A trough type photo-thermal power station system coupling modeling and coordinated control method

Abstract

This invention discloses a system coupling modeling and coordinated control method for a parabolic trough solar thermal power plant, comprising: building a unified coupled dynamic model of the entire link between the steam generation system and the steam-water power generation system on the Dymola platform; wherein, the preheater and superheater adopt a single-phase flow heat transfer model considering axial diffusion and working fluid compressibility; the evaporator accurately captures the phase change interface through adaptive switching heat transfer correlation; the steam drum uses the non-equilibrium lumped parameter method to decouple the steam and liquid phases, and uses the dryness deviation to simulate the dynamic process of evaporation and condensation; the steam turbine is characterized by the variable operating condition flow capacity based on the Flueger formula with the introduction of a temperature correction term; on this basis, the main steam parameters are used as the coupling boundary and the condensate return water is used as the circulating working fluid to achieve closed-loop connection of the system, and with the cooperation of a multivariable coordinated control strategy, the system coupling consistency and operational stability are effectively improved, and the dynamic adjustment capability and engineering applicability under wide load conditions are enhanced.

Description

Technical Field

[0001] This invention relates to the field of solar thermal power generation and thermal system modeling and control, specifically to a method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system. Background Technology

[0002] With the rapid development of renewable energy, parabolic trough solar thermal power generation technology has become one of the important technical routes in the field of solar thermal power generation due to its mature engineering application foundation and good scalability potential. In a typical parabolic trough solar thermal power plant, the collector field converts solar radiation energy into heat energy, which is then transferred to the steam generation system (SGS) through a heat transfer medium. This process realizes the phase change of the water medium from liquid to superheated steam, thereby driving the turbine to generate electricity. The temperature, pressure, and flow rate of the main steam at the outlet of the steam generation system directly determine the output power of the steam-water power generation system; while changes in the opening of the turbine regulating valve in the steam-water power generation system will also cause sudden changes in the upstream steam flow rate, thus affecting the evaporation section pressure, steam drum water level, and evaporation point location in the steam generation system. This strong bidirectional coupling relationship makes the steam generation system and the steam-water power generation system an inseparable integrated thermodynamic unit, and its dynamic characteristics directly affect the efficiency and operational stability of the power plant.

[0003] However, current engineering practices and research often model the SGS (Self-Governing Gas Separator) and the steam turbine power generation system separately. The SGS side typically employs lumped parameter or one-dimensional distributed parameter models, describing the dynamics of each heat exchange section based on homogeneous flow or drift flow assumptions. The turbine side, on the other hand, establishes power and pressure-flow models encompassing the regulating stage, pressure stage, and extraction steam regeneration system. The two models are mostly coupled through setpoint boundaries or unidirectionally transmitted steam parameters: either assuming a constant SGS outlet back pressure, or providing parameters only based on the static curve of the turbine inlet pressure, while the turbine model treats the SGS outlet parameters as external inputs. This approach completely ignores the dynamic reactions of regulating valve action and sudden changes in steam flow to the pressure, temperature, and phase change processes on the SGS side. Although the Modelica platform, represented by Dymola, has been used for modeling local equipment in concentrated solar power plants, current work mainly focuses on the collector loop or individual heat exchangers, and a unified coupled model covering the entire chain of "preheating—evaporation—superheating—reheating—multi-stage extraction steam regeneration in the turbine" has not yet been constructed. During periods of intense solar irradiance fluctuations, significant load changes, or start-up and shutdown, the separation model cannot reproduce phenomena such as "false water level" in the steam drum and sudden changes in superheater outlet temperature caused by abrupt changes in turbine inlet steam parameters. It also cannot accurately characterize the complex coupling transition process between steam flow, pressure, temperature, and power generation load, resulting in significant distortions in system-level dynamic characteristic analysis and control strategy verification.

