating and analyzing the membership of these fuzzy sets and comprehensively considering the influence of various factors, an optimal or near-optimal decision-making plan for the hydrogen blending ratio is obtained.

[0240] Through iterative optimization by pigeon swarm optimization algorithm, the performance of individuals in the population is gradually improved until the Pareto solution set of the optimization problem is found. In the iterative optimization process, the pigeon swarm algorithm mainly includes compass operation, map operation, and Pareto dominance, among which:

[0241] The compass operation is mainly used to update the direction of individual pigeons. According to the current position and flight direction of the pigeon, the next direction of each pigeon is guided by its historical optimal position and the optimal solution in the group. The compass operation mainly simulates the behavior of pigeons adjusting their directions according to external reference objects, and optimizes the iterative direction according to the following formula:

[0242]

[0243] In the formula, represents the current position of the i-th pigeon in the t-th generation, represents the global optimal solution, and a represents the parameter for adjusting the step size.

[0244] The map operation is mainly to update the global position of individual pigeons. The pigeons will update their positions according to the positions in their memory (i.e. the historical optimal solution) to enhance the global search capability. The position of each pigeon is updated as follows:

[0245]

[0246] Where λ is the attenuation coefficient, e -λ·t It is an exponential decay function. As time goes by, the degree to which the pigeon relies on memory gradually decreases, thus ensuring the convergence of the algorithm and prompting the pigeon to gradually converge to the global optimal solution.

[0247] Pareto dominance is the comparison between individual pigeons in multi-objective optimization using the Pareto dominance relationship. For example, the condition for solution x1 to dominate solution x2 is: solution x1 is better than or equal to solution x2 in all objectives, and x1 is strictly better than x2 in at least one objective. Non-dominated solutions are stored in external archives to ensure the diversity of solutions, and the Pareto solution set of the current iteration is recorded to screen out the Pareto solution set composed of non-dominated solutions in the pigeon group.

[0248] In step S4, the Pareto solution set is sorted by multiple objectives using the fuzzy approximation ideal solution method, including the following steps:

[0249] Standardized decision matrix: Non-dimensionalize the values ​​of multiple objectives and compare objectives in different dimensions:

[0250]

[0251] In the formula, and They represent the ideal solution and anti-ideal solution of the i-th objective respectively.

[0252] Calculate the S and R values:

[0253]

[0254]

[0255] In the formula, S j To solve x j The sum of deviations from all targets, R j To solve x j Deviation from the worst target, ω i The weight for each goal.

[0256] Calculate the comprehensive index Q value:

[0257]

[0258] In the formula, S * and S- Respectively represent the best and worst values ​​of S among all solutions, R * and R - They represent the best and worst values ​​of R among all solutions, respectively. v is the adjustment coefficient, which is set to 0.5, indicating the trade-off between the overall deviation of the solution and the worst deviation of a single objective.

[0259] When the number of iterations reaches the maximum or the Pareto solution set does not change significantly in multiple iterations, the iteration is stopped, and the Pareto solution set is sorted by the fuzzy approximation ideal solution to obtain a solution with the highest priority as the final optimal solution. Through the fuzzy decision-making method, according to the sorting results of the fuzzy approximation ideal solution and the decision maker's preference, one solution or multiple optimal solutions are selected as the final decision, and the real-time power status and hydrogen blending ratio of each device corresponding to the optimal solution are output.

[0260] The present invention also proposes an industrial park comprehensive energy system optimization system, which is characterized by comprising:

[0261] Data acquisition module, used to obtain the design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park;

[0262] The system optimization model building module is used to establish the mathematical model of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints;

[0263] The objective function solving module is used to determine the objective function of the annual total cost, carbon emissions and energy consumption of the integrated energy system based on the constraints, taking the load data in the industrial park as the initial conditions, inputting the design parameters and meteorological data, and taking the hydrogen blending ratio as the decision variable; solving the objective function with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, and outputting the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption;

[0264] The optimization result output module is used to perform multi-objective sorting on the Pareto solution set, screen out the optimal solution corresponding to the decision variables, and obtain the real-time power status of each device corresponding to the optimal solution.

