An optimization method for a pvt-driven brayton carnot cell system and related devices

Abstract

The application belongs to the field of new energy power generation system, and discloses a kind of optimization method and related device of PVT driven brayton carno cell system, first, the parameter sensitivity analysis is carried out to the numerical model of pre-constructed PVT driven brayton cycle carno cell system, and the key variables turbine inlet temperature and evaporation pressure are identified;Subsequently, the system electric conversion efficiency and the degree of electric cost are taken as the optimization target, and the multi-objective optimization model containing the objective function and the constraint is established;Finally, the model is solved by using Pareto dominance method, and the Pareto optimal solution set is output to determine the optimal operating condition of the system. The contradiction between the thermal efficiency and the degree of electric cost is effectively solved by using the optimization method, and the overall performance and economic benefit of the system are significantly improved, which provides efficient operation control strategy for engineering practice.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation systems, specifically the field of Brayton cycle Carnot battery energy storage systems, and particularly relates to an optimization method and related apparatus for a PVT-driven Brayton Carnot battery system. Background Technology

[0002] With the depletion of fossil fuels and escalating environmental problems, the global energy landscape is undergoing profound changes, and the energy supply system is accelerating its structural transformation from traditional thermal power generation to one dominated by renewable energy sources such as wind and solar power. However, the inherent randomness, instability, and uncertainty of renewable energy make it difficult to directly match the complex and ever-changing grid load demands, posing a severe challenge to the supply-demand balance and stable operation of the power system. Therefore, developing efficient energy storage technologies to store surplus renewable energy power and release it during peak electricity demand has become a key link in ensuring the success of the energy transition. Among numerous energy storage technologies, Carnot batteries, with their high energy density, good compatibility with renewable energy, strong geographical and environmental adaptability, large-capacity energy storage potential, and high cost-effectiveness, demonstrate great application prospects in solving the volatility problem of renewable energy, especially in Bretton cycle-based systems.

[0003] In the actual operation of Carnot battery energy storage systems based on the Brayton cycle architecture, the setting of key operating parameters has a significant impact on the overall performance of the system, especially thermal efficiency and levelized cost of electricity (LCOE). The core problem currently faced is that the effects of changes in these parameters on thermal efficiency and economics often exhibit inconsistent or even contradictory trends. For example, increasing thermal efficiency may lead to an increase in LCOE, and vice versa. This complex trade-off between thermodynamic performance and economics, coupled with the varying impact patterns of different parameter changes on both, makes it difficult to simultaneously achieve the dual objectives of maximizing system efficiency and minimizing operating costs using traditional single-objective optimization methods. This presents significant challenges to parameter optimization and control in engineering practice.

[0004] Therefore, existing operational optimization methods for Carnot battery energy storage systems based on the Brayton cycle architecture cannot effectively solve the multi-objective collaborative optimization problem between thermal efficiency and cost per kilowatt-hour in the system, and thus cannot maximize system performance. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method and related apparatus for a PVT-driven Brayton Carnot battery system. This method can effectively solve the multi-objective synergistic optimization problem between thermal efficiency and cost per kilowatt-hour in the system, thereby maximizing system performance.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] An optimization method for a PVT-driven Brayton Carnot battery system includes:

[0008] A parameter sensitivity analysis was performed on the pre-constructed numerical model of the Brayton cycle Carnot battery energy storage system driven by PVT to obtain the parameter sensitivity analysis results.

[0009] Based on the results of parameter sensitivity analysis, a multi-objective optimization model including objective function and constraints is established, with turbine inlet temperature and evaporation pressure as variables and system electrical conversion efficiency and cost per kilowatt-hour as optimization objectives.

[0010] The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0011] Furthermore, in the parameter sensitivity analysis of the pre-constructed PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model, the specific representation of the PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model is as follows:

[0012] PVT model:

[0013] Energy absorption efficiency of collector plate or th and net rate of heat absorption Q u :

[0014]

[0015]

[0016] Where S represents the total incident solar radiation, m water The mass flow rate of hot water. C p,water For the specific heat of hot water, T PVT→SST The PV / T hot water outlet temperature. T SST→PVT This refers to the PV / T cold water inlet temperature.

[0017] PVT power generation efficiency or PV and the production of electricity W PV :

[0018]

[0019] in, or r This indicates the reference efficiency of the PV cell. cIndicates the temperature coefficient. T cell Indicates the temperature of the PV cell. T r Indicates reference temperature. IAM Indicates the correction amount for the angle of incidence, ( the ) n This represents the product of the transmittance and absorptivity of the solar radiation at the normal angle of incidence of the PV panel. G T This represents the total solar radiation incident on the surface of the collector.

[0020] Model of a thermoelectric cycle:

[0021] Compressor status parameters:

[0022]

[0023]

[0024]

[0025] In the formula, P comp,in , P comp,out These are the intake and exhaust pressures of the heat pump cycle compressor, respectively. e comp This refers to the compression ratio; T comp,in、 T comp,out These are the compressor inlet and outlet temperatures, respectively. k The adiabatic index; m This is the mass flow rate of the working fluid in the compressor; or comp It is isentropic efficiency; C p Specific heat of the working fluid; e Pressure ratio;

[0026] Heat exchanger heat load for:

[0027]

[0028] In the formula, m hot The mass flow rate of the hot fluid; m cold This refers to the mass flow rate of the cold fluid. h hot,in and h hot,out These are the inlet and outlet enthalpies of the heat fluid, respectively. h cold,in andh cold,out These are the inlet and outlet enthalpies of the cold fluid, respectively.

[0029] Work done by a Brayton cycle steam turbine:

[0030]

[0031] In the formula, W turb The amount of work done by the steam turbine; This represents the mass flow rate of CO2. h turb,in and h turb,out These are the inlet and outlet enthalpies of the heat fluid, respectively. or turb The isentropic efficiency of the steam turbine;

[0032] Heat pump charging cycle COP:

[0033]

[0034] In the formula, Q HP, out For the heat supply of the heat pump cycle, W PV The electrical energy generated by PV / T photovoltaic panels.