[0004] In terms of control strategies, current engineering practices commonly employ local loop regulation methods, such as single-variable PID control. To address irradiation disturbances, feedforward compensation based on irradiance intensity measurements is often introduced. However, these control strategies essentially correct for local deviations and fail to coordinate energy storage, phase change heat transfer, and turbine power generation within the superheater steam drum (SGS) from a global perspective. Under transient conditions such as rapid cloud cover and large-scale load changes, the thermal inertia of the large amount of working fluid and metal walls within the SGS causes steam parameter responses to lag by several minutes, while turbine power responses are completed within seconds. Traditional PID and feedforward strategies, lacking the ability to coordinate dynamics across multiple time scales, are highly susceptible to problems such as significant deviations in main steam pressure, superheater overheating, and severe fluctuations in drum water levels, jeopardizing equipment safety and system efficiency.

[0005] For the control of such multivariable coupled objects, advanced algorithms such as Model Predictive Control (MPC) are theoretically capable of handling multivariable constraints. However, when directly applied to the SGS-turbine integrated coupled system, they still face significant mismatch problems due to the specific characteristics of this scenario. First, the system exhibits strong nonlinearity under large-scale load variations and irradiance fluctuations. The heat transfer coefficient of the two-phase flow in the evaporation section and the steam properties change significantly with the operating conditions. Traditional linear MPC, based on a single equilibrium point linearized model, will inevitably lead to deterioration of control quality or even instability across the entire operating range due to model mismatch. Second, if nonlinear MPC is adopted, a complete high-dimensional dynamic model covering the entire process from evaporation phase change to turbine work needs to be constructed, while satisfying hard constraints such as drum water level, superheater wall temperature, and regulating valve opening and rate. The solution scale of the online optimization problem will expand dramatically, making it difficult to meet the real-time control cycle of seconds or even faster. Third, and more importantly, most existing algorithms such as MPC still consider the SGS and turbine separately, constructing independent prediction models and constraints for each, without treating the SGS multi-stage phase change thermal dynamics and the turbine extraction and regenerative system as a unified whole for state estimation and constraint processing. This makes it impossible to effectively suppress inter-loop interference caused by bidirectional coupling—for example, the turbine control valve's rapid response to load changes will simultaneously react on the SGS pressure, thereby disturbing the drum water level and evaporation boundary; separately designed controllers, lacking coordination mechanisms, are prone to conflicting regulation actions, causing system oscillations. Therefore, existing algorithms are difficult to directly adapt to this highly nonlinear, multi-timescale, bidirectionally coupled "SGS-turbine" overall control scenario in terms of model structure, real-time computational complexity, and coupling coordination mechanisms.

[0006] In summary, existing separate modeling methods cannot accurately describe the bidirectional coupled dynamic behavior of the SGS and steam turbine power generation system. Conventional distributed PID control and directly transplanted advanced control algorithms both suffer from adaptation deficiencies to the system's strong nonlinearity, multi-timescale, and bidirectional coupling characteristics, making it difficult to achieve high-precision coordinated control under complex operating conditions. Therefore, there is an urgent need to establish an integrated modeling framework for the SGS and steam turbine power generation system, and based on this model, design advanced control strategies capable of handling multivariable constraints and coordinating dynamics across multiple timescales, in order to improve the operational performance and control accuracy of parabolic trough solar thermal power plants under complex operating conditions. Summary of the Invention

[0007] To address the problems of fragmented system modeling, insufficient coupling of control strategies, and limited dynamic adaptability in existing technologies, this invention proposes a coupled modeling and coordinated control method for parabolic trough solar thermal power plants. By constructing a unified system-level dynamic model and designing a multivariable coupled control strategy based on it, the stability and efficiency of system operation can be achieved.

[0008] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0009] A method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system, the method comprising the following steps:

[0010] A unified, coupled dynamic model of the entire link between the steam generation system and the steam-water power generation system of a parabolic trough solar thermal power plant was constructed in the Dymola platform.