[0265] The integrated energy system optimization method proposed in the present invention constructs an integrated energy system optimization model that takes the local sunshine amplitude, hourly temperature changes, and hourly cold, hot, and electric load types of the industrial park as input variables to ensure that the system design can adapt to the specific environment and needs. These input data will be used to formulate optimization strategies to minimize the annual total cost, annual carbon emissions, and energy consumption (F1, F2, F3) as the main optimization goals, and perform multi-objective sorting on the output Pareto solution set, screen out the optimal solution corresponding to the decision variables, and obtain the real-time power status of each device corresponding to the optimal solution to improve the overall efficiency and reliability of the system, so that the system can adapt to environmental changes to achieve the optimization of the system's annual total cost, annual carbon emissions, and energy consumption.

[0266] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0267] In addition, unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as commonly understood by those skilled in the art to which the present invention belongs. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

Claims

1. A method for optimizing the integrated energy system of an industrial park, characterized in that: The following steps are involved: Obtain design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park; Establish mathematical models of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints; According to the constraints, the load data in the industrial park is used as the initial condition, the design parameters and meteorological data are input, and the hydrogen blending ratio is used as the decision variable to build an integrated energy system optimization model and determine the objective function; with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, the objective function is solved and the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is output; The Pareto solution set is sorted by multiple objectives, the optimal solution corresponding to the decision variables is screened out, and the real-time power status of each device corresponding to the optimal solution is obtained.

2. The method for optimizing the integrated energy system of an industrial park according to claim 1, characterized in that: The establishment of mathematical models of various devices in the comprehensive energy system specifically includes: A mathematical model is established for various devices in the combined cooling, heating and power microgrid, and the output power of the photovoltaic module is: P pv =P pv,s ×G×[1+k pv (T pv -T pv,s )] / G s Where P pv is the power output of photovoltaic power generation in period t, P pv,s is the maximum photovoltaic power under standard conditions, G is the actual solar radiation intensity during the period t, k pv is the power temperature coefficient of the photovoltaic module, T pv is the actual temperature of the PV module during the period t, T pv,s Indicates the ambient temperature under standard conditions, G s Indicates the solar radiation intensity under standard conditions; The mathematical model of hydrogen production and storage system includes the mathematical model of hydrogen production by electrolysis and the mathematical model of hydrogen storage tank; The constructed mathematical model of hydrogen production by electrolysis is: 0≤P EL ≤P EL,max |P EL (t)-P EL (t-1)|≤P EL,up In the formula, is the amount of hydrogen produced at time t, P EL,up is the upper limit of the electrolytic cell’s climbing capacity, P EL,max is the upper limit of the power of the electrolyzer; The mathematical model of the hydrogen storage tank is constructed as follows: E c,min ≤E h (t)≤E c,max In the formula, E t is the storage capacity of the hydrogen storage tank at time t, E c,min and E c,max are the minimum and maximum capacities of the hydrogen storage tank, and are the amount of hydrogen charged and discharged at the current moment, and are the hydrogen charging and discharging efficiency, and are the upper limits of hydrogen charging and discharging, is the hydrogen production of the electrolyzer at time t, V GT is the amount of gas consumed in period t; The mathematical model of gas turbine flue gas waste heat is constructed as follows: Q GT =V GT or GT ×Q LV ×(1-n) GT -non GT In the formula, Q GT is the flue gas waste heat generated by the gas turbine during period t, V GT is the amount of gas consumed in period t, η GT is the gas turbine efficiency, μ is the heat loss coefficient, Q LV It is the calorific value of combustion, which will change with the change of hydrogen blending ratio; Among them, the total combustion calorific value of hydrogen-blended natural gas is: Q LV =α×Q Hmin +(1-a)×Q gasmin In the formula, Q Hmin is the lower calorific value of hydrogen, Qgasmin is the lower calorific value of natural gas; The mathematical model of the constructed absorption refrigerator is: Q AC =Q GT ×μ cool ×C COP,AC In the formula, Q AC is the cooling power of the absorption refrigerator in time period t, μ cool is the proportion of waste heat output used for cooling, C CoP,AC is the coefficient of performance of the absorption refrigeration unit, Q GT is the residual heat of flue gas; The mathematical model of the constructed heat exchanger is: Q HE =Q GT ×μ heat ×η HE In the formula, Q HE is the cooling power of the heat exchanger in time period t, μ heat is the proportion of waste heat output power used for heating, η HE is the heat transfer efficiency of the heat exchanger; The mathematical model of the constructed electric refrigerator is: Q EC =W EC ×COP EC In the formula, Q EC is the cooling power in period t, W EC is the input power of the electric refrigerator, COP EC is the refrigeration coefficient of the electric refrigerator; The mathematical model of the gas boiler constructed is: Q GB =Q in ×η GB In the formula, Q GB is the heating power of the boiler during period t, Q in The input energy provided by the gas boiler fuel, η GB It is the heating efficiency of the gas boiler.