[0035] Furthermore, after constructing the PVT-driven Brayton cycle Carnot battery energy storage system, the performance of the PVT-driven Brayton cycle Carnot battery energy storage system is verified, including:

[0036] Based on the set working fluid type, narrow point temperature difference of the heat exchanger, and assumed PVT photovoltaic system parameters, combined with the thermodynamic process, the thermodynamic parameters at each state point are calculated.

[0037] Verify whether the narrow-point temperature difference of the regenerator meets the design requirements based on the thermodynamic parameters at each state point.

[0038] Furthermore, after determining that the narrow-point temperature difference of the regenerator meets the design requirements, the mass flow rate of each working fluid is calculated based on the flow balance relationship of the system. Finally, it is verified whether the temperature of the heat pump condenser at the minimum narrow-point temperature difference meets the operating specifications, and the system operation results are output.

[0039] Furthermore, in the parameter sensitivity analysis of the pre-constructed PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model, a pre-constructed performance evaluation index model is used to perform parameter sensitivity analysis on the pre-constructed PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model. The specific expression of the performance evaluation index model is as follows:

[0040] efficiency of the Brayton cycle :

[0041]

[0042] In the formula, W net It is the net work of the Bretton cycle. Q in_BC The heat input from the storage tank is the heat of the Brayton cycle;

[0043]

[0044] In the formula, h turb,in , h turb,out The enthalpy values ​​of the working fluid at the inlet and outlet of the steam turbine. m It is the working fluid flow rate. or turb It refers to the efficiency of the steam turbine. a It represents the actual process;

[0045] The overall system's electrical conversion efficiency :

[0046]

[0047] In the formula, or ts For thermal storage efficiency.

[0048] Furthermore, based on the parameter sensitivity analysis results, using the turbine inlet temperature and evaporation pressure as variables, and the system electrical conversion efficiency and cost per kilowatt-hour as optimization objectives, a multi-objective optimization model including objective functions and constraints is established. The construction process of the multi-objective optimization model includes:

[0049] With the goals of high electricity conversion efficiency and the lowest levelized cost of electricity, the following objectives are established:

[0050]

[0051] in, or p2p For electrical conversion efficiency, LCOS For the cost per kilowatt-hour, the constraints are as follows: T pp For narrow temperature differences, This refers to the turbine inlet temperature. P e_BC This refers to the evaporation pressure of the Brayton cycle.

[0052] The cost per kilowatt-hour is calculated as follows:

[0053]

[0054] in, LT Indicates the service life. C tot and C an These refer to the total initial investment cost and annual expenditure, respectively. r Indicates the discount rate; E gen This indicates the amount of power returned to the grid each year;

[0055] The total initial investment cost is calculated using the modular costing method: C tot The total construction cost of all components of the system is calculated using the formulas for each module below and then summed together:

[0056] Cost of heat exchangers:

[0057]

[0058] Cost of steam turbine:

[0059]

[0060] The cost of the compressor:

[0061]

[0062] Pump cost:

[0063]

[0064] Cost of storage tanks:

[0065]

[0066] In the formula, a, b, and c are all correction coefficients.

[0067] Furthermore, the method of using Pareto dominance to solve the multi-objective optimization model and outputting the Pareto optimal solution set includes:

[0068] S31: Initialize the particle population and obtain the initial information of the population;

[0069] S32: Calculate the fitness of each particle in the particle swarm;

[0070] S33: Perform non-dominated sorting on the initial population; put the fitness of the initial population into the Pareto solution set, compare each particle with other particles, and if it is dominated by other individuals, remove it from the Pareto solution set, thereby obtaining the population of non-dominated solutions.

[0071] S34: The crowding distance of all individual particles is calculated using the dense distance method;

[0072] S35: Randomly select the global optimum from the pre-set proportional solutions with a large crowding distance, and record the position and fitness of the global optimum in the Pareto solution set, where the individual optimum is the initial value;

[0073] S36: Record the number of iterations and begin iterative calculation;

[0074] S37: Perform the following calculations for each particle to obtain the individual optimal value and the population optimal value;

[0075] S38: Determine if the maximum number of iterations has been reached. If it has, end the calculation. If not, increment the iteration count by 1 and return to S36 to recalculate until the maximum number of iterations is reached.

[0076] An optimized system for a PVT-driven Brayton Carnot battery system includes:

[0077] The sensitivity analysis module is used to perform parameter sensitivity analysis on the pre-built numerical calculation model of the PVT-driven Brayton cycle Carnot battery energy storage system to obtain the parameter sensitivity analysis results.

[0078] The model building module is used to establish a multi-objective optimization model based on the results of parameter sensitivity analysis, using turbine inlet temperature and evaporation pressure as variables, and system electrical conversion efficiency and cost per kilowatt-hour as optimization objectives, including objective functions and constraints.

[0079] The model solving module is used to solve the multi-objective optimization model using the Pareto dominance method and output the Pareto optimal solution set, so as to obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0080] An optimization device for a PVT-driven Brayton Carnot battery system includes:

[0081] Memory, used to store computer programs;

[0082] A processor is used to implement the steps of the above-described optimization method for a PVT-driven Brayton Carnot battery system when executing the computer program.

[0083] A computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of the optimization method for the PVT-driven Brayton Carnot battery system described above.

[0084] Compared with the prior art, the present invention has the following beneficial effects:

[0085] This invention provides an optimization method for a PVT-driven Brayton Carnot battery system. The method first performs parameter sensitivity analysis on a pre-constructed numerical model of the PVT-driven Brayton cycle Carnot battery system to identify key variables: turbine inlet temperature and evaporation pressure. Then, with system electro-conversion efficiency and levelized cost of electricity (LCOE) as optimization objectives, a multi-objective optimization model including objective functions and constraints is established. Finally, the Pareto dominance method is used to solve the model, outputting the Pareto optimal solution set to determine the optimal operating conditions of the system. Parameter sensitivity analysis focuses on core variables, avoiding interference from irrelevant parameters; the multi-objective model simultaneously optimizes thermal efficiency and economic efficiency, overcoming the limitations of single-objective methods; the Pareto method uses non-dominated solutions to balance conflicts between objectives, ensuring that efficiency improvements do not come at the cost of increased costs, achieving synergistic optimization. This optimization method effectively resolves the contradictory relationship between thermal efficiency and LCOE, significantly improving the overall system performance and economic benefits, and providing an efficient operation and control strategy for engineering practice.

[0086] Preferably, in this invention, the PVT model and thermoelectric cycle model include calculations of energy absorption efficiency, power generation efficiency, and state parameters of key components such as the compressor and expander. The accurate mathematical model captures the dynamic processes of PVT power generation and the Brayton cycle, laying a reliable foundation for subsequent parameter analysis and optimization. The ultimate effect is to ensure that the system simulation results accurately reflect actual operating characteristics, avoid deviations caused by model simplification, and improve the scientific rigor and credibility of the optimization process.

[0087] Preferably, in this invention, performance verification is performed after the system is built by calculating the thermodynamic parameters at each state point and checking the narrow-point temperature difference of the regenerator. The verification process is based on thermodynamic laws and design constraints to ensure that the narrow-point temperature difference meets the safety margin. The ultimate effect is to identify potential thermodynamic design flaws in advance, enhance the stability and reliability of system operation, and prevent efficiency losses or equipment damage caused by temperature difference violations.

[0088] Preferably, in this invention, after verifying the narrow-point temperature difference, the working fluid mass flow rate is further calculated and the minimum narrow-point temperature difference of the heat pump condenser is verified. Flow balance and temperature difference verification cover the key heat exchange components of the system, ensuring overall thermodynamic consistency. The ultimate effect is to comprehensively optimize the thermodynamic cycle efficiency, ensure the system operates efficiently within specifications, reduce energy loss, and improve system reliability.

[0089] Preferably, in this invention, the performance evaluation index model includes calculation formulas for Brayton cycle efficiency and system electro-conversion efficiency, used for parameter sensitivity analysis. These indices quantify key thermodynamic and economic parameters, providing a direct basis for optimization objectives. The ultimate effect is to make sensitivity analysis more targeted, identify core factors affecting system performance, lay a data foundation for multi-objective optimization, and improve decision-making accuracy.

[0090] Preferably, in this invention, the objective function and constraints are clearly defined in the construction of the multi-objective optimization model, and the cost per kilowatt-hour calculation formula is combined to cover economic factors such as total initial investment and annual expenditure. The thermodynamic efficiency and economic cost models are integrated to achieve a mathematical expression of the dual objectives. The ultimate effect is to ensure that the optimization process comprehensively considers technical feasibility and economic efficiency, generating a solution that balances high efficiency and low cost, thereby enhancing the competitiveness of the system in practical applications.

[0091] Preferably, in this invention, the solution steps of the Pareto dominance method include population initialization, non-dominated sorting, crowding distance calculation, and iterative optimization. The particle swarm optimization algorithm efficiently searches for the non-dominated solution set and avoids local optima guided by the crowding distance. The final result is rapid convergence to the tradeoff-optimal solution set, providing diverse operating conditions and enhancing the practicality and robustness of the optimization method. Attached Figure Description

[0092] Figure 1 A schematic diagram of a PVT-driven Brayton cycle Carnot battery system provided in an embodiment of the present invention.

[0093] Figure 2 A flowchart of a multi-objective optimization model for a Brayton cycle Carnot battery system driven by PVT is provided for embodiments of the present invention.

[0094] Figure 3 A flowchart for calculating the optimization objective is provided for embodiments of the present invention.

[0095] Figure 4 A flowchart for solving a multi-objective optimization model using the Pareto dominance method is provided for embodiments of the present invention.

[0096] Figure 5 This invention provides a multi-objective optimization model verification for Pareto curves of three standard functions for embodiments of the invention.

[0097] Figure 6 This invention provides a calculated optimal Pareto front solution set for a PVT-driven Brayton cycle Carnot battery system, yielding high efficiency and low cost.

[0098] Figure 7 A flowchart of an optimization method for a PVT-driven Brayton Carnot battery system provided by the present invention;

[0099] Figure 8 This is a schematic diagram of the structure of an optimized PVT-driven Brayton Carnot battery system provided by the present invention.

[0100] Figure label:

[0101] 1. Photovoltaic / thermal power system; 2. First evaporator; 3. First regenerator; 4. Pump; 5. Condenser; 6. Storage tank; 7. Second evaporator; 8. Steam turbine; 9. Second regenerator; 10. Cooler. Detailed Implementation

[0102] To facilitate a deeper understanding of the technical solution of this invention, the following explanations are provided for the technical terms:

[0103] PVT: PV / T stands for Photovoltaic / Thermoelectric System. A photovoltaic / thermoelectric system is a composite energy system that combines the photoelectric conversion (photovoltaic effect) and thermal conversion (thermoelectric effect) of solar energy. It aims to improve energy utilization efficiency and achieve multi-energy complementary output by synergistically utilizing the two different forms of solar energy (light energy and thermal energy).

[0104] As described in the background section, during operation, multiple parameters of the Brayton cycle affect the thermal efficiency and economics of the entire energy storage system. This work selects turbine inlet temperature, condensing temperature, and evaporating pressure as the parameters for discussion. When these parameters change, the trends of system thermal efficiency and cost per kilowatt-hour are inconsistent; thermal efficiency may increase, but economics may decrease. The relationship between the two is quite complex, and the influence patterns of different parameters also differ.

[0105] To achieve the above objectives, this embodiment provides an optimization method for a PVT-driven Brayton Carnot battery system. After performing sensitivity analysis of the parameters, this method uses a multi-objective optimization particle swarm optimization algorithm to obtain the Pareto optimal solution set. During engineering operation, the turbine inlet temperature and evaporation pressure can be adjusted according to the Pareto optimal solution set to achieve the highest efficiency and lowest levelized cost of electricity, providing a reference for performance optimization of novel Carnot battery energy storage systems.

[0106] S1. Establish a numerical calculation model and parameter sensitivity analysis for a PV / T driven Brayton cycle Carnot battery energy storage system;

[0107] S2. Based on the results of the parameter sensitivity analysis, the turbine inlet temperature and evaporation pressure are selected as variables, and the system power conversion efficiency and cost per kilowatt-hour are the objectives. A multi-objective optimization model including objective function and constraints is established.

[0108] S3. Use the Pareto dominance method to solve the multi-objective optimization model, output the Pareto optimal solution set, and analyze to obtain the optimal operating conditions.

[0109] The process for establishing a PV / T driven Brayton cycle Carnot battery energy storage system includes:

[0110] (1) During the charging phase, the pump converts the low-temperature working fluid (R113) into a high-temperature working fluid through the photovoltaic power generation system. Subsequently, the working fluid releases heat in the condenser and stores it in the thermal energy storage tank. Then, the working fluid flows into the second evaporator 7 through the first regenerator 3 and the valve. After efficient heat exchange with the hot water from the photovoltaic / thermal power system in the second evaporator 7, the working fluid returns to the pump through the first regenerator 3, completing the cycle.

[0111] (2) During the discharge phase, the heat storage tank first releases the stored heat in the second evaporator 7 to heat the working fluid (CO2), thereby driving the turbine to generate electricity. Subsequently, the working fluid exchanges heat with the cooler through the second regenerator 9, and finally enters the second regenerator 9 again under the action of the compressor to complete the energy cycle.

[0112] In addition, the system's charging time is 5 hours during the day when sunlight is strong, and the discharging time is 5 hours in the evening and morning when the peak power is present.

[0113] The establishment of the multi-objective particle swarm optimization model includes:

[0114] (1) An objective function is established with the goals of high system electrical conversion efficiency and minimum cost per kilowatt-hour. The system electrical conversion efficiency is the effective conversion of electrical energy input into the system. The cost includes the cost of all components and materials, and the cost per kilowatt-hour is calculated based on an assumed service life of 25 years. The constraints are the adjustment range of the turbine inlet temperature and evaporation pressure, with the narrow temperature difference set at 5°C.

[0115] (2) The calculation process for the optimization objective is as follows: Based on the input variable factors of turbine inlet temperature and evaporation pressure, the state of each point is calculated according to the numerical calculation model of the entire system described in S1, to obtain the COP of the heat pump, the power consumption of the compressor, and the power generation of the turbine, etc. The total electrical conversion efficiency of the system is calculated according to the performance formulas of the heat pump and the Brayton cycle. The initial investment cost is calculated based on the power consumption of each component and the tank capacity. The cost of the working fluid is calculated based on the flow rate. The annual operating cost is calculated based on the literature reference. The total power generation within the service life is calculated according to the system model described in S1, and the cost per kilowatt-hour is further obtained.

[0116] The process of solving the problem using the Pareto dominance method is as follows:

[0117] (1) Initialize the particle swarm and filter the initial particle swarm for individual optima and global optima. Set the initial swarm size, spatial dimension, objective function dimension, and Pareto solution set size. Define position and velocity boundaries. Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm. Calculate the fitness of each particle. Perform non-dominated sorting on the initialized swarm. Place the fitness of the initialized swarm into the Pareto solution set. For each particle, compare it with other particles. If it is dominated by other individuals, remove it from the Pareto solution set to obtain the non-dominated solution swarm. Calculate the crowding distance of all individual particles using the dense distance method. Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is the initial value.

[0118] (2) Record the number of iterations, and start the calculation of step (3) within the number of iterations.

[0119] (3) For each particle, perform the following calculations: Update the particle's velocity and position using the particle swarm optimization method. Use inertial adaptive weight coefficients to control the degree of change in each iteration to avoid premature convergence or local optimization. Determine whether the updated particle velocity and position are within the constraints (defined boundary range). If they exceed the boundary range, regenerate the particle's velocity and position within the boundary; if they are within the boundary range, proceed to the next step. Calculate the fitness of each particle in the updated particle swarm. Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the constrained boundary conditions. If they exceed the range, remove the particle from the Pareto solution set; if the constraints are met, proceed to the next step. Perform non-dominated sorting and crowding calculation on the updated population. Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the individual optimal value and the population optimal value.

[0120] After iterative calculations, a three-dimensional Pareto optimal solution set is obtained, with efficiency, cost, and evaporation rate as the coordinate axes. All points on this solution set are non-dominated solutions. During engineering operation, the molten salt flow rate and water flow rate can be adjusted according to different evaporation rate demands based on the Pareto optimal solution set, in order to achieve the goal of minimum cost and maximum efficiency.

[0121] The optimization method provided in this embodiment will be further explained below with reference to the accompanying drawings:

[0122] like Figure 1As shown in the diagram, this embodiment presents a schematic flow chart of a PV / T-driven Brayton Carnot battery system. Its operation is as follows: The system uses a photovoltaic power generation-driven pump to convert a low-temperature working fluid (R113) into a high-temperature working fluid, storing its heat in a heat storage tank. During the charging phase, the working fluid releases heat in the condenser and exchanges heat with the evaporator through a regenerator. During the discharging phase, the heat storage tank releases heat to warm the working fluid (CO2), driving the turbine to generate electricity. After passing through the regenerator and cooler, the working fluid re-enters the regenerator under the action of a compressor, completing the energy cycle.

[0123] In this embodiment, the multi-objective optimization model flow of the PVT-driven Brayton cycle Carnot battery system is as follows: Figure 2 As shown, the specific steps include:

[0124] S1: Establish a numerical calculation model for a Brayton cycle Carnot battery energy storage system driven by photovoltaic / thermal power (PV / T) and conduct parameter sensitivity analysis;

[0125] S2: Based on the results of parameter sensitivity analysis, the turbine inlet temperature and evaporation pressure are selected as optimization variables, and the system electrical conversion efficiency and cost per kilowatt-hour are the objectives. A multi-objective optimization model containing objective functions and constraints is constructed.

[0126] S3: The Pareto dominance method is used to solve the multi-objective optimization model, output the Pareto optimal solution set, and then analyze and determine the optimal operating conditions.

[0127] The numerical calculation model and performance evaluation index model for the Carnot battery energy storage system described in S1 include:

[0128] The PV / T model (PVT model):

[0129] Energy absorption efficiency of collector plate ( or th ) and net rate ( Q u ):

[0130]

[0131]

[0132] Where S is the total incident solar radiation in kJ / h -1 , m water The mass flow rate of hot water. C p,water For the specific heat of hot water, T PVT→SST The PV / T hot water outlet temperature. T SST→PVT This refers to the PV / T cold water inlet temperature.

[0133] PV / T power generation efficiency ( or PV ) and generating electricity (W) PV ):

[0134]

[0135] in, or r This is the reference efficiency of PV cells. c It is the temperature coefficient. T cell It is the temperature of the PV cell, in K. T r This is a reference temperature. IAM It is the incident angle correction amount, ( the ) n It is the product of the transmittance and absorptivity of solar radiation at the normal angle of incidence of the PV panel. G T It is the total solar radiation (beam + diffuse) incident on the surface of the collector, kJh -1 m -2 .

[0136] Model of a thermoelectric cycle:

[0137] Compressor status parameters:

[0138]

[0139]

[0140]

[0141] In the formula, P comp,in , P comp,out These are the inlet and outlet pressures of the heat pump cycle compressor, respectively, in Pa. e comp Compression ratio; that is, the ratio of exhaust pressure to intake pressure; T comp,in、 T comp,out These are the compressor inlet and outlet temperatures, respectively, in K; k Inductance index; kW; m The mass flow rate of the working fluid in the compressor is expressed in kg / s. or comp It is isentropic efficiency; C p Specific heat of the working fluid; e This refers to the pressure ratio.

[0142] Expander outlet pressure, outlet temperature, expander work:

[0143]

[0144]

[0145]

[0146] In the formula, P turb,in , P turb,out These are the inlet and outlet pressures of the heat pump cycle expander, respectively, in Pa. e turb The expansion ratio is the ratio of exhaust pressure to intake pressure. T turb,in , T turb,out The inlet and outlet temperatures of the expander are in K. k The adiabatic index; W turb Power consumption of the expander is kW; m is the mass flow rate of CO2. h turb,in and h turb,out These are the inlet and outlet enthalpies of the heat fluid, respectively. or turb This refers to the isentropic efficiency of the steam turbine.

[0147] The heat load of the heat exchanger is:

[0148]

[0149] In the formula, m hot The mass flow rate of the hot fluid is kg / s; m cold The mass flow rate of the cold fluid is kg / s; h hot,in and h hot,out These are the inlet and outlet enthalpies of the heat fluid, respectively, in kJ / kg; h cold,in and h cold,out These are the inlet and outlet enthalpies of the cold fluid, respectively, in kJ / kg.

[0150] Work done by a Brayton cycle steam turbine:

[0151]

[0152]

[0153] Heat pump charging cycle COP:

[0154]

[0155] In the formula, Q HP, out For the heat supply of the heat pump cycle, W PV The electrical energy generated by PV / T photovoltaic panels.

[0156] The efficiency of the Brayton cycle:

[0157]

[0158] In the formula W net It is the net work of the Brayton cycle, in kW. Q in_BC It is the heat input from the storage tank in the Brayton cycle.

[0159]

[0160] In the formula, h turb,in , h turb,out The enthalpy values ​​at the inlet and outlet of the working fluid in the steam turbine are expressed in kJ / kg. m It is the working fluid flow rate, kg / s. or turb It refers to the efficiency of the steam turbine. a It represents the actual process.

[0161] The overall system's electrical conversion efficiency:

[0162]

[0163] In the formula, or ts For thermal storage efficiency, it is considered to be 100% in this embodiment.

[0164] In S2 above, based on the PV / T-driven Brayton cycle Carnot battery model and evaluation indicators established in S1, a multi-objective optimization model is established with high power conversion efficiency and the lowest levelized cost of electricity as objectives, and the turbine inlet temperature and evaporation pressure of the Brayton cycle as variables. The specific process is as follows:

[0165] 1) With the goals of high electricity conversion efficiency and the lowest levelized cost of electricity, the following objectives are established:

[0166]

[0167] in, or p2p For electrical conversion efficiency, LCOS For the cost per kilowatt-hour, the constraints are as follows: T pp For narrow temperature differences, This refers to the turbine inlet temperature. P e_BC This refers to the evaporation pressure of the Brayton cycle.

[0168] The cost per kilowatt-hour is calculated as follows:

[0169]

[0170] Among them, service life LT The initial estimate was 25 years, based on earlier research. Meanwhile, C tot and C an These refer to the total initial investment cost and annual expenditure, respectively. r This indicates a discount rate of 5%. The power returned to the grid annually is called... E gen .

[0171] The total initial investment cost is calculated using the modular costing method: C tot This is the construction cost of all components of the system, calculated using the formulas for each module below and then summed together. The costs are as follows:

[0172] Heat exchanger:

[0173]

[0174] Steam turbine:

[0175]

[0176] compressor:

[0177]

[0178] Pump:

[0179]

[0180] Storage tanks:

[0181]

[0182] In each formula, a, b, and c are correction coefficients.

[0183] The flowchart of the system calculation is as follows Figure 3As shown, the basic parameters are first set, including the working fluid type, the narrow-point temperature difference of the heat exchanger, and the assumed PV / T photovoltaic system parameters. Then, based on the main thermodynamic processes, the thermodynamic parameters at each state point are calculated sequentially, and the narrow-point temperature difference of the regenerator is verified to meet the design requirements. On this basis, further thermodynamic analysis of each stage of the heat pump cycle is conducted, and the minimum narrow-point temperature difference of each heat exchanger is checked one by one. Based on the system's flow balance relationship, the mass flow rate of each working fluid is calculated. Finally, it is verified whether the temperature of the heat pump condenser at the minimum narrow-point temperature difference meets the operating specifications, and the system operation results are output.

[0184] In S3 above, the flowchart for solving the multi-objective optimization model in S2 using the Pareto dominance method is as follows: Figure 4 As shown, particle position represents the optimization variable, fitness stores the optimization objective, and particle velocity represents the search direction. Specifically, the Pareto dominance method is used to solve the multi-objective optimization model in S2, resulting in the following optimization solution process for the Brayton cycle Carnot battery system driven by PV / T:

[0185] S31: Initialize the particle swarm to obtain its initial information. Specifically, set the initial swarm size to 100, the spatial dimension to 2 (number of optimization variables), the objective function dimension to 2 (number of optimization objectives), and the Pareto solution set size to 80 (number of retained optimal solutions). Define position and velocity boundaries. Initialize the initial positions and velocities of the particle swarm to obtain its initial information.

[0186] S32: Calculate the fitness of each particle in the particle swarm according to the calculation method described in S2.

[0187] S33: Perform non-dominated sorting on the initial population. Place the fitness of the initial population into the Pareto solution set. For each particle, compare it with other particles. If it is dominated by other individuals, remove it from the Pareto solution set. This will give you a population with non-dominated solutions.

[0188] S34: The crowding distance of all individual particles is calculated using the dense distance method.

[0189] S35: Randomly select the global optimum from the top 20% of solutions with the largest crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is the initial value.

[0190] S36: Record the number of iterations and start iterative calculation.

[0191] S37: Perform the following calculations for each particle:

[0192] S371: Update the particle's velocity and position. The particle position x at step k+1. ik+1 Calculated as follows:

[0193]

[0194] Where, x i k Let v be the position of the i-th particle at step k. i k Let v be the velocity of the i-th particle at step k. The formula for the particle velocity v of the i-th particle at iteration step k+1 is as follows:

[0195]

[0196] in, p i,best It is the best position in the history of an individual particle, and G best This is the optimal location for the current population. c per and c en These are the self-learning factor and the group learning coefficient. r per and r en It is a random number between 0 and 1. w The inertial adaptive weight coefficients control the degree of change in each iteration, avoiding premature convergence or local optimization, and are calculated as follows:

[0197]

[0198] Where d is the overall dimension (the number of variable factors), which is 2 in this example. w max and w min These are the maximum and minimum values ​​of the set adaptive inertial weighting coefficients, respectively. x max and x min These are the maximum and minimum values ​​for the set particle positions, respectively.

[0199] Determine if the updated particle velocity and position are within the constraints (defined boundary range). If they exceed the boundary range, regenerate the particle velocity and position within the boundary; otherwise, proceed to the next step.

[0200] S372: Calculate the fitness of each particle in the updated particle swarm according to the calculation method described in S2. Determine the calculated fitness. P o and Pi Check if the particle is within the bounded boundary conditions. If it is outside the range, remove it from the Pareto solution set and ignore it. If the constraints are met, proceed to the next step.

[0201] S373: Perform non-dominated sorting on the updated population.

[0202] S374: Calculate the crowding of the updated population.

[0203] S375: Randomly select the global optimum from the top 20% of solutions with the largest crowding distance.

[0204] S376: Record the individual optimal value and the group optimal value.

[0205] S38: Determine if the number of iterations has been reached. If it has, end the calculation. If not, increment the number of iterations by 1 and return to S36 to recalculate.

[0206] The established multi-objective particle swarm optimization iterative computational model was validated. This paper calculates three standard functions applicable to the evaluation of multi-objective optimization schemes. The results are as follows: Figure 5 As shown, all functions achieved excellent computational accuracy and exhibited a more uniformly distributed Pareto front curve.

[0207] The Pareto optimal solution set of the multi-objective optimization result in this embodiment is as follows: Figure 6 As shown. Figure 6 For electrical conversion efficiency or p2p The Pareto front solution set for LCOS (Levelized Cost of Electricity) is shown. It can be seen that the optimized Pareto optimal solution set forms a smooth cancellation path. All points on this solution set are non-dominated solutions, indicating that an increase in power conversion efficiency is accompanied by an increase in cost. The three points of different colors in the figure represent the selected optimal operating conditions. During engineering operation, the turbine inlet temperature and evaporation pressure can be adjusted according to the Pareto optimal solution set to achieve the goal of minimum cost and maximum efficiency under different operating conditions.

[0208] Example 2

[0209] like Figure 7 As shown, this embodiment provides an optimization method for a PVT-driven Brayton Carnot battery system, including the following steps:

[0210] S1: Perform parameter sensitivity analysis on the pre-constructed numerical calculation model of the Brayton cycle Carnot battery energy storage system driven by PVT to obtain the parameter sensitivity analysis results;

[0211] S2: Based on the results of parameter sensitivity analysis, a multi-objective optimization model including objective function and constraints is established, with turbine inlet temperature and evaporation pressure as variables and system power conversion efficiency and cost per kilowatt-hour as optimization objectives.

[0212] S3: The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0213] like Figure 8 As shown, this embodiment also provides an optimization system for a PVT-driven Brayton Carnot battery system, including: a sensitivity analysis module, used to perform parameter sensitivity analysis on a pre-built numerical calculation model of a PVT-driven Brayton Carnot battery energy storage system to obtain parameter sensitivity analysis results; a model building module, used to establish a multi-objective optimization model including objective functions and constraints based on the parameter sensitivity analysis results, using turbine inlet temperature and evaporation pressure as variables, and system electro-conversion efficiency and cost per kilowatt-hour as optimization objectives; and a model solving module, used to solve the multi-objective optimization model using the Pareto dominance method, outputting a Pareto optimal solution set, so as to obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0214] The present invention also provides an optimization device for a PVT-driven Brayton Carnot battery system, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the optimization method for the PVT-driven Brayton Carnot battery system.

