Forced circulation steam generation system variable load operation optimization method and related equipment

Abstract

The invention belongs to the field of new energy power generation systems, and discloses a forced circulation steam generation system variable load operation optimization method and related equipment.The method includes the steps that a tube shell type evaporator numerical calculation model and a forced circulation system hydrodynamic balance model are established, and high evaporation capacity, evaporator efficiency and minimum cost serve as optimization targets; a multi-objective optimization model is constructed by combining the change of the flow of the fused salt and the flow of the circulating water; the model is solved by adopting a Pareto dominating method, and a Pareto optimal solution set can be output, so that optimized operation schemes of the forced circulation system under different loads can be formulated according to the optimal solutions; by the adoption of the method, high evaporator efficiency and evaporation capacity can be achieved, meanwhile, pump work loss can be effectively reduced, the key target in operation of the photo-thermal power station is achieved, namely, balance of high efficiency and low loss is achieved, and powerful support is provided for stable operation and economic benefits of the photo-thermal power station.

Description

Technical Field

[0001] The present invention belongs to the field of new energy power generation systems, specifically to the field of steam generation systems, and in particular to a method for optimizing variable load operation of a forced circulation steam generation system and related equipment. Background Art

[0002] As an environmentally friendly new energy power generation system, a solar thermal power station uses a large-scale array of mirrors to collect solar thermal energy, provides steam through a heat exchange device, and combines the process of a traditional steam turbine generator to achieve the purpose of power generation. In addition, the molten salt heated by solar energy in a solar thermal power station equipped with long-term molten salt energy storage can be stored in a huge container, and can still drive the steam turbine to generate electricity a few hours after the sun sets. It has rapid response and synchronous support, and has the ability to participate in the active frequency regulation, reactive voltage control, and low-frequency oscillation suppression of the power grid. It can provide rotational inertia support for the power system, is a stable and controllable zero-carbon power source, and is a flexible power source with the same endowment as conventional thermal power peak and frequency regulation. Among them, a steam generation system with high-temperature molten salt is often equipped in a solar thermal power station to generate water vapor for subsequent steam turbine power generation and drive the steam turbine to do work. The use of a forced circulation system with a circulating pump can achieve the purpose of rapid peak regulation by adjusting the pump power, which is an effective means of operating under variable load conditions.

[0003] During operation, increasing the output capacity of the forced circulation pump can speed up the water circulation flow, improve the heat transfer coefficient, and increase the evaporator efficiency, but it will increase the pump power loss; if the output of the forced circulation pump is reduced, although the pump power loss can be reduced, it will lead to a decrease in the water circulation flow, a decrease in the heat transfer coefficient, and a decrease in the evaporator efficiency; at the same time, the simultaneous change of the molten salt flow and the circulation flow will also affect the evaporation capacity of the steam generation system. Therefore, it is crucial to reasonably select the output power of the forced circulation pump to achieve higher evaporator efficiency, lower pump power loss, higher evaporation capacity or the set evaporation capacity in the operation of the CSP power station.

[0004] It can be seen that the existing forced circulation pump operation mode cannot effectively regulate the molten salt flow rate and the circulation flow rate, and thus cannot take into account both the efficiency and energy consumption of the high evaporator at the same time. Summary of the invention

[0005] The purpose of the present invention is to provide a method for optimizing the variable load operation of a forced circulation steam generating system and related equipment to solve the technical problem that the existing forced circulation pump operation mode cannot effectively regulate the molten salt flow rate and the circulation flow rate, and thus cannot simultaneously take into account the efficiency and energy consumption of the high evaporator.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A method for optimizing variable load operation of a forced circulation steam generation system, comprising: Establish the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system; Based on the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system, with high evaporation capacity, evaporator efficiency and minimum cost as optimization objectives, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model is established. The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0007] Furthermore, the shell and tube evaporator numerical calculation model is used to solve the molten salt temperature at the evaporator outlet and the dryness and density of wet steam, and is constructed based on the efficiency-heat transfer unit method; wherein the efficiency-heat transfer unit method is used to calculate the heat exchange process of the evaporator with phase change, and the specific formula is as follows:

[0008] In the formula, ε is the evaporator efficiency, and NTU is the number of heat transfer units of the evaporator;

[0009] Where U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of ​​the evaporator, m o is the mass flow rate of molten salt, c p,o is the specific heat capacity of molten salt;

[0010] In the formula, h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer diameter, d i is the inner diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side.

[0011] Furthermore, the hydrodynamic balance model of the forced circulation system is established by assuming that the resistance and the driving force are equal. The specific formula is as follows:

[0012] In the formula, ∆ P i is the resistance loss caused by the steam generator, ∆ P f,H is the resistance loss along the circulation path; ∆ P g The driving force is generated by the density difference between water with zero mass gas content and wet steam with mass gas content in the riser and downcomer, ∆ Pi,pump Provide driving force for forced circulation pump;

[0013] In the formula, ρ in is the wet steam density at the evaporator inlet, ρ out is the wet steam density at the evaporator outlet, g is the acceleration of gravity, and h is the height difference between the evaporator and the steam drum.

[0014] Furthermore, the multi-objective optimization model includes an objective function and constraints, wherein the constraints are the adjustment range of the molten salt flow rate and the wet steam flow rate, and the shell-side molten salt pressure drop and the tube-side wet steam pressure drop cannot be higher than the preset pressure. The specific expressions of the objective function and the constraints are as follows:

[0015] In the formula, C total For cost, Q steam is the evaporation amount; Constraints P o is the shell-side molten salt pressure drop, ∆ P i is the wet steam pressure drop in the tube;

[0016] In the formula, x out is the evaporator outlet dryness; The cost includes the cost of purchasing the evaporator C in and the operating costs of the molten salt pump and circulating pump power loss C op ;

[0017]

[0018]

[0019] In the formula, a 1, a 2, a 3 are coefficients respectively; n y is the system running time; c el For electricity bills; n a is the annual inflation rate; η is the pump efficiency; pi and p o The pumping power of the forced circulation pump and the molten salt are:

[0020]

[0021] In the formula, ρ o is the density of molten salt.

[0022] Furthermore, the calculation process of the optimization target in the multi-objective optimization model is as follows: According to the input variable factors, the mass flow rate of molten salt and the mass flow rate of water, the heat exchange effect is calculated according to the numerical calculation model of the shell and tube evaporator, and the wet steam dryness, density and pressure drop at the evaporator outlet are obtained. The evaporator efficiency is calculated according to the wet steam dryness; Calculate the evaporation capacity based on the wet steam density, and calculate the driving force caused by the density difference between the riser and downcomer based on the wet steam density; The resistance loss of the evaporator system is obtained by adding the wet steam pressure drop and the resistance loss along the circulation path; The driving force required to be provided by the forced circulation pump is calculated according to the hydrodynamic balance model of the forced circulation system, and the pumping power of the forced circulation pump is further calculated; The total cost is obtained based on the pressure drop and pumping power on the molten salt side and the cost of the purchased evaporator; Among them, the evaporation capacity is the product of the wet steam circulation flow rate and the evaporator outlet dryness.

[0023] Furthermore, the step of solving the multi-objective optimization model using the Pareto dominance method includes: Initialize the particle swarm and screen the initial particle swarm for individual optimality and global optimality; Calculate each particle in the screened particle group until the maximum number of iterations is reached, and output the Pareto optimal solution set; wherein the Pareto optimal solution set takes efficiency, cost and evaporation as coordinate axes.

[0024] Furthermore, the specific steps of initializing the particle swarm include: Set the population size, space dimension, objective function dimension, and Pareto solution set scale of the initial particle swarm; Define position boundaries and velocity boundaries; Initializing the initial position and initial velocity of the particle swarm to obtain initial information of the particle swarm; Calculate the fitness corresponding to each particle; Perform non-dominated sorting on the initialized particle swarm; Put the fitness of the initialized particle swarm into the Pareto solution set, and compare each particle with other particles. If it is dominated by other particles, delete it from the Pareto solution set, and then get a particle swarm with non-dominated solutions; The crowding distance of all individual particles is calculated using the dense distance method; the global optimum is randomly selected from the top 20% solutions with the largest crowding distance; Record the global optimal position and fitness in the Pareto solution set, where the individual optimal value is the initial value; The specific steps of calculating each particle in the screened particle group include: Use particle swarm method to update particle velocity and position; Use the inertia adaptive weight coefficient to control the degree of change in each iteration; Determine whether the updated particle speed and position are within the constraints: if they exceed the constraints, regenerate the particle speed and position within the boundary; if they are within the constraints, proceed to the next step; Calculate the fitness of each particle in the updated particle swarm; Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the bounding conditions of the constraints: if they are beyond the range, remove the particle from the Pareto solution set; if they meet the constraints, proceed to the next step; Perform non-dominated sorting and crowding calculation on the updated particle swarm: randomly select the global optimum from the top 20% solutions with the largest crowding distance, record the individual optimum and the group optimum, and output the Pareto optimal solution set.

[0025] A variable load operation optimization system for a forced circulation steam generation system, comprising: The first model building module is used to build a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system; The second model building module is used to establish a multi-objective optimization model based on the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model, with high evaporation capacity, evaporator efficiency and minimum cost as optimization goals, and molten salt flow rate and circulating water flow rate as variables; The operation optimization module is used to solve the multi-objective optimization model using the Pareto dominance method and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

[0026] A device comprising: Memory for storing computer programs; A processor is used to implement the steps of the above-mentioned forced circulation steam generation system variable load operation optimization method when executing the computer program.

[0027] A computer-readable storage medium stores a computer program, which, when executed by a processor, is used to implement the steps of the above-mentioned forced circulation steam generation system variable load operation optimization method.

[0028] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for optimizing the variable load operation of a forced circulation steam generation system. The method establishes a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system, takes high evaporation capacity, evaporator efficiency and minimum cost as optimization targets, and combines changes in molten salt flow rate and circulating water flow rate to construct a multi-objective optimization model; the Pareto dominance method is used to solve the model, and a Pareto optimal solution set can be output, so that an optimized operation plan of the forced circulation system under different loads can be formulated according to these optimal solutions; the method is not only helpful to achieve higher evaporator efficiency and evaporation capacity, but also can effectively reduce pump power loss, so as to achieve the key target in the operation of a solar thermal power station, that is, the balance between high efficiency and low loss, and provide strong support for the stable operation and economic benefits of the solar thermal power station; the method is helpful to select a suitable plan according to specific needs in actual operation, so as to achieve efficient, economical and stable operation of the forced circulation steam generation system under variable load.