[0011] The steam generation system is internally connected in sequence according to the working fluid flow order: preheater, evaporator, steam drum, and superheater. For the preheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The Colebrook formula is used to smoothly solve for the friction coefficient to calculate the pressure drop along the tube and shell sides. The working fluid heated by the preheater is input into the evaporator. A dynamic model adapted to the evaporator for drastic changes in two-phase flow properties is constructed. This model automatically switches between single-phase forced convection heat transfer correlations and nucleus boiling heat transfer correlations to capture real-time changes. The movement law of the phase change interface in the evaporator; the steam-water mixture output from the evaporator is input into the steam drum. For the steam drum, the non-equilibrium lumped parameter method is used to strictly decouple the steam and liquid phases inside the steam drum. Mass conservation equations and energy conservation equations for the steam phase and liquid phase are established respectively. The dryness fraction deviating from the saturated equilibrium state is used as the driving variable to simulate the dynamic process of evaporation and condensation in the steam drum; the saturated steam separated from the steam drum is input into the superheater. The heat transfer and pressure drop process of the superheater is calculated using the single-phase flow heat transfer model. Finally, the main steam is output to the steam-water power generation system.

[0012] The steam-water power generation system, in the order of steam doing work, includes a steam turbine, a condenser, and a regenerative system. Main steam is input into the steam turbine for expansion and work; the exhaust steam after work enters the condenser and condenses into condensate. The condensate then flows back to the steam generation system via the regenerative system. For the steam turbine, a turbine stage model is constructed based on the Flueger formula, introducing an inlet temperature correction term to characterize the turbine's flow capacity under varying operating conditions. At the coupling interface between the steam generation system and the steam-water power generation system, the main steam temperature, pressure, and flow rate parameters at the steam generation system outlet are used as the turbine inlet boundary conditions. Simultaneously, the feedwater return from the turbine condenser side is used as the working fluid at the steam generation system inlet, achieving a dynamic connection of the closed-loop thermodynamic cycle.

[0013] Furthermore, for the preheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The process of using the Colebrook formula to smoothly solve for the friction coefficient to calculate the pressure drop along the tube and shell sides includes:

[0014] A1. Based on the conservation of mass, energy and momentum, a framework of governing equations for a single-phase flow heat transfer process is established.

[0015] A2. By introducing partial derivatives of density with respect to pressure and enthalpy into the mass balance equation, a dynamic mass balance equation considering the compressibility of the working fluid is constructed.

[0016] A3 introduces flow convection, wall heat transfer and axial property diffusion terms into the energy balance equation, divides the equipment into multiple micro-segments along the flow direction, calculates the temperature gradient and heat transfer of each micro-segment, and forms the micro-segment energy balance equation with axial non-uniform temperature distribution correction.

[0017] A4. Establish the momentum conservation equation, use the Colebrook formula to solve for the friction coefficients of the tube side and the shell side, and calculate the friction drop along the tube side and the shell side respectively.

[0018] A5 combines the mass balance equation, energy balance equation, and momentum conservation equation for a coupled solution, thus completing the construction of a single-phase flow heat transfer model.

[0019] Further, in step A2, the dynamic mass balance equation is:

[0020]

[0021] In the formula, A represents the flow cross-sectional area of ​​the heat exchanger; This indicates the density of the working fluid. and t represents the working fluid pressure and specific enthalpy of the (i+1)th micro-element segment, respectively; t represents time, and i represents the sequence number of the discrete micro-element segment along the flow direction. This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th micro-element segment and the (i+1)-th micro-element segment, respectively.

[0022] Furthermore, in step A3, the energy balance equation for introducing the flow convection term, wall heat transfer term, and axial property diffusion term is as follows:

[0023]

[0024] In the formula: This represents the axial diffusion heat flow, which is the superposition of inlet diffusion and outlet diffusion; A represents the flow cross-sectional area of ​​the heat exchanger. Let represent the working fluid density of the i-th micro-element, t represent time, and i represent the index of the discrete micro-element along the flow direction; This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th and (i+1)-th infinitesimal segments, respectively. This represents the specific enthalpy of the working fluid at the entrance of the (i+1)th infinitesimal segment; This represents the heat exchange between the working fluid and the pipe wall within the i-th micro-element segment.

[0025] Furthermore, in step A4, the momentum conservation equation is:

[0026]

[0027] In the formula: This represents the frictional pressure drop within the i-th infinitesimal segment. This represents the gravitational pressure drop within the i-th infinitesimal segment. This represents the local pressure drop within the i-th infinitesimal segment; This represents the cross-sectional area of ​​the micro-element. This represents the axial length of the infinitesimal segment; the friction coefficient on the pipe side is smoothly solved using the Colebrook formula. The pressure drop is Considering the number of baffles on the shell side The pressure drop is Gravitational pressure drop is expressed as .

[0028] Furthermore, the working fluid, heated by the preheater, is input into the evaporator. A dynamic model adapted to the drastic change in the two-phase flow properties is constructed for the evaporator. The process of automatically switching between the single-phase forced convection heat transfer correlation and the nucleus boiling heat transfer correlation to capture the movement law of the phase change interface in the evaporator in real time includes the following steps:

[0029] B1. Based on the energy conservation relationship based on enthalpy difference, the overall energy balance equation of the evaporator is established.

[0030] B2. Using the distributed parameter modeling method, the evaporator is discretized into multiple computational units along the flow direction, and local mass and energy conservation equations are established in each computational unit.

[0031] B3 determines the flow heat transfer type of the current section based on the real-time temperature, pressure, and enthalpy of the working fluid in each calculation unit; when it is determined to be a single-phase forced convection state, it calls the single-phase forced convection heat transfer correlation to calculate the heat transfer coefficient; when it is determined to be a nucleus boiling state, it switches to the nucleus boiling heat transfer correlation to calculate the heat transfer coefficient.

[0032] B4 updates the heat flux density and enthalpy distribution of each calculation unit based on the real-time calculation results of the heat transfer coefficient;

[0033] B5. Based on the enthalpy distribution of each unit, determine and track the dynamic position of the phase change interface in the evaporator to complete the construction of the two-phase flow dynamic model.

[0034] Furthermore, for the steam drum, firstly, based on the concept of lumped parameters, the internal region of the steam drum is divided into independent vapor phase space and liquid phase space; secondly, mass conservation equations and energy conservation equations are established for the vapor phase space to characterize the generation, overflow, and dynamic changes in pressure of steam; mass conservation equations and energy conservation equations are established for the liquid phase space to characterize the inflow, evaporation, and dynamic changes in liquid level of the water phase; then, the deviation between the actual dryness fraction and the saturated equilibrium dryness fraction is used as the driving force for phase change to construct the dynamic coupling relationship between evaporation and condensation within the steam drum; finally, the conservation equations for the vapor and liquid phases are solved simultaneously to simulate the dynamic process of evaporation and condensation within the steam drum, thus completing the construction of the steam drum model.

[0035] Furthermore, for the steam turbine, a turbine stage model is constructed based on the Vlugel formula. The process of introducing an inlet temperature correction term into the turbine stage model to characterize the turbine's flow capacity under varying operating conditions includes the following steps:

[0036] D1. Based on the design operating parameters of the turbine stage, determine the stage design flow rate, design pressure ratio, and corresponding ideal gas constant, and establish the benchmark calculation form of the Vlugel formula.

[0037] D2 collects real-time operating parameters of the steam turbine, including stage inlet pressure, outlet pressure, inlet temperature, and current steam flow rate;

[0038] D3. Based on the difference between the inlet temperature and the design temperature, an inlet temperature correction coefficient is constructed. This correction coefficient is then introduced into the flow calculation term of the Flugel formula to form a stage flow calculation model with temperature correction.

[0039] D4. The temperature correction factor is embedded into the Flueger formula to form a cascade flow calculation model with temperature correction.

[0040] D5, based on the real-time pressure, pressure ratio and the corrected flow rate calculation relationship, solve the actual flow rate of the turbine stage under variable operating conditions;

[0041] D6. Based on the matching relationship between actual flow rate and operating parameters, a turbine stage model adapted to varying operating conditions was constructed.

[0042] Furthermore, at the coupling interface between the steam generation system and the steam-water power generation system, the main steam temperature, pressure, and flow rate parameters at the outlet of the steam generation system are used as the turbine inlet boundary conditions, while the feedwater return from the turbine condenser side is used as the working fluid at the steam generation system inlet. The process of achieving dynamic connection of the closed-loop thermodynamic cycle includes the following steps:

[0043] E1 collects multi-source operating parameters in real time during the operation of the parabolic trough solar thermal power plant, including grid load commands, main steam pressure, main steam temperature, evaporator liquid level, heat trap water level, instantaneous main steam flow rate, and molten salt circulation flow rate.

[0044] E2 responds to the dynamic changes in real-time grid load commands by adjusting the opening degree of the turbine's main steam regulating valve to change the unit's steam intake and correct the turbine's output power in real time.

[0045] E3 calculates the deviation signal between the actual liquid level and the set liquid level of the evaporator in real time, and synchronously collects the instantaneous consumption flow signal of the main steam. It adopts a cascade three-impulse feedforward feedback composite control logic, outputs the basic feedwater adjustment amount according to the liquid level deviation, and combines the feedforward amount of the main steam flow to correct the feedwater pump speed in advance, so that the feedwater flow matches the instantaneous evaporation of the system in real time and suppresses the dynamic disturbance of the liquid level.

[0046] E4: Real-time acquisition of the deviation between the actual water level of the heat sink and the rated set water level; use PID control algorithm to close-loop adjust the operating speed of the condensate pump; dynamically adjust the condensate output flow rate; make the condensate flow rate match the turbine exhaust steam condensation rate in real time; lock the operating water level of the heat sink; and stabilize the unit's cycle boundary conditions.

[0047] E5: Based on the real-time changes in the grid load command, establish a load-molten salt flow mapping relationship, calculate in advance the molten salt flow feedforward compensation amount to adapt to load fluctuations; collect the main steam pressure deviation and obtain the pressure feedback regulation amount through PID calculation; after linearly superimposing the feedforward compensation amount and the feedback regulation amount, synchronously adjust the molten salt pump frequency and the molten salt regulating valve opening, dynamically correct the heat supply flow of the heat collection circuit, and match the changing operating conditions of the power generation side.

[0048] E6: Build a thermal field model, a segmented discrete model of the steam generation system, and a closed-loop power generation cycle model of the steam turbine in the Dymola simulation platform. Iterate and tune the control parameters of each control loop through dynamic simulation under all operating conditions to complete the deployment and application of the system's coordinated control strategy.

[0049] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0050] First, the system coupling modeling and coordinated control method for parabolic trough solar thermal power plants of the present invention establishes a single-phase flow model for the preheater, a two-phase flow model for the evaporator, a non-equilibrium state model for the steam drum, and a variable operating condition model for the steam turbine for each component of the parabolic trough solar thermal power plant, forming a unified system-level coupled dynamic model. This realizes unified modeling and data interaction between the heat collection system, the steam generation system, and the power generation system, significantly improving the coupling consistency between the subsystems and more realistically reflecting the global dynamic characteristics of the system.

[0051] Secondly, the coupled modeling and coordinated control method for the trough-type solar thermal power plant system of the present invention introduces the main steam flow feedforward signal in the water supply control, and combines it with the liquid level feedback to form a cascade three-impulse composite control strategy. It responds to changes in evaporation in advance, which can effectively overcome the false water level interference caused by flash evaporation and condensation in the heating surface, avoid the lag adjustment problem of traditional simple liquid level feedback, and significantly improve the stability of evaporator liquid level control.

[0052] Third, the coupled modeling and coordinated control method of the parabolic trough solar thermal power plant system of the present invention adopts a composite control mