Optimization method, device, equipment and medium for concentrating solar photo-thermal generator set

Abstract

The invention relates to the field of new energy system planning, and provides a concentrating solar photo-thermal generator set optimization method, device, equipment and medium, and the method comprises the steps: obtaining the operation conditions of a solar photo-thermal generator set, the operation conditions comprising the operation parameters and operation conditions of the solar photo-thermal generator set; determining that the operation condition accords with a preset design condition, and optimizing the previously determined adjustable parameters of the solar photo-thermal generator set by using a preset genetic algorithm, an electrification process model and a heat transfer constraint according to the operation condition of the solar photo-thermal generator set to obtain an optimal parameter combination; and optimizing the solar photo-thermal generator set by using the optimal parameter combination. According to the method, the genetic algorithm is utilized, the electrification process model and the heat transfer constraint are combined, global optimization is carried out to ensure that the global optimal parameter combination is found out, the cycle efficiency and the net output power of the unit under the full-load design working condition are remarkably improved, and the operation performance and reliability of the unit are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy system planning, and in particular to a method, device, equipment and medium for optimizing a concentrated solar thermal power generation unit. Background Art

[0002] Concentrating solar power (CSP) units are attracting more and more attention. They can not only generate electricity from renewable energy, but also effectively improve the operational flexibility of the system.

[0003] At present, CSP unit modeling mostly adopts a component-based approach, which mainly constructs a system model by stacking single component models and optimizes the corresponding system model based on a single parameter.

[0004] However, while the component-based approach is intuitive, it struggles to reflect the overall operating characteristics of the unit, especially under off-design conditions, where problems such as insufficient accuracy and poor convergence occur, limiting the optimization effect. Furthermore, the aforementioned optimization method targets a single parameter, ignoring the multivariable coupling effect and making it difficult to achieve global optimization. Furthermore, CSP units often deviate from their design operating conditions in peak-shaving and frequency-regulation scenarios, and their thermal performance is closely related to parameters such as working fluid flow and cooling air flow. Existing research has failed to effectively combine system-level heat transfer models with multi-objective optimization algorithms, resulting in limited improvements in cycle efficiency. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for optimizing a concentrated solar thermal power generation unit, which is used to solve the defect in the prior art that the optimization method is difficult to achieve global optimization for a single parameter, thereby affecting the operating performance of the unit, and achieves a significant improvement in the cycle efficiency and net output power of the unit under full-load design conditions, thereby significantly improving the operating performance and reliability of the unit.

[0006] The present invention provides a method for optimizing a concentrated solar thermal power generation unit, comprising: obtaining an operating condition of the solar thermal power generation unit, the operating condition including operating parameters and operating conditions of the solar thermal power generation unit; determining that the operating condition meets a preset design condition, and optimizing the adjustable parameters of the solar thermal power generation unit determined in advance according to the operating condition of the solar thermal power generation unit by using a preset genetic algorithm, an electrification process model and heat transfer constraints to obtain an optimal parameter combination; wherein the preset design condition is used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation unit, the electrification process model is constructed in advance based on the solar thermal power generation unit by using a heat flow method, and the heat transfer constraint is constructed based on the electrification process model by using Kirchhoff's voltage law; and optimizing the solar thermal power generation unit by using the optimal parameter combination.

[0007] According to a concentrated solar thermal power generation unit optimization method provided by the present invention, the adjustable parameters of the solar thermal power generation unit include the working fluid flow rate and the cooling air flow rate. According to the operating conditions of the solar thermal power generation unit, the preset genetic algorithm, the electrification process model and the heat transfer constraints are used to optimize the adjustable parameters of the solar thermal power generation unit determined in advance to obtain the optimal parameter combination, including: SA, using the preset genetic algorithm to randomly generate a first target number of individuals to obtain a first population, where the individuals include the working fluid flow rate and the cooling air flow rate; SB, for each individual, according to the operating conditions of the individual and the solar thermal power generation unit, using the electrification process model and Heat transfer constraints are combined with a preset cycle efficiency optimization algorithm to determine the cycle efficiency of the individual; SC, based on the cycle efficiency of each individual, uses a preset selection algorithm to select the second target number of individuals from the first population, and crossover and mutation are performed on the second target number of individuals to obtain the corresponding mutant individuals; wherein the second target number is less than the first target number; SD, uses all the mutant individuals to update the first population, and iterates SB-SD until the maximum number of iterations is reached, to obtain the cycle efficiency of the corresponding updated population and each updated individual in the updated population, and according to the cycle efficiency of each updated individual, select the updated individual with the largest cycle efficiency as the optimal parameter combination.

[0008] According to a concentrated solar thermal power generation unit optimization method provided by the present invention, the individual cycle efficiency is determined according to the operating conditions of the individual and the solar thermal power generation unit, using the electrification process model and heat transfer constraints, combined with a preset cycle efficiency optimization algorithm, including: inputting the operating conditions of the individual and the solar thermal power generation unit into the electrification process model, and combining the heat transfer constraints to obtain the workload of each mechanical work output component in the corresponding electrification process model; querying the coolprop open source physical property library based on the regional temperature and pressure states obtained in advance, and determining the inlet and outlet enthalpy difference of each heat exchanger in the electrification process model, so as to obtain the total heat consumption based on the inlet and outlet enthalpy difference of each heat exchanger; determining the cycle efficiency of the corresponding individual according to the workload and total heat consumption of each mechanical work output component.

[0009] According to a concentrated solar thermal power generation unit optimization method provided by the present invention, crossover and mutation are performed on a second target number of individuals to obtain corresponding mutant individuals, including: pairing the second target number of individuals in pairs to obtain corresponding paired individuals; for each paired individual, randomly selecting an intersection point on the paired individual, and exchanging parameter fragments of the two paired individuals based on the intersection point to generate two corresponding crossover individuals; for each crossover individual, randomly selecting at least one position for mutation to obtain a corresponding mutant individual.

[0010] According to the present invention, a method for optimizing a concentrated solar thermal power generation unit includes: determining that the operating conditions do not conform to preset design conditions, and updating the operating conditions using the preset design conditions, based on the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model, and heat transfer constraints, before obtaining the optimal parameter combination. Accordingly, after optimizing the solar thermal power generation unit using the optimal parameter combination, the method includes: searching a preset parameter database based on the operating conditions that do not conform to the preset design conditions to determine the corresponding optimal operating parameters; wherein the preset parameter database is previously constructed based on different non-preset design conditions and their corresponding optimal operating parameters; and updating the corresponding solar thermal power generation unit based on the optimal operating parameters.

[0011] According to a method for optimizing a concentrated solar thermal power generation unit provided by the present invention, a preset parameter database is searched according to an operating condition that does not conform to the preset design condition to determine the corresponding optimal operating parameters, including: searching the preset parameter database according to the operating condition that does not conform to the preset design condition; if the operating condition matches the non-preset design condition in the preset parameter database, the optimal operating parameters corresponding to the non-preset design condition are obtained; otherwise, it is determined whether the operating condition is between two non-preset design conditions in the preset parameter database; if the operating condition is between two non-preset design conditions in the preset parameter database, the optimal operating parameters are determined according to the optimal operating parameters of the two non-preset design conditions using an interpolation method; otherwise, the non-preset design condition in the preset parameter database that is closest to the operating condition is determined, and the corresponding optimal operating parameters are determined in combination with an extrapolation method or a previously created parameter prediction model.

[0012] According to a concentrated solar thermal power generation unit optimization method provided by the present invention, before optimizing the adjustable parameters of the solar thermal power generation unit determined in advance according to the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model and heat transfer constraints to obtain the optimal parameter combination, the method also includes: obtaining thermodynamic parameters in the electrification process model; performing sensitivity analysis on each thermodynamic parameter to obtain a sensitivity index of the corresponding thermodynamic parameter; determining whether the sensitivity index of each thermodynamic parameter is greater than a preset sensitivity threshold, and based on the existence of a sensitivity index greater than the preset sensitivity threshold, using the corresponding thermodynamic parameter as an adjustable parameter.

[0013] The present invention also provides a concentrated solar thermal power generation unit optimization device, including: an operating condition acquisition module, which acquires the operating conditions of the solar thermal power generation unit, and the operating conditions include the operating parameters and operating conditions of the solar thermal power generation unit; an optimization module, which determines that the operating conditions meet the preset design conditions, and according to the operating conditions of the solar thermal power generation unit, uses a preset genetic algorithm, an electrification process model and heat transfer constraints to optimize the adjustable parameters of the solar thermal power generation unit determined in advance to obtain an optimal parameter combination; wherein, the preset design conditions are used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation unit, the electrification process model is constructed based on the solar thermal power generation unit and using the heat flow method, and the heat transfer constraint is constructed based on the electrification process model and using Kirchhoff's voltage law; the optimization module uses the optimal parameter combination to optimize the solar thermal power generation unit.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for optimizing a concentrated solar thermal power generation unit as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing a concentrated solar thermal power generation unit.

[0016] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described methods for optimizing a concentrated solar thermal power generation unit.

[0017] The optimization method, device, equipment and medium of the concentrated solar thermal power generation unit provided by the present invention obtain the operating conditions to timely understand the operating status of the unit, provide data support for subsequent optimization and adjustment, and compare the actual operating conditions with the preset design conditions to quickly discover whether the unit deviates from the design benchmark, provide direction for subsequent optimization, and use genetic algorithms to perform global optimization when the operating conditions meet the preset design conditions, and combine the electrification process model and heat transfer constraints to provide reliable model support for parameter optimization to ensure that the global optimal parameter combination is found, so as to optimize the unit using the optimal parameter combination, achieve a significant improvement in the cycle efficiency and net output power of the unit under full-load design conditions, significantly improve the operating performance and reliability of the unit, and provide a solid theoretical basis for the intelligent regulation and efficient and stable operation of the solar thermal power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 It is a schematic flow chart of the optimization method of the concentrated solar thermal power generation unit provided by the present invention; Figure 2 Schematic diagram of the overall effect of working fluid flow and cooling air flow on net output power provided by the present invention; Figure 3 This is a schematic diagram of the overall impact of the working fluid flow rate and cooling air flow rate on the cycle efficiency provided by the present invention; Figure 4 This is a schematic diagram showing the effect of the working fluid flow rate on the net output power and cycle conversion efficiency provided by the present invention; Figure 5 Schematic diagram showing the effect of cooling air flow rate on net output power and cycle conversion efficiency provided by the present invention; Figure 6 This is the optimal condensing pressure curve under different working conditions provided by the present invention; Figure 7 This is the optimal flow curve diagram of the working medium flow and cooling air flow under different working conditions provided by the present invention; Figure 8 This is a graph of the best net output power and cycle efficiency under different working conditions provided by the present invention; Figure 9 It is a structural schematic diagram of the concentrated solar thermal power generation unit optimization device provided by the present invention; Figure 10 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0021] Figure 1 This is a flow chart of the optimization method of the concentrated solar thermal power generation unit provided by the present invention, such as Figure 1 As shown, the method includes the following: S11, obtaining the operating conditions of the solar thermal power generation unit, where the operating conditions include operating parameters and operating conditions of the solar thermal power generation unit; S12, determining whether the operating conditions meet the preset design conditions, and optimizing the previously determined adjustable parameters of the solar thermal power generation unit based on the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model, and heat transfer constraints to obtain an optimal parameter combination; wherein the preset design conditions are used to represent the benchmark operating conditions when designing the corresponding solar thermal power generation unit, the electrification process model is previously constructed based on the solar thermal power generation unit using a heat flow method, and the heat transfer constraints are constructed based on the electrification process model using Kirchhoff's voltage law; S13, optimize the solar thermal power generation unit using the optimal parameter combination.

[0022] It should be noted that the step numbers "S1N" in this manual do not represent the order of the optimization method of concentrated solar thermal power generation unit. Figure 2-Figure 8 The present invention describes the optimization method of the concentrated solar thermal power generation unit.

[0023] Step S11, obtaining the operating conditions of the solar thermal power generation unit, where the operating conditions include the operating parameters and operating conditions of the solar thermal power generation unit.

[0024] In an optional embodiment, before optimizing the adjustable parameters of the solar thermal power generation unit determined in advance by using a preset genetic algorithm, an electrification process model and heat transfer constraints according to the operating conditions of the solar thermal power generation unit to obtain the optimal parameter combination, it also includes: constructing a corresponding electrification process model according to the various thermal system components of the solar thermal power generation unit using a heat flow method; and constructing a heat transfer constraint according to the electrification process model using Kirchhoff's voltage law.

[0025] It should be added that the types of thermal system components include preheaters, evaporators, superheaters, reheaters, condensers and other corresponding heat recovery extraction and feed water system components. The types of thermal system components also include mechanical power output components such as turbines, fans and water pumps. The electrification process model includes a preheater sub-model, an evaporator sub-model, a superheater sub-model, a reheater sub-model and a condenser sub-model; the heat transfer constraints include the first constraint, the second preset, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, the seventh constraint and the eighth constraint.

[0026] In addition, according to the electrified process model, Kirchhoff's voltage law is used to construct heat transfer constraints, including: based on Kirchhoff's voltage law, according to the initial temperature of the heat exchange medium of the preheater submodel, the heat exchange and thermal resistance of the superheater submodel, and the steam temperature of the condenser submodel, constructing a first constraint; according to the initial temperature of the heat exchange medium of the preheater submodel, the thermokinetic potential of the heat exchange medium in the parallel superheater submodel and reheater submodel, the heat exchange and thermal resistance of the second stage of the evaporator submodel, and the steam temperature of the condenser submodel, constructing a second constraint; according to the initial temperature of the heat exchange medium of the preheater submodel, and the heat exchange and thermal resistance of the second stage of the evaporator submodel, and the steam temperature of the condenser submodel, constructing a second constraint; according to the initial temperature of the heat exchange medium of the preheater submodel, and The third constraint is constructed based on the thermodynamic potential of the heat exchange medium in the superheater submodel and the reheater submodel connected in parallel, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the second stage, the heat transfer and thermal resistance of the evaporator submodel in the first stage, the thermodynamic potential of the working medium in the evaporator submodel in the first stage, and the steam temperature of the condenser submodel; the third constraint is constructed based on the initial temperature of the heat exchange medium in the preheater submodel, the thermodynamic potential of the heat exchange medium in the superheater submodel and the reheater submodel connected in parallel, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the second stage, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the first stage, the heat transfer and thermal resistance of the preheater submodel. , the thermokinetic potential of the working fluid in the preheater submodel, the thermokinetic potential of the working fluid in the evaporator submodel during the first mixing process, the thermokinetic potential of the working fluid in the evaporator submodel during the first stage, and the steam temperature of the condenser submodel, the fourth constraint is constructed; according to the temperature of the working fluid at the inlet end of the preheater submodel, the thermokinetic potential of the working fluid in the preheater submodel, the thermokinetic potential of the working fluid in the evaporator submodel during the first mixing process, the thermokinetic potential of the working fluid in the evaporator submodel during the first stage, and the steam temperature of the condenser submodel, the fifth constraint is constructed; according to the initial temperature of the cold air in the condenser submodel, the thermokinetic potential of the cold air in the condenser submodel during the third condensing stage, the condenser The sixth constraint is constructed based on the thermokinetic potential of the cold air in the first condensing section of the condenser sub-model, the heat transfer and thermal resistance of the first condensing section of the condenser sub-model, the thermokinetic potential of the working medium in the first condensing section of the condenser sub-model, and the condensed water temperature of the condenser sub-model; the seventh constraint is constructed based on the initial temperature of the cold air in the condenser sub-model, the thermokinetic potential of the cold air in the third condensing section of the condenser sub-model, the heat transfer and thermal resistance of the second condensing section of the condenser sub-model, and the condensed water temperature of the condenser sub-model; the eighth constraint is constructed based on the initial temperature of the cold air in the condenser sub-model, the heat transfer and thermal resistance of the third condensing section of the condenser sub-model, and the condensed water temperature of the condenser sub-model.

[0027] It should be noted that the first stage of the evaporator sub-model is the heating process of heating the working medium from unsaturated water to saturated water, and the second stage of the evaporator sub-model is the vaporization process of vaporizing saturated water into saturated steam; the first stage of the condenser sub-model is the process of condensing superheated steam into saturated steam when the low-pressure cylinder exhaust steam is in a superheated state, the second stage of the condenser sub-model is the process of condensing saturated steam into saturated water when the low-pressure cylinder exhaust steam is in a superheated state, and the third stage of the condenser sub-model is a one-stage heat exchange process adopted when the low-pressure cylinder exhaust steam is in a saturated state.

[0028] Furthermore, the first constraint is expressed as: in, represents the initial temperature of the heat exchange medium of the preheater submodel; represents the heat transfer of the superheater sub-model; represents the thermal resistance of the superheater submodel; Represents the vapor temperature of the evaporator submodel.

[0029] The second constraint is expressed as: in, represents the initial temperature of the heat exchange medium of the preheater submodel; Represents the thermodynamic potential of the heat exchange medium in the parallel superheater sub-model and reheater sub-model; represents the heat transfer in the second stage of the evaporator submodel; represents the thermal resistance of the second stage of the evaporator submodel; Represents the vapor temperature of the evaporator submodel.

[0030] The third constraint is expressed as: in, represents the initial temperature of the heat exchange medium of the preheater submodel; Represents the thermodynamic potential of the heat exchange medium in the parallel superheater sub-model and reheater sub-model; represents the thermokinetic potential of the heat exchange medium in the second stage of the evaporator submodel; represents the heat transfer of the first stage of the evaporator submodel; represents the thermal resistance of the first stage of the evaporator submodel; represents the thermokinetic potential of the working fluid in the first stage of the evaporator submodel; Represents the steam temperature of the condenser submodel.

[0031] The fourth constraint is expressed as: in, represents the initial temperature of the heat exchange medium of the preheater submodel; Represents the thermodynamic potential of the heat exchange medium in the parallel superheater sub-model and reheater sub-model; represents the thermokinetic potential of the heat exchange medium in the second stage of the evaporator submodel; Represents the thermokinetic potential of the heat exchange medium in the first stage of the evaporator submodel; represents the heat transfer of the preheater submodel; represents the thermal resistance of the preheater submodel; represents the thermokinetic potential of the working fluid in the preheater submodel; Represents the thermokinetic potential of the working fluid in the first mixing process in the evaporator sub-model; represents the thermokinetic potential of the working fluid in the first stage of the evaporator submodel; Represents the steam temperature of the condenser submodel.

[0032] The fifth constraint is expressed as: in, represents the temperature of the working fluid at the inlet end of the preheater sub-model; represents the thermokinetic potential of the working fluid in the preheater submodel; Represents the thermokinetic potential of the working fluid in the first mixing process in the evaporator sub-model; represents the thermokinetic potential of the working fluid in the first stage of the evaporator submodel; Represents the steam temperature of the condenser submodel.

[0033] The sixth constraint is expressed as: in, represents the initial temperature of cold air in the condenser submodel; Represents the thermokinetic potential of cold air in the third condensing section in the condenser sub-model; Represents the thermokinetic potential of the cold air in the condenser section in the condenser sub-model; Indicates the heat transfer of the condensing section of the condenser sub-model; Represents the thermal resistance of the condensing section of the condenser sub-model; Indicates the thermokinetic potential of the working medium in the condenser section in the condenser sub-model; Represents the condensate temperature of the condenser sub-model.

[0034] The seventh constraint is expressed as: represents the initial temperature of cold air in the condenser submodel; Represents the thermokinetic potential of cold air in the third condensing section in the condenser sub-model; Represents the heat transfer of the second condensing stage of the condenser sub-model; Represents the thermal resistance of the second condensing section of the condenser sub-model; Represents the condensate temperature of the condenser sub-model.

[0035] The eighth constraint is expressed as: in, represents the initial temperature of cold air in the condenser submodel; Represents the heat transfer of the three condensing sections of the condenser sub-model; Represents the thermal resistance of the three condensing sections of the condenser submodel; Represents the condensate temperature of the condenser sub-model.

[0036] In an optional embodiment, before optimizing the adjustable parameters of the solar thermal power generation group determined in advance according to the operating conditions of the solar thermal power generation group using a preset genetic algorithm, an electrification process model and heat transfer constraints to obtain the optimal parameter combination, it also includes: obtaining the thermodynamic parameters in the electrification process model; performing sensitivity analysis on each thermodynamic parameter to obtain a sensitivity index of the corresponding thermodynamic parameter; determining whether the sensitivity index of each thermodynamic parameter is greater than a preset sensitivity threshold, and based on the existence of a sensitivity index greater than the preset sensitivity threshold, using the corresponding thermodynamic parameter as an adjustable parameter.

[0037] It should be noted that sensitivity analysis can be used to determine the degree of influence of various thermodynamic parameters on cycle efficiency, thereby identifying the key influencing factors, which helps to determine the key decision variables affecting cycle efficiency, that is, adjustable parameters, and thus facilitate guidance of subsequent optimization strategies and directions.

[0038] Furthermore, the adjustable parameters of the solar thermal power generation unit include the working fluid flow rate and the cooling air flow rate. It is worth noting that in order to improve the operating performance of the solar thermal power generation (CSP) unit, an in-depth analysis of the key parameters was conducted, and it was found that the characteristic of the CSP unit is that there are two adjustable variables: working fluid flow rate and cooling air flow rate. By conducting a sensitivity analysis under the design working condition (heat consumption rate THA100%), the joint influence of these two variables on the net output of the unit was studied, and the main factors affecting the net output of the unit were analyzed. The results show that the influence of these two variables on the net output of the unit is consistent. The power and cycle efficiency are respectively as follows: Figure 2 and Figure 3 As shown, the working range of the working fluid flow rate is (272.69, 305.69) t / h, and the working range of the cooling air flow rate is (5659.55, 8002.57) m3 / s.

[0039] In order to more clearly identify the changing trends, Figure 4 The individual effects of working fluid flow rate on net output power and cycle efficiency are shown. Figure 5 The individual effects of cooling air flow rate on net power output and cycle efficiency are shown. It can be observed that both net power output and cycle efficiency gradually increase with the working mass flow rate. Conversely, increasing the cooling air flow rate leads to optimal values for net power output and cycle efficiency, after which further increases result in diminishing returns.

[0040] In order to achieve the best operating performance, it is crucial to determine the optimal combination of working fluid flow and cooling air flow. For CSP units, cycle efficiency is given priority as a key indicator because it can accurately reflect the overall operating economy of the unit.

[0041] Step S12, determining whether the operating conditions meet the preset design conditions, and according to the operating conditions of the solar thermal power generation unit, using the preset genetic algorithm, the electrification process model and the heat transfer constraints, optimizing the adjustable parameters of the solar thermal power generation unit determined in advance to obtain the optimal parameter combination; wherein, the preset design conditions are used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation unit, the electrification process model is constructed based on the solar thermal power generation unit using the heat flow method, and the heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law.

[0042] In this embodiment, based on the operating conditions of the solar thermal power generation unit, a preset genetic algorithm, an electrification process model, and heat transfer constraints are used to optimize the previously determined adjustable parameters of the solar thermal power generation unit to obtain the optimal parameter combination, including: SA,uses a preset genetic algorithm to randomly generate a first target number of,individuals to obtain a first population, where the individuals include the,working fluid flow rate and the cooling air flow rate.

[0043] SB, for each individual, determines the individual cycle efficiency based on the operating conditions of the individual and the solar thermal power generation unit, using the electrification process model and heat transfer constraints, combined with the preset cycle efficiency optimization algorithm.

[0044] Specifically, according to the operating conditions of individuals and solar thermal power generation units, the electrification process model and heat transfer constraints are used, combined with the preset cycle efficiency optimization algorithm, to determine the individual cycle efficiency, including: inputting the operating conditions of individuals and solar thermal power generation units into the electrification process model, and combining the heat transfer constraints to obtain the workload of each mechanical work output component in the corresponding electrification process model; according to the regional temperature and pressure conditions obtained in advance, querying the coolprop open source physical property library, determining the inlet and outlet enthalpy difference of each heat exchanger in the electrification process model, and obtaining the total heat consumption based on the inlet and outlet enthalpy difference of each heat exchanger; determining the cycle efficiency of the corresponding individual based on the workload and total heat consumption of each mechanical work output component.

[0045] It should be added that the types of heat exchangers include preheaters, evaporators, superheaters and reheaters, and mechanical work output components include mechanical work output components such as turbines, fans and water pumps. The total heat consumption is used to characterize the total amount of heat reabsorbed by the circulating working fluid in the preheater, evaporator, superheater and reheater, which can be calculated based on the inlet and outlet enthalpy differences of each heat exchanger obtained from the query.

[0046] Furthermore, the cycle efficiency of the corresponding individual is determined based on the workload and heat consumption of each thermal system component, including: obtaining the turbine workload, fan workload and water pump workload based on the workload of each thermal system component to determine the net cycle power; and determining the corresponding cycle efficiency based on the net cycle power and total heat consumption.

[0047] It should be noted that the workload of the steam turbine can be determined based on the difference in the inlet and outlet steam specific enthalpies corresponding to the steam turbine sub-model in the electrification process model combined with the turbine efficiency. The workload of the fan can be determined based on the direct ratio of the power consumed in the actual operation process of the fan sub-model in the electrification process model to the cube of the corresponding volume flow rate. The workload of the water pump can refer to the workload of the fan, and will not be repeated here.

[0048] In addition, the cycle efficiency is expressed as: in, Indicates cycle efficiency; Indicates total heat consumption; It represents the net cycle power, which can be obtained by subtracting the fan workload and the pump workload from the turbine workload, where the turbine workload is , expressed as: in, represents the efficiency of the steam turbine; m represents the flow rate of the corresponding stage group of the steam turbine sub-model, and They represent the inlet and outlet steam specific enthalpies of the turbine sub-model respectively, which can be obtained by searching the coolprop open source physical property library according to the corresponding local temperature and pressure states.

[0049] SC, based on the circulation efficiency of each individual, uses a preset selection algorithm to select a second target number of individuals from the first population, and performs crossover and mutation on the second target number of individuals to obtain corresponding mutant individuals; wherein the second target number is smaller than the first target number.

[0050] It should be noted that the method for selecting the second target number of individuals from the first population can be selected based on actual selection requirements, such as roulette wheel selection. This method can be selected based on actual design requirements and is not further specified here. Furthermore, the crossover and mutation methods can be selected based on actual design requirements or prior experience, such as single-point crossover or random mutation, and are not further specified here.

[0051] In addition, crossover and mutation are performed on the second target number of individuals to obtain corresponding mutant individuals, including: pairing the second target number of individuals in pairs to obtain corresponding paired individuals; for each paired individual, randomly selecting a crossover point on the paired individual, and exchanging parameter fragments of the two paired individuals based on the crossover point to generate two corresponding crossover individuals; for each crossover individual, randomly selecting at least one position for mutation to obtain the corresponding mutant individual.

[0052] It should be noted that the population is randomly generated to ensure the diversity and global search capability of the search, and the circulation efficiency of each individual in the population is evaluated in combination with the electrification process model and heat transfer constraints. Then, selection, crossover and mutation are used to simulate the natural selection and genetic process to find the optimal solution. Through iterative updates, the population gradually evolves towards a better solution. After the iteration, a final evaluation is performed based on the circulation efficiency of the corresponding updated individuals to ensure the optimality of the selected individuals, thereby better improving the operating performance of the solar thermal power unit.

[0053] SD, use all the mutant individuals to update the first population, and iterate SB-SD until the maximum number of iterations is reached, obtain the cycle efficiency of the corresponding updated population and each updated individual in the updated population, and according to the cycle efficiency of each updated individual, select the updated individual with the largest cycle efficiency as the optimal parameter combination.

[0054] It should be added that by taking the cycle efficiency as the optimization target, the working fluid flow rate and the cooling air flow rate as the decision variables, and then using the genetic algorithm, combined with the electrification process model and heat transfer constraints built based on the CSP unit, optimization is performed to take into account multi-variable coordinated regulation and dynamic operating condition adaptation, thereby improving the unit's operating energy efficiency and economy.

[0055] After optimizing the flow rate, both net output power and cycle efficiency were significantly improved. Experimental results show that the optimized solution has a maximum cycle efficiency of 46.581% and a maximum net output power of 105.091 MW. The corresponding optimal working fluid flow rate and optimal cooling air flow rate are 305.690 t / h and 7348.399 m3 / s, respectively. Compared with the optimization of a single decision variable, this emphasizes the importance of comprehensively considering all decision variables to achieve the best performance of the CSP unit.

[0056] Step S13: Optimize the solar thermal power generation unit using the optimal parameter combination.

[0057] In an optional embodiment, before optimizing the previously determined adjustable parameters of the solar thermal power generation unit based on the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model, and heat transfer constraints to obtain an optimal parameter combination, the method includes: determining that the operating conditions do not conform to preset design conditions, and updating the operating conditions using the preset design conditions. It should be noted that the method for optimizing and determining the optimal parameter combination using the operating conditions after updating to the preset design conditions can be referred to above and will not be repeated here.

[0058] In addition, after using the optimal parameter combination to optimize the solar thermal power generation unit, it includes: searching the preset parameter database according to the operating conditions that do not meet the preset design conditions, and determining the corresponding optimal operating parameters; wherein, the preset parameter database is first constructed based on different non-preset design conditions and their corresponding optimal operating parameters; according to the optimal operating parameters, the corresponding solar thermal power generation unit is updated to avoid the situation where the system model has poor convergence under non-design conditions and thus affects the optimization effect, thereby achieving a significant improvement in the cycle efficiency and net output power of the unit under full load and non-design conditions, and ensuring that it can operate efficiently and stably under different conditions.

[0059] Furthermore, based on the operating conditions that do not conform to the preset design conditions, the preset parameter database is searched to determine the corresponding optimal operating parameters, including: based on the operating conditions that do not conform to the preset design conditions, the preset parameter database is searched; if the operating conditions match the non-preset design conditions in the preset parameter database, the optimal operating parameters corresponding to the non-preset design conditions are obtained, otherwise, it is determined whether the operating conditions are between the two non-preset design conditions in the preset parameter database; if the operating conditions are between the two non-preset design conditions in the preset parameter database, the optimal operating parameters are determined based on the optimal operating parameters of the two non-preset design conditions using the interpolation method; otherwise, the non-preset design condition in the preset parameter database that is closest to the operating conditions is determined, and the corresponding optimal operating parameters are determined in combination with the extrapolation method or a previously created parameter prediction model.

[0060] It should be noted that the extrapolation method can be selected based on actual design requirements, such as linear extrapolation, polynomial extrapolation, etc., and is not further limited here. Furthermore, the parameter prediction model can be trained based on historical operating conditions and the optimal historical operating parameters corresponding to these conditions, so that the optimal operating parameters can be predicted directly based on the acquired operating conditions during actual use.

[0061] It is worth noting that in the above method, the non-preset design conditions are non-design conditions with a heat rate (THA) of 20% to 90%. Based on the optimization of the above preset design conditions, the optimization work is expanded to include non-design conditions with a THA of 20% to 90%. By carefully matching the flow rates of the working fluid and the cooling air, the highest cycle efficiency is achieved. Similarly, the genetic algorithm is used to optimize the THA conditions of 20% to 90%, and the results are as follows: Figure 6-8 As shown, through parameter matching (i.e., comparing with the optimal flow curve or the optimal condensing pressure curve), the parameters are set by looking up the table or interpolating according to the current THA during operation, and the control quantity is updated to form a closed loop to maximize the unit efficiency under this operating condition.

[0062] Depend on Figure 6 It can be seen that the optimal condensing pressure gradually increases with the increase in the mass flow rate of the molten metal. Taking into account the influence of ambient temperature, which affects the inlet temperature of the cooling air and varies with the season and weather, it is observed that lower ambient temperature helps to reduce the condensing pressure. It can be seen that this reduction improves the vacuum in the condenser, allowing for more extensive steam expansion in the low-pressure cylinder, thereby increasing the net output power and cycle efficiency of the CSP unit.

[0063] refer to Figure 7 and Figure 8 , the ambient temperature is fixed at 16 °C, and the best results are depicted under different working conditions. It can be seen that as the working conditions develop from 20% THA to full THA, the optimal flow rates of the working fluid and cooling air gradually increase, while the optimal net output power and cycle efficiency also gradually increase, emphasizing the effectiveness of matching flow rates under different working conditions.

[0064] In summary, the embodiments of the present invention obtain operating conditions to timely understand the operating status of the unit, provide data support for subsequent optimization and adjustment, and compare the actual operating conditions with the preset design conditions to quickly discover whether the unit deviates from the design benchmark, providing direction for subsequent optimization. When the operating conditions meet the preset design conditions, a genetic algorithm is used for global optimization, and combined with the electrification process model and heat transfer constraints, a reliable model support is provided for parameter optimization to ensure that the global optimal parameter combination is found, so as to optimize the unit using the optimal parameter combination, achieve a significant improvement in the cycle efficiency and net output power of the unit under full-load design conditions, significantly improve the operating performance and reliability of the unit, and provide a solid theoretical basis for the intelligent control and efficient and stable operation of the solar thermal power generation system.

[0065] The concentrating solar thermal power generation unit optimization device provided by the present invention is described below. The concentrating solar thermal power generation unit optimization device described below and the concentrating solar thermal power generation unit optimization method described above can be referenced to each other.

[0066] Figure 9 The schematic diagram of the structure of a concentrated solar thermal power generation unit optimization device is shown, which includes: The operating condition acquisition module 91 acquires the operating condition of the solar thermal power generation unit, which includes the operating parameters and operating conditions of the solar thermal power generation unit; An optimization module 92 determines whether the operating conditions meet preset design conditions. Based on the operating conditions of the solar thermal generator set, the module optimizes the previously determined adjustable parameters of the solar thermal generator set using a preset genetic algorithm, an electrification process model, and heat transfer constraints to obtain an optimal parameter combination. The preset design conditions are used to represent the baseline operating conditions for the design of the corresponding solar thermal generator set. The electrification process model is constructed based on the solar thermal generator set using a heat flow method. The heat transfer constraints are constructed based on the electrification process model using Kirchhoff's voltage law. The optimization module 93 optimizes the solar thermal power generation unit by using the optimal parameter combination.

[0067] In an optional embodiment, the device further includes: a model construction module, which uses a preset genetic algorithm, an electrification process model and heat transfer constraints to optimize the adjustable parameters of the solar thermal power generation group determined in advance according to the operating conditions of the solar thermal power generation group, and before obtaining the optimal parameter combination, uses the heat flow method to construct a corresponding electrification process model based on the various thermal system components of the solar thermal power generation group; and a constraint construction module, which uses Kirchhoff's voltage law to construct heat transfer constraints based on the electrification process model.

[0068] It should be added that the types of thermal system components include preheaters, evaporators, superheaters, reheaters, condensers and other corresponding heat recovery extraction and feed water system components. The types of thermal system components also include mechanical power output components such as turbines, fans and water pumps. The electrification process model includes a preheater sub-model, an evaporator sub-model, a superheater sub-model, a reheater sub-model and a condenser sub-model; the heat transfer constraints include the first constraint, the second preset, the third constraint, the fourth constraint, the fifth constraint, the sixth constraint, the seventh constraint and the eighth constraint.

[0069] Accordingly, the constraint construction module is used to: construct the first constraint based on Kirchhoff's voltage law according to the initial temperature of the heat exchange medium of the preheater submodel, the heat transfer and thermal resistance of the superheater submodel, and the steam temperature of the condenser submodel; construct the second constraint according to the initial temperature of the heat exchange medium of the preheater submodel, the thermodynamic potential of the heat exchange medium in the parallel superheater submodel and reheater submodel, the heat transfer and thermal resistance of the second stage of the evaporator submodel, and the steam temperature of the condenser submodel; construct the second constraint according to the initial temperature of the heat exchange medium of the preheater submodel, the heat transfer and thermal resistance of the second stage of the evaporator submodel, and the steam temperature of the condenser submodel. The third constraint is constructed based on the thermodynamic potential of the medium, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the second stage, the heat transfer and thermal resistance of the evaporator submodel in the first stage, the thermodynamic potential of the working medium in the evaporator submodel in the first stage, and the steam temperature of the condenser submodel; the third constraint is constructed based on the initial temperature of the heat exchange medium in the preheater submodel, the thermodynamic potential of the heat exchange medium in the parallel superheater submodel and reheater submodel, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the second stage, the thermodynamic potential of the heat exchange medium in the evaporator submodel in the first stage, the heat transfer and thermal resistance of the preheater submodel, the thermodynamic potential of the working medium in the preheater submodel, , the thermodynamic potential of the working fluid in the first stage of the evaporator sub-model and the steam temperature of the condenser sub-model, construct the fourth constraint; according to the temperature of the working fluid at the inlet end of the preheater sub-model, the thermodynamic potential of the working fluid in the preheater sub-model, , the thermokinetic potential of the working fluid in the first stage of the evaporator sub-model and the steam temperature of the condenser sub-model, construct the fifth constraint; according to the initial temperature of the cold air in the condenser sub-model, the thermokinetic potential of the cold air in the third condensing section in the condenser sub-model, the thermokinetic potential of the cold air in the first condensing section in the condenser sub-model, the heat transfer and thermal resistance of the first condensing section of the condenser sub-model, the thermokinetic potential of the working fluid in the first condensing section of the condenser sub-model and the condensed water temperature of the condenser sub-model, construct the sixth constraint; according to the initial temperature of the cold air in the condenser sub-model, the thermokinetic potential of the cold air in the third condensing section in the condenser sub-model, the heat transfer and thermal resistance of the second condensing section of the condenser sub-model and the condensed water temperature of the condenser sub-model, construct the seventh constraint; according to the initial temperature of the cold air in the condenser sub-model, the heat transfer and thermal resistance of the third condensing section of the condenser sub-model and the condensed water temperature of the condenser sub-model, construct the eighth constraint.

[0070] It should be noted that the first stage of the evaporator sub-model is the heating process of heating the working medium from unsaturated water to saturated water, and the second stage of the evaporator sub-model is the vaporization process of vaporizing saturated water into saturated steam; the first stage of the condenser sub-model is the process of condensing superheated steam into saturated steam when the low-pressure cylinder exhaust steam is in a superheated state, the second stage of the condenser sub-model is the process of condensing saturated steam into saturated water when the low-pressure cylinder exhaust steam is in a superheated state, and the third stage of the condenser sub-model is a one-stage heat exchange process adopted when the low-pressure cylinder exhaust steam is in a saturated state.

[0071] In an optional embodiment, the device further includes: a parameter acquisition module, which optimizes the adjustable parameters of the solar thermal power generation group determined in advance according to the operating conditions of the solar thermal power generation group using a preset genetic algorithm, an electrification process model and heat transfer constraints, and obtains the thermodynamic parameters in the electrification process model before obtaining the optimal parameter combination; an analysis module, which performs sensitivity analysis on each thermodynamic parameter and obtains a sensitivity index of the corresponding thermodynamic parameter; a parameter determination module, which determines whether the sensitivity index of each thermodynamic parameter is greater than a preset sensitivity threshold, and based on the existence of a sensitivity index greater than the preset sensitivity threshold, uses the corresponding thermodynamic parameter as an adjustable parameter.

[0072] In this embodiment, the optimization module 92 includes: a population generation unit, which uses a preset genetic algorithm to randomly generate a first target number of individuals to obtain a first population, and the individuals include a working fluid flow rate and a cooling air flow rate; a cycle efficiency determination unit, which determines the cycle efficiency of each individual according to the operating conditions of the individual and the solar thermal power generation unit, using an electrification process model and heat transfer constraints, combined with a preset cycle efficiency optimization algorithm; a genetic simulation unit, which uses a preset selection algorithm to select a second target number of individuals from the first population according to the cycle efficiency of each individual, and crossover and mutation of the second target number of individuals to obtain corresponding mutant individuals; wherein the second target number is less than the first target number; an optimization unit, which uses all the mutant individuals to update the first population, and cyclically executes the cycle efficiency determination unit to the optimization unit until the maximum number of iterations is reached, to obtain the cycle efficiency of the corresponding updated population and each updated individual in the updated population, and selects the updated individual with the largest cycle efficiency according to the cycle efficiency of each updated individual as the optimal parameter combination.