[0029] Preferably, in the present invention, by adopting the efficiency-heat transfer unit method to construct a numerical calculation model of a shell and tube evaporator, the molten salt temperature at the evaporator outlet and the dryness and density of wet steam can be accurately solved, providing precise data support for the establishment of the optimization model, and helping to improve the accuracy and reliability of the optimization results.

[0030] Preferably, in the present invention, the hydrodynamic balance model of the forced circulation system is established by equalizing the resistance and driving force, which can comprehensively consider the influence of various resistances and driving forces in the system, provide important constraints for the establishment of the optimization model, and help ensure the feasibility and stability of the optimization scheme in actual operation.

[0031] Preferably, in the present invention, the multi-objective optimization model includes an objective function and constraints, wherein the constraints take into account the adjustment range of the molten salt flow rate and the wet steam flow rate and the limitations of the shell-side molten salt pressure drop and the tube-side wet steam pressure drop, thereby ensuring the safety and economy of the optimization scheme in actual operation; the objective function comprehensively considers multiple objectives such as cost and evaporation volume, thereby helping to maximize the overall benefit.

[0032] Preferably, in the present invention, the calculation process of the optimization target in the multi-objective optimization model includes the calculation of heat exchange effect, evaporation capacity, driving force, resistance loss, pumping power and cost, which helps to ensure the accuracy and comprehensiveness of the optimization results and provides a reliable basis for actual operation.

[0033] Preferably, in the present invention, the Pareto dominance method is used to solve the multi-objective optimization model. By initializing the particle swarm and performing individual optimal and global optimal screening, and subsequent iterative calculations, the Pareto optimal solution set can be output. In this way, in actual operation, the appropriate solution can be selected according to specific needs to achieve the balance and optimization of multiple objectives.

[0034] Further preferably, in the present invention, the calculation steps of initializing the particle swarm and the screened particle swarm include setting initial parameters, defining boundary conditions, initializing position and speed, calculating fitness, non-dominated sorting, and crowding distance calculation; these steps ensure the accuracy and effectiveness of the particle swarm optimization algorithm and provide a reliable method for solving multi-objective optimization models; at the same time, by judging whether the updated particle speed and position are within the constraints and calculating the fitness corresponding to each particle in the updated particle swarm, the feasibility and economy of the optimization scheme are further ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the structure of a forced circulation steam generating system provided in an embodiment of the present invention; Figure 2 An operation flow chart of a multi-objective optimization model for a forced circulation steam generation system provided by an embodiment of the present invention; Figure 3 A flow chart of a method for optimizing variable load operation of a forced circulation steam generation system provided by an embodiment of the present invention; Figure 4 A flowchart of the operation of the Pareto dominance method for solving a multi-objective optimization model provided by an embodiment of the present invention; Figure 5 A multi-objective optimization model verification diagram of three standard function Pareto curves provided in an embodiment of the present invention; Figure 6 The optimal Pareto frontier solution set with high efficiency and low cost under different evaporation capacities calculated by the optimization method provided in the embodiment of the present invention; Figure 7 A flow chart of a method for optimizing variable load operation of a forced circulation steam generation system provided by the present invention; Figure 8 A structural schematic diagram of a variable load operation optimization system for a forced circulation steam generation system provided by the present invention. DETAILED DESCRIPTION

[0036] Example 1 As described in the background technology, during operation, increasing the output capacity of the forced circulation pump can speed up the circulation flow of water, improve the heat transfer coefficient, and increase the efficiency of the evaporator, but it will increase the pump power loss; if the output of the forced circulation pump is reduced, although the pump power loss can be reduced, it will lead to a decrease in the circulation flow of water, a decrease in the heat transfer coefficient, and a decrease in the efficiency of the evaporator; at the same time, the simultaneous change of the molten salt flow and the circulation flow will also affect the evaporation amount of the steam generation system. Therefore, it is a crucial operating goal in the operation of a solar thermal power station to reasonably select the output power of the forced circulation pump to achieve higher evaporator efficiency, lower pump power loss, higher evaporation amount, or a set evaporation amount.

[0037] In order to achieve the above-mentioned purpose, the present invention provides a method for optimizing the variable load operation of a forced circulation steam generation system. This method is aimed at the forced circulation steam generation system, and the multi-objective optimization particle swarm algorithm is used to calculate and obtain the Pareto optimal solution set. During the operation of the project, the molten salt flow rate and the water flow rate can be adjusted according to the Pareto optimal solution set when the evaporation demand changes, so as to achieve the purpose of minimum cost and highest efficiency, and provide a reference for the optimization of industrial variable load flow control operation of solar thermal power stations; the use of this method can effectively solve the problem of achieving high evaporator efficiency and low cost at the same time when variable load control is used.

[0038] Among them, the Pareto dominance method, also known as the Pareto optimization algorithm and the Pareto frontier algorithm, is a multi-objective optimization algorithm that solves problems based on the Pareto optimal theory.

[0039] like Figure 1 As shown, this embodiment provides a structure of a forced circulation steam generation system. Combined with the components of the system structure, its specific working process is as follows: Feed water enters the drum 1, and the water in the drum that has not changed phase enters the evaporator 4 along the downcomer 2. The water in the evaporator is heated by the stored high-temperature molten salt to produce wet steam with a certain dryness. The wet steam enters the drum along the riser 5. The water and steam are separated in the drum, and the steam enters the next superheater for further heating. The forced circulation pump 3 can control the water circulation flow rate in the circulation system.

[0040] Combined with the above work process, such as Figure 2 As shown, this embodiment provides a method for optimizing variable load operation of a forced circulation steam generation system, comprising the following steps: S1: Establish the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system; S2: Based on the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system, with high evaporation capacity, evaporator efficiency and minimum cost as the goals, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model including objective functions and constraints is established; S3: The Pareto dominance method is used to solve the multi-objective optimization model, output the Pareto optimal solution set, and obtain the optimization plan for improving the variable load and low load high-efficiency operation of the forced circulation system.

[0041] The establishment of the numerical calculation model of the shell and tube evaporator and the hydrodynamic balance model of the forced circulation system includes: (1) The efficiency-heat transfer unit method is used to establish a numerical calculation model for a shell-and-tube evaporator to solve the molten salt temperature at the evaporator outlet and the dryness and density of wet steam. The efficiency-heat transfer unit calculation formula uses the empirical formula for phase change; the shell-side heat transfer coefficient of the molten salt is calculated using the Bell-Delaware method; and the phase change heat transfer coefficient in the tube on the wet steam side is calculated using the Chen method.

[0042] (2) The hydrodynamic balance model of the forced circulation system is established by equating the resistance and driving force. The resistance includes the resistance loss along the steam generator and the resistance loss along the circulation path. The driving force includes the driving force generated by the density difference between water with zero mass gas content and wet steam with a certain mass gas content in the riser and downcomer, as well as the driving force provided by the forced circulation pump.

[0043] In addition, the resistance loss generated inside the evaporator requires the calculation of the two-phase friction resistance loss, the two-phase local pressure loss and the acceleration pressure drop. The resistance loss generated in the circulation path requires the calculation of the friction resistance loss and local pressure loss of the riser and downcomer.

[0044] The multi-objective particle swarm optimization model is established as follows: (1) The objective function is established with high evaporator efficiency, high evaporation capacity and minimum cost as the goals. The evaporation capacity is the product of the wet steam circulation flow rate and the evaporator outlet dryness. The cost includes the cost of purchasing the evaporator and the operating cost of the molten salt pump and the circulation pump power loss. The constraints are the adjustment range of the molten salt flow rate and the wet steam flow rate. The shell-side molten salt pressure drop and the tube-side wet steam pressure drop cannot be higher than 2 bar.

[0045] (2) The calculation process of the optimization target is as follows: According to the input variable factors, the mass flow rate of molten salt and the mass flow rate of water, the heat exchange effect is calculated according to the numerical calculation model of the evaporator described in S1, and the wet steam dryness, density and pressure drop at the evaporator outlet are obtained. The evaporator efficiency is calculated according to the wet steam dryness. The evaporation capacity is calculated according to the wet steam density. The driving force caused by the density difference between the riser and the downcomer is calculated according to the wet steam density. The resistance loss of the evaporator system is obtained by adding the wet steam pressure drop and the resistance loss along the circulation path. The driving force required to be provided by the forced circulation pump is calculated according to the hydrodynamic balance model of the forced circulation system described in S1, and the pumping power of the forced circulation pump is further calculated. On the other hand, after the pressure drop and pumping power on the molten salt side are calculated, the total cost is obtained in combination with the cost of the purchased evaporator.

[0046] The solution process using the Pareto dominance method is: (1) Initialize the particle population and screen the initial particle population for individual optimality and global optimality. Set the number of initial populations, spatial dimension, objective function dimension, and Pareto solution set size. Define position boundaries and velocity boundaries. Initialize the initial position and initial velocity of the particle population to obtain the initial information of the particle population. Calculate the fitness corresponding to each particle. Perform non-dominated sorting on the initialized population. Put the fitness of the initialized population into the Pareto solution set, compare each particle with other particles, and delete it from the Pareto solution set if it is dominated by other individuals, thereby obtaining a population of non-dominated solutions. Use the dense distance method to calculate the crowding distance of all individual particles. Randomly select the global optimal solution from the top 20% of solutions with the largest crowding distance. Record the position and fitness of the global optimal solution in the Pareto solution set. The individual optimal solution is the initial value.

[0047] (2) Record the iteration number and start the calculation of step (3) within the iteration number.

[0048] (3) Perform the following calculations for each particle: Use the particle swarm method to update the particle's velocity and position. Use the inertia adaptive weight coefficient to control the degree of change in each iteration to avoid premature convergence or local optimization. Determine whether the updated particle velocity and position are within the constraints (the set boundary range). If they are beyond the boundary range, regenerate the particle's velocity and position within the boundary. If they are within the boundary range, proceed to the next step. Calculate the fitness of each particle in the updated particle swarm. Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the constraint boundary conditions. If they are beyond the range, remove the particle from the Pareto solution set and do not consider it. If the constraints are met, proceed to the next step. Perform non-dominated sorting and crowding calculation on the updated population. Randomly select the global optimum from the top 20% solutions with the largest crowding distance. Record the individual optimal value and the group optimal value.

[0049] After the iterative calculation, a three-dimensional Pareto optimal solution set with efficiency, cost and evaporation as the coordinate axes is obtained. All points on this solution set are non-inferior solutions. During the operation of the project, the molten salt flow and water flow can be adjusted according to the Pareto optimal solution set when the evaporation demand changes to achieve the goal of minimum cost and highest efficiency.

