Molten salt heat storage capacity optimization process in linear Fresnel photo-thermal power generation system

Abstract

The invention discloses a fused salt heat storage capacity optimization process in a linear Fresnel photo-thermal power generation system. Historical operation data and local meteorological data of a power generation system are collected, and a correlation model is established after preprocessing. The heat storage capacity is preliminarily estimated by using the law of conservation of energy, optimization and correction are carried out by considering factors such as peak regulation, load fluctuation and response time, and economic evaluation is carried out by combining investment, operation and maintenance cost and income coefficients. Professional thermal system simulation software is adopted, the optimal heat storage capacity value is found, and storage tank model selection arrangement is optimized. In actual operation, system parameters are monitored in real time, dynamic adjustment is carried out according to heat storage state indexes, and meanwhile correction is carried out by considering thermophysical property changes of molten salt. The process can accurately determine the fused salt heat storage capacity, improve the stability and efficiency of a power generation system, reduce the cost and improve the overall performance of the system.

Description

Technical Field

[0001] The present invention relates to the field related to solar thermal power generation systems, and specifically to an optimization process for the molten salt heat storage capacity in a linear Fresnel solar thermal power generation system. Background Art

[0002] Molten salt heat storage technology has been widely studied and applied in multiple aspects such as industrial steam supply, solar thermal power generation, and building integrated energy systems. The patent document with the publication number CN117709653A in the prior art considers an optimization method for the capacity of a building integrated energy system with molten salt heat storage flexibility. The building integrated energy system has the characteristics of high energy density, frequent and diverse energy demands. The energy hub of the building integrated energy system composed of heat pumps, photovoltaics, cogeneration units, gas boilers, and absorption chillers is difficult to deeply optimize the energy supply and demand relationship, fails to fully utilize the flexibility of molten salt heat storage, cannot effectively achieve the thermal-electric decoupling at the energy scheduling stage of the energy hub, and is also difficult to fully consume renewable energy by combining the cascade utilization of energy, resulting in a relatively high energy configuration cost. Summary of the Invention

[0003] The purpose of the present invention is to provide an optimization process for the molten salt heat storage capacity in a linear Fresnel solar thermal power generation system to solve the problems raised in the above background art.

[0004] To achieve the above purpose, the present invention provides the following technical solution: An optimization process for the molten salt heat storage capacity in a linear Fresnel solar thermal power generation system, including the following steps:

[0005] The first step: Data collection and analysis

[0006] Collect the historical operation data of the linear Fresnel solar thermal power generation system, including solar radiation intensity, ambient temperature, steam output, power generation, molten salt temperature, and flow parameters; at the same time, collect the local meteorological data, conduct statistical analysis on the collected data, and establish an association model between solar radiation, environmental conditions and steam output, power generation in the power generation system by using mathematical modeling methods;

[0007] The second step: Preliminary estimation of the heat storage capacity

[0008] According to the association model and analysis results established in the first step, combined with the power generation capacity, designed operation hours of the linear Fresnel solar thermal power generation system, and the electricity demand characteristics of users, use the law of conservation of energy to preliminarily estimate the range of heat storage capacity required for the molten salt heat storage system; consider the difference between the peak load and the valley load of the system, as well as the impact of unstable solar radiation factors on power generation, to ensure that the system can stably output power when the light is insufficient or the power demand is high, and determine the preliminarily estimated molten salt heat storage capacity as a reasonable interval value;

[0009] The third step: Optimization parameter setting

[0010] Analyze the key parameters affecting the molten salt heat storage capacity and system performance, including the physical property parameters of the molten salt such as specific heat capacity, melting point, and thermal conductivity, as well as the structural design parameters of the heat storage tank and the efficiency of the heat exchange system; Set reasonable optimization target values and constraint conditions for these parameters according to the actual operation requirements and economic cost accounting of the system.

[0011] Step 4: Simulation and optimization calculation

[0012] Use professional thermal system simulation software to perform simulation calculations on the operation of different molten salt heat storage capacity values in the entire system based on the correlation model established in the first step and the optimization parameters set in the third step; During the simulation process, consider the energy conversion efficiency factors under different working conditions; By changing the molten salt heat storage capacity value, compare the system power generation efficiency, stability, and cost-benefit indicators in the simulation results to find the exact value of the molten salt heat storage capacity that makes the comprehensive performance of the system reach the optimal.

[0013] Step 5: Actual verification and adjustment

[0014] According to the exact value of the molten salt heat storage capacity obtained from the simulation and optimization calculation in the fourth step, carry out corresponding transformation or construction on the molten salt heat storage system in the linear Fresnel solar thermal power generation system; During the actual operation of the system, real-time monitor the operating parameters of the system and compare and analyze them with the simulation calculation results; If there is a deviation between the actual operation situation and the simulation results, analyze and make appropriate adjustments to the optimization parameters and the heat storage capacity value until the system reaches the best operating state and optimization effect.

[0015] Furthermore, when initially estimating the heat storage capacity in the second step, use a formula based on the design load of the power station and the characteristics of the light resource; Let the design power generation of the power station be P d (unit: MW), the local average effective light duration be t l (unit: hours), and the designed charge-discharge efficiency of the molten salt heat storage system be η s , then the initially estimated molten salt heat storage capacity Q p (unit: MWh) can be calculated by the formula:

[0016]

[0017] Calculate;

[0018] If the power station has peak shaving operation requirements during some periods, it is necessary to correct the energy demand during non-illuminated periods according to the power and duration of peak shaving, so as to obtain an initial value of the molten salt heat storage capacity that is more in line with the actual situation.

[0019] Furthermore, for the initially estimated molten salt heat storage capacity Q pWhen making optimization and correction, consider the dynamic load characteristics of the power station; introduce the load fluctuation coefficient α and the system response time coefficient β; the load fluctuation coefficient α reflects the degree of load fluctuation relative to the designed load during the actual operation of the power station, and is obtained through statistical analysis of historical operation data; the system response time coefficient β considers the response ability of the molten salt thermal energy storage system to load changes;

[0020] The optimized molten salt heat storage capacity Q opt1 can be calculated by the formula Q opt1 = Q p ×α×β; when the power station is in the peak electricity consumption period, the load may increase significantly in a short time. If the load fluctuation coefficient α is large, it means that this load fluctuation is relatively severe. In order to ensure that the peak load demand can be met in time, it is necessary to increase the molten salt heat storage capacity; and if the system response time coefficient β is small, it indicates that the response speed of the molten salt heat storage system from receiving the command to outputting heat is slow, and additional heat storage capacity is required to make up for the energy gap during the response time.

[0021] Furthermore, in the third step, for the optimized molten salt heat storage capacity Q opt1 further conduct economic evaluation and correction; introduce the investment cost coefficient C inv , the operation and maintenance cost coefficient C om and the revenue coefficient R;

[0022] The molten salt heat storage capacity Q f is calculated by the formula ; when C inv and C om are large, it means that increasing the heat storage capacity will bring higher costs, and it is necessary to evaluate whether the revenue coefficient R can cover these costs; if R is high, it means that the power generation revenue brought by increasing the heat storage capacity can make up for the investment and operation and maintenance costs, and at this time, Q f can be appropriately increased; on the contrary, if the cost is too high and the revenue increase is limited, then Q f needs to be reduced.

[0023] Furthermore, in the fourth step, after determining the molten salt heat storage capacity Q f , optimize the selection and layout of the molten salt storage tank; introduce the tank geometric shape coefficient k g and the space utilization coefficient k s ;

[0024] For the volume V of the storage tank, considering the density ρ of the molten salt (unit: kg / m 3 ), it is calculated by the formula

[0025] where C Pis the specific heat capacity of the molten salt (unit: kJ / (kg·K)), and ΔT is the temperature difference between heat charging and discharging of the molten salt (unit: K); then, in combination with k based on V g and k s carry out the optimization of storage tank selection and layout.

[0026] Furthermore, in the fifth step, during the operation process, dynamic monitoring and adjustment are carried out on the molten salt thermal energy storage system; introduce the thermal energy storage state index S, and its calculation formula is where Q c is the actual heat storage of the current molten salt thermal energy storage system; calculate S by real-time monitoring of Q c and make corresponding adjustments according to the value of S;

[0027] When S < 0.2, it indicates that the heat storage of the molten salt thermal energy storage system is relatively low. At this time, the solar heat collection time can be extended or the heat collection power can be increased to supplement the heat storage; when 0.2 ≤ S ≤ 0.8, it is regarded as the normal heat storage state, and the current operating parameters can be maintained; when S > 0.8, the heat storage is close to full load. If continuous heat charging may pose a safety risk, it is necessary to appropriately reduce the solar heat collection power or increase the heat discharging power to convert the excess heat into electric energy output.

[0028] Furthermore, consider the influence of the change of the thermal physical properties of the molten salt on the heat storage capacity; introduce the thermal physical property correction coefficient γ, which is related to the temperature and composition factors of the molten salt; as the use time of the molten salt increases and the temperature changes, its specific heat capacity C P and density ρ, the thermal physical property parameters will change;

[0029] The molten salt heat storage capacity Q mpd after thermal physical property correction is calculated by the formula Q mod = Q f ×γ; determine the value of γ under different working conditions by regularly detecting and analyzing the thermal physical properties of the molten salt; under high-temperature working conditions, the specific heat capacity of the molten salt may change. If the specific heat capacity increases, it means that the molten salt per unit mass can store more heat. At this time, γ > 1, and correspondingly Q mod will increase; on the contrary, if the specific heat capacity decreases, γ < 1, Q mod will decrease.

[0030] Furthermore, between the first step and the second step, it also includes the step of preprocessing the collected data, which is specifically as follows:

[0031] For the collected light intensity data I(t), ambient temperature data \(T e (t)\) and power generation data P(t) (where t represents time), calculate the mean value and standard deviation σ of each data respectively, and the calculation formulas are as follows:

[0032] Mean: where n is the number of data samples, and x i is the i-th data sample;

[0033] Standard Deviation:

[0034] Remove the data points in the data that deviate from the mean by more than k times the standard deviation to eliminate the influence of abnormal data;

[0035] Normalize the processed data, map the light intensity, ambient temperature, and power generation data to the interval [0,1]. The normalization formula used is:

[0036] , where x is the original data, x min and x max are the minimum and maximum values of this set of data respectively, and x n is the normalized data.

[0037] Furthermore, in the second step, the energy balance mathematical model of the molten salt thermal energy storage system is further refined into the following sub-steps:

[0038] It is clear that the energy input of the molten salt thermal energy storage system mainly comes from the solar energy collector field converting solar energy into the thermal energy of molten salt. Its calculation formula is

[0039] Q in (t) = E s (t) × η so × A,

[0040] where E s (t) is the solar irradiance received by the solar energy collector field at time (t), η so is the heat collection efficiency of the solar energy collector field, and A is the total area of the solar energy collector field;

[0041] The energy output of the molten salt thermal energy storage system is mainly used to drive the steam generator to generate steam to drive the steam turbine to generate electricity. Its calculation formula is

[0042]

[0043] where P(t) is the power generation at time t, and η g is the overall efficiency of the power generation system;

[0044] Consider the heat loss Q l (t) of the molten salt thermal energy storage system during storage and transmission. The heat loss is related to the surface area S of the molten salt storage tank, the temperature difference ΔT(t) inside and outside, and the heat transfer coefficient h. The calculation formula is Q l (t) = h × S × ΔT(t);

[0045] Based on the above analysis, the energy balance equation of the molten salt thermal energy storage system is established as follows:

[0046] Where Q st (t) is the thermal energy stored in the molten salt thermal energy storage system at time t.

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] By comprehensively collecting the historical operation data of the linear Fresnel solar thermal power generation system and local meteorological data, the present invention establishes an accurate correlation model, and fully considers the power generation capacity of the system, the designed operation hours, the characteristics of user power consumption demand, and the unstable factors of solar radiation, etc. when initially estimating the thermal energy storage capacity. During the optimization process, the key parameters affecting the molten salt thermal energy storage capacity and system performance are deeply analyzed, reasonable optimization target values and constraint conditions are set, precise calculations are carried out using professional simulation software, and through actual verification and adjustment, the system is ensured to reach the optimal operating state. The invention can accurately determine the molten salt thermal energy storage capacity, significantly improve the stability and efficiency of the power generation system, effectively reduce costs, and improve the overall performance of the linear Fresnel solar thermal power generation system. Description of the Drawings

[0049] Figure 1 is the flow chart of the present invention. Detailed Embodiments

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] Please refer to Figure 1 , a molten salt thermal energy storage capacity optimization process in a linear Fresnel solar thermal power generation system provided by the present invention includes the following steps:

[0052] The first step: Data collection and analysis

[0053] Collect the historical operation data of the linear Fresnel solar thermal power generation system, including solar radiation intensity, ambient temperature, steam output, power generation, molten salt temperature and flow parameters; at the same time, collect local meteorological data, conduct statistical analysis on the collected data, and establish a correlation model between solar radiation, environmental conditions and steam output, power generation in the power generation system by using mathematical modeling methods;

[0054] The steps for preprocessing the data are as follows:

[0055] For the collected light intensity data I(t), ambient temperature data \(T e (t)\), and power generation data P(t) (where t represents time), calculate the mean and standard deviation σ of each data respectively. The calculation formulas are as follows: And the standard deviation σ, the calculation formulas are as follows:

[0056] Mean: where n is the number of data samples, and x i is the i-th data sample;

[0057] Standard deviation:

[0058] Remove the data points in the data that deviate from the mean by more than k times the standard deviation to eliminate the influence of abnormal data;

[0059] Perform normalization processing on the processed data, map the light intensity, ambient temperature, and power generation data to the interval [0, 1]. The normalization formula used is:

[0060] , where x is the original data, and x min and x max are the minimum and maximum values of this group of data respectively, and x n is the normalized data.

[0061] In addition to the solar radiation intensity, ambient temperature, steam output, power generation, molten salt temperature and flow parameters, and local meteorological data already mentioned, supplement and collect the operation status data of the power generation system equipment. The equipment operation status will affect the power generation efficiency and heat storage demand. Collecting these data helps to establish a more accurate correlation model

[0062] Step 2: Preliminary estimation of the heat storage capacity

[0063] According to the correlation model and analysis results established in the first step, combined with the power generation of the linear Fresnel solar thermal power generation system, the designed operation hours, and the electricity demand characteristics of users, use the law of conservation of energy to preliminarily estimate the range of heat storage capacity required for the molten salt heat storage system; consider the difference between the peak load and the valley load of the system, as well as the impact of unstable solar radiation on power generation, to ensure that the system can stably output electricity when the light is insufficient or the power demand is high, and determine the preliminarily estimated molten salt heat storage capacity as a reasonable interval value;

[0064] The established energy balance mathematical model of the molten salt heat storage system is further refined into the following sub-steps:

[0065] It is clear that the energy input of the molten salt heat storage system mainly comes from the solar energy collector field converting solar energy into the heat energy of molten salt. The calculation formula is

[0066] Q inE(t) = E s × η(t) so × A,

[0067] where E s (t) is the solar irradiance received by the solar thermal collection field at time (t), η so is the heat collection efficiency of the solar thermal collection field, and A is the total area of the solar thermal collection field;

[0068] The energy output of the molten salt thermal energy storage system is mainly used to drive a steam generator to generate steam to drive a steam turbine to generate electricity. Its calculation formula is

[0069]

[0070] where P(t) is the power generation at time t, and η g is the overall efficiency of the power generation system;

[0071] Considering the heat loss Q l (t) during the storage and transmission of the molten salt thermal energy storage system, the heat loss is related to the surface area S of the molten salt storage tank, the temperature difference ΔT(t) inside and outside, and the heat transfer coefficient h. The calculation formula is Q l (t) = h × S × ΔT(t);

[0072] Based on the above analysis, the energy balance equation of the molten salt thermal energy storage system is established as:

[0073] where Q st (t) is the thermal energy stored in the molten salt thermal energy storage system at time t.

[0074] In the preliminary estimation of the thermal energy storage capacity in the second step, a formula based on the design load of the power station and the characteristics of the light resources is adopted; assume the design power generation of the power station is P d (unit: MW), the local average effective light duration is t l (unit: hours), and the designed charge-discharge efficiency of the molten salt thermal energy storage system is η s , then the preliminarily estimated molten salt thermal energy storage capacity Q p (unit: MWh) can be calculated through the formula:

[0075]

[0076] ;

[0077] If the power station has peak shaving operation requirements during some periods, it is necessary to correct the energy demand during non-light periods according to the power and duration of peak shaving, so as to obtain an initial value of the molten salt thermal energy storage capacity that is more in line with the actual situation.

[0078] For the preliminarily estimated molten salt thermal energy storage capacity Q pWhen making optimization and correction, consider the dynamic load characteristics of the power station; introduce the load fluctuation coefficient α and the system response time coefficient β; the load fluctuation coefficient α reflects the degree of load fluctuation relative to the designed load during the actual operation of the power station, and is obtained through statistical analysis of historical operation data; the system response time coefficient β considers the response ability of the molten salt thermal energy storage system to load changes;

[0079] The optimized molten salt heat storage capacity Q opt1 can be calculated by the formula Q opt1 =Q p ×α×β; when the power station is in the peak electricity consumption period, the load may increase significantly in a short time. If the load fluctuation coefficient α is large, it means that this load fluctuation is relatively intense. In order to ensure that the peak load demand can be met in time, it is necessary to increase the molten salt heat storage capacity; and if the system response time coefficient β is small, it indicates that the response speed of the molten salt heat storage system from receiving the command to outputting heat is slow, and additional heat storage capacity is required to make up for the energy gap during the response time.

[0080] When correcting the energy demand during non-illumination periods according to the peak shaving power and duration, elaborate on the specific correction process. The peak shaving power is Ppeak, and the peak shaving duration is tpeak. First, calculate the additional energy demand during the peak shaving period Epeak = Ppeak × tpeak, and distribute this energy demand to each time period during non-illumination periods according to a certain ratio. Determine the additional heat storage capacity that should be increased for each time period based on the load characteristics and system operation conditions of different time periods.

[0081] Step 3: Optimize parameter setting

[0082] Analyze the key parameters affecting the molten salt heat storage capacity and system performance, such as the specific heat capacity, melting point, and thermal conductivity physical properties of the molten salt, as well as the structural design parameters of the heat storage tank and the efficiency of the heat exchange system; according to the actual operation requirements of the system and economic cost accounting, set reasonable optimization target values and constraint conditions for these parameters;

[0083] For the optimized molten salt heat storage capacity Q opt1 further conduct economic evaluation and correction; introduce the investment cost coefficient C inv 、operation and maintenance cost coefficient C om and the revenue coefficient R;

[0084] The molten salt heat storage capacity Q f is calculated by the formula ; when C inv and C om are large, it means that increasing the heat storage capacity will bring higher costs, and it is necessary to evaluate whether the revenue coefficient R can cover these costs; if R is high, it means that the power generation revenue brought by increasing the heat storage capacity can make up for the investment and operation and maintenance costs. At this time, Q can be appropriately increasedf ; Conversely, if the cost is too high while the increase in revenue is limited, then Q needs to be reduced. f .

[0085] Step 4: Simulation and optimization calculation

[0086] Using professional thermal system simulation software, based on the correlation model established in the first step and the optimization parameters set in the third step, simulate and calculate the operation of different molten salt thermal energy storage capacity values in the whole system; during the simulation process, consider the energy conversion efficiency factors under different working conditions; by changing the molten salt thermal energy storage capacity value, compare the system power generation efficiency, stability, and cost-benefit indicators in the simulation results, and find the exact value of the molten salt thermal energy storage capacity that makes the comprehensive performance of the system reach the optimal.

[0087] TRNSYS software, build a model in the software according to the actual structure and connection mode of the system. For the solar collector field, use the corresponding collector module and input the optical and thermal physical properties parameters of the collector tube and the geometric parameters of the concentrator. For the molten salt thermal energy storage system, use the thermal energy storage tank and heat exchanger modules, and set the physical properties parameters of the molten salt, the tank size, and the heat transfer area. For the steam generator and steam turbine, use the corresponding modules and set the relevant operating parameters. In terms of boundary condition settings, input the solar radiation intensity according to the local historical meteorological data, set the ambient temperature according to the typical values in different seasons, and determine the inlet molten salt temperature and flow rate according to the system design conditions. Select the implicit solution algorithm for the solver and set the convergence accuracy to ensure the accuracy of the calculation results.

[0088] After determining the molten salt thermal energy storage capacity Q f , optimize the selection and layout of the molten salt storage tank; introduce the geometric shape coefficient k g and the space utilization coefficient k s ;

[0089] For the volume V of the storage tank, considering the density ρ of the molten salt (unit: kg / m 3 ), calculate by the formula

[0090] where C P is the specific heat capacity of the molten salt (unit: kJ / (kg·K)), and ΔT is the temperature difference of the molten salt during charging and discharging (unit: K); then optimize the selection and layout of the storage tank according to V combined with k g and k s .

[0091] Step 5: Practical verification and adjustment

[0092] According to the accurate value of the molten salt heat storage capacity obtained from the simulation optimization calculation in the fourth step, the molten salt heat storage system in the linear Fresnel solar thermal power generation system is transformed or constructed accordingly; during the actual operation of the system, various operating parameters of the system are monitored in real time and compared with the simulation calculation results for analysis; if there are deviations between the actual operation situation and the simulation results, analyze and appropriately adjust the optimization parameters and the heat storage capacity value until the system reaches the best operating state and optimization effect. The real-time monitoring system uses temperature sensors to measure the molten salt temperature, which are installed at key positions such as the inlet and outlet of the solar field and the inlet and outlet of the heat storage tank; pressure sensors measure the molten salt pressure, which are installed at key nodes of the pipeline; flow sensors measure the molten salt flow rate, which are installed at appropriate positions on the pipeline. Data transmission adopts a wireless transmission method, and the sensor data is transmitted to the data acquisition terminal through the ZigBee protocol, and then uploaded to the monitoring center through the GPRS network. The data acquisition frequency is set to 1 minute / time, and the data is stored in an SQL database for convenient query and analysis.

[0093] During the operation process, dynamically monitor and adjust the molten salt heat storage system; introduce the heat storage state index S, and its calculation formula is where Q c is the actual heat storage of the current molten salt heat storage system; calculate S by monitoring Q in real time c and make corresponding adjustments according to the value of S;

[0094] When S < 0.2, it indicates that the heat storage of the molten salt heat storage system is low. At this time, the solar heat collection time can be extended or the heat collection power can be increased to supplement the heat storage; when 0.2 ≤ S ≤ 0.8, it is regarded as the normal heat storage state, and the current operating parameters can be maintained; when S > 0.8, the heat storage is close to full load. If continuous heat charging may pose a safety risk, it is necessary to appropriately reduce the solar heat collection power or increase the heat release power to convert the excess heat into electrical energy output.

[0095] Consider the influence of the change of the thermal physical properties of the molten salt on the heat storage capacity; introduce the thermal physical property correction coefficient γ, which is related to the temperature and composition factors of the molten salt; as the use time of the molten salt increases and the temperature changes, its specific heat capacity C P and density ρ, the thermal physical property parameters will change;

[0096] The molten salt heat storage capacity Q after thermal physical property correction mod is calculated by the formula Q mod = Q f ×γ; by regularly detecting and analyzing the thermal physical properties of the molten salt, determine the γ value under different working conditions; under high-temperature working conditions, the specific heat capacity of the molten salt may change. If the specific heat capacity increases, it means that the molten salt per unit mass can store more heat. At this time, γ > 1, and correspondingly Q mod will increase; conversely, if the specific heat capacity decreases, γ < 1, Q mod will decrease.

Claims

1. A molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system, characterized in that, It includes the following steps: The first step: Data collection and analysis Collect the historical operation data of the linear Fresnel solar thermal power generation system, including solar radiation intensity, ambient temperature, steam output, power generation, molten salt temperature and flow parameters; at the same time, collect the local meteorological data, conduct statistical analysis on the collected data, and establish a correlation model between solar radiation, ambient conditions and steam output, power generation in the power generation system by using mathematical modeling methods; The second step: Preliminary estimation of the heat storage capacity According to the correlation model and analysis results established in the first step, combined with the power generation power, designed operation hours of the linear Fresnel solar thermal power generation system and the electricity demand characteristics of users, use the law of conservation of energy to preliminarily estimate the range of heat storage capacity required for the molten salt heat storage system; consider the difference between the peak load and the valley load of the system, as well as the impact of unstable solar radiation factors on power generation, to ensure that the system can stably output electricity when the light is insufficient or the power demand is high, and determine the preliminarily estimated molten salt heat storage capacity as a reasonable interval value; The third step: Optimization parameter setting Analyze the key parameters affecting the molten salt heat storage capacity and system performance, such as the physical property parameters of the specific heat capacity, melting point, and thermal conductivity of the molten salt, as well as the structural design parameters of the heat storage tank and the efficiency of the heat exchange system; according to the actual operation requirements and economic cost accounting of the system, set reasonable optimization target values and constraint conditions for these parameters; The fourth step: Simulation optimization calculation Use professional thermal system simulation software to perform simulation calculations on the operation of different molten salt heat storage capacity values in the whole system based on the correlation model established in the first step and the optimization parameters set in the third step; during the simulation process, consider the energy conversion efficiency factors under different working conditions; by changing the molten salt heat storage capacity value, compare the system power generation efficiency, stability, and cost-benefit indicators in the simulation results to find the exact value of the molten salt heat storage capacity that makes the comprehensive performance of the system reach the optimal; The fifth step: Actual verification and adjustment According to the exact value of the molten salt heat storage capacity obtained from the simulation optimization calculation in the fourth step, carry out corresponding transformation or construction on the molten salt heat storage system in the linear Fresnel solar thermal power generation system; During the actual operation process of the system, monitor the operation parameters of the system in real time and compare and analyze them with the simulation calculation results; if there is a deviation between the actual operation situation and the simulation results, analyze and make appropriate adjustments to the optimization parameters and heat storage capacity value until the system reaches the best operation state and optimization effect.

2. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 1, characterized in that In the preliminary estimation of the heat storage capacity in the second step, a formula based on the design load of the power station and the characteristics of the light resource is adopted; let the designed power generation power of the power station be P d (unit: MW), the local average effective light duration be t l (unit: hours), and the designed charge-discharge efficiency of the molten salt heat storage system be η s , then the preliminarily estimated molten salt heat storage capacity Q p (unit: MWh) can be obtained through the formula: Calculation; If the power station has the peak shaving operation demand in some periods, it is necessary to correct the energy demand during the non-illumination period according to the peak shaving power and duration, so as to obtain an initial value of the molten salt heat storage capacity that is more in line with the actual situation.

3. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 2, characterized in that When optimizing and correcting the preliminary estimated molten salt heat storage capacity Q p the dynamic load characteristics of the power station are considered; the load fluctuation coefficient α and the system response time coefficient β are introduced; The load fluctuation coefficient α reflects the fluctuation degree of the load relative to the designed load during the actual operation process of the power station, and is obtained by statistical analysis of the historical operation data; The system response time coefficient β considers the response ability of the molten salt heat storage system to load changes; Optimized molten salt heat storage capacity Q opt1 The formula Q opt1 =Q p ×α×β calculation; when the power station is in the peak period of electricity consumption, the load may increase significantly in a short period of time. If the load fluctuation coefficient α is large, it means that the load fluctuation is more severe. In order to ensure that the peak load demand can be met in time, it is necessary to increase the molten salt heat storage capacity; and if the system response time coefficient β is small, it indicates that the molten salt heat storage system has a slow response speed from receiving instructions to outputting heat, and additional heat storage capacity is needed to make up for the energy gap within the response time.

4. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 3, characterized in that In the third step, the optimized molten salt heat storage capacity Q opt1 is further evaluated and corrected economically; the investment cost coefficient C inv , the operation and maintenance cost coefficient C om and the revenue coefficient R are introduced; Molten salt heat storage capacity Q f Calculated by the formula When C inv and C om are relatively large, it means that increasing the heat storage capacity will bring higher costs, and it is necessary to evaluate whether the benefit coefficient R can cover these costs; if R is high, it means that the power generation benefits brought by increasing the heat storage capacity can cover the investment and operation and maintenance costs, and at this time, Q f can be appropriately increased; on the contrary, if the cost is too high and the benefit increase is limited, then Q needs to be reduced f .

5. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 4, characterized in that, In the fourth step, after determining the molten salt heat storage capacity Q f the selection and layout of the molten salt storage tank are optimized; introduce the storage tank geometric shape coefficient k g and the space utilization coefficient k s ; For the volume V of the storage tank, considering the density ρ of the molten salt (unit: kg / m 3 ), from the formula Calculation, where C P is the specific heat capacity of the molten salt (unit: kJ / (kg·K)), and ΔT is the temperature difference between heat charging and discharging of the molten salt (unit: K); then, based on V, in combination with k g and k s carry out optimization of storage tank type selection and layout.

6. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 5, characterized in that In the fifth step, during operation, the molten salt thermal energy storage system is dynamically monitored and adjusted; a thermal energy storage state index S is introduced, and its calculation formula is where Q c is the actual stored heat of the current molten salt thermal energy storage system; by monitoring Q in real time c to calculate S, and make corresponding adjustments according to the value of S; When S < 0.2, it indicates that the heat storage capacity of the molten salt heat storage system is relatively low. At this time, the solar heat collection time can be extended or the heat collection power can be increased to supplement the heat storage. When 0.2 ≤ S ≤ 0.8, it is regarded as the normal heat storage state, and the current operating parameters can be maintained. When S > 0.8, the heat storage capacity is close to full load. If heat is continuously charged, there may be safety risks. It is necessary to appropriately reduce the solar heat collection power or increase the heat release power to convert the excess heat into electrical energy output.

7. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 6, characterized in that, Consider the influence of the change in the thermal properties of molten salt on the heat storage capacity; introduce a thermal property correction coefficient γ, which is related to the temperature and composition factors of molten salt; as the usage time of molten salt increases and the temperature changes, its specific heat capacity C P and density ρ, the thermal property parameters will change; The molten salt heat storage capacity Q after thermophysical property correction mod Calculated by the formula Q mod = Q f ×γ; By regularly detecting and analyzing the thermophysical properties of the molten salt, the γ value under different working conditions is determined; Under high-temperature working conditions, the specific heat capacity of the molten salt may change. If the specific heat capacity increases, it means that the molten salt per unit mass can store more heat. At this time, γ > 1, and correspondingly Q mod will increase; On the contrary, if the specific heat capacity decreases, γ < 1, Q mod will decrease.

8. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 1, characterized in that, Between the first step and the second step, there is also a step of preprocessing the collected data, which is specifically as follows: For the collected light intensity data \(I(t)\), ambient temperature data \(T e (t)\) and power generation data \(P(t)\) (where \(t\) represents time), calculate the mean value of each data respectively and standard deviation \(\sigma\). The calculation formulas are as follows: Mean: where n is the number of data samples, and x i is the i-th data sample; Standard deviation: Remove the data points in the data that deviate from the mean by more than k times the standard deviation to eliminate the influence of abnormal data. Normalize the processed data, map the light intensity, ambient temperature, and power generation data to the [0, 1] interval, and the normalization formula used is: , where x is the original data, x min and x max are the minimum and maximum values of this group of data respectively, and x n is the data after normalization.

9. The molten salt heat storage capacity optimization process in a linear Fresnel solar thermal power generation system according to claim 1, characterized in that, In the second step, the established mathematical model of the energy balance of the molten salt heat storage system is further refined into the following sub-steps: It is clear that the energy input of the molten salt heat storage system mainly comes from the solar heat collection field converting solar energy into the heat energy of molten salt, and its calculation formula is Q in Q(t) = E s Q(t) × η so × A Among them, E s (t) is the solar irradiance intensity received by the solar collector field at time (t), and η so is the heat collection efficiency of the solar collector field, and A is the total area of the solar collector field; The energy output of the molten salt heat storage system is mainly used to drive the steam generator to generate steam to drive the steam turbine to generate electricity, and its calculation formula is where P(t) is the power generation at time t, and η g is the overall efficiency of the power generation system; Consider the heat loss Q l (t) during the storage and transmission processes of the molten salt thermal energy storage system. The heat loss is related to the surface area S of the molten salt storage tank, the temperature difference ΔT(t) between the inside and outside, and the heat transfer coefficient h. The calculation formula is Q l (t) = h × S × ΔT(t); Based on the above analysis, the energy balance equation of the molten salt heat storage system is established as: Among which Q st (t) is the thermal energy stored in the molten salt thermal energy storage system at time t.

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