[0073] Specifically, the cycle efficiency determination unit includes: a simulation prediction subunit, which inputs the operating conditions of individuals and solar thermal power generation units into the electrification process model, and combines the heat transfer constraints to obtain the workload of each mechanical work output component in the corresponding electrification process model; a query subunit, which queries the coolprop open source physical property library based on the regional temperature and pressure states obtained in advance, and determines the inlet and outlet enthalpy difference of each heat exchanger in the electrification process model, so as to obtain the total heat consumption based on the inlet and outlet enthalpy difference of each heat exchanger; an efficiency determination subunit, which determines the cycle efficiency of the corresponding individual based on the workload and total heat consumption of each mechanical work output component.

[0074] Furthermore, the efficiency determination subunit is used to: obtain the turbine workload, fan workload and water pump workload based on the workload of each thermal system component, and determine the net cycle power; and determine the corresponding cycle efficiency based on the net cycle power and total heat consumption.

[0075] It should be noted that the workload of the steam turbine can be determined based on the difference in the inlet and outlet steam specific enthalpies corresponding to the steam turbine sub-model in the electrification process model combined with the turbine efficiency. The workload of the fan can be determined based on the direct ratio of the power consumed in the actual operation process of the fan sub-model in the electrification process model to the cube of the corresponding volume flow rate. The workload of the water pump can refer to the workload of the fan, and will not be repeated here.

[0076] In addition, the genetic simulation unit includes: a pairing subunit, which pairs the second target number of individuals in pairs to obtain corresponding paired individuals; a crossover subunit, which randomly selects a crossover point on each paired individual, and exchanges parameter fragments of the two paired individuals based on the crossover point to generate two corresponding crossover individuals; and a mutation subunit, which randomly selects at least one position for mutation of each crossover individual to obtain a corresponding mutant individual.

[0077] In an optional embodiment, the device further includes: an operating condition updating module, which uses a preset genetic algorithm, an electrification process model and heat transfer constraints to optimize the adjustable parameters of the solar thermal power generation group determined in advance according to the operating conditions of the solar thermal power generation group, and before obtaining the optimal parameter combination, determines that the operating conditions do not meet the preset design conditions, and uses the preset design conditions to update the operating conditions.

[0078] Accordingly, the device also includes: a non-standard optimization module, which, after optimizing the solar thermal power generation unit using the optimal parameter combination, searches the preset parameter database according to the operating conditions that do not meet the preset design conditions to determine the corresponding optimal operating parameters; wherein the preset parameter database is first constructed based on different non-preset design conditions and their corresponding optimal operating parameters; the optimization module 93 is used to update the corresponding solar thermal power generation unit according to the optimal operating parameters.

[0079] Furthermore, the non-standard optimization module includes: a search unit, which searches the preset parameter database based on the operating conditions that do not meet the preset design conditions; a parameter determination unit, which obtains the optimal operating parameters corresponding to the non-preset design conditions if the operating conditions match the non-preset design conditions in the preset parameter database; otherwise, determines whether the operating conditions are between the two non-preset design conditions in the preset parameter database; if the operating conditions are between the two non-preset design conditions in the preset parameter database, determines the optimal operating parameters based on the optimal operating parameters of the two non-preset design conditions using the interpolation method; otherwise, determines the non-preset design condition in the preset parameter database that is closest to the operating conditions, and determines the corresponding optimal operating parameters in combination with the extrapolation method or a previously created parameter prediction model.

[0080] In summary, the embodiment of the present invention obtains the operating conditions through the operating condition acquisition module to timely understand the operating status of the unit, providing data support for subsequent optimization and adjustment, and compares the actual operating conditions with the preset design conditions through the optimization module to quickly discover whether the unit deviates from the design benchmark, providing direction for subsequent optimization, and when the operating conditions meet the preset design conditions, a genetic algorithm is used for global optimization, and combined with the electrification process model and heat transfer constraints, a reliable model support is provided for parameter optimization to ensure that the global optimal parameter combination is found, so that the unit is optimized using the optimal parameter combination through the optimization module, achieving a significant improvement in the cycle efficiency and net output power of the unit under full-load design conditions, significantly improving the operating performance and reliability of the unit, and providing a solid theoretical basis for the intelligent regulation and efficient and stable operation of the solar thermal power generation system.

[0081] Figure 10 An example of a physical structure diagram of an electronic device is shown below. Figure 10As shown, the electronic device may include: a processor (processor) 1010 , a communication interface (Communications Interface) 1020 , a memory (memory) 1030 and a communication bus 1040 , wherein the processor 1010 , the communication interface 1020 , and the memory 1030 communicate with each other via the communication bus 1040 . The processor 1010 can call the logic instructions in the memory 1030 to execute the optimization method of the concentrated solar thermal power generation group, which includes: obtaining the operating conditions of the solar thermal power generation group, the operating conditions including the operating parameters and operating conditions of the solar thermal power generation group; determining that the operating conditions meet the preset design conditions, and according to the operating conditions of the solar thermal power generation group, using the preset genetic algorithm, the electrification process model and the heat transfer constraints, optimizing the adjustable parameters of the solar thermal power generation group determined in advance to obtain the optimal parameter combination; wherein, the preset design conditions are used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation group, the electrification process model is first constructed based on the solar thermal power generation group using the heat flow method, and the heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law; and optimizing the solar thermal power generation group using the optimal parameter combination.

[0082] Furthermore, the logic instructions in the aforementioned memory 1030 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0083] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the concentrated solar thermal power generation group optimization method provided by the above methods, the method including: obtaining the operating conditions of the solar thermal power generation group, the operating conditions including the operating parameters and operating conditions of the solar thermal power generation group; determining that the operating conditions meet the preset design conditions, and according to the operating conditions of the solar thermal power generation group, using a preset genetic algorithm, an electrification process model and heat transfer constraints, optimizing the previously determined adjustable parameters of the solar thermal power generation group to obtain the optimal parameter combination; wherein the preset design conditions are used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation group, the electrification process model is first constructed based on the solar thermal power generation group using the heat flow method, and the heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law; and optimizing the solar thermal power generation group using the optimal parameter combination.

[0084] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the concentrated solar thermal power generation group optimization method provided by the above-mentioned methods, the method comprising: obtaining the operating conditions of the solar thermal power generation group, the operating conditions including the operating parameters and operating conditions of the solar thermal power generation group; determining that the operating conditions meet the preset design conditions, and according to the operating conditions of the solar thermal power generation group, optimizing the previously determined adjustable parameters of the solar thermal power generation group using a preset genetic algorithm, an electrification process model and heat transfer constraints to obtain an optimal parameter combination; wherein the preset design conditions are used to characterize the benchmark operating conditions when designing the corresponding solar thermal power generation group, the electrification process model is first constructed based on the solar thermal power generation group using the heat flow method, and the heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law; and optimizing the solar thermal power generation group using the optimal parameter combination.

[0085] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0086] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing a concentrated solar thermal power generation unit, characterized in that: include: Acquiring an operating condition of a solar thermal power generation unit, wherein the operating condition includes operating parameters and operating conditions of the solar thermal power generation unit; Determining that the operating conditions meet preset design conditions, and optimizing the previously determined adjustable parameters of the solar thermal power generation unit based on the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model, and heat transfer constraints to obtain an optimal parameter combination; The preset design operating conditions are used to represent the benchmark operating conditions when designing the corresponding solar thermal power generation unit. The electrification process model is previously constructed based on the solar thermal power generation unit using the heat flow method. The heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law. The optimal parameter combination is used to optimize the solar thermal power generation unit.

2. The method for optimizing a concentrated solar thermal power generation unit according to claim 1, characterized in that: The adjustable parameters of the solar thermal power generation unit include the working fluid flow rate and the cooling air flow rate. According to the operating conditions of the solar thermal power generation unit, the preset genetic algorithm, the electrification process model and the heat transfer constraints are used to optimize the adjustable parameters of the solar thermal power generation unit determined in advance to obtain the optimal parameter combination, including: SA, using a preset genetic algorithm, randomly generates a first target number of individuals to obtain a first population, wherein the individuals include a working fluid flow rate and a cooling air flow rate; SB, for each of the individuals, determining the cycle efficiency of the individual according to the operating conditions of the individual and the solar thermal power generation unit, using an electrification process model and heat transfer constraints in combination with a preset cycle efficiency optimization algorithm; SC, selecting a second target number of individuals from the first population using a preset selection algorithm based on the cycle efficiency of each of the individuals, and performing crossover and mutation on the second target number of individuals to obtain corresponding mutant individuals; wherein the second target number is smaller than the first target number; SD, use all the mutant individuals to update the first population, and iterate SB-SD until the maximum number of iterations is reached, obtain the cycle efficiency of the corresponding updated population and each updated individual in the updated population, and select the updated individual with the largest cycle efficiency as the optimal parameter combination based on the cycle efficiency of each updated individual.

3. The method for optimizing a concentrated solar thermal power generation unit according to claim 2, characterized in that: According to the operating conditions of the individual and the solar thermal power generation unit, the cycle efficiency of the individual is determined by utilizing an electrification process model and heat transfer constraints in combination with a preset cycle efficiency optimization algorithm, including: Inputting the operating conditions of the individual and the solar thermal power generation unit into the electrification process model, and combining the heat transfer constraints to obtain the work of each mechanical work output component in the corresponding electrification process model; Based on the previously acquired regional temperature and pressure states, the CoolProp open source physical property library is queried to determine the inlet and outlet enthalpy differences of each heat exchanger in the electrification process model, so as to obtain the total heat consumption based on the inlet and outlet enthalpy differences of each heat exchanger; The cycle efficiency of the corresponding individual is determined according to the workload of each mechanical work output component and the total heat consumption.

4. The method for optimizing a concentrated solar thermal power generation unit according to claim 2, wherein: Performing crossover and mutation on the second target number of individuals to obtain corresponding mutant individuals includes: Pairing the second target number of individuals in pairs to obtain corresponding paired individuals; For each of the paired individuals, a crossover point on the paired individual is randomly selected, and parameter segments of the two paired individuals are exchanged based on the crossover point to generate two corresponding crossover individuals; For each crossover individual, at least one position is randomly selected for mutation to obtain the corresponding mutant individual.

5. The method for optimizing a concentrated solar thermal power generation unit according to claim 1, characterized in that: Before optimizing the adjustable parameters of the solar thermal power generation unit determined in advance by using a preset genetic algorithm, an electrification process model and heat transfer constraints according to the operating conditions of the solar thermal power generation unit to obtain the optimal parameter combination, the method includes: determining that the operating condition does not conform to the preset design condition, and updating the operating condition using the preset design condition; After optimizing the solar thermal power generation unit by using the optimal parameter combination, the method includes: According to the operating condition that does not meet the preset design condition, searching the preset parameter database to determine the corresponding optimal operating parameters; wherein the preset parameter database is previously constructed based on different non-preset design conditions and their corresponding optimal operating parameters; According to the optimal operating parameters, the corresponding solar thermal power generation group is updated.

6. The method for optimizing a concentrated solar thermal power generation unit according to claim 5, characterized in that: According to the operating conditions that do not meet the preset design conditions, searching the preset parameter database to determine the corresponding optimal operating parameters, including: Searching a preset parameter database according to an operating condition that does not conform to the preset design condition; If the operating condition matches the non-preset design condition in the preset parameter database, obtaining the optimal operating parameters corresponding to the non-preset design condition; otherwise, determining whether the operating condition is between two non-preset design conditions in the preset parameter database; If the operating condition is between two non-preset design conditions in the preset parameter database, determining the optimal operating parameters using an interpolation method based on the optimal operating parameters corresponding to the two non-preset design conditions; Otherwise, the non-preset design operating condition closest to the operating condition in the preset parameter database is determined, and the corresponding optimal operating parameters are determined in combination with the extrapolation method or the parameter prediction model created previously.

7. The method for optimizing a concentrated solar thermal power generation unit according to claim 1, characterized in that: Before optimizing the adjustable parameters of the solar thermal power generation unit determined in advance by using a preset genetic algorithm, an electrification process model, and heat transfer constraints according to the operating conditions of the solar thermal power generation unit to obtain the optimal parameter combination, the method further includes: Obtaining thermodynamic parameters in the electrification process model; For each of the thermodynamic parameters, sensitivity analysis is performed to obtain a sensitivity index of the corresponding thermodynamic parameter; It is determined whether the sensitivity index of each of the thermodynamic parameters is greater than a preset sensitivity threshold, and based on whether the sensitivity index is greater than the preset sensitivity threshold, the corresponding thermodynamic parameter is used as an adjustable parameter.

8. A concentrated solar thermal power generation unit optimization device, characterized in that: include: An operating condition acquisition module is used to acquire the operating condition of the solar thermal power generation unit, wherein the operating condition includes the operating parameters and operating conditions of the solar thermal power generation unit; An optimization module determines that the operating conditions meet the preset design conditions, and optimizes the previously determined adjustable parameters of the solar thermal power generation unit based on the operating conditions of the solar thermal power generation unit using a preset genetic algorithm, an electrification process model, and heat transfer constraints to obtain an optimal parameter combination; The preset design operating conditions are used to represent the benchmark operating conditions when designing the corresponding solar thermal power generation unit. The electrification process model is previously constructed based on the solar thermal power generation unit using the heat flow method. The heat transfer constraint is constructed based on the electrification process model using Kirchhoff's voltage law. The optimization module optimizes the solar thermal power generation unit by utilizing the optimal parameter combination.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for optimizing a concentrated solar thermal power generation unit according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing a concentrated solar thermal power generation unit according to any one of claims 1 to 7 is implemented.

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