Collaborative optimization method for capacity configuration and operation control of electricity-heat-hydrogen-oxygen combined supply system

Abstract

The invention relates to the technical field of new energy collaborative optimization, and discloses a collaborative optimization method for capacity configuration and operation control of an electric heating hydrogen-oxygen combined supply system, which comprises the following steps: acquiring equipment capacity parameters of the electric heating hydrogen-oxygen combined supply system, and taking the maximum annual net income of the electric heating hydrogen-oxygen combined supply system as a target; constructing an upper-layer capacity configuration model based on the equipment capacity parameters; acquiring equipment operation parameters of the electric heating hydrogen oxygen combined supply system, and constructing a lower-layer operation control model based on the equipment operation parameters by taking the maximum single-day operation net income of the electric heating hydrogen oxygen combined supply system as a target; and solving a double-layer optimization model of the electric heating hydrogen-oxygen combined supply system formed by the upper-layer capacity configuration model and the lower-layer operation control model to obtain an optimal capacity configuration and operation control scheme. According to the invention, collaborative optimization of capacity configuration and operation control of each device in the electric heating hydrogen-oxygen combined supply system is realized, and the stability and reliability of the electric heating hydrogen-oxygen combined supply system in an uncertain environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy collaborative optimization, and in particular to a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system. Background Art

[0002] As energy demand increases year by year, the pressure for low-carbon energy is growing. Through the electric, thermal, hydrogen and oxygen coupling characteristics of the hydrogen energy system, the surplus output of renewable energy can be consumed locally, and a part of it can be directly converted into thermal energy, reducing the demand for electric energy storage systems and thermal energy production equipment, and improving the power supply and heating systems' ability to regulate loads. While improving the photovoltaic absorption capacity, it also meets the system's electrical load, thermal load, hydrogen load, and oxygen load needs, greatly enhancing the system's economy.

[0003] However, in the capacity configuration and operation control optimization methods of related electric thermal hydrogen and oxygen cogeneration systems, the capacity configuration and operation strategy are optimized separately. Since the capacity configuration and operation control often affect each other, it is difficult to achieve the overall optimality of the system by optimizing them alone, resulting in low stability of the electric thermal hydrogen and oxygen cogeneration system. Summary of the invention

[0004] In view of this, the present invention provides a collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system to solve the problem that it is difficult to achieve the overall optimality of the system by optimizing the capacity configuration and operation strategy alone, resulting in low stability of the electric thermal hydrogen and oxygen cogeneration system.

[0005] In a first aspect, the present invention provides a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system, the method comprising:

[0006] The equipment capacity parameters of the electric heat hydrogen oxygen cogeneration system are obtained, and the upper capacity configuration model is constructed based on the equipment capacity parameters with the goal of maximizing the annual net profit of the electric heat hydrogen oxygen cogeneration system;

[0007] The equipment operating parameters of the electric heat hydrogen and oxygen cogeneration system are obtained, and the lower-level operation control model is constructed based on the equipment operating parameters with the goal of maximizing the single-day net profit of the electric heat hydrogen and oxygen cogeneration system;

[0008] The double-layer optimization model of the electric heat hydrogen oxygen cogeneration system consisting of the upper-layer capacity configuration model and the lower-layer operation control model is solved to obtain the optimal capacity configuration and operation control scheme.

[0009] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment constructs a two-layer optimization model of an electric thermal hydrogen and oxygen cogeneration system consisting of an upper-layer capacity configuration model and a lower-layer operation control model, and solves the two-layer optimization model of the electric thermal hydrogen and oxygen cogeneration system to obtain the optimal capacity configuration and operation control scheme, comprehensively considering the mutual influence of capacity configuration and system operation conditions, and through the organic coupling of the upper-layer capacity configuration model and the lower-layer operation control model, completing the collaborative optimization of capacity configuration and operation control of each equipment in the electric thermal hydrogen and oxygen cogeneration system, achieving the global optimization of the electric thermal hydrogen and oxygen cogeneration system, ensuring the optimization of capital investment and equipment operation, reducing the cost waste caused by excessive or insufficient configuration and poor operation conditions, and collaboratively optimizing the operation control and capacity configuration of the electric thermal hydrogen and oxygen cogeneration system, effectively coping with fluctuations in energy supply and demand, and enhancing the stability and reliability of the electric thermal hydrogen and oxygen cogeneration system in an uncertain environment.

[0010] In an optional implementation, with the goal of maximizing the annual net income of the electric heat hydrogen and oxygen cogeneration system, an upper capacity configuration model is constructed based on equipment capacity parameters, including:

[0011] Calculate the total investment cost of each equipment based on the equipment capacity, investment cost per unit capacity of the equipment, equipment service life and discount rate in the equipment capacity parameters;

[0012] Obtain the daily net income of the electric thermal hydrogen and oxygen cogeneration system, take the maximum annual net income of the electric thermal hydrogen and oxygen cogeneration system as the goal, and construct the first objective function based on the daily net income of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each equipment;

[0013] The capacity constraints of each device are obtained, and the capacity constraints of each device are used as the constraint conditions of the first objective function to build an upper-level capacity configuration model.

[0014] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment aims to maximize the annual net profit of the electric thermal hydrogen and oxygen cogeneration system, constructs a first objective function based on the single-day operation net profit of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each equipment, and takes into account multiple sources of income such as electricity sales, hydrogen sales, and oxygen sales, so that the upper-level capacity configuration model can quickly adapt to price fluctuations in the electricity market and fuel market and changes in load demand, thereby significantly improving the economy of the electric thermal hydrogen and oxygen cogeneration system.

[0015] In an optional implementation, with the goal of maximizing the single-day net profit of the electric heat hydrogen and oxygen cogeneration system, a lower-level operation control model is constructed based on the equipment operation parameters, including:

[0016] Taking the maximum net profit of the single-day operation of the electric-thermal hydrogen-oxygen cogeneration system as the goal, the second objective function is constructed based on the energy supply income of the electric-thermal hydrogen-oxygen cogeneration system, the operation and maintenance cost of the equipment, the fuel cost, the pollution gas treatment cost and the grid transaction cost in the equipment operation parameters;

[0017] The power balance constraints and equipment constraints are obtained, and the power balance constraints and equipment constraints are used as the constraints of the second objective function to construct the lower-level operation control model; among them, the equipment constraints include hydrogen energy storage system constraints, electric energy storage system constraints, electric boiler constraints, gas boiler constraints and heat storage tank constraints.

[0018] The collaborative optimization method for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system provided in this embodiment comprehensively incorporates the energy supply income of the electric thermal hydrogen and oxygen cogeneration system, the operation and maintenance costs of the equipment, the fuel costs, the pollution gas treatment costs and the grid transaction costs into the second objective function of the lower-level operation control model, making the second objective function more perfect and fully reflecting the comprehensive net benefit composition of the electric thermal hydrogen and oxygen cogeneration system.

[0019] In an optional implementation, a two-layer optimization model of the electric heat hydrogen and oxygen cogeneration system consisting of an upper layer capacity configuration model and a lower layer operation control model is solved to obtain an optimal capacity configuration and operation control scheme, including:

[0020] Initialize the capacity configuration of each device in the electric heat hydrogen oxygen cogeneration system to obtain the initial capacity configuration value of the device, and set the upper maximum iteration number and the capacity limit of each device; wherein the initial capacity configuration value of the device meets the capacity limit of each device;

[0021] Based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net profit corresponding to the initial capacity configuration value of the equipment;

[0022] The upper-layer capacity configuration model is used to evaluate the optimal single-day operating net income corresponding to the initial capacity configuration value of the equipment. The initial capacity configuration value of the equipment is iteratively updated based on the evaluation result until the current upper-layer iteration number reaches the upper-layer maximum iteration number. The optimal capacity configuration value of each device and the optimal operating power of the device at each moment corresponding to the optimal capacity configuration value of each device are obtained.

[0023] The optimal capacity configuration and operation control scheme are determined based on the optimal capacity configuration value of each device and the optimal operating power of the device at each moment.

[0024] The collaborative optimization method for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system provided in this embodiment introduces a particle swarm algorithm to optimize and solve the underlying operation control model, thereby ensuring that the global optimal solution for equipment capacity configuration is achieved while maximizing daily net revenue, effectively avoiding local optimal problems; and, through refined coupling modeling and collaborative optimization strategies, on the basis of considering the utilization of waste heat from electrolyzers and fuel cells, the energy conversion and utilization efficiency of the electric thermal hydrogen and oxygen cogeneration system is significantly improved, and the operation control and capacity configuration are collaboratively optimized to effectively respond to fluctuations in energy supply and demand, and enhance the stability and reliability of the electric thermal hydrogen and oxygen cogeneration system in uncertain environments.

[0025] In an optional implementation, based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net benefit corresponding to the initial capacity configuration value of the equipment, including:

[0026] The charging and discharging power of the battery at each moment, the electric power of the gas boiler and the electric boiler at each moment, and the electric power of the fuel cell and the electrolyzer at each moment in the electric heat hydrogen and oxygen cogeneration system are selected as particles;

[0027] Randomly generate particle speed and particle position based on the device initial capacity configuration value, and set the maximum number of iterations of the lower layer;

[0028] Construct an electric heat, hydrogen and oxygen combined supply system model; the electric heat, hydrogen and oxygen combined supply system model includes a hydrogen energy storage system model, an electric energy storage system model, a thermal energy storage system model, an electric boiler and a gas boiler model, a photovoltaic operation model and a load model;

[0029] Input the particles into the electric heat hydrogen oxygen cogeneration system model to obtain updated state variables;

[0030] The updated state variables are input into the lower-level operation control model to obtain the fitness function value;

[0031] Based on the fitness function value, the position of each particle is evaluated to see whether it is the optimal solution, and the particle position is updated based on the evaluation result until the current number of iterations of the lower layer reaches the maximum number of iterations of the lower layer, so as to obtain the optimal operating power of the equipment at each moment and the optimal single-day operating net profit of the electric heat, hydrogen and oxygen cogeneration system.

[0032] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment, by constructing an electric thermal hydrogen and oxygen cogeneration system model, accurately describes the electric thermal hydrogen and oxygen coupling dynamics of each device, improves the practical application value of the model, and optimizes and solves the lower-level operation control model using a particle swarm algorithm, thereby achieving optimal calculation of the optimal single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system corresponding to different capacity configuration values, laying a foundation for capacity configuration optimization of the subsequent upper-level capacity configuration model, organically coupling the upper-level capacity configuration model and the lower-level operation control model, and achieving collaborative optimization of capacity configuration and operation control of each device in the electric thermal hydrogen and oxygen cogeneration system.

[0033] In an optional implementation, based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment, and further includes:

[0034] If the particle position does not meet the preset range, a penalty function is determined based on the power balance constraint and the device constraint, and the fitness function value is adjusted based on the penalty function.

[0035] The collaborative optimization method for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system provided in this embodiment, if the particle position does not meet the preset range, adjusts the fitness function value through the penalty function to ensure that the solution in the optimization and solution process of the lower-level operation control model meets the power balance constraints and equipment constraints.

[0036] In a second aspect, the present invention provides a collaborative optimization device for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system, the device comprising:

[0037] The first construction module is used to obtain the equipment capacity parameters of the electric heat hydrogen oxygen cogeneration system, and to build an upper capacity configuration model based on the equipment capacity parameters with the goal of maximizing the annual net profit of the electric heat hydrogen oxygen cogeneration system;

[0038] The second construction module is used to obtain the equipment operation parameters of the electric heat hydrogen and oxygen cogeneration system, and to build a lower-level operation control model based on the equipment operation parameters with the goal of maximizing the single-day net profit of the electric heat hydrogen and oxygen cogeneration system;

[0039] The solution module is used to solve the double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper capacity configuration model and the lower operation control model to obtain the optimal capacity configuration and operation control plan.

[0040] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system of the first aspect or any corresponding embodiment thereof.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system according to the first aspect or any corresponding embodiment thereof.

[0042] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 is a flow chart of a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention;

[0045] Figure 2 is a structural schematic diagram of an electric thermal hydrogen and oxygen cogeneration system according to an embodiment of the present invention;

[0046] Figure 3 is a flow chart of another collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention;

[0047] Figure 4 is a flow chart of another collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention;

[0048] Figure 5 is a structural block diagram of a collaborative optimization device for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention;

[0049] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0051] Certain research results have been achieved in the modeling, planning and optimal scheduling of integrated energy systems. In terms of combined heat and power, the research mainly focuses on the following aspects:

[0052] The introduction of hydrogen energy system into microgrid realizes the mutual conversion between electricity and hydrogen. Although it solves the energy balance problem to a certain extent, it only considers the mutual conversion between electricity and hydrogen and ignores the thermoelectric coupling characteristics of fuel cells and electrolyzers, resulting in low efficiency of hydrogen energy system.

[0053] In the coupled system of active distribution network and district heating network, the heat generation characteristics of electrolyzer during hydrogen production were simply considered to improve the operation efficiency of electrolyzer, but the system was not modeled in detail.

[0054] The optimization configuration is mainly optimized from the perspective of operation control or capacity configuration. However, the operation control and optimization configuration of the system often affect each other and need to be optimized in a coordinated manner.

[0055] Specifically, the capacity configuration and operation control optimization methods of related electric heat hydrogen and oxygen cogeneration systems have the following main shortcomings:

[0056] (1) Insufficient coordinated optimization of operation strategy and capacity configuration: Capacity configuration and operation strategy are often optimized separately, ignoring the mutual influence between the two. The separate optimization method is difficult to achieve the overall optimality of the system.

[0057] (2) Complexity of multi-energy coupling: The coupling of multiple energy forms such as electricity, heat, hydrogen, and oxygen makes the energy conversion and management of the system more complicated. There is little research on the optimization and scheduling of multi-energy coupling, and it is difficult to effectively solve the optimization problem of multi-energy coordinated operation. Although some studies have introduced hydrogen energy systems, they only consider the mutual conversion of electricity and hydrogen, ignoring the thermoelectric coupling characteristics of fuel cells and electrolyzers, resulting in low overall system efficiency.

[0058] (3) Insufficient refinement of system modeling: Many studies have failed to fully consider the thermoelectric hydrogen coupling characteristics of fuel cells and electrolyzers in hydrogen energy systems, resulting in the system model being not refined enough and unable to accurately reflect the actual operating conditions, limiting the effect of system optimization and practical application.

[0059] (4) Limitations of optimization objectives: Most related studies focus on maximizing economic benefits, without comprehensively considering the system’s energy supply revenue (including electricity, heat, hydrogen, and oxygen sales), operation and maintenance costs, investment costs, fuel costs, pollution gas treatment costs, grid transaction costs, etc., and are unable to fully reflect the system’s comprehensive benefits.

[0060] An embodiment of the present invention provides a collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system. By considering the operating characteristics of a (grid-connected) electric thermal hydrogen and oxygen cogeneration system, a model of an electric thermal hydrogen and oxygen cogeneration system including photovoltaic power generation, fuel cells, electrolyzers, hydrogen storage tanks, oxygen storage tanks, batteries, gas boilers, electric boilers, heat storage tanks, electric loads, heat loads, hydrogen loads, and oxygen loads is established. By studying the operating characteristics of each component, the energy supply income including electricity sales, heat sales, hydrogen sales, and oxygen sales, as well as investment costs, operation and maintenance costs, fuel costs, pollution gas treatment costs, and grid transaction costs are comprehensively considered. The objective function is to maximize the net profit minus the investment cost. The upper model uses the capacity of photovoltaics, fuel cells, electrolyzers, hydrogen storage tanks, oxygen storage tanks, batteries, gas boilers, electric boilers, and heat storage tanks as independent variables. The lower model uses the battery charging and discharging power, gas boiler power, electric boiler power, fuel cell power, and electrolyzer power as independent variables. The particle swarm algorithm is used for a two-layer joint iterative optimization solution to obtain the optimal capacity of each device, the optimal hourly power of each device, and the power of electricity sales, heat sales, hydrogen sales, and oxygen sales.

[0061] According to an embodiment of the present invention, an embodiment of a collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0062] In this embodiment, a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system is provided, which can be used in an electric heat hydrogen and oxygen cogeneration system. Figure 1 is a flow chart of a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0063] Step S101, obtaining equipment capacity parameters of the electric thermal hydrogen and oxygen cogeneration system, taking the annual net profit of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing an upper-level capacity configuration model based on the equipment capacity parameters.

[0064] Specifically, Figure 2As shown in the figure, the electric thermal hydrogen and oxygen cogeneration system is mainly composed of photovoltaic power generation, energy storage equipment, energy supply equipment and load; among them, the energy storage equipment includes hybrid electric energy storage system (battery), hydrogen energy storage system (fuel cell, hydrogen storage tank, oxygen storage tank, electrolyzer) and thermal energy storage system (heat storage tank), the energy supply equipment is electric boiler and gas boiler, and the load includes electric load, thermal load, hydrogen load and oxygen load.

[0065] Furthermore, the upper-level capacity configuration model aims to maximize the annual net profit of the electric, thermal, hydrogen and oxygen cogeneration system, takes the upper and lower limits of the capacity configuration of each equipment in the electric, thermal, hydrogen and oxygen cogeneration system as constraints, and takes the capacity of photovoltaics, batteries, heat storage tanks, gas boilers, electric boilers, fuel cells, electrolyzers, hydrogen storage tanks and oxygen storage tanks as independent variables.

[0066] Step S102, obtaining the equipment operating parameters of the electric thermal hydrogen and oxygen cogeneration system, taking the maximization of the single-day operating net profit of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing a lower-level operation control model based on the equipment operating parameters.

[0067] Specifically, the lower-level operation control model aims to maximize the single-day net operating income of the electric, thermal, hydrogen and oxygen cogeneration system. The single-day net operating income of the electric, thermal, hydrogen and oxygen cogeneration system includes the system's energy supply income (electricity sales, heat sales, hydrogen sales, oxygen sales), equipment operation and maintenance costs, fuel costs, pollutant gas treatment costs and grid transaction costs. The lower-level operation control model uses the hourly operating power of fuel cells, electrolyzers, hydrogen storage tanks, oxygen storage tanks, batteries, supercapacitors, electric boilers, and heat storage tanks as independent variables, as well as the power of electricity sales, heat sales, hydrogen sales, and oxygen sales.

[0068] Step S103, solving the double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper layer capacity configuration model and the lower layer operation control model to obtain the optimal capacity configuration and operation control scheme.

[0069] Specifically, the two-layer optimization model of the electric thermal hydrogen and oxygen cogeneration system is divided into an upper-layer capacity configuration model and a lower-layer operation control model. The upper-layer capacity configuration model optimizes the capacity of each equipment based on the objective function of system net profit minus investment cost, including the capacity of photovoltaics, batteries, heat storage tanks, gas boilers, electric boilers, fuel cells, electrolyzers, hydrogen storage tanks and oxygen storage tanks; the upper-layer capacity configuration model calls the lower-layer operation control model, and the lower-layer operation control model optimizes the hourly operation of each equipment according to the equipment capacity, and then outputs the optimal daily operating net profit. The output of the lower-layer operation control model is used to calculate the objective function of the upper-layer capacity configuration model; the two-layer optimization model of the electric thermal hydrogen and oxygen cogeneration system comprehensively considers the equipment economy and operation characteristics, optimizes the capacity configuration and operation of the electric thermal hydrogen and oxygen cogeneration system, thereby improving the overall efficiency of the system.

[0070] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment constructs a two-layer optimization model of an electric thermal hydrogen and oxygen cogeneration system consisting of an upper-layer capacity configuration model and a lower-layer operation control model, and solves the two-layer optimization model of the electric thermal hydrogen and oxygen cogeneration system to obtain the optimal capacity configuration and operation control scheme, comprehensively considering the mutual influence of capacity configuration and system operation conditions, and through the organic coupling of the upper-layer capacity configuration model and the lower-layer operation control model, completing the collaborative optimization of capacity configuration and operation control of each equipment in the electric thermal hydrogen and oxygen cogeneration system, achieving the global optimization of the electric thermal hydrogen and oxygen cogeneration system, ensuring the optimization of capital investment and equipment operation, reducing the cost waste caused by excessive or insufficient configuration and poor operation conditions, and collaboratively optimizing the operation control and capacity configuration of the electric thermal hydrogen and oxygen cogeneration system, effectively coping with fluctuations in energy supply and demand, and enhancing the stability and reliability of the electric thermal hydrogen and oxygen cogeneration system in an uncertain environment.

[0071] In this embodiment, a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system is provided, which can be used for the above-mentioned electric heat hydrogen and oxygen cogeneration system. Figure 3 is a flow chart of a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention, such as Figure 3 As shown, the process includes the following steps:

[0072] Step S301, obtaining equipment capacity parameters of the electric thermal hydrogen and oxygen cogeneration system, taking the annual net profit of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing an upper-level capacity configuration model based on the equipment capacity parameters.

[0073] Specifically, the above step S301 includes:

[0074] Step S3011, calculating the total investment cost of each device based on the capacity of each device in the device capacity parameters, the investment cost per unit capacity of the device, the service life of the device and the discount rate.

[0075] Specifically, the total investment cost of each equipment C inv The calculation formula is as follows:

[0076]

[0077] Among them, k ic,i Represents the investment cost per unit capacity of the equipment, S i represents the capacity of each device, namely, the capacity of photovoltaic, battery, heat storage tank, gas boiler, electric boiler, fuel cell, electrolyzer, hydrogen storage tank and oxygen storage tank, η i represents the discount rate, L sf,i Indicates the age of the equipment.

[0078] Step S3012, obtaining the daily operating net income of the electric thermal hydrogen and oxygen cogeneration system, taking the maximum annual net income of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing a first objective function based on the daily operating net income of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each equipment.

[0079] Specifically, the expression of the first objective function is as follows:

[0080] Maximize(E total ×365-C inv )(2)

[0081] Among them, E total It represents the net profit of single-day operation of the electric, thermal, hydrogen and oxygen cogeneration system.

[0082] Step S3013, obtaining the capacity constraints of each device, taking the capacity constraints of each device as the constraint conditions of the first objective function, and constructing an upper-layer capacity configuration model.

[0083] Specifically, the capacity constraints of each device can be expressed as follows:

[0084] S imin ≤S i ≤S imax (3)

[0085] Among them, S imin Indicates the lower limit of equipment capacity, S imax Indicates the upper limit of device capacity.

[0086] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment aims to maximize the annual net profit of the electric thermal hydrogen and oxygen cogeneration system, constructs a first objective function based on the single-day operation net profit of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each equipment, and takes into account multiple sources of income such as electricity sales, hydrogen sales, and oxygen sales, so that the upper-level capacity configuration model can quickly adapt to price fluctuations in the electricity market and fuel market and changes in load demand, thereby significantly improving the economy of the electric thermal hydrogen and oxygen cogeneration system.

[0087] Step S302, obtaining the equipment operating parameters of the electric thermal hydrogen and oxygen cogeneration system, taking the maximization of the single-day operating net profit of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing a lower-level operation control model based on the equipment operating parameters.

[0088] Specifically, the above step S302 includes:

[0089] Step S3021, with the goal of maximizing the single-day operating net profit of the electric thermal hydrogen and oxygen cogeneration system, construct a second objective function based on the energy supply income of the electric thermal hydrogen and oxygen cogeneration system in the equipment operating parameters, the equipment operation and maintenance costs, fuel costs, pollutant gas treatment costs and grid transaction costs.

[0090] Specifically, the calculation formula for energy supply income E is as follows:

[0091]

[0092] in, c T and c P They represent the system hydrogen supply price, system oxygen supply price, system heating price and system electricity supply price respectively. 2_load , O 2_load , H _load , P _load They respectively represent the hydrogen load demand, oxygen load demand, heat load demand and electricity load demand at each moment.

[0093] Furthermore, the equipment operation and maintenance cost C OM The calculation formula is as follows:

[0094]

[0095] Among them, T d represents the scheduling period, ρ pv , ec , fc , hst , ost , bat , eb , gb , tst represents the unit operation and maintenance cost of photovoltaic, battery, heat storage tank, gas boiler, electric boiler, fuel cell, electrolyzer, hydrogen storage tank and oxygen storage tank (unit: yuan / kWh), P pv , H eb , H gb , P ec , P fc They represent the photovoltaic operation power, the thermal power of the electric boiler, the thermal power of the gas boiler, the electric power consumed by the solution tank, and the power generated by the fuel cell, respectively. bat Indicates the operating power of the battery, P hst Indicates the power stored in the hydrogen storage tank, P ost Indicates the power stored in the oxygen tank.

[0096] Furthermore, the fuel cost C fuel The calculation formula is as follows:

[0097]

[0098] Among them, R gas Indicates the unit price of natural gas (unit: yuan / m 3 ), L H Indicates the calorific value of natural gas (unit: kWh / m3 ).

[0099] Furthermore, the pollution gas treatment cost C gas The calculation formula is as follows:

[0100]

[0101] in, They represent the emission coefficients of SO2 (sulfur dioxide), CO2 (carbon dioxide), and NO2 (nitrogen dioxide), respectively. Represent the control costs of SO2, CO2 and NO2 respectively.

[0102] Furthermore, the grid transaction cost C trade The calculation formula is as follows:

[0103] C trade =∑(c p ·P grid ) (8)

[0104] Among them, c p represents the system power supply price at each time, P grid Indicates the system's power purchase or sales at each moment (power purchase is positive, power sales is negative).

[0105] Furthermore, the expression of the second objective function F is as follows:

[0106] F=max(EC OM -C fuel -C gas -C trade ) (9)

[0107] Step S3022, obtain power balance constraints and equipment constraints, use the power balance constraints and equipment constraints as constraints of the second objective function, and construct a lower-level operation control model; wherein the equipment constraints include hydrogen energy storage system constraints, electric energy storage system constraints, electric boiler constraints, gas boiler constraints, and heat storage tank constraints.

[0108] Specifically, the power balance constraint includes the electrical power balance constraint and the thermal power balance constraint. The power balance constraint is expressed as:

[0109]

[0110] Furthermore, the hydrogen energy storage constraints include fuel cell working mode constraints, electrolyzer working mode constraints, static mode constraints, mode uniqueness constraints, hydrogen load balance constraints, oxygen load energy balance constraints, hydrogen storage tank energy state constraints and oxygen storage tank energy state constraints. Therefore, the hydrogen energy storage constraints can be expressed as:

[0111]

[0112] Among them, Temp fc Indicates the operating status identifier of the fuel cell, Temp fc =1 means the fuel cell is working and the electrolyzer is not working, otherwise it is 0; Temp ec Indicates the operating status identifier of the electrolytic cell, Temp ec =1 means the electrolyzer is working and the fuel cell is not working, otherwise it is 0; Temp Hstatic Indicates the static state identifier. 1 means that both the fuel cell and the electrolyzer are not working, otherwise it is 0. fc Indicates the capacity of the fuel cell, cap ec Indicates the capacity of the electrolyzer, SOC hst Indicates the energy storage state of the hydrogen tank, SOC Ost Indicates the energy storage status of the oxygen storage tank.

[0113] Furthermore, the energy storage constraints include charging state constraints, discharging state constraints, static constraints, mode uniqueness constraints, and battery energy state constraints. Therefore, the energy storage system constraints can be expressed as:

[0114]

[0115] Among them, Temp cha Indicates the battery charging status identifier, Temp cha =1 means the battery is being charged, otherwise 0; Temp dis Indicates the battery discharge status identifier, Temp dis =1 means the battery is discharging, otherwise it is 0; Temp static Indicates the static state identifier, Temp static =1 means the battery is not working, that is, the battery is neither charging nor discharging, otherwise it is 0; bat Indicates the ratio of the battery's charge and discharge power relative to its own capacity.

[0116] Furthermore, the electric boiler constraint can be expressed as:

[0117] 0≤H eb (t)≤cap eb ·eta eb (14)

[0118] Among them, cap eb Indicates the capacity of the electric boiler, eta eb Indicates the thermal efficiency of the electric boiler.

[0119] Furthermore, the gas boiler constraint can be expressed as:

[0120] λ gb_min ·cap gb ≤H gb (t)≤cap gb (15)

[0121] Among them, cap gb represents the capacity of the gas boiler, λ gb_min Indicates the thermal power ratio coefficient of the gas boiler relative to its own capacity.

[0122] Furthermore, the heat storage tank constraints include heat storage mode constraints, heat release mode constraints, static mode constraints, mode uniqueness constraints, and heat storage tank energy state constraints. Therefore, the heat storage tank constraints can be expressed as:

[0123]

[0124] Among them, Temp _Qcha Indicates the heat storage status identifier of the heat storage tank, Temp _Qcha =1 means the heat storage tank is storing heat, otherwise it is 0; Temp _Qdis Indicates the heat release state identifier of the heat storage tank, Temp _Qdis =1 means the battery is discharging, otherwise it is 0; Temp _Qstatic Indicates the static state identifier of the heat storage tank, Temp _Qstatic =1 means the heat storage tank is not working, that is, the heat storage tank neither stores nor releases heat, otherwise it is 0; tst It indicates the heat storage and release power ratio coefficient of the heat storage tank relative to its own capacity.

[0125] Step S303, solve the double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper layer capacity configuration model and the lower layer operation control model to obtain the optimal capacity configuration and operation control scheme. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0126] The collaborative optimization method for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system provided in this embodiment comprehensively incorporates the energy supply income of the electric thermal hydrogen and oxygen cogeneration system, the operation and maintenance costs of the equipment, the fuel costs, the pollution gas treatment costs and the grid transaction costs into the second objective function of the lower-level operation control model, making the second objective function more perfect and fully reflecting the comprehensive net benefit composition of the electric thermal hydrogen and oxygen cogeneration system.

[0127] In this embodiment, a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system is provided, which can be used for the above-mentioned electric heat hydrogen and oxygen cogeneration system. Figure 4 is a flow chart of a collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system according to an embodiment of the present invention, such as Figure 4 As shown, the process includes the following steps:

[0128] Step S401, obtain the equipment capacity parameters of the electric heat hydrogen oxygen cogeneration system, and build an upper capacity configuration model based on the equipment capacity parameters with the goal of maximizing the annual net profit of the electric heat hydrogen oxygen cogeneration system. Figure 3 Step S301 of the illustrated embodiment will not be described in detail here.

[0129] Step S402, obtain the equipment operation parameters of the electric heat hydrogen oxygen cogeneration system, take the maximum single-day net profit of the electric heat hydrogen oxygen cogeneration system as the goal, and build the lower-level operation control model based on the equipment operation parameters. Figure 3 Step S302 of the illustrated embodiment will not be described in detail here.

[0130] Step S403, solving the double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper layer capacity configuration model and the lower layer operation control model to obtain the optimal capacity configuration and operation control scheme.

[0131] Specifically, the particle swarm optimization (PSO) algorithm is used to solve the optimization problem of equipment configuration in the electric heat hydrogen and oxygen cogeneration system. This process involves two levels of models: the upper capacity configuration model focuses on the optimal configuration of equipment capacity, while the lower operation control model focuses on optimizing the daily operation status of the equipment according to the set equipment capacity.

[0132] Furthermore, in the process of solving the two-level optimization model of the electric thermal hydrogen and oxygen cogeneration system, the Upper_Level function and the Lower_Level function respectively undertake different tasks of the annual net profit and daily operation optimization of the electric thermal hydrogen and oxygen cogeneration system; the Upper_Level function optimizes the equipment capacity configuration and calls the Lower_Level function; the Lower_Level function optimizes the operation of the electric thermal hydrogen and oxygen cogeneration system within one day according to the equipment capacity configuration, and calculates the single-day operation net profit of the electric thermal hydrogen and oxygen cogeneration system. The output of the Lower_Level function is passed back to the Upper_Level function for the annual net profit of the electric thermal hydrogen and oxygen cogeneration system; through the above-mentioned hierarchical function structure, the Upper_Level function can evaluate the annual net profit of the system under different equipment configurations through the daily operation optimization of the Lower_Level function on the basis of considering the total investment cost of each equipment. Finally, the particle swarm algorithm is used in the Upper_Level to search for the equipment configuration scheme that maximizes the net profit minus the investment cost.

[0133] Furthermore, the Upper_Level function is used to calculate the annual net income of the electric thermal hydrogen and oxygen cogeneration system. The output of the Upper_Level function is based on two parts: the net income of the electric thermal hydrogen and oxygen cogeneration system per day, which is calculated and returned by the Lower_Level function; and the total investment cost of each device, which is calculated using the above formula (1). The Upper_Level function calculates the annual net income of the electric thermal hydrogen and oxygen cogeneration system by multiplying the net income of the electric thermal hydrogen and oxygen cogeneration system per day by 365 days and subtracting the total investment cost of each device, as shown in the above formula (2).

[0134] Furthermore, the Lower_Level function is nested in the Upper_Level function, which is responsible for simulating the operation of the electric heat, hydrogen and oxygen cogeneration system in a day. The Lower_Level function uses the particle swarm algorithm to optimize the operating power of each device in each time period according to the equipment capacity configuration provided by the Upper_Level function. Finally, the Lower_Level function returns the net daily operating income of the electric heat, hydrogen and oxygen cogeneration system.

[0135] Furthermore, the above step S403 includes:

[0136] Step S4031, initialize the capacity configuration of each device in the electric heat hydrogen oxygen cogeneration system, obtain the initial capacity configuration value of the device, and set the upper maximum iteration number and the capacity limit of each device; wherein the initial capacity configuration value of the device complies with the capacity limit of each device.

[0137] Specifically, the upper-layer capacity configuration model uses a particle swarm optimization algorithm to optimize the configuration of equipment capacity.

[0138] Furthermore, in the optimization process of the upper-level capacity configuration model, the number of particles can be set to 100, indicating that the particle swarm optimization algorithm will initialize 100 potential solutions (particles), and each particle represents the initial capacity configuration value of each device (capacity of photovoltaic, battery, heat storage tank, gas boiler, electric boiler, fuel cell, electrolyzer, hydrogen storage tank, and oxygen storage tank); the maximum number of iterations of the upper layer can be set to 50, which limits the total number of rounds of algorithm optimization; the capacity range of each device is set between the lower limit of capacity (such as 0.1kW) and the upper limit of capacity (such as 500kW) to ensure the rationality of the search space; the particle speed is set to 0.

[0139] Step S4032, based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net profit corresponding to the initial capacity configuration value of the equipment.

[0140] In some optional implementations, the above step S4032 includes:

[0141] Step a1, selecting the charging and discharging power of the battery at each moment, the electric power of the gas boiler and the electric boiler at each moment, and the electric power of the fuel cell and the electrolyzer at each moment in the electric heat hydrogen and oxygen cogeneration system as particles.

[0142] Specifically, each particle represents the operation plan of the electric heat and hydrogen oxygen cogeneration system within a day, which contains the values ​​of all decision variables, including the charging and discharging power of the battery at each moment, the electric power of the gas boiler and electric boiler at each moment, and the electric power of the fuel cell and electrolyzer at each moment.

[0143] Step a2, randomly generate particle speed and particle position based on the initial capacity configuration value of the device, and set the maximum number of iterations of the lower layer.

[0144] Specifically, the initial position of the particle is randomly generated to ensure that each decision variable is within its allowed range (i.e., between 0 and the initial capacity configuration value of the device).

[0145] Step a3, constructing an electric thermal hydrogen and oxygen cogeneration system model; wherein the electric thermal hydrogen and oxygen cogeneration system model includes a hydrogen energy storage system model, an electric energy storage system model, a thermal energy storage system model, an electric boiler and a gas boiler model, a photovoltaic operation model and a load model.

[0146] Specifically, the hydrogen energy storage system consists of a fuel cell, a hydrogen storage tank, an oxygen storage tank and an electrolyzer. In the hydrogen energy storage system, hydrogen is mainly provided by the electrolyzer, and the heat energy generated mainly comes from the waste heat of the fuel cell stack and the electrolyzer. The waste heat utilization can not only enhance the overall energy efficiency of the hydrogen energy storage system, but also facilitate the further promotion and application of hydrogen energy technology. Among them, the mathematical model of the fuel cell can be expressed as:

[0147]

[0148] in, Indicates the power obtained by the fuel cell from the hydrogen storage tank, P fc Represents the power generated by the fuel cell, H fc represents the fuel cell thermal power, P fc_other Represents the remaining power of the fuel cell, eff fc Indicates the hydrogen-to-electricity conversion efficiency of the fuel cell, eta fc Represents the thermal efficiency of the fuel cell.

[0149] Furthermore, the mathematical model of the electrolyzer can be expressed as:

[0150]

[0151] H ec =P ec ×eta ec (twenty two)

[0152] in, Indicates the power delivered by the electrolyzer to the hydrogen storage tank, P ec Indicates the electrical power consumed by the electrolytic cell, H ec Indicates the thermal power of the electrolytic cell, P ec_other Indicates the residual power of the electrolyzer, eff ec Indicates the electrolyzer electricity-to-hydrogen conversion efficiency, eta ec Indicates the thermal efficiency of the electrolytic cell.

[0153] Furthermore, the mathematical model of the hydrogen storage tank can be expressed as:

[0154]

[0155] Among them, P hst Indicates the power stored in the hydrogen storage tank, H 2_w Indicates the power of hydrogen sold, cap hst represents the capacity of the hydrogen storage tank, and t represents a specific time interval (for example, one hour), which is used to calculate the parameter state at the current time point.

[0156] Furthermore, the mathematical model of the oxygen storage tank can be expressed as:

[0157]

[0158] in, Indicates the power delivered by the electrolyzer to the oxygen storage tank, P ost Indicates the power stored in the oxygen tank, O 2_w Indicates the power of oxygen sales, SOC ost Indicates the energy status of the oxygen tank, cap ost Indicates the oxygen tank capacity.

[0159] Specifically, the electric energy storage system, namely the battery, can smooth the fluctuation of photovoltaic power generation and complete the storage and release of electric energy. The electric energy storage system model can be expressed as:

[0160]

[0161] Among them, SOC bat Indicates the battery energy state, P dis Indicates the battery discharge power, eff bat Indicates the battery charging and discharging efficiency, P cha Indicates the battery charging power, cap bat Indicates the battery capacity.

[0162] Specifically, the thermal energy storage system, i.e., the heat storage tank, can adjust the imbalance between the supply and demand of thermal energy and complete the storage and release of thermal energy. The thermal energy storage system helps to smooth the fluctuation of thermal energy supply and improve energy efficiency by storing excess thermal energy and releasing thermal energy when demand increases. The thermal energy storage system model can be expressed as:

[0163]

[0164] Among them, SOC tst Indicates the energy state of the heat storage tank, H dis Indicates the heat release power of the heat storage tank, eta tst Indicates the heat release efficiency of the heat storage tank, H cha Indicates the thermal storage capacity of the heat storage tank, cap tst Indicates the capacity of the heat storage tank.

[0165] Specifically, in the electric boiler and gas boiler models, the electric boiler uses electricity as its energy source and converts it into heat energy. It is an energy coupling device that converts electricity into heat energy. The gas boiler uses gas fuels such as natural gas and liquefied petroleum gas to generate heat energy through combustion. Its heat output is related to its own output characteristics. Among them, the mathematical model of the electric boiler can be expressed as:

[0166] H eb =eta eb ·P eb (30)

[0167] Among them, H eb Indicates the thermal power of the electric boiler, eta eb Indicates the thermal efficiency of the electric boiler, P eb Indicates the electrical power of the electric boiler.

[0168] Furthermore, the mathematical model of the gas boiler can be expressed as:

[0169] H gb =eta gb ·P gb (31)

[0170] Among them, H gb Indicates the thermal power of the gas boiler, eta gb Indicates the thermal efficiency of the gas boiler, P gb Indicates the amount of gas consumed by the gas boiler.

[0171] Specifically, the PV output ratio (actual PV operating power / PV configured capacity) data of a typical day in the PV operation model is in hours.

[0172] Specifically, the load data of a typical day in the load model includes electrical load, thermal load, hydrogen load and oxygen load, with hourly intervals.

[0173] Step a4, input the particles into the electric thermal hydrogen and oxygen cogeneration system model to obtain updated state variables.

[0174] Specifically, the state variables include photovoltaic operating power, power obtained by the fuel cell from the hydrogen storage tank and the thermal power of the electrolyzer, power delivered by the electrolyzer to the hydrogen storage tank and the thermal power of the electrolyzer, thermal power of the electric boiler, thermal power of the gas boiler, storage power of the hydrogen storage tank and the oxygen storage tank, and the SOC of the hydrogen storage tank, oxygen storage tank, battery and heat storage tank.

[0175] Furthermore, the photovoltaic operating power P pv The calculation formula is as follows:

[0176] P pv (t)=Ratio pv (t)×cap pv (32)

[0177] Among them, Ratio pv Indicates the operating power ratio coefficient of photovoltaic relative to its own capacity, cap pv Represents the capacity of photovoltaic.

[0178] Furthermore, the calculation formula for the power obtained by the fuel cell from the hydrogen storage tank and the thermal power of the electrolyzer is as follows:

[0179]

[0180] Among them, η fc Represents the thermal efficiency of the fuel cell.

[0181] Furthermore, the calculation formula for the power delivered by the electrolyzer to the hydrogen storage tank and the thermal power of the electrolyzer is as follows:

[0182]

[0183] H ec (t) = P ec (t)×η ec (36)

[0184] Among them, η ec Indicates the thermal efficiency of the electrolytic cell.

[0185] Furthermore, the calculation formula of the thermal power of the electric boiler is as follows:

[0186] H eb (t) = η eb ·P eb (t) (37)

[0187] Among them, η eb Indicates the thermal efficiency of the electric boiler.

[0188] Furthermore, the calculation formula of the thermal power of the gas boiler is as follows:

[0189] H gb(t) = η gb ·P gb (t) (38)

[0190] Among them, η gb Indicates the thermal efficiency of the gas boiler.

[0191] Furthermore, the calculation formula for the storage power of the hydrogen storage tank and the oxygen storage tank is as follows:

[0192]

[0193] Furthermore, the calculation formulas for the SOC of the hydrogen storage tank, oxygen storage tank, battery and heat storage tank are as follows:

[0194]

[0195] Among them, η tst Indicates the heat storage and release efficiency of the heat storage tank.

[0196] Step a5, input the updated state variables into the lower-level operation control model to obtain the fitness function value.

[0197] Specifically, the updated state variables are input into the above formula (9) to calculate the fitness function value.

[0198] Step a6, based on the fitness function value, evaluate whether the position of each particle is the optimal solution, and update the particle position based on the evaluation result, until the current number of iterations of the lower layer reaches the maximum number of iterations of the lower layer, and obtain the optimal operating power of the equipment at each moment and the optimal single-day operating net income of the electric heat hydrogen and oxygen cogeneration system.

[0199] Specifically, rereading the above steps a4 and a5, the fitness value of each particle is determined by its second objective function value, and the individual optimum and global optimum of the particle are updated according to the fitness function value.

[0200] Furthermore, in each iteration, the velocity and position of the particle are updated according to the following formula:

[0201]

[0202] Position i,new =Position i,old +Velocity i,new (46)

[0203] Among them, Velocity i,new represents the new velocity of the ith particle, that is, the updated velocity based on the current position. ω represents the inertia weight, which is used to adjust the moving speed of the particle in the search space and control the change of the velocity in the previous and next iterations.i,old represents the old speed of the i-th particle, that is, the speed before the update, c1 and c2 represent the acceleration coefficients, which are used to adjust the strength of the particle moving to the individual optimal position and the global optimal position, rand represents the random number generation function, which is used to introduce randomness to avoid particles falling into the local optimum, and p represents gBest i represents the historical best position of the ith particle, which is the best position found by the particle in all iterations. gBest represents the global best position among all particles, which is the best position in the entire group so far. Position i Indicates the current position of the i-th particle, that is, the position of the particle in the current iteration, Position i，nerw Represents the new position of the ith particle, that is, the new position of the particle obtained after updating the velocity, Position i,old Represents the old position of the i-th particle, that is, the particle position before the update.

[0204] Furthermore, when the current number of lower layer iterations reaches the maximum number of lower layer iterations, the optimal operating power of each device at each time point and the optimal single-day operating net profit of the electric thermal hydrogen and oxygen cogeneration system are obtained, and the optimal single-day operating net profit of the electric thermal hydrogen and oxygen cogeneration system is passed back to the Upper_Level function for calculating the objective function of Upper_Level.

[0205] Furthermore, the power balance constraint and device constraint in the lower-level operation control model are added to the second objective function in the form of penalty, where the penalty coefficient of each constraint can be 10000 to ensure that the solution violating the constraint will be significantly penalized. The penalty function F new As shown below:

[0206]

[0207] Among them, f penalty1 、f penalty2 , ..., f penalty22 They represent the penalty functions corresponding to the electric power balance constraint, thermal power balance constraint, hydrogen energy storage system constraint, electric energy storage system constraint, electric boiler constraint, gas boiler constraint and heat storage tank constraint, respectively. 22 They respectively represent the penalty coefficients corresponding to the electric power balance constraint, thermal power balance constraint, hydrogen energy storage system constraint, electric energy storage system constraint, electric boiler constraint, gas boiler constraint and heat storage tank constraint.

[0208] Furthermore, the penalty function of the electric power balance constraint is expressed as follows:

[0209]

[0210] Furthermore, the penalty function of the thermal power balance constraint is expressed as follows:

[0211]

[0212] Furthermore, the expression of the penalty function corresponding to the hydrogen energy storage system constraint is as follows:

[0213]

[0214] Furthermore, the penalty function corresponding to the electric energy storage system constraint is expressed as follows:

[0215]

[0216] Furthermore, the penalty function expression corresponding to the electric boiler constraint is as follows:

[0217] f penalty,16 (t) = λ 16 ·[max(0,H eb (t)-cap eb ·η eb )+max(0,-H eb (t))] (52)

[0219] Furthermore, the expression of the penalty function corresponding to the gas boiler constraint is as follows:

[0220] f penalty,17 (t) = λ 17 ·[max(0,H gb (t)-cap gb )+max(0,λ gb_min ·cap gb -H gb (t))] (53)

[0221] Furthermore, the penalty function expression corresponding to the heat storage tank constraint is as follows:

[0222]

[0223] The collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system provided in this embodiment, by constructing an electric thermal hydrogen and oxygen cogeneration system model, accurately describes the electric thermal hydrogen and oxygen coupling dynamics of each device, improves the practical application value of the model, and optimizes and solves the lower-level operation control model using a particle swarm algorithm, thereby achieving optimal calculation of the optimal single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system corresponding to different capacity configuration values, laying a foundation for capacity configuration optimization of the subsequent upper-level capacity configuration model, organically coupling the upper-level capacity configuration model and the lower-level operation control model, and achieving collaborative optimization of capacity configuration and operation control of each device in the electric thermal hydrogen and oxygen cogeneration system.

[0224] Step S4033, using the upper-layer capacity configuration model to evaluate the optimal single-day operating net income corresponding to the initial capacity configuration value of the device, iteratively update the initial capacity configuration value of the device based on the evaluation result, until the current upper-layer iteration number reaches the upper-layer maximum iteration number, to obtain the optimal capacity configuration value of each device and the optimal operating power of the device at each moment corresponding to the optimal capacity configuration value of each device.

[0225] Specifically, in the optimal configuration problem of equipment capacity in the upper-level capacity configuration model, the fitness function is used to evaluate the performance of each particle, that is, the annual net profit (annual total profit minus annualized investment cost) under a given equipment capacity configuration value; then the optimal single-day operating net profit corresponding to the initial capacity configuration value of the equipment is input into the formula (2) corresponding to the above-mentioned first objective function to obtain the upper-level fitness function value.

[0226] Furthermore, if the particle position does not meet the preset range, a penalty function is determined based on the power balance constraint and the device constraint, and the fitness function value is adjusted based on the penalty function; specifically, for each device (such as photovoltaic, battery, heat storage tank, gas boiler, electric boiler, fuel cell, electrolyzer, hydrogen storage tank, oxygen storage tank), the upper and lower limits of the capacity are set. If the position of the upper particle (i.e., the device capacity configuration) exceeds the upper and lower limits, the fitness value corresponding to the Upper_Level function is adjusted through the penalty function to ensure that the solution in the optimization process meets all constraints; wherein, the new fitness value fun after adjustment by the penalty function new The expression is as follows:

[0227]

[0228] Among them, fun represents the original fitness value, λ i represents the penalty coefficient, which can be 10000 to ensure that solutions violating the constraints will be significantly penalized. i Indicates the capacity configuration of the i-th device, Upper Limit i Indicates the upper limit of the device capacity, Lower Limiti Indicates the lower limit of the device capacity, and N indicates the total type of the device.

[0229] Furthermore, in each iteration, the speed of each upper particle is updated according to the current speed, the individual historical best position and the group historical best position; the above steps S4032 and S4033 are repeated, the fitness value of each upper particle is determined by the first objective function value, and the individual optimum and global optimum of the upper particles are updated according to the fitness function value.

[0230] Furthermore, when the current upper layer iteration number reaches the upper layer maximum iteration number, the iteration is terminated to obtain the optimal capacity configuration of each device and the optimal operating power of each device at each moment under the capacity configuration.

[0231] Step S4034, determining the optimal capacity configuration and operation control scheme based on the optimal capacity configuration value of each device and the optimal operating power of the device at each moment.

[0232] The collaborative optimization method for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system provided in this embodiment introduces a particle swarm algorithm to optimize and solve the underlying operation control model, thereby ensuring that the global optimal solution for equipment capacity configuration is achieved while maximizing daily net revenue, effectively avoiding local optimal problems; and, through refined coupling modeling and collaborative optimization strategies, on the basis of considering the utilization of waste heat from electrolyzers and fuel cells, the energy conversion and utilization efficiency of the electric thermal hydrogen and oxygen cogeneration system is significantly improved, and the operation control and capacity configuration are collaboratively optimized to effectively respond to fluctuations in energy supply and demand, and enhance the stability and reliability of the electric thermal hydrogen and oxygen cogeneration system in uncertain environments.

[0233] In this embodiment, a collaborative optimization device for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0234] This embodiment provides a coordinated optimization device for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system. Figure 5 As shown, including:

[0235] The first construction module 501 is used to obtain equipment capacity parameters of the electric heat hydrogen oxygen cogeneration system, and to construct an upper capacity configuration model based on the equipment capacity parameters with the goal of maximizing the annual net profit of the electric heat hydrogen oxygen cogeneration system;

[0236] The second construction module 502 is used to obtain the equipment operation parameters of the electric heat hydrogen oxygen cogeneration system, and to build a lower-level operation control model based on the equipment operation parameters with the goal of maximizing the single-day net profit of the electric heat hydrogen oxygen cogeneration system;

[0237] The solution module 503 is used to solve the double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper layer capacity configuration model and the lower layer operation control model to obtain the optimal capacity configuration and operation control scheme.

[0238] In some optional implementations, the first building block 501 includes:

[0239] A calculation unit, used for calculating the total investment cost of each device based on the capacity of each device in the device capacity parameter, the investment cost per unit capacity of the device, the service life of the device and the discount rate;

[0240] The first construction unit is used to obtain the single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system, with the goal of maximizing the annual net income of the electric thermal hydrogen and oxygen cogeneration system, and to construct a first objective function based on the single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each equipment;

[0241] The second construction unit is used to obtain the capacity constraints of each device, use the capacity constraints of each device as the constraint conditions of the first objective function, and construct an upper-layer capacity configuration model.

[0242] In some optional implementations, the second building module 502 includes:

[0243] The third construction unit is used to maximize the single-day operation net income of the electric heat hydrogen and oxygen cogeneration system, and to construct a second objective function based on the energy supply income of the electric heat hydrogen and oxygen cogeneration system, the operation and maintenance cost of the equipment, the fuel cost, the pollution gas treatment cost and the power grid transaction cost in the equipment operation parameters;

[0244] The fourth construction unit is used to obtain power balance constraints and equipment constraints, use the power balance constraints and equipment constraints as constraints of the second objective function, and construct a lower-level operation control model; wherein the equipment constraints include hydrogen energy storage system constraints, electric energy storage system constraints, electric boiler constraints, gas boiler constraints, and heat storage tank constraints.

[0245] In some optional implementations, the solution module 503 includes:

[0246] An initialization unit is used to initialize the capacity configuration of each device in the electric heat hydrogen oxygen cogeneration system, obtain the initial capacity configuration value of the device, and set the upper maximum iteration number and the capacity limit of each device; wherein the initial capacity configuration value of the device meets the capacity limit of each device;

[0247] A solving unit is used to optimize and solve the lower-level operation control model based on the initial capacity configuration value of the equipment by using a particle swarm algorithm to obtain the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment;

[0248] An evaluation unit is used to evaluate the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment by using the upper-layer capacity configuration model, and iteratively update the initial capacity configuration value of the equipment based on the evaluation result until the current upper-layer iteration number reaches the upper-layer maximum iteration number, thereby obtaining the optimal capacity configuration value of each equipment and the optimal operation power of the equipment at each moment corresponding to the optimal capacity configuration value of each equipment;

[0249] The determination unit is used to determine the optimal capacity configuration and operation control scheme based on the optimal capacity configuration value of each device and the optimal operation power of the device at each moment.

[0250] In some optional implementations, the solving unit includes:

[0251] Select subunits for selecting the charging and discharging power of the battery at each moment, the electric power of the gas boiler and the electric boiler at each moment, and the electric power of the fuel cell and the electrolyzer at each moment in the electric heat hydrogen and oxygen cogeneration system as particles;

[0252] Set up a subunit for randomly generating particle speeds and particle positions based on the device's initial capacity configuration value, and setting the maximum number of iterations for the lower layer;

[0253] Constructing subunits for constructing an electric-thermal-hydrogen-oxygen cogeneration system model; wherein the electric-thermal-hydrogen-oxygen cogeneration system model includes a hydrogen energy storage system model, an electric energy storage system model, a thermal energy storage system model, an electric boiler and a gas boiler model, a photovoltaic operation model, and a load model;

[0254] An updating subunit is used to input particles into the electric heat hydrogen oxygen cogeneration system model to obtain updated state variables;

[0255] A generating subunit is used to input the updated state variables into the lower-level operation control model to obtain the fitness function value;

[0256] The evaluation subunit is used to evaluate whether the position of each particle is the optimal solution based on the fitness function value, and update the particle position based on the evaluation result until the current number of iterations of the lower layer reaches the maximum number of iterations of the lower layer, so as to obtain the optimal operating power of the equipment at each moment and the optimal single-day operating net income of the electric heat hydrogen and oxygen cogeneration system.

[0257] In some optional implementations, the solving unit further includes:

[0258] The determination subunit is used to determine a penalty function based on the power balance constraint and the device constraint if the particle position does not meet the preset range, and adjust the fitness function value based on the penalty function.

[0259] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0260] The collaborative optimization device for capacity configuration and operation control of the electric thermal hydrogen and oxygen cogeneration system in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0261] The embodiment of the present invention also provides a computer device having the above Figure 5 The collaborative optimization device for capacity configuration and operation control of the electric heat hydrogen and oxygen combined supply system shown.

[0262] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0263] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0264] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0265] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0266] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0267] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0268] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0269] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0270] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A collaborative optimization method for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system, characterized in that: The method comprises: Acquire equipment capacity parameters of the electric heat hydrogen and oxygen cogeneration system, and build an upper capacity configuration model based on the equipment capacity parameters with the goal of maximizing the annual net income of the electric heat hydrogen and oxygen cogeneration system; Acquire equipment operating parameters of the electric heat hydrogen and oxygen cogeneration system, and build a lower-level operation control model based on the equipment operating parameters with the goal of maximizing the single-day net profit of the electric heat hydrogen and oxygen cogeneration system; The double-layer optimization model of the electric heat hydrogen oxygen cogeneration system composed of the upper-layer capacity configuration model and the lower-layer operation control model is solved to obtain the optimal capacity configuration and operation control scheme.

2. The method according to claim 1, characterized in that The upper capacity configuration model is constructed based on the equipment capacity parameters with the goal of maximizing the annual net income of the electric heat hydrogen oxygen cogeneration system, including: Calculate the total investment cost of each device based on the capacity of each device, the investment cost per unit capacity of the device, the service life of the device and the discount rate in the device capacity parameters; Obtaining the net daily operating income of the electric thermal hydrogen and oxygen cogeneration system, taking the maximum annual net income of the electric thermal hydrogen and oxygen cogeneration system as the goal, and constructing a first objective function based on the net daily operating income of the electric thermal hydrogen and oxygen cogeneration system and the total investment cost of each device; Capacity constraints of each device are obtained, and the capacity constraints of each device are used as constraint conditions of the first objective function to construct the upper-layer capacity configuration model.

3. The method according to claim 1, characterized in that The goal is to maximize the single-day net profit of the electric heat hydrogen and oxygen cogeneration system, and to build a lower-level operation control model based on the equipment operation parameters, including: Taking the maximum single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system as the goal, a second objective function is constructed based on the energy supply income of the electric thermal hydrogen and oxygen cogeneration system, the operation and maintenance cost of the equipment, the fuel cost, the pollution gas treatment cost and the power grid transaction cost in the equipment operation parameters; Obtain power balance constraints and equipment constraints, use the power balance constraints and the equipment constraints as constraints of the second objective function, and construct the lower-level operation control model; wherein the equipment constraints include hydrogen energy storage system constraints, electric energy storage system constraints, electric boiler constraints, gas boiler constraints, and heat storage tank constraints.

4. The method according to claim 1, characterized in that: The method of solving the double-layer optimization model of the electric heat hydrogen and oxygen cogeneration system composed of the upper-layer capacity configuration model and the lower-layer operation control model to obtain the optimal capacity configuration and operation control scheme includes: Initialize the capacity configuration of each device in the electric heat hydrogen and oxygen cogeneration system to obtain the initial capacity configuration value of the device, and set the upper maximum iteration number and the capacity limit of each device; wherein the initial capacity configuration value of the device meets the capacity limit of each device; Based on the initial capacity configuration value of the equipment, the lower-level operation control model is optimized and solved by using a particle swarm algorithm to obtain the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment; The upper-layer capacity configuration model is used to evaluate the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment, and the initial capacity configuration value of the equipment is iteratively updated based on the evaluation result until the current upper-layer iteration number reaches the upper-layer maximum iteration number, thereby obtaining the optimal capacity configuration value of each device and the optimal operating power of the device at each moment corresponding to the optimal capacity configuration value of each device; The optimal capacity configuration and operation control scheme are determined based on the optimal capacity configuration value of each device and the optimal operating power of the device at each moment.

5. The method according to claim 4, characterized in that Based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment, including: The charging and discharging power of the battery at each moment, the electric power of the gas boiler and the electric boiler at each moment, and the electric power of the fuel cell and the electrolyzer at each moment in the electric heat hydrogen and oxygen cogeneration system are selected as particles; Randomly generate particle speed and particle position based on the initial capacity configuration value of the device, and set the maximum number of iterations of the lower layer; Constructing an electric heat hydrogen and oxygen cogeneration system model; wherein the electric heat hydrogen and oxygen cogeneration system model includes a hydrogen energy storage system model, an electric energy storage system model, a thermal energy storage system model, an electric boiler and a gas boiler model, a photovoltaic operation model and a load model; Inputting the particles into the electric heat hydrogen oxygen cogeneration system model to obtain updated state variables; Inputting the updated state variables into the lower layer operation control model to obtain a fitness function value; Based on the fitness function value, it is evaluated whether the position of each particle is the optimal solution, and the particle position is updated based on the evaluation result until the current number of lower layer iterations reaches the maximum number of lower layer iterations, so as to obtain the optimal operating power of the equipment at each moment and the optimal single-day operating net income of the electric thermal hydrogen and oxygen cogeneration system.

6. The method according to claim 5, characterized in that Based on the initial capacity configuration value of the equipment, the particle swarm algorithm is used to optimize and solve the lower-level operation control model to obtain the optimal single-day operation net income corresponding to the initial capacity configuration value of the equipment, and further includes: If the particle position does not conform to a preset range, a penalty function is determined based on the power balance constraint and the device constraint, and the fitness function value is adjusted based on the penalty function.

7. A collaborative optimization device for capacity configuration and operation control of an electric heat hydrogen and oxygen cogeneration system, characterized in that: The device comprises: The first construction module is used to obtain equipment capacity parameters of the electric heat hydrogen and oxygen cogeneration system, and to construct an upper-level capacity configuration model based on the equipment capacity parameters with the goal of maximizing the annual net income of the electric heat hydrogen and oxygen cogeneration system; The second construction module is used to obtain the equipment operation parameters of the electric heat hydrogen and oxygen cogeneration system, and to build a lower-level operation control model based on the equipment operation parameters with the goal of maximizing the single-day net profit of the electric heat hydrogen and oxygen cogeneration system; The solution module is used to solve the double-layer optimization model of the electric heat hydrogen and oxygen cogeneration system composed of the upper-layer capacity configuration model and the lower-layer operation control model to obtain the optimal capacity configuration and operation control plan.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the collaborative optimization method for capacity configuration and operation control of an electric thermal hydrogen and oxygen cogeneration system according to any one of claims 1 to 6.

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