[0215] When the processor executes the computer program, it implements the optimization steps of the PVT-driven Brayton Carnot battery system described above. For example, it performs parameter sensitivity analysis on the pre-built numerical calculation model of the PVT-driven Brayton cycle Carnot battery energy storage system to obtain the parameter sensitivity analysis results; based on the parameter sensitivity analysis results, it establishes a multi-objective optimization model including objective functions and constraints, using turbine inlet temperature and evaporation pressure as variables, and system electro-conversion efficiency and cost per kilowatt-hour as optimization objectives; it solves the multi-objective optimization model using the Pareto dominance method, outputs the Pareto optimal solution set, and obtains the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0216] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, such as: a sensitivity analysis module, used to perform parameter sensitivity analysis on the pre-built numerical calculation model of the PVT-driven Brayton cycle Carnot battery energy storage system to obtain parameter sensitivity analysis results; a model building module, used to establish a multi-objective optimization model including objective functions and constraints based on the parameter sensitivity analysis results, using turbine inlet temperature and evaporation pressure as variables, and system electro-conversion efficiency and cost per kilowatt-hour as optimization objectives; and a model solving module, used to solve the multi-objective optimization model using the Pareto dominance method, output the Pareto optimal solution set, and obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0217] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions, the instruction segments describing the execution process of the computer program in the optimization device of the PVT-driven Brayton Carnot battery system. For example, the computer program can be divided into a sensitivity analysis module, a model building module, and a model solving module; the specific functions of each module are as follows: the sensitivity analysis module is used to perform parameter sensitivity analysis on the pre-built numerical calculation model of the PVT-driven Brayton Carnot battery energy storage system to obtain parameter sensitivity analysis results; the model building module is used to establish a multi-objective optimization model including objective functions and constraints based on the parameter sensitivity analysis results, using turbine inlet temperature and evaporation pressure as variables, and system electro-conversion efficiency and cost per kilowatt-hour as optimization objectives; the model solving module is used to solve the multi-objective optimization model using the Pareto dominance method, outputting the Pareto optimal solution set, so as to obtain the optimal operating conditions of the Brayton Carnot battery system based on the Pareto optimal solution set.

[0218] The optimized device for the PVT-driven Brayton Carnot battery system can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The optimized device for the PVT-driven Brayton Carnot battery system may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above are examples of optimized devices for PVT-driven Brayton Carnot battery systems and do not constitute a limitation on optimized devices for PVT-driven Brayton Carnot battery systems. It may include more components than described above, or combine certain components, or different components. For example, the optimized device for the PVT-driven Brayton Carnot battery system may also include input / output devices, network access devices, buses, etc.

[0219] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. This processor is the optimized control center of the PVT-driven Brayton Carnot battery system, connecting various parts of the optimized equipment of the entire PVT-driven Brayton Carnot battery system through various interfaces and lines.

[0220] The memory can be used to store the computer program and / or modules. The processor implements various functions of the optimized device of the PVT-driven Brayton Carnot battery system by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory.

[0221] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0222] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the optimization method for a PVT-driven Brayton Carnot battery system.

[0223] If the optimized system integration modules / units of the PVT-driven Brayton Carnot battery system are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0224] Based on this understanding, the optimization method for the PVT-driven Brayton Carnot battery system described above can be implemented, either in whole or in part, by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the optimization method for the PVT-driven Brayton Carnot battery system described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0225] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0226] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0227] Therefore, this invention provides an optimization method for a PVT-driven Brayton Carnot battery system, which has the following advantages compared to existing operating methods:

[0228] This method enables complete self-sufficiency through solar energy, leveraging energy storage technology to efficiently utilize solar energy across time and space, significantly improving the overall utilization rate of solar energy, with a maximum conversion efficiency of up to 65%. The PVT-driven Brayton Carnot battery system is a novel energy storage and conversion scheme, contributing to the efficient consumption of new energy sources. Using a multi-objective particle swarm optimization method with turbine inlet temperature and evaporation pressure as control variables, and system electrical conversion efficiency and cost per kilowatt-hour as optimization objectives, the method optimizes a Carnot battery energy storage system operating under varying conditions. The Pareto optimal solution set calculated by this invention can provide the optimal scheme for regulating turbine inlet temperature and evaporation pressure when different demands change during the operation of the energy storage system, achieving the goal of lowest cost and highest efficiency. Secondly, the optimization method of this invention eliminates the need for mechanical and manual experimentation based on experience, greatly reducing optimization time and labor costs. Simultaneously, the multi-objective particle swarm optimization algorithm established in this invention combines inertial adaptive weight coefficients, non-dominated sorting, and congestion calculation to control the degree of change in each iteration, avoiding premature convergence or local optimization, and quickly searching for a uniform and dispersed optimal solution set, thus improving the system's optimization efficiency. Finally, the method of this invention calculated three standard functions suitable for evaluating multi-objective optimization schemes. All functions achieved excellent computational accuracy and exhibited more uniformly distributed Pareto front curves, validating the accuracy of the method of this invention.

[0229] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

Claims

1. A method of optimization of a PVT driven Brayton Carnot cell system, characterized in that, The application relates to a method for optimizing operation of a PVT-driven Brayton cycle Carnot battery energy storage system. The method comprises the following steps: A pre-built PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model is subjected to parameter sensitivity analysis to obtain parameter sensitivity analysis results; Based on the parameter sensitivity analysis results, a multi-objective optimization model including an objective function and constraint conditions is established by taking turbine inlet temperature and evaporation pressure as variables and taking system electric conversion efficiency and unit electricity cost as optimization objectives; 2. The method of optimization of a PVT driven Brayton Carnot cell system according to claim 1, characterized in that, The multi-objective optimization model is solved by using a Pareto dominance method to output a Pareto optimal solution set, and optimal operation conditions of the Brayton Carnot battery system are obtained based on the Pareto optimal solution set. In the step of subjecting the pre-built PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model to parameter sensitivity analysis, the PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model is specifically represented as follows: Energy absorption efficiency of collector plates Model of PVT: th And net rate of thermal energy absorption Q u : where S denotes the total incident solar radiation, m water is the mass flow of hot water, C p,water is the specific heat of hot water, T PVT→SST is the PV / T hot water outlet temperature, T SST→PVT is the PV / T cold water inlet temperature; PVT's power generation efficiency η PV and the production of electrical energy W PV : wherein, η r represents the reference efficiency of the PV cell, η represents the temperature coefficient, T cell represents the temperature of the PV cell, T r represents the reference temperature, γ represents the incident angle correction, IAM n represents the product of the transmittance-absorptance of the PV panel for solar radiation at normal incidence angle, G T represents the total solar radiation incident on the collector surface;​ τα Model of heat and electricity cycle: wherein, P comp,in , P comp,out Pc_in and Pc_out are the compressor inlet and outlet pressures, respectively; State parameters of compressor: comp C is the compression ratio; T comp,in、 T comp,out Tc_in and Tc_out are the compressor inlet and outlet temperatures, respectively; ε is the adiabatic index; m is the mass flow rate of the working fluid in the compressor; κ comp is the isentropic efficiency; C p is the specific heat of the working fluid; η is the compression ratio; Heat exchanger thermal duty is: wherein m hot is the mass flow of the hot fluid; m cold is the mass flow of the cold fluid; h hot,in and h hot,out are the inlet and outlet enthalpy values of the hot fluid, respectively; h cold,in and h cold,out are the inlet and outlet enthalpy values of the cold fluid, respectively; ε wherein W turb is the work done by the steam turbine; is the mass flow rate of CO2; h turb,in and h turb,out are the inlet and outlet enthalpy values of the hot fluid, respectively; Brayton cycle turbine work: turb is the isentropic efficiency of the steam turbine; η wherein Q HP, out Qh is the heat supply of the heat pump cycle, W PV Qel is the electrical energy produced by the PV / T photovoltaic panel.

3. The method of optimization of a PVT driven Brayton Carnot cell system according to claim 2, characterized in that, COP of heat pump charging cycle: After the PVT-driven Brayton cycle Carnot battery energy storage system is built, performance verification of the PVT-driven Brayton cycle Carnot battery energy storage system is carried out, including the following steps: Based on the set working medium type, heat exchanger narrow point temperature difference and assumed PVT photovoltaic system parameters, the thermodynamic parameters of each state point are calculated in combination with thermodynamic processes; 4. The method of optimization of a PVT driven Brayton Carnot cell system according to claim 3, characterized in that, Whether the narrow point temperature difference of the regenerator meets the design requirements is verified according to the thermodynamic parameters of each state point.

5. The method of optimization of a PVT driven Brayton Carnot cell system of claim 2, wherein, After it is judged that the narrow point temperature difference of the regenerator meets the design requirements, the mass flow rates of each working medium are calculated according to the flow balance relationship of the system, and whether the temperature of the heat pump condenser at the minimum narrow point temperature difference meets the operation specification is finally verified, and system operation results are output. Efficiency of the Brayton cycle : wherein W net is the net work of the Brayton cycle, Q in_BC is the heat input to the Brayton cycle from the storage tank; wherein h turb,in , h turb,out is the enthalpy of the working fluid at the inlet of the turbine, m is the flow rate of the working fluid, In the step of subjecting the pre-built PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model to parameter sensitivity analysis, the pre-built PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model is subjected to parameter sensitivity analysis by using a pre-built performance evaluation index model, and the performance evaluation index model is specifically expressed as follows: turb is the efficiency of the turbine, a is representative of the actual process; The overall system electrical conversion efficiency : In the formula, η ts is the heat storage efficiency.

6. The method of optimization of a PVT driven Brayton Carnot cell system of claim 1, wherein, η In the step of establishing the multi-objective optimization model including the objective function and the constraint conditions based on the parameter sensitivity analysis results, taking the turbine inlet temperature and the evaporation pressure as the variables and taking the system electric conversion efficiency and the unit electricity cost as the optimization objectives, the construction process of the multi-objective optimization model comprises the following steps: wherein, The following objective is established by taking high electric conversion efficiency and the lowest unit electricity cost as the objectives: p2p is the electrical conversion efficiency, η is the cost per degree of electricity, in the constraint T pp is the narrow point temperature difference, is the turbine inlet temperature, P e_BC is the evaporation pressure of the Brayton cycle; LCOS wherein, The calculation of the unit electricity cost is as follows: represents the lifetime of use, C tot and C an denote the total initial investment cost and annual expenditure, respectively, r represents the discount rate; E gen represents the power returned to the grid per year; The total initial investment cost is calculated using the module cost calculation method: C tot The construction cost of all components of the system is calculated using the following formula for each module and then summed up: LT Cost of heat exchanger: Cost of turbine: Cost of compressor: Cost of pump: Cost of storage tank:

7. The method of optimizing a PVT-driven Brayton Carnot cell system of claim 1, wherein, In the formula, a, b and c are correction coefficients. The step of solving the multi-objective optimization model by using the Pareto dominance method and outputting the Pareto optimal solution set comprises the following steps: S31: initialize a particle population to obtain initial information of the population; S32: calculate the fitness of each particle in the particle population; S33: non-dominated sorting is performed on the initialized population; the fitness of the initialized population is put into the Pareto solution set, and for each particle, if it is dominated by other particles, it is deleted from the Pareto solution set, thereby obtaining the population of non-dominated solutions; S34: the crowding distance of all particle individuals is calculated by using the dense distance method; S35: the global optimum is randomly selected from the front preset proportion solution with larger crowding distance, and the position and fitness of the global optimum are recorded in the Pareto solution set, wherein the individual optimum is the initial value; S36: the iteration number is recorded, and the iteration calculation is started; S37: the following calculation is performed for each particle to obtain the individual optimum value and the population optimum value; S38: it is judged whether the iteration number is reached, if yes, the calculation is ended, if not, the iteration number is increased by 1 and the calculation is returned to S36 for re-calculation, until the maximum iteration number is reached.

8. An optimization system for a PVT-driven Brayton Carnot cell system, characterized by, The sensitivity analysis module is configured to perform parameter sensitivity analysis on a pre-constructed PVT-driven Brayton cycle Carnot battery energy storage system numerical calculation model to obtain a parameter sensitivity analysis result. The model construction module is configured to, based on the parameter sensitivity analysis result, take turbine inlet temperature and evaporation pressure as variables, and take system electric conversion efficiency and degree electric cost as optimization objectives, construct a multi-objective optimization model including an objective function and a constraint condition. The model solving module is configured to solve the multi-objective optimization model by using a Pareto dominance method, output a Pareto optimal solution set, and obtain an optimal operating condition of the Brayton Carnot battery system according to the Pareto optimal solution set. The memory is configured to store a computer program.

9. An apparatus for optimization of a PVT driven Brayton Carnot cell system, characterized by, The processor is configured to implement the steps of the optimization method of the PVT-driven Brayton Carnot battery system according to any one of claims 1-7 when executing the computer program. The computer program is configured to implement the steps of the optimization method of the PVT-driven Brayton Carnot battery system according to any one of claims 1-7 when executed by the processor. ​ 10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. ​

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