[0050] In order to facilitate understanding of the optimization method provided in this embodiment, the preferred method is further explained in conjunction with the accompanying drawings, specifically including: In this embodiment, the establishment of the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model in S1 includes: The following shell and tube evaporator numerical calculation model is established: The heat transfer process of the evaporator with phase change is calculated using the efficiency-heat transfer unit method (ε-NTU).

[0051]

[0052] Where ε is the evaporator efficiency and NTU is the number of heat transfer units in the evaporator.

[0053]

[0054] Where U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of ​​the evaporator, m o is the mass flow rate of molten salt, c p,o is the specific heat of molten salt.

[0055]

[0056] Among them, h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer diameter, d i is the inner diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side. The shell-side heat transfer coefficient on the molten salt side is calculated using the Bell-Delaware method; the phase change heat transfer coefficient in the tube on the wet steam side is calculated using the Chen method.

[0057] Total heat load Q ε-NTU Calculated by:

[0058] Among them, T o,in is the molten salt inlet temperature, T i,in is the wet steam inlet temperature.

[0059] The outlet temperatures on both sides are calculated as follows: ,

[0060] Among them, T o,out is the molten salt outlet temperature, h i,out is the wet steam outlet enthalpy, h i,in is the wet steam inlet enthalpy. m i is the wet steam mass flow rate, c p,i is the specific heat capacity of wet steam. The wet steam outlet dryness is obtained according to the wet steam outlet enthalpy value. x out and density ρ out .

[0061] The following hydrodynamic balance model of the forced circulation system is established by making the resistance and driving force equal:

[0062] Among them, ∆ P i is the resistance loss caused by the steam generator, ∆ P f,H is the resistance loss along the circulation path; ∆ P g The driving force is generated by the density difference between water with zero mass gas content and wet steam with a certain mass gas content in the riser and downcomer, ∆ P i,pump Provides driving force for forced circulation pump.

[0063]

[0064] in, ρ in is the wet steam density at the evaporator inlet, ρ out is the wet steam density at the evaporator outlet, g is the acceleration of gravity, and h is the height difference between the evaporator and the steam drum.

[0065] The resistance loss generated inside the evaporator requires the calculation of the two-phase friction resistance loss, the two-phase local pressure loss and the acceleration pressure drop. The resistance loss generated in the circulation path requires the calculation of the friction resistance loss and the local pressure loss of the riser and downcomer.

[0066] In the above S2, based on the numerical calculation model of the shell and tube evaporator and the hydrodynamic balance model of the forced circulation system established in S1, with high evaporator efficiency, high evaporation capacity and minimum cost as the goals, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model including objective functions and constraints is established. The specific process is as follows: 1) With high evaporator efficiency, high evaporation volume and minimum cost as the goal, establish the following goals:

[0067] in, C total For cost, Q steam is the evaporation amount. P o is the shell-side molten salt pressure drop, ∆ P i is the wet steam pressure drop in the tube.

[0068]

[0069] in,x out is the evaporator outlet dryness.

[0070] The cost includes the cost of purchasing the evaporator C in and the operating costs of the molten salt pump and circulating pump power loss C op .

[0071]

[0072]

[0073]

[0074] Among them, the coefficient a 1, a 2, a 3 are 8500, 409, and 0.85 respectively; it is considered that the system is running n y For 10 years. c el 0.15 $ / kW; annual operating time is 4380 h; annual inflation rate n a is 0.1; η is the pump efficiency, which is 70%. p i and p o The pumping power of the forced circulation pump and the molten salt are:

[0075]

[0076] in, ρ o is the density of molten salt.

[0077] The flowchart for calculating the optimization target is as follows Figure 3 As shown, according to the input variable factors molten salt mass flow rate and water mass flow rate, the heat exchange effect is calculated according to the evaporator numerical calculation model described in S1, and the wet steam dryness, density and pressure drop at the evaporator outlet are obtained. The evaporator efficiency is calculated according to the wet steam dryness. The evaporation capacity is calculated according to the wet steam density. The driving force caused by the density difference between the riser and the downcomer is calculated according to the wet steam density. The resistance loss of the evaporator system is obtained by adding the wet steam pressure drop and the resistance loss along the circulation path. The driving force ∆ required to be provided by the forced circulation pump is calculated according to the hydrodynamic balance model of the forced circulation system described in S1 P i,pump , and further calculate the forced circulation pump pumping power pi On the other hand, after calculating the pressure drop and pumping power on the molten salt side, the total cost is obtained by combining the cost of the purchased evaporator, thereby obtaining the calculation results of the three optimization objectives.

[0078] In the above S3, the flowchart of using the Pareto dominance method to solve the multi-objective optimization model in S2 is as follows: Figure 4 As shown, the particle position is used to represent the optimization variable, the fitness is used to store the optimization target, and the particle speed represents the search direction. The specific Pareto dominance method is used to solve the multi-objective optimization model in S2 to obtain the optimization solution process for the forced circulation steam generation system of the CSP power station as follows: S31: Initialize the particle population and obtain the initial information of the population. Specifically, set the number of initial populations to 35, the spatial dimension to 2 (the number of optimization variables), the objective function dimension to 3 (the number of optimization targets), and the Pareto solution set size to 30 (the number of optimal solutions retained). Define the position boundary and the velocity boundary. Initialize the initial position and initial velocity of the particle swarm to obtain the initial information of the particle swarm.

[0079] S32: Calculate the fitness corresponding to each particle in the particle swarm according to the calculation method described in S2.

[0080] S33: Perform non-dominated sorting on the initialized population. Put the fitness of the initialized population into the Pareto solution set, compare each particle with other particles, and if it is dominated by other individuals, delete it from the Pareto solution set, thereby obtaining a population of non-dominated solutions.

[0081] S34: The crowding distance of all individual particles is calculated using the dense distance method.

[0082] S35: Randomly select the global optimum from the top 20% solutions with the largest crowding distance. Record the position and fitness of the global optimum in the Pareto solution set. The individual optimum is the initial value.

[0083] S36: Record the number of iterations and start iterative calculation.

[0084] S37: For each particle, the following calculations are performed: S371: Update the particle's velocity and position. The particle position x at step k+1 i k+1 Calculated by:

[0085] where x i k is the position of the i-th particle at the k-th step. i kis the velocity of the i-th particle at the k-th step. The formula for the particle velocity v of the i-th particle at iteration step k+1 is as follows:

[0086] in p i,best is the best position in the history of the particle, and G best It is the best position for the current population. c per and c en They are the self-learning factor and the group learning coefficient respectively. r per and r en is a random number between 0 and 1. w is the inertia adaptive weight coefficient, which controls the degree of change of each iteration to avoid premature convergence or local optimization. It is calculated as follows:

[0087] Where d is the overall dimension (the number of variable factors), which is 2 in this embodiment. w max and w min are the maximum and minimum values ​​of the set inertia adaptive weight coefficient respectively. x max and x min They are the maximum and minimum values ​​of the particle positions respectively.

[0088] Determine whether the updated particle speed and position are within the constraints (the set boundary range). If they are beyond the boundary range, regenerate the particle speed and position within the boundary. If they are within the boundary range, proceed to the next step.

[0089] S372: Calculate the fitness of each particle in the updated particle swarm according to the calculation method described in S2. P o and ∆ P i Is it within the bounds of the constraints? If it is out of range, remove the particle from the Pareto solution set and do not consider it. If it meets the constraints, proceed to the next step.

[0090] S373: Perform non-dominated sorting on the updated population.

[0091] S374: Calculate the crowding degree of the updated population.

[0092] S375: Randomly select the global optimum from the top 20% solutions with large crowding distance.

[0093] S376: Record individual optimal values ​​and group optimal values.

[0094] S38: Determine whether the number of iterations has been reached. If so, end the calculation. If not, add 1 to the number of iterations and return to S36 to recalculate.

[0095] The established multi-objective particle swarm iterative calculation model was validated. This example calculates three standard functions suitable for multi-objective optimization scheme evaluation; the specific results are as follows: Figure 5 As shown, all functions achieve excellent computational accuracy and present a more evenly distributed Pareto front curve.

[0096] In this embodiment, Figure 6 As shown, Figure 6 It is the Pareto optimal solution set of multi-objective optimization results, specifically evaporation Q steam , Evaporator efficiency ε and costs C total It can be seen that the Pareto optimal solution set after optimization calculation forms an upward plane, and all points on this solution set are non-inferior solutions, which means that when the evaporation volume increases, the efficiency will decrease and the cost will increase; when the project is running, the molten salt flow and water flow can be adjusted according to the Pareto optimal solution set when the evaporation demand changes, so as to achieve the goal of minimum cost and highest efficiency.

[0097] Example 2 like Figure 7 As shown, this embodiment provides a method for optimizing variable load operation of a forced circulation steam generation system, comprising the following steps: S1: Establish the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system; S2: Based on the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system, with high evaporation capacity, evaporator efficiency and minimum cost as optimization objectives, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model is established; S3: The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set. like Figure 8As shown, the present embodiment further provides a forced circulation steam generating system variable load operation optimization system, comprising: a first model building module, used to establish a shell and tube evaporator numerical calculation model and a forced circulation system hydrodynamic balance model; a second model building module, used to establish a multi-objective optimization model based on the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model, and with high evaporation volume, evaporator efficiency and minimum cost as optimization objectives, and with molten salt flow rate and circulating water flow rate as variables; an operation optimization module, used to solve the multi-objective optimization model using the Pareto dominance method, and output the Pareto optimal solution set, so as to obtain a forced circulation system variable load operation optimization scheme based on the Pareto optimal solution set.

[0098] The present invention also provides a device, comprising: a memory for storing a computer program; and a processor for implementing the steps of the method for optimizing variable load operation of a forced circulation steam generation system when executing the computer program.

[0099] When the processor executes the computer program, the above-mentioned steps of optimizing the variable load operation of the forced circulation steam generating system are implemented, for example: establishing a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system; establishing a multi-objective optimization model based on the numerical calculation model of the shell and tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation volume, evaporator efficiency and minimum cost as optimization objectives, and with molten salt flow rate and circulating water flow rate as variables; using the Pareto dominance method to solve the multi-objective optimization model, outputting the Pareto optimal solution set, and obtaining an optimization scheme for the variable load operation of the forced circulation system according to the Pareto optimal solution set.

[0100] Alternatively, when the processor executes the computer program, the functions of each module in the above system are realized, for example: a first model building module is used to establish a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system; a second model building module is used to establish a multi-objective optimization model based on the numerical calculation model of the shell and tube evaporator and the hydrodynamic balance model of the forced circulation system, with high evaporation volume, evaporator efficiency and minimum cost as optimization objectives, and molten salt flow rate and circulating water flow rate as variables; an operation optimization module is used to solve the multi-objective optimization model using the Pareto dominance method, and output the Pareto optimal solution set, so as to obtain a variable load operation optimization scheme for the forced circulation system according to the Pareto optimal solution set.

[0101] Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete preset functions, and the instruction segments are used to describe the execution process of the computer program in the variable load operation optimization device of the forced circulation steam generation system. For example, the computer program can be divided into a first model building module, a second model building module and an operation optimization module; the specific functions of each module are as follows: the first model building module is used to establish a shell and tube evaporator numerical calculation model and a forced circulation system hydrodynamic balance model; the second model building module is used to establish a multi-objective optimization model based on the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model, and with high evaporation and evaporator efficiency, minimum cost as optimization objectives, and molten salt flow and circulating water flow as variables; the operation optimization module is used to solve the multi-objective optimization model using the Pareto dominance method, output the Pareto optimal solution set, so as to obtain the forced circulation system variable load operation optimization scheme according to the Pareto optimal solution set.

[0102] The variable load operation optimization device for the forced circulation steam generating system can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The variable load operation optimization device for the forced circulation steam generating system may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above is an example of a variable load operation optimization device for a forced circulation steam generating system, and does not constitute a limitation on the variable load operation optimization device for a forced circulation steam generating system, and may include more components than the above, or a combination of certain components, or different components, for example, the variable load operation optimization device for the forced circulation steam generating system may also include input and output devices, network access devices, buses, etc.

[0103] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The processor is the control center of the variable load operation optimization of the forced circulation steam generation system, and uses various interfaces and lines to connect various parts of the variable load operation optimization equipment of the entire forced circulation steam generation system.

[0104] The memory can be used to store the computer program and / or module, and the processor implements various functions of the variable load operation optimization device of the forced circulation steam generation system by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0105] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0106] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for optimizing variable load operation of a forced circulation steam generation system are implemented.

[0107] If the integrated modules / units of the variable load operation optimization system of the forced circulation steam generation system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0108] Based on such understanding, the present invention implements all or part of the processes in the above-mentioned forced circulation steam generation system variable load operation optimization method, and can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned forced circulation steam generation system variable load operation optimization method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or preset intermediate form, etc.

[0109] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0110] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0111] It can be seen that the present invention provides a method for optimizing variable load operation of a forced circulation steam generation system, which has the following advantages over the existing operation mode: The present invention is based on the numerical calculation model of the shell and tube evaporator established by the efficiency-heat transfer unit method containing phase change and the hydrodynamic balance model of the forced circulation system established by equal resistance and driving force, and uses a multi-objective particle swarm optimization method to optimize the forced circulation steam generation system operating under variable load conditions with molten salt flow and water circulation flow as control variables, evaporator efficiency, cost and evaporator as optimization targets. The Pareto optimal solution set calculated by the present invention can provide the optimal solution for regulating molten salt flow and water flow when different evaporation requirements change during the operation of the peak-shaving project, so as to achieve the purpose of minimum cost and highest efficiency. Among them, the mathematical model established by the present invention can not only quickly calculate the working state of the circulation system, but also has a high calculation accuracy for the phase change process. Secondly, the optimization method of the present invention does not need to be mechanically and manually optimized according to experience, which greatly reduces the optimization time and labor cost. At the same time, the multi-objective particle swarm optimization algorithm established by the present invention combines inertial adaptive weight coefficients, non-dominated sorting and congestion calculation, controls the degree of change of each iteration, avoids premature convergence or local optimization, and searches for uniform and dispersed optimal solution sets as soon as possible, thereby improving the optimization efficiency of the system. Finally, the method of the present invention calculates three standard functions suitable for multi-objective optimization scheme evaluation. All functions obtain excellent calculation accuracy and present a more evenly distributed Pareto front curve, verifying the accuracy of the method of the present invention.

[0112] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention.

Claims

1. A method for optimizing variable load operation of a forced circulation steam generation system, characterized in that: include: Establish the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system; Based on the numerical calculation model of shell and tube evaporator and the hydrodynamic balance model of forced circulation system, with high evaporation capacity, evaporator efficiency and minimum cost as optimization objectives, and molten salt flow rate and circulating water flow rate as variables, a multi-objective optimization model is established. The Pareto dominance method is used to solve the multi-objective optimization model and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

2. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: The shell and tube evaporator numerical calculation model is used to solve the molten salt temperature at the evaporator outlet and the dryness and density of wet steam, and is constructed based on the efficiency-heat transfer unit method; wherein the efficiency-heat transfer unit method is used to calculate the heat exchange process of the evaporator with phase change, and the specific formula is as follows: In the formula, ε is the evaporator efficiency, and NTU is the number of heat transfer units of the evaporator; Where U is the total heat transfer coefficient of the evaporator, A is the heat transfer area of ​​the evaporator, m o is the mass flow rate of molten salt, c p,o is the specific heat capacity of molten salt; In the formula, h o is the heat transfer coefficient on the molten salt side, R o,f is the fouling thermal resistance on the molten salt side, d o is the outer diameter, d i is the inner diameter, R i,f is the fouling thermal resistance on the wet steam side, h i is the heat transfer coefficient on the wet steam side.

3. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: The hydrodynamic balance model of the forced circulation system is established by equating the resistance and driving force. The specific formula is as follows: In the formula, ∆ P i is the resistance loss caused by the steam generator, ∆ P f,H is the resistance loss along the circulation path; ∆ P g The driving force is generated by the density difference between water with zero mass gas content and wet steam with mass gas content in the riser and downcomer, ∆ P i,pump Provide driving force for forced circulation pump; In the formula, ρ in is the wet steam density at the evaporator inlet, ρ out is the wet steam density at the evaporator outlet, g is the acceleration of gravity, and h is the height difference between the evaporator and the steam drum.

4. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: The multi-objective optimization model includes an objective function and constraints, wherein the constraints are the adjustment range of the molten salt flow rate and the wet steam flow rate, and the shell-side molten salt pressure drop and the tube-side wet steam pressure drop cannot be higher than the preset pressure. The specific expressions of the objective function and the constraints are as follows: In the formula, C total For cost, Q steam is the evaporation amount; Constraints P o is the shell-side molten salt pressure drop, ∆ P i is the wet steam pressure drop in the tube; In the formula, x out is the evaporator outlet dryness; The cost includes the cost of purchasing the evaporator C in and the operating costs of the molten salt pump and circulating pump power loss C op ; In the formula, a 1, a 2, a 3 are coefficients respectively; n y is the system running time; c el For electricity bills; n a is the annual inflation rate; η is the pump efficiency; p i and p o The pumping power of the forced circulation pump and the molten salt are: In the formula, ρ o is the density of molten salt.

5. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 4, characterized in that: in, The calculation process of the optimization objective in the multi-objective optimization model is as follows: According to the input variable factors, the mass flow rate of molten salt and the mass flow rate of water, the heat exchange effect is calculated according to the numerical calculation model of the shell and tube evaporator, and the wet steam dryness, density and pressure drop at the evaporator outlet are obtained; the evaporator efficiency is calculated according to the wet steam dryness; Calculate the evaporation capacity based on the wet steam density, and calculate the driving force caused by the density difference between the riser and downcomer based on the wet steam density; The resistance loss of the evaporator system is obtained by adding the wet steam pressure drop and the resistance loss along the circulation path; Calculate the driving force that the forced circulation pump needs to provide according to the hydrodynamic balance model of the forced circulation system, and further calculate the pumping power of the forced circulation pump; The total cost is obtained based on the pressure drop and pumping power on the molten salt side and the cost of the purchased evaporator; Among them, the evaporation capacity is the product of the wet steam circulation flow rate and the evaporator outlet dryness.

6. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 1, characterized in that: The steps of solving the multi-objective optimization model using the Pareto dominance method include: Initialize the particle swarm and screen the individual optimal and global optimal initial particle swarm; Calculate each particle in the screened particle group until the maximum number of iterations is reached, and output the Pareto optimal solution set; wherein the Pareto optimal solution set takes efficiency, cost and evaporation as coordinate axes.

7. The method for optimizing variable load operation of a forced circulation steam generation system according to claim 6, characterized in that: The specific steps of initializing the particle swarm include: Set the population size, space dimension, objective function dimension, and Pareto solution set scale of the initial particle swarm; Define position boundaries and velocity boundaries; Initializing the initial position and initial velocity of the particle swarm to obtain initial information of the particle swarm; Calculate the fitness corresponding to each particle; Perform non-dominated sorting on the initialized particle swarm; Put the fitness of the initialized particle swarm into the Pareto solution set, and compare each particle with other particles. If it is dominated by other particles, delete it from the Pareto solution set, and then get a particle swarm with non-dominated solutions; The crowding distance of all individual particles is calculated using the dense distance method; the global optimum is randomly selected from the top 20% solutions with the largest crowding distance; Record the global optimal position and fitness in the Pareto solution set, where the individual optimal value is the initial value; The specific steps of calculating each particle in the screened particle group include: Use particle swarm method to update particle velocity and position; Use the inertia adaptive weight coefficient to control the degree of change in each iteration; Determine whether the updated particle speed and position are within the constraints: if they exceed the constraints, regenerate the particle speed and position within the boundary; if they are within the constraints, proceed to the next step; Calculate the fitness of each particle in the updated particle swarm; Determine whether the calculated molten salt pressure drop and wet steam pressure drop are within the bounding conditions of the constraints: if they are beyond the range, remove the particle from the Pareto solution set; if they meet the constraints, proceed to the next step; Perform non-dominated sorting and crowding calculation on the updated particle swarm: randomly select the global optimum from the top 20% solutions with the largest crowding distance, record the individual optimum and the group optimum, and output the Pareto optimal solution set.

8. A forced circulation steam generation system variable load operation optimization system, characterized in that: include: The first model building module is used to build a numerical calculation model of a shell and tube evaporator and a hydrodynamic balance model of a forced circulation system; The second model building module is used to establish a multi-objective optimization model based on the shell and tube evaporator numerical calculation model and the forced circulation system hydrodynamic balance model, with high evaporation capacity, evaporator efficiency and minimum cost as optimization goals, and molten salt flow rate and circulating water flow rate as variables; The operation optimization module is used to solve the multi-objective optimization model using the Pareto dominance method and output the Pareto optimal solution set, so as to obtain the variable load operation optimization scheme of the forced circulation system according to the Pareto optimal solution set.

9. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the method for optimizing variable load operation of a forced circulation steam generating system according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the variable load operation optimization method of the forced circulation steam generation system according to any one of claims 1 to 7.

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