3. The method for optimizing the integrated energy system of an industrial park according to claim 2, characterized in that: The constraint conditions of the mathematical model for determining the energy balance constraint, the equipment capacity constraint, and the equipment operation constraint specifically include: Energy balance constraints include power balance constraints, cooling balance constraints, and heat balance constraints, where: The power balance constraint equation is: P GT +P pv -P EL -P ch +P dis +P grid =L ele ; Where P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, P grid Indicates the amount of electricity purchased from the power grid, P GT represents the power generation of the gas turbine; The cold equilibrium constraint equation is: Q AC +Q EC =L cool In the formula, Q AC and Q EC Represent the cooling power of absorption refrigerator and electric refrigerator respectively, L cool Indicates the cooling load of the system; The thermal balance constraint equation is: Q GB +Q HE =L hot Among them, Q GB and Q HE are the heating power of central heating and heat exchanger, L hot Indicates the heat load of the system; The output constraint equation of the device is: P i，t，min ≤P i，t ≤P i，t，max P f，t，min ≤P f，t ≤P f，t，max Where P i，t and P f，t represents the current output of non-schedulable equipment and schedulable equipment, P i，t，min and P i，t，max Represent the maximum and minimum output values ​​of non-schedulable equipment, P f，t，min and P f，t，max They respectively represent the scheduling interval of the effective output of the scheduling equipment; The rise constraint equation for the device is: Where P i，t and P i，t-1 Respectively represent the power of the device at the current time and the previous time point. and They represent the maximum power change allowed to increase or decrease per unit time of the device respectively; The battery constraint equation for the energy storage system is: P EES.ch.min U EES.ch (t)≤P EES.ch (t)≤P EES.ch.max U EES.ch (t) P EES.dis.min U EES.dis (t)≤P EES.dis (t)≤P EES.dis.max U EES.dis (t) Where P EES.ch.min and P EES.ch.max represent the minimum and maximum power levels for charging the storage battery, respectively, while P EES.dis.min and P EES.dis，max They represent the minimum and maximum power levels of the storage battery discharge, E represents the maximum capacity of the storage battery, and η ech and η e.ds Respectively represent the charging and discharging coefficients of the storage battery, U EES.ch (t) and U EES.dis (t) represent the charging and discharging status flags of the storage battery, respectively. When the value is 0, it means off, and when the value is 1, it means on.

4. The method for optimizing the integrated energy system of an industrial park according to claim 3, characterized in that: The construction of the comprehensive energy system optimization model and determination of the objective function specifically include: The total annual cost equation is: F1=minATC=C in +C grid +C gas +C om +C EES +C h In the formula, C in represents the initial investment cost, C grid , C gas and C om They represent the system’s electricity purchase cost, gas purchase cost, and operation and maintenance cost, respectively. EES and C h denote the costs of electricity storage and hydrogen storage, respectively; Among them, the system's electricity purchase cost equation is: P grid =L ele -P GT -P pv +P EL +P ch -P dis Where P grid Indicates the power purchased by the system from the grid in real time, L ele represents the total electrical load of other equipment in the electrolyzer system, α buy represents the cost of the power purchased by the system from the grid in real time, P EL is the real-time power consumption of the electrolytic cell, P ch and P dis Respectively represent the charging and discharging power of the energy storage battery, P GT represents the power generation of the gas turbine; The cost equation for natural gas is: Where P GI represents the real-time operating power of the system gas turbine, β GI represents the power coefficient of the gas turbine, β buy represents the price of natural gas, U GI Indicates the start / stop status flag of the gas turbine. When the value is 0, it means it is off, and when the value is 1, it means it is on. The system operation and maintenance cost equation is: In the formula, C i represents the operation and maintenance cost of each device in the system, P i Indicates the real-time operating power of each device in the system; The cost equation for producing hydrogen is: In the formula, C h represents the cost of producing hydrogen, γ EL represents the cost of producing hydrogen in the electrolytic cell, η EL is the electrolysis efficiency of the electrolytic cell; The carbon emission equation of the system is: F2=minCDE=G gas +G grid G gas =(1-a)×Q fuel ×EF gas In the formula, CDE represents the total carbon emissions of the system, G gas represents the carbon emissions caused by natural gas consumption in the system, Q fuel is the total fuel demand, EF gas is the carbon emission coefficient of natural gas, α is the hydrogen blending ratio, G grid Indicates the carbon emissions caused by electricity purchased from the grid; The energy consumption of the system is the amount of natural gas consumed, and the equation for the amount of natural gas consumed is: F3=minFEC=M CCHP +M burn M burn =(1-a)×Q fuei Where FEC is the total natural gas consumption, M CCHP represents the amount of natural gas consumed by the combined heating and power system, M burn It indicates the actual natural gas consumption used in the industrial combustion process after taking into account the hydrogen blending ratio.

5. The method for optimizing the integrated energy system of an industrial park according to claim 4, characterized in that: The output satisfies the Pareto solution set of minimum annual total cost, minimum carbon emissions and minimum energy consumption, specifically including: The load data in the industrial park is used as the initial condition, and the solar radiation amplitude, ambient temperature, photovoltaic installed capacity, battery capacity and hydrogen storage tank capacity are input. The produced and stored hydrogen is mixed with natural gas in a certain proportion into the natural gas pipeline network to form hydrogen-blended natural gas for industrial combustion. By judging whether the power of photovoltaic and battery for producing and storing hydrogen is sufficient, if it is sufficient, it is necessary to judge whether the cooling and heating loads of the system for burning hydrogen-blended natural gas meet the load demand. If it is satisfied, the hydrogen blending ratio is used as the decision variable, and the Pareto solution set that meets the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption is found through a multi-objective pigeon flock optimization algorithm based on fuzzy approximation ideal solution.

6. The method for optimizing the integrated energy system of an industrial park according to claim 5, characterized in that: The multi-objective pigeon group optimization algorithm based on the fuzzy approximation ideal solution comprises the following steps: By initializing the parameters of the multi-objective optimization pigeon swarm algorithm, the initial pigeon swarm is randomly generated, and a group of individual pigeons are randomly generated in the solution space. Each pigeon represents a solution, and the dimension of the solution is the same as the hydrogen doping ratio of the decision variable. Generate solutions corresponding to the objective function values ​​of annual total cost, carbon emissions and energy consumption according to the range of hydrogen blending ratio; Calculate the fitness of each pigeon and conduct fitness evaluation; Through fuzzy logic, various factors in the decision-making problem are converted into fuzzy sets, and by calculating and analyzing the membership of these fuzzy sets, the influence of various factors is comprehensively considered to obtain an optimal or near-optimal decision-making plan for the hydrogen blending ratio; Iterative optimization is performed through the pigeon swarm optimization algorithm to gradually improve the performance of individuals in the population until a Pareto solution set that satisfies the minimum annual total cost, minimum carbon emissions, and minimum energy consumption is found.

7. The method for optimizing the integrated energy system of an industrial park according to claim 6, characterized in that: The multi-objective sorting of the Pareto solution set comprises the following steps: Multi-objective sorting is performed through fuzzy approximation to ideal solution, including: Standardized decision matrix: Non-dimensionalize the values ​​of multiple objectives and compare objectives in different dimensions: In the formula, and denote the ideal solution and anti-ideal solution of the i-th objective respectively; Calculate the S and R values: In the formula, S j To solve x j The sum of deviations from all targets, R j To solve x j Deviation from the worst target, ω i is the weight of each goal; Calculate the comprehensive index Q value: In the formula, S * and S - Respectively represent the best and worst values ​​of S among all solutions, R * and R - They represent the best and worst values ​​of R among all solutions respectively, and v is the adjustment coefficient, which represents the overall deviation of the trade-off solution and the worst deviation of a single objective.

8. The method for optimizing the integrated energy system of an industrial park according to claim 7, characterized in that: The obtaining of the real-time power status of each device corresponding to the optimal solution specifically includes: When the number of iterations reaches the maximum or the Pareto solution set does not change significantly in multiple iterations, the iteration is stopped, and the Pareto solution set is sorted by the fuzzy approximation ideal solution to obtain a solution with the highest priority as the final optimal solution; Through the fuzzy decision-making method, according to the ranking results of the fuzzy approximation ideal solution and the decision maker's preference, one solution or multiple optimal solutions are selected as the final decision; Based on the selected one or more optimal solutions as the final decision, the real-time power status and hydrogen blending ratio of each device corresponding to the optimal solution are output.

9. The method for optimizing the integrated energy system of an industrial park according to claim 1, characterized in that: The acquisition of design parameters of the industrial park comprehensive energy system equipment, meteorological data, and load data within the industrial park, wherein: The integrated energy system equipment includes: photovoltaic power generation system, gas turbine, battery, heat exchanger, boiler, absorption chiller, electric chiller, electrolyzer and hydrogen storage tank; The design parameters of the equipment include: installed capacity of photovoltaic panels, installed capacity of hydrogen storage tanks, and installed capacity of storage batteries; Meteorological data include: ambient temperature and light radiation intensity; The load data in the industrial park include typical daily load data in transition season, winter and summer; The typical daily load data is used as the initial condition, the installed capacity of photovoltaic panels, the installed capacity of hydrogen storage tanks, and the installed capacity of storage batteries are input, and the hydrogen blending ratio is used as the decision variable to construct an integrated energy system optimization model.

10. An industrial park integrated energy system optimization system, characterized in that: include: Data acquisition module, used to obtain the design parameters of the industrial park's integrated energy system equipment, meteorological data, and load data within the industrial park; The system optimization model building module is used to establish the mathematical model of each device in the integrated energy system and determine the constraints of energy balance constraints, equipment capacity constraints, and equipment operation constraints; The objective function solving module is used to construct an integrated energy system optimization model and determine the objective function based on the constraints, taking the load data in the industrial park as the initial conditions, inputting the design parameters and meteorological data, and taking the hydrogen blending ratio as the decision variable; solving the objective function with the goal of minimizing the annual total cost, carbon emissions and energy consumption of the integrated energy system, and outputting the Pareto solution set that satisfies the minimum annual total cost, the minimum carbon emissions and the minimum energy consumption; The optimization result output module is used to perform multi-objective sorting on the Pareto solution set, screen out the optimal solution corresponding to the decision variables, and obtain the real-time power status of each device corresponding to the optimal solution.

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