Method and device for selecting a sofc-based cogeneration system

Abstract

The embodiment of the application provides a selection method and device for a SOFC-based combined cooling, heating and power system, wherein the method comprises the following steps: constructing a SOFC-based combined cooling, heating and power system framework; constructing a numerical model for key equipment in the combined cooling, heating and power system framework, the key equipment comprising at least one of the following: an energy storage battery, an electrolytic cell or a SOFC; coupling the numerical model of the key equipment with the combined cooling, heating and power system framework, and determining an energy efficiency index and a cost index of the combined cooling, heating and power system framework; taking the capacity of the key equipment as an independent variable factor, and taking the maximization of the energy efficiency index and the minimization of the cost index as a solving target, and constructing a multi-objective optimization model; and selecting the capacity of the key equipment based on the result obtained by solving the multi-objective optimization model. In this way, the system equipment capacity with the best economic and technical performance of the system is obtained, and theoretical guidance can be provided for the system design and manufacturing of a SOFC combined cooling, heating and power system producer.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of combined cooling, heating and power systems, in particular to a selection method of a SOFC-based combined cooling, heating and power system, a selection device of the SOFC-based combined cooling, heating and power system, an electronic device and a computer readable storage medium. BACKGROUND

[0002] In recent years, hydrogen energy has attracted widespread attention in the industry and academia due to its high enthalpy value, zero carbon emission, long-term storage and environmental friendliness. The samarium doped ceria (SDC) solid oxide fuel cell (SOFC) can convert hydrogen energy into electric energy and thermal energy at an operating temperature of 500-800 DEG C, and can be used for the cooling, heating and power demand coverage of buildings in cooperation with heat exchangers, heat storage tanks, absorption refrigerators and other equipment. Therefore, SOFC has been widely used in combined cooling, heating and power (CCHP) systems for building energy supply, and has a far-reaching application prospect in promoting green low-carbon building reconstruction.

[0003] In order to adapt to the building application scenarios in a wide time domain, the SOFC combined cooling, heating and power system can be integrated with other auxiliary equipment to realize long-term independent operation, including energy storage batteries, electrolytic cells, photovoltaic panels and the like. The capacity size of the key equipment of the combined cooling, heating and power system (such as SOFC, energy storage battery, electrolytic cell, etc.) not only determines the initial investment cost of the system, but also affects the working performance and service life of the equipment. Therefore, the capacity selection of the key equipment of the SOFC combined cooling, heating and power system is an effective means to balance the economic performance and technical performance of the system. Compared with the field experiment method, the numerical modeling and simulation analysis have the characteristics of high analysis efficiency and low operation cost, and is an effective method for capacity selection of the SOFC combined cooling, heating and power system. However, there are few related selection methods of the combined cooling, heating and power system. SUMMARY

[0004] Therefore, it is necessary to provide a selection method of a SOFC-based combined cooling, heating and power system, a selection device of the SOFC-based combined cooling, heating and power system, an electronic device and a computer readable storage medium in view of the above technical problems.

[0005] In a first aspect, an embodiment of the present application provides a selection method of a SOFC-based combined cooling, heating and power system, which comprises the following steps:

[0006] A cold heat and power cogeneration system framework based on SOFC is constructed, wherein the cold heat and power cogeneration system framework comprises a photovoltaic panel, an electrolytic cell, an energy storage battery, a hydrogen storage tank, an air compressor, an SOFC, a burner, an electric energy supply mechanism, a cold energy supply mechanism, and a hot water supply mechanism, the electrolytic cell and the energy storage battery are connected to the photovoltaic panel, the hydrogen storage tank is connected to the electrolytic cell, the hydrogen storage tank and the air compressor are connected to the SOFC, the SOFC is connected to the burner, the electric energy supply mechanism, the cold energy supply mechanism, and the hot water supply mechanism are connected to the burner, and the electric energy supply mechanism is further connected to the energy storage battery;

[0007] A numerical model is constructed for a key device in the cold heat and power cogeneration system framework, the key device comprising at least one of the energy storage battery, the electrolytic cell, or the SOFC;

[0008] The numerical model of the key device is coupled with the cold heat and power cogeneration system framework to determine an energy efficiency index and a cost index of the cold heat and power cogeneration system framework;

[0009] A multi-objective optimization model is constructed with the capacity of the key device as an independent variable factor and the maximization of the energy efficiency index and the minimization of the cost index as solving objectives;

[0010] The capacity of the key device is selected based on a result obtained by solving the multi-objective optimization model.

[0011] In the embodiments of the present application, an SOFC cold heat and power cogeneration system device capacity selection method for optimizing technical and economic performance is proposed, the capacity of a key device (an energy storage battery, an electrolytic cell, or an SOFC, etc.) of the system is adjusted to explore the system energy efficiency and cost as optimization objects, and the system device capacity with the best system economic and technical performance is obtained, which can provide theoretical guidance for system design and manufacturing of an SOFC cold heat and power cogeneration system production party.

[0012] In a possible implementation manner, a numerical model of the energy storage battery is constructed, comprising:

[0013] A battery capacity sub-model of the energy storage battery and a performance attenuation sub-model of the energy storage battery are constructed, wherein the battery capacity sub-model is used to determine an effective total capacity of the energy storage battery based on the number of single pieces and real-time relative capacity of the energy storage battery, and the performance attenuation sub-model of the energy storage battery is used to determine an attenuation capacity of the energy storage battery based on a discharge depth of each charge and discharge cycle of the energy storage battery.

[0014] In a possible implementation manner, the discharge depth of each charge and discharge cycle of the energy storage battery is determined based on a highest charge amount and a lowest charge amount of the charge and discharge cycle;

[0015] determining the decay capacity of the energy storage battery based on the depth of discharge of each charge-discharge cycle of the energy storage battery comprises:

[0016] determining a fitting coefficient based on the depth of discharge of the charge-discharge cycle of the energy storage battery;

[0017] determining the decay capacity of the energy storage battery based on the fitting coefficient.

[0018] In a possible implementation, the numerical model of the electrolytic cell is constructed, comprising:

[0019] constructing an electrochemical sub-model of the electrolytic cell and a performance decay sub-model of the electrolytic cell, wherein the electrochemical sub-model of the electrolytic cell is used to determine the hydrogen production efficiency of the electrolytic cell based on the number of electrolytic cell monopiles and the voltage of electrolytic cell monopiles; and the performance decay sub-model of the electrolytic cell is used to determine the decay voltage of the electrolytic cell based on the number of electrolytic cell monopiles and the voltage decay rate of electrolytic cell.

[0020] In a possible implementation, the numerical model of the SOFC is constructed, comprising:

[0021] constructing an electrochemical sub-model of the SOFC and a performance decay sub-model of the SOFC, wherein the electrochemical sub-model of the SOFC is used to determine the electric power and the thermal power of the SOFC based on the number of monopiles of the SOFC and the effective monopile voltage of the SOFC; and the performance decay sub-model of the SOFC is used to determine the voltage decay rate of the SOFC based on the current density of the SOFC and the hydrogen mass fraction.

[0022] In a possible implementation, the energy efficiency indicator is determined based on the system output electric power, the coefficient output thermal power and the system output cold power; and the cost indicator is determined based on the cumulative decay capacity of the energy storage battery, the cumulative decay voltage of the electrolytic cell and the cumulative decay voltage of the SOFC.

[0023] In a possible implementation, the result obtained by solving the multi-objective optimization model is a Pareto frontier solution set; and the capacity of the key equipment is selected based on the result obtained by solving the multi-objective optimization model, comprising:

[0024] determining a normalized Euclidean distance based on the Pareto frontier solution set;

[0025] selecting the capacity of the key equipment based on the normalized Euclidean distance.

[0026] In a second aspect, the embodiments of the present application provide a selection device of a SOFC-based combined cooling, heating and power system, comprising:

[0027] a system construction module, configured to construct a SOFC-based combined cooling heating and power system framework, wherein the combined cooling heating and power system framework comprises a photovoltaic panel, an electrolyzer, an energy storage battery, a hydrogen storage tank, an air compressor, a SOFC, a burner, an electric energy supply mechanism, a cold energy supply mechanism, and a hot water supply mechanism, the electrolyzer and the energy storage battery are connected to the photovoltaic panel, the hydrogen storage tank is connected to the electrolyzer, the hydrogen storage tank and the air compressor are connected to the SOFC, the SOFC is connected to the burner, the electric energy supply mechanism, the cold energy supply mechanism, and the hot water supply mechanism are connected to the burner, and the electric energy supply mechanism is further connected to the energy storage battery;

[0028] a device numerical model construction module, configured to construct a numerical model for a key device in the combined cooling heating and power system framework, the key device comprising at least one of the energy storage battery, the electrolyzer, or the SOFC;

[0029] an index determination module, configured to couple the numerical model of the key device with the combined cooling heating and power system framework, and determine an energy efficiency index and a cost index of the combined cooling heating and power system framework;

[0030] a multi-objective optimization model construction module, configured to construct a multi-objective optimization model by taking a capacity of the key device as an independent variable factor, and taking maximizing the energy efficiency index and minimizing the cost index as solving objectives;

[0031] a solving module, configured to select the capacity of the key device based on a result obtained by solving the multi-objective optimization model.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, comprising:

[0033] a memory, configured to store a program;

[0034] a processor, configured to execute the program stored in the memory, and when the processor executes the program stored in the memory, the processor is configured to execute the method in the first aspect.

[0035] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are used to execute the selection method of the SOFC-based combined cooling heating and power system in the first aspect.

[0036] The solutions in the second aspect to the fourth aspect are used to implement or assist in implementing the selection method of the SOFC-based combined cooling heating and power system in the first aspect, and thus can achieve the same or corresponding beneficial effects as the first aspect, which will not be described here.

[0037] It should be understood that the general description and detailed description of the foregoing are only exemplary and do not limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A flowchart of a selection method of a SOFC-based combined cooling, heating and power system according to a first embodiment of the present application is provided.

[0039] Figure 2 A schematic diagram of a SOFC-based combined cooling, heating and power system framework according to an embodiment of the present application is provided.

[0040] Figure 3 A schematic diagram of an implementation framework of a selection of a SOFC-based combined cooling, heating and power system according to an embodiment of the present application is provided.

[0041] Figure 4 A flowchart of a selection method of a SOFC-based combined cooling, heating and power system according to a second embodiment of the present application is provided.

[0042] Figure 5 A schematic diagram of a Pareto solution set of system total energy efficiency and system annual total cost under multi-objective optimization according to an embodiment of the present application is provided.

[0043] Figure 6 A schematic diagram of a normalized Pareto solution set of system total energy efficiency and system annual total cost according to an embodiment of the present application is provided.

[0044] Figure 7 A schematic diagram of a structure of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0045] In order to make the purposes, technical methods and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0046] It should be noted that the meaning of multiple (or multiple items) involved in the description of the embodiments of the present application is more than two, greater than, less than, more than, etc. are not included in the number, above, below, etc. are understood to include the number. If there is a description of "first", "second", etc. is only used to distinguish technical features for the purpose, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the sequence of indicated technical features.

[0047] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. Wherein A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can mean that a exists alone, b exists alone, c exists alone, a and b exist together, a and c exist together, b and c exist together, or a and b and c exist together, where a, b, and c can be single or multiple.

[0048] The terms "substantially", "about" and similar terms used in the embodiments of the present application are used as approximate terms, not as degree terms, and are intended to take into account the inherent deviations of measured or calculated values known by those skilled in the art. In addition, the use of "may" in describing the embodiments of the present application means "one or more embodiments". The terms "use", "using", and "used" used in the embodiments of the present application can be considered as synonymous with the terms "utilize", "utilizing", and "utilized", respectively. In addition, the term "exemplary" is intended to refer to an example or an illustration.

[0049] Referring to Figure 1 The flowchart of the selection method of the SOFC-based combined cooling heating and power system provided in the first embodiment of the present application is shown in the figure. The method comprises the following steps:

[0050] Step S101: Constructing a SOFC-based combined cooling heating and power system framework, wherein the combined cooling heating and power system framework comprises a photovoltaic panel, an electrolytic cell, an energy storage battery, a hydrogen storage tank, an air compressor, a SOFC, a burner, an electric energy supply mechanism, a cold energy supply mechanism, and a hot water supply mechanism. The electrolytic cell and the energy storage battery are connected to the photovoltaic panel, the hydrogen storage tank is connected to the electrolytic cell, the hydrogen storage tank and the air compressor are connected to the SOFC, the SOFC is connected to the burner, the electric energy supply mechanism, the cold energy supply mechanism, and the hot water supply mechanism are connected to the burner, and the electric energy supply mechanism is also connected to the energy storage battery.

[0051] Exemplarily, a solid oxide fuel cell combined cooling heating and power system model framework can be designed and constructed on a TRNSYS simulation platform.

[0052] Referring to Figure 2A schematic diagram of a SOFC-based combined cooling, heating and power system framework is provided in the embodiments of the present application. The framework includes: photovoltaic panels, electrolytic cells, energy storage batteries, hydrogen storage tanks, air compressors, SOFCs, burners, electrical power supply mechanisms (including inverters), cold supply mechanisms (including generators, absorbers, condensers, throttles, evaporators, cooling coils, air heaters, etc.), and hot water supply mechanisms (including gas-liquid heat exchangers, liquid electric heaters, hot water storage tanks, splitters, mixers, etc.).

[0053] Figure 2 The working principle of the SOFC-based combined cooling, heating and power system framework is as follows:

[0054] The photovoltaic panel generates electricity which is stored in the energy storage battery (path 1), and the excess electricity is delivered to the electrolyzer for electrolytic hydrogen production (path 2). The hydrogen produced by the electrolyzer is stored in the hydrogen storage tank (path 3). Then the air compressor and the hydrogen storage tank supply air and hydrogen respectively, and after being preheated by the gas heat exchanger and the gas electric heater, they are sent into the solid oxide fuel cell stack (paths 4-9). The air and hydrogen undergo an electrochemical reaction in the solid oxide fuel cell stack to generate electricity and heat, further increasing the gas temperature. The excess air and hydrogen are discharged into the combustor and fully mixed to form high-temperature mixed gas (paths 10, 11). Then the high-temperature mixed gas is preheated by the gas heat exchangers of the cathode and anode in turn (paths 12-14). Without cooling, the high-temperature mixed gas enters the gas-liquid heat exchanger through paths 15, 16 (path 26 valve closed) to exchange heat with the water in the heat storage water tank (paths 18, 19) to heat the hot water in the heat storage water tank, and then is discharged from the system through path 17. The city tap water is divided into two parts by the diverter (path 20), one part enters the heat storage water tank for heating (path 21), and the other part is mixed in the mixer (paths 22, 23) to achieve the effect of preliminary water temperature rise. Then the hot water is heated again to the set value by the liquid electric heater and delivered to the building end (paths 24, 25). In the case of cooling the building, the high-temperature mixed gas discharged from the solid oxide fuel cell stack passes through path 26 (path 15 valve closed) to heat the generator of the absorption refrigeration module, and then enters the gas-liquid heat exchanger to heat the heat storage water tank (path 27). The water vapor formed by the evaporation of the water solution in the generator passes through the condenser, the throttle valve and the evaporator in turn (paths 30, 31), enters the absorber to be combined and absorbed with the lithium bromide solution again, and is recycled into the generator (paths 28, 29). The cold energy generated by the evaporator is further increased to the cooling coil through water circulation (paths 33, 34) to cool the natural air from the outside (paths 35, 36). The cold air is adjusted in temperature and humidity by the air heater and then sent to the building end (path 37). At the same time, the electricity generated by the energy storage battery and the solid oxide fuel cell stack (paths 40, 41) is rectified by the inverter and supplied to the building end and the auxiliary components of the system itself (path 42), realizing the grid-independent operation of the system.

[0055] Step S102: constructing a numerical model for a key device in the combined cooling, heating and power system framework, the key device including at least one of: an energy storage battery, an electrolyzer or a SOFC.

[0056] In one possible implementation, the numerical model of the energy storage battery is constructed, including:

[0057] The battery capacity sub-model of the energy storage battery is constructed, and the performance degradation sub-model of the energy storage battery is constructed.

[0058] In a possible implementation, the discharge depth of the energy storage battery in each charge-discharge cycle is determined based on the highest state of charge and the lowest state of charge of the charge-discharge cycle.

[0059] The discharge depth of the energy storage battery in each charge-discharge cycle is determined based on the highest state of charge and the lowest state of charge of the charge-discharge cycle.

[0060] The fitting coefficient is determined based on the discharge depth of the charge-discharge cycle of the energy storage battery.

[0061] The fitting coefficient is determined based on the discharge depth of the charge-discharge cycle of the energy storage battery.

[0062] In a possible implementation, a numerical model of the electrolytic cell is constructed, including:

[0063] The electrochemical sub-model of the electrolytic cell is constructed, and the performance degradation sub-model of the electrolytic cell is constructed, where the electrochemical sub-model of the electrolytic cell is used to determine the hydrogen production efficiency of the electrolytic cell based on the number of single pieces of the electrolytic cell and the single piece voltage of the electrolytic cell; and the performance degradation sub-model of the electrolytic cell is used to determine the degradation voltage of the electrolytic cell based on the number of single pieces of the electrolytic cell and the voltage degradation rate of the electrolytic cell.

[0064] In a possible implementation, a numerical model of the SOFC is constructed, including:

[0065] The electrochemical sub-model of the SOFC is constructed, and the performance degradation sub-model of the SOFC is constructed, where the electrochemical sub-model of the SOFC is used to determine the electric power and the thermal power of the SOFC based on the number of single pieces of the SOFC and the effective single piece voltage of the SOFC; and the performance degradation sub-model of the SOFC is used to determine the voltage degradation rate of the SOFC based on the current density of the SOFC and the hydrogen mass ratio of the SOFC.

[0066] Step S103: coupling the numerical model of the key equipment with the combined cooling, heating and power system framework to determine the energy efficiency index and the cost index of the combined cooling, heating and power system framework.

[0067] Step S104: constructing a multi-objective optimization model with the capacity of the key equipment as an independent variable factor and maximizing the energy efficiency index and minimizing the cost index as a solving target.

[0068] Step S105: selecting the capacity of the key equipment based on the result obtained by solving the multi-objective optimization model.

[0069] In a possible implementation, the energy efficiency index is determined based on system output electric power, coefficient output thermal power and system output cold power; and the cost index is determined based on cumulative attenuation capacity of the energy storage battery, cumulative attenuation voltage of the electrolytic cell and cumulative attenuation voltage of the SOFC.

[0070] In a possible implementation, the result obtained by solving the multi-objective optimization model is a Pareto frontier solution set; and based on the result obtained by solving the multi-objective optimization model, the capacity of the key equipment is selected, including:

[0071] determining a normalized Euclidean distance based on the Pareto frontier solution set;

[0072] selecting the capacity of the key equipment based on the normalized Euclidean distance.

[0073] In the embodiments of the present application, a method for selecting the capacity of the equipment of the SOFC combined cooling, heating and power system for optimal technical and economic performance is provided, the capacity of the key equipment (energy storage battery, electrolytic cell or SOFC, etc.) of the system is adjusted, the total energy efficiency and the total annual cost of the system are taken as the optimization objects, and the capacity of the equipment of the system with the best technical and economic performance is obtained, thereby providing theoretical guidance for the system design and manufacturing of the SOFC combined cooling, heating and power system production party.

[0074] Referring to Figure 3 An implementation framework schematic diagram for selecting the SOFC-based combined cooling, heating and power system is provided in the embodiments of the present application. As shown in Figure 3As shown, when selecting the type of SOFC-based CCHP system, the framework configuration of the SOFC CCHP system can be designed and constructed on the simulation platform first; then, numerical modeling is performed for the key devices in the framework, specifically including: construction of the battery capacity sub-model of the energy storage battery, construction of the performance degradation sub-model of the energy storage battery, construction of the electrochemical sub-model of the electrolyzer, construction of the performance degradation sub-model of the electrolyzer, construction of the electrochemical sub-model of the SOFC, and construction of the performance degradation sub-model of the SOFC; then, the numerical models of the key devices are coupled to the SOFC CCHP system to obtain the mapping relationship between the capacity of the key devices and the economic and technical evaluation indexes of the SOFC CCHP system; further, the economic and technical evaluation indexes are constructed, including the total energy efficiency index of the system and the total annual cost index of the system; further, a multi-objective optimization algorithm framework is constructed with the maximization of the total energy efficiency index of the system and the minimization of the total annual cost index of the system as the objectives; the multi-objective optimization algorithm is solved, and it should be understood that the multi-objective optimization algorithm is a kind of algorithm for solving optimization problems with multiple conflicting objectives. In the embodiments of the present application, the multi-objective optimization algorithm needs to solve the conflict between the maximization of the total energy efficiency index of the system and the minimization of the total annual cost index of the system. The multi-objective optimization algorithm does not have a single optimal solution, but a set of solutions (Pareto optimal solution set). These solutions provide different trade-offs between the maximization of the total energy efficiency index of the system and the minimization of the total annual cost index of the system. The device capacity is selected and decided based on the normalized Euclidean distance judgment in the embodiments of the present application.

[0075] It should be noted that the key devices of the embodiments of the present application include not only the energy storage battery, the electrolyzer and the SOFC, but also other devices; or only one or two of the energy storage battery, the electrolyzer and the SOFC. The specific types of the key devices are not limited in the embodiments of the present application.

[0076] Referring to Figure 4 The flowchart of the selection method of the SOFC-based CCHP system provided by the second embodiment of the present application is shown. The method includes the following steps:

[0077] Step S201: designing and constructing a solid oxide fuel cell (SOFC) combined cooling heating and power (CCHP) system model framework model on the TRNSYS simulation platform.

[0078] It should be noted that the implementation of step S201 can refer to the related description of step S101 above, which will not be repeated here.

[0079] Step S202: constructing numerical models of key devices and coupling them to the CCHP system.

[0080] In this embodiment, the energy storage battery, electrolytic tank and SOFC are taken as examples to illustrate the numerical model construction process of key equipment.

[0081] Step S202.1: Construct the numerical model of the energy storage battery and couple it with the combined cooling, heating and power system.

[0082] Firstly, the battery capacity sub-model of the energy storage battery is constructed, and the expression of the battery capacity of the energy storage battery is:

[0083] (Formula 1)

[0084] wherein Caeff is the effective total capacity of the energy storage battery, Nba is the number of single pieces of the energy storage battery, Caini,cell is the initial single piece capacity, RC is the real-time relative capacity, Pba is the net charging and discharging power, and tba is the operation time of the energy storage battery.

[0085] The expression of the relative capacity of the energy storage battery is:

[0086] (Formula 2)

[0087] wherein n is the number of charging and discharging cycles, RC is the decay capacity of each charging and discharging cycle.

[0088] Secondly, the performance decay sub-model of the energy storage battery is constructed, which is used to represent the decay capacity of the energy storage battery, and the expression is:

[0089] (Formula 3)

[0090] wherein represents the decay capacity of each charging and discharging cycle, and β1, β2 and β3 are fitting coefficients related to the depth of discharge, which can be calculated as:

[0091] (Formula 4)

[0092] wherein uj,i is the fitting coefficient (j=1, 2, 3; i=1, 2, 3, 4, 5, 6, 7, 8). As shown in Table 1 below, the value examples of the depth of discharge and the fitting coefficient uj,i are shown.

[0093] Table 1

[0094]

[0095] DOD is the depth of discharge, which can be calculated as:

[0096] (Formula 5)

[0097] where DOD is the depth of discharge, FSOCpeak,n is the highest state of charge of the n-th charge-discharge cycle, and FSOCvalley,n is the lowest state of charge of the n-th charge-discharge cycle.

[0098] Step S202.2: Constructing a numerical model of the electrolyzer and coupling it with the combined cooling, heating and power system.

[0099] First, an electrochemical model of the electrolyzer is constructed to characterize the hydrogen generation rate, which can be calculated as:

[0100] (Equation 6)

[0101] where is the hydrogen generation rate; is the input current; is the molar mass of hydrogen, 2 x 10-3 kg / mol; is the number of electrons carried by a hydrogen molecule, 2; and F is the Faraday constant, 96485 C / mol.

[0102] The input current can be calculated as:

[0103] (Equation 7)

[0104] where Pel is the input electric power, Nel is the number of electrolyzer cells, and Eel is the voltage of an electrolyzer cell.

[0105] The voltage of an electrolyzer cell can be calculated as:

[0106] (Equation 8)

[0107] where Eel,re is the reversible potential, Eel,act is the activation polarization of the electrolyzer, Eel,ohm is the ohmic polarization of the electrolyzer, Eel is the cumulative decay voltage of the electrolyzer.

[0108] The reversible potential is expressed as:

[0109] (Equation 9)

[0110] where Tel is the operating temperature of the electrolyzer.

[0111] The activation polarization of the electrolyzer is expressed as:

[0112] (Equation 10)

[0113] where R is the gas constant, 8.314 J / (mol K), Tel is the electrolyzer operating temperature, iel,a is the anode exchange current density, iel,c is the cathode exchange current density, and iel is the input current density.

[0114] The anode exchange current density expression is:

[0115] (Equation 11)

[0116] where is the anode current density reference factor, is the anode activation energy, 76 kJ / mol.

[0117] The cathode exchange current density expression is:

[0118] (Equation 12)

[0119] where is the cathode current density reference factor, is the cathode activation energy, 18 kJ / mol.

[0120] The electrolyzer ohmic polarization expression is:

[0121] (Equation 13)

[0122] where del is the electrolyzer membrane thickness, and θ(λ) is the membrane conductivity, which can be calculated as:

[0123] (Equation 14)

[0124] where λ is the membrane state water content.

[0125] Secondly, the performance degradation sub-model of the electrolyzer is constructed to represent the degradation voltage of the electrolyzer, and the expression is:

[0126] (Equation 15)

[0127] where Eel is the cumulative degradation voltage of the electrolyzer, Nel is the number of electrolyzer sheets, tm, tfl, tct, tfh, and tcr are the operating times of the low-power maintenance stage (≤ 0.6 W / cm2), the low-power cycling stage (0.6 – 4 W / cm2), the constant-power operation stage (4 W / cm2), the high-power cycling stage (4 – 6 W / cm2), and the high-power excess stage (6 W / cm2), respectively; φm, φfl, φct, φfh, and φcr are the voltage decay rates corresponding to each of the above stages, which are 1.5, 50, 20, 66, and 196 μV / h, respectively.

[0128] Step S202.3: Constructing the numerical model of SOFC and coupling with the combined cooling, heating and power system.

[0129] Firstly, the electrochemical model of SOFC is constructed to characterize the electrical and thermal power of SOFC.

[0130] The expression of SOFC electrical power is:

[0131] (Equation 16)

[0132] Where is the electrical power of SOFC, is the effective cell voltage, is the current density, As is the active area, and Ns is the number of cells.

[0133] The expression of SOFC thermal power is:

[0134] (Equation 17)

[0135] Where is the thermal power of SOFC.

[0136] The expression of effective cell voltage is:

[0137] (Equation 18)

[0138] Where is the cell voltage of SOFC, is the performance degradation rate, is the operation time.

[0139] The cell voltage of SOFC is:

[0140] (Equation 19)

[0141] is the Nernst voltage, is the activation polarization, is the ohmic polarization, is the concentration polarization.

[0142] The expression of Nernst voltage is:

[0143] (Equation 20)

[0144] Where Ts is the fuel cell operating temperature, is the local hydrogen pressure, is the local oxygen pressure, is the local water vapor pressure,

[0145] The activation polarization expression is:

[0146] (Formula 21)

[0147] where is, a is the anode exchange current density, is, c is the cathode exchange current density.

[0148] The ohmic polarization expression is:

[0149] (Formula 22)

[0150] where da, dc, dele, dint are the thickness of the anode, cathode, electrolyte, interconnector of the solid oxide fuel cell, and ka, kc, kele, kint are the electrical conductivity of the anode, cathode, electrolyte, interconnector of the solid oxide fuel cell.

[0151] The concentration polarization expression is:

[0152] (Formula 23)

[0153] where , , is the limiting current density of hydrogen, oxygen and water vapor, , is the number of electrons of a single molecule of hydrogen and oxygen, respectively.

[0154] Secondly, a performance degradation sub-model of the SOFC is constructed to represent the voltage decay rate of the SOFC, and the expression is:

[0155] (Formula 24)

[0156] where is the mass fraction of hydrogen.

[0157] Step S203: Build economic and technical evaluation indexes of the combined cooling heating and power system, i.e. total energy efficiency of the system and total annual cost of the system.

[0158] Step S203.1: The total energy efficiency of the combined cooling heating and power system can be calculated as:

[0159] (Formula 25)

[0160] where is the total energy efficiency of the system, is the output electric power of the system, is the output heat power of the system, is the output cold power of the system, is the solar radiation power density, PV panel area, PV panel number, Initial net output electrical energy of energy storage battery, Initial net output hydrogen energy of hydrogen storage tank.

[0161] Initial net output electrical energy of energy storage battery is:

[0162] (Equation 26)

[0163] Initial net output hydrogen energy of hydrogen storage tank is:

[0164] (Equation 27)

[0165] wherein , is real-time hydrogen consumption and production rate, is hydrogen lower heating value, 1.21 x 108 J / kg.

[0166] Step S203.2: Annual total cost of CCHP system can be calculated as:

[0167] (Equation 28)

[0168] wherein Costini is initial investment cost, CR is capital recovery factor, Costmian is annual maintenance cost, Costdep is annual depreciation cost.

[0169] Capital recovery factor can be calculated as:

[0170] (Equation 29)

[0171] wherein r is interest rate, L is system working life.

[0172] Annual maintenance cost can be calculated as:

[0173] (Equation 30)

[0174] wherein ζ is maintenance factor, 6%.

[0175] Annual depreciation cost can be calculated as:

[0176] (Equation 31)

[0177] wherein is cumulative decay capacity of energy storage battery, is cumulative decay voltage of electrolyzer, is cumulative decay voltage of solid oxide fuel cell, is maximum allowed decay capacity of energy storage battery, is the maximum allowed decay voltage of the electrolyzer, is the maximum allowed decay voltage of the solid oxide fuel cell, , , are the initial investment costs of the energy storage battery, the electrolyzer and the solid oxide fuel cell, respectively.

[0178] Step S204: A multi-objective optimization algorithm framework is constructed based on a non-dominated solution sorting genetic algorithm (NSGA-II), and the capacity sizes of the energy storage battery, the electrolyzer and the SOFC are taken as adjustment objects to perform multi-objective optimization on the total energy efficiency of the system and the total annual cost of the system, so as to obtain the optimal economic and technical device capacity size of the system.

[0179] For example, the expression of the optimization algorithm is as follows:

[0180] (Formula 32)

[0181] As shown in Figure 5 , it is a schematic diagram of the Pareto solution set of the total energy efficiency of the system and the total annual cost of the system under multi-objective optimization provided by the embodiment of the application.

[0182] Step S205: Based on the Pareto front solution set obtained by multi-objective optimization, the optimal device capacity size is selected by normalized Euclidean distance judgment. The normalized Euclidean distance can be calculated as follows:

[0183] (Formula 33)

[0184] wherein Edis is the normalized Euclidean distance, n is the dimension number of the solution set, is the normalized solution set coordinate, is the normalized theoretical optimal solution set coordinate. The normalized solution set coordinate can be calculated as follows:

[0185] (Formula 34)

[0186] wherein , , are the current solution coordinate, the maximum coordinate of the solution set and the minimum coordinate of the solution set, respectively.

[0187] It can be understood that the total energy efficiency of the system and the total annual cost of the system under multi-objective optimization can be normalized, and then the normalized total energy efficiency of the system and the total annual cost of the system are solved to obtain the Pareto solution set, with the maximum total energy efficiency of the system and the minimum total annual cost of the system as the target. As Figure 6The Pareto solution set of the normalized system total energy efficiency and the system total annual cost provided by the embodiment of the present application is shown.

[0188] Based on the normalized Euclidean distance judgment, the optimal 5 sets of equipment capacity selection results and the corresponding system total energy efficiency, system total annual cost and normalized Euclidean distance can be referred to as shown in Table 2.

[0189] Table 2

[0190]

[0191] The embodiment of the present application aims at the technical field of fuel cell combined heat and power system, and proposes a solid oxide fuel cell combined heat and power system equipment capacity selection method for optimizing technical and economic performance. By adjusting the capacity of the key equipment (energy storage battery, electrolytic cell and solid oxide fuel cell) of the system, the system total energy efficiency and the total annual cost are optimized, and the system equipment capacity with the best economic and technical performance of the system is obtained. The method can provide theoretical guidance for the system design and manufacturing of the fuel cell combined heat and power system production party.

[0192] For example, the SOFC-based combined heat and power system selection method provided by the embodiment of the present application can be applied to a terminal, a server, or software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server can be configured as a standalone physical server, a server cluster composed of multiple physical servers, or a distributed system, and can also be configured as a cloud server providing basic cloud computing services such as cloud service, cloud database, cloud computing, cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content delivery network (CDN), and big data and artificial intelligence platform; and the software can be an application program for implementing the SOFC-based combined heat and power system selection method, but is not limited to the above forms.

[0193] By way of example, an implementation of the application can be applied in a variety of general purpose or special purpose computing system environments or configurations. Examples of well known computing systems, environments, and / or configurations that can be suitable for use with the application include, but are not limited to, personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote computer storage media including memory storage devices.

[0194] The embodiment of the application further provides a selection device of a solid oxide fuel cell (SOFC) based combined cooling, heating and power system, which comprises:

[0195] a system construction module, which is used for constructing a SOFC based combined cooling, heating and power system framework, wherein the combined cooling, heating and power system framework comprises a photovoltaic panel, an electrolytic cell, an energy storage battery, a hydrogen storage tank, an air compressor, a SOFC, a burner, an electric energy supply mechanism, a cold energy supply mechanism and a hot water supply mechanism, the electrolytic cell and the energy storage battery are connected to the photovoltaic panel, the hydrogen storage tank is connected to the electrolytic cell, the hydrogen storage tank and the air compressor are connected to the SOFC, the SOFC is connected to the burner, the electric energy supply mechanism, the cold energy supply mechanism and the hot water supply mechanism are connected to the burner, and the electric energy supply mechanism is further connected to the energy storage battery;

[0196] a device numerical model construction module, which is used for constructing a numerical model of a key device in the combined cooling, heating and power system framework, and the key device comprises at least one of the energy storage battery, the electrolytic cell or the SOFC;

[0197] an index determination module, which is used for coupling the numerical model of the key device with the combined cooling, heating and power system framework, and determining an energy efficiency index and a cost index of the combined cooling, heating and power system framework;

[0198] a multi-objective optimization model construction module, which is used for constructing a multi-objective optimization model by taking the capacity of the key device as an independent variable factor and taking the maximization of the energy efficiency index and the minimization of the cost index as solving targets;

[0199] a solving module, which is used for selecting the capacity of the key device based on a result obtained by solving the multi-objective optimization model.

[0200] It should be noted that the information interaction, execution process and the like between the above modules / units are based on the same concept as the method embodiments of the present application, and specific functions and brought technical effects can be referred to the method embodiments part, which will not be described here.

[0201] Referring to Figure 7 The present application also provides an electronic device 300. The electronic device 300 can be a server or a terminal, and the internal structure of the electronic device 300 includes but is not limited to:

[0202] The memory 310 is configured to store programs.

[0203] The processor 320 is configured to execute the programs stored in the memory 310, and when the processor 320 executes the programs stored in the memory 310, the processor 320 is configured to perform the selection method of the SOFC-based combined cooling, heating and power system as in any of the preceding embodiments.

[0204] The processor 320 and the memory 310 can be connected by a bus or other means.

[0205] The memory 310 as a kind of non-transient computer readable storage medium can be used to store non-transient software programs and non-transient computer executable programs, such as the selection method of the SOFC-based combined cooling, heating and power system described in any embodiment of the present application. The processor 320 executes the non-transient software programs and instructions stored in the memory 310, thereby realizing the selection method of the SOFC-based combined cooling, heating and power system as in any of the preceding embodiments.

[0206] The memory 310 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; the data storage area can store the selection method of the SOFC-based combined cooling, heating and power system described above. In addition, the memory 310 can include a high-speed random access memory, and can also include a non-transient memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 310 can optionally include a memory remotely arranged with respect to the processor 320, and these remote memories can be connected to the processor 320 through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0207] The non-transient software programs and instructions required to realize the selection method of the SOFC-based combined cooling, heating and power system described above are stored in the memory 310, and when executed by one or more processors 320, the selection method of the SOFC-based combined cooling, heating and power system provided by any embodiment of the present application is executed.

[0208] The embodiment of the present application further provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions are used for executing the selection method of the SOFC-based combined cooling heating and power system.

[0209] In an embodiment, the storage medium stores computer executable instructions, and the computer executable instructions are executed by one or more control processors, such as the one or more processors 320 in the electronic device 300, so that the one or more processors 320 execute the selection method of the SOFC-based combined cooling heating and power system provided by any embodiment of the present application.

[0210] The above described embodiments are merely illustrative, and units described as separate components can or can not be physically separate, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0211] Those skilled in the art can understand that all or some steps in the above disclosed method and system can be implemented as software, firmware, hardware and appropriate combinations thereof. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software can be distributed on a computer readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, as known to those skilled in the art, communication media generally includes computer readable instructions, data structures, program modules or other data in modulated data signals such as carrier waves or other transport mechanisms, and can include any information delivery medium.

[0212] In addition, one embodiment of the present application further provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the selection method of the SOFC-based combined cooling heating and power system in any of the foregoing embodiments.

[0213] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0214] It should also be understood that various embodiments provided by the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0215] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present application, which are used to illustrate the technical solutions of the present application, rather than limit them. The protection scope of the present application is not limited thereto, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and these modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application.

Claims

1. A selection method for a SOFC-based combined cooling, heating and power system, characterized in that, The method includes the following steps: A combined cooling, heating, and power (CCHP) system framework based on SOFC is constructed. The CCHP system framework includes photovoltaic panels, an electrolyzer, an energy storage battery, a hydrogen storage tank, an air compressor, an SOFC, a burner, an electricity supply mechanism, a cooling supply mechanism, and a hot water supply mechanism. The electrolyzer and the energy storage battery are respectively connected to the photovoltaic panels. The hydrogen storage tank is connected to the electrolyzer. The hydrogen storage tank and the air compressor are respectively connected to the SOFC. The SOFC is connected to the burner. The electricity supply mechanism, the cooling supply mechanism, and the hot water supply mechanism are respectively connected to the burner. The electricity supply mechanism is also connected to the energy storage battery. Numerical models are constructed for the key equipment in the combined cooling, heating and power system framework, including: the energy storage battery, the electrolyzer, and the SOFC; The numerical model of the key equipment is coupled with the framework of the combined cooling, heating and power system to determine the energy efficiency index and cost index of the combined cooling, heating and power system framework. The energy efficiency index is determined based on the system output electrical power, coefficient output thermal power and system output cold power. The cost index is determined based on the cumulative decay capacity of the energy storage battery, the cumulative decay voltage of the electrolytic cell and the cumulative decay voltage of the SOFC. A multi-objective optimization model is constructed, with the capacity of the key equipment as the independent variable factor and the maximization of the energy efficiency index and the minimization of the cost index as the solution objectives. Based on the results obtained from solving the multi-objective optimization model, the capacity of the key equipment is selected.

2. The method according to claim 1, characterized in that, Constructing a numerical model of the energy storage battery includes: A battery capacity sub-model and a performance degradation sub-model of the energy storage battery are constructed. The battery capacity sub-model is used to determine the effective total capacity of the energy storage battery based on the number of individual cells and the real-time relative capacity. The performance degradation sub-model of the energy storage battery is used to determine the degradation capacity of the energy storage battery based on the depth of discharge of the energy storage battery in each charge-discharge cycle.

3. The method according to claim 2, characterized in that, The depth of discharge of the energy storage battery in each charge-discharge cycle is determined based on the highest and lowest charge levels of the charge-discharge cycle. Determining the degradation capacity of the energy storage battery based on the depth of discharge in each charge-discharge cycle includes: The fitting coefficients are determined based on the depth of discharge during the charge-discharge cycle of the energy storage battery. The degradation capacity of the energy storage battery is determined based on the fitting coefficients.

4. The method according to claim 1, characterized in that, Constructing a numerical model of the electrolytic cell includes: An electrochemical sub-model and a performance degradation sub-model of the electrolyzer are constructed. The electrochemical sub-model is used to determine the hydrogen production efficiency of the electrolyzer based on the number of individual electrolyzer plates and the voltage of each plate. The performance degradation sub-model is used to determine the degradation voltage of the electrolyzer based on the number of individual electrolyzer plates and the voltage degradation rate of the electrolyzer.

5. The method according to claim 1, characterized in that, Constructing the numerical model of the SOFC includes: An electrochemical sub-model and a performance degradation sub-model of the SOFC are constructed. The electrochemical sub-model is used to determine the electrical power and thermal power of the SOFC based on the number of single cells and the effective single-cell voltage of the SOFC. The performance degradation sub-model is used to determine the voltage decay rate of the SOFC based on the current density and hydrogen mass ratio of the SOFC.

6. The method according to claim 1, characterized in that, The result obtained by solving the multi-objective optimization model is the Pareto front solution set; the selection of the capacity of the key equipment based on the result obtained by solving the multi-objective optimization model includes: The Pareto front solution set is used to determine the normalized Euclidean distance; The capacity of the key equipment is selected based on the normalized Euclidean distance.

7. A selection device for a SOFC-based combined cooling, heating and power system, characterized in that, The device includes: The system construction module is used to construct a combined cooling, heating, and power (CCHP) system framework based on SOFC. The CCHP system framework includes photovoltaic panels, an electrolyzer, an energy storage battery, a hydrogen storage tank, an air compressor, an SOFC, a burner, a power supply mechanism, a cooling supply mechanism, and a hot water supply mechanism. The electrolyzer and the energy storage battery are respectively connected to the photovoltaic panels, the hydrogen storage tank is connected to the electrolyzer, the hydrogen storage tank and the air compressor are respectively connected to the SOFC, the SOFC is connected to the burner, and the power supply mechanism, cooling supply mechanism, and hot water supply mechanism are respectively connected to the burner. The power supply mechanism is also connected to the energy storage battery. The equipment numerical model construction module is used to construct numerical models for key equipment in the combined cooling, heating and power system framework, including: the energy storage battery, the electrolytic cell and the SOFC; The index determination module is used to couple the numerical model of the key equipment with the combined cooling, heating and power system framework to determine the energy efficiency index and cost index of the combined cooling, heating and power system framework. The energy efficiency index is determined based on the system output electrical power, coefficient output thermal power and system output cold power, and the cost index is determined based on the cumulative decay capacity of the energy storage battery, the cumulative decay voltage of the electrolyzer and the cumulative decay voltage of the SOFC. A multi-objective optimization model construction module is used to construct a multi-objective optimization model with the capacity of the key equipment as the independent variable factor and the maximization of the energy efficiency index and the minimization of the cost index as the solution objectives. The solution module is used to select the capacity of the key equipment based on the results obtained from solving the multi-objective optimization model.

8. An electronic device, characterized in that, include: Memory, used to store programs; A processor for executing a program stored in the memory, wherein when the processor executes the program stored in the memory, the processor is configured to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The device stores computer-executable instructions for performing the method as described in any one of claims 1 to 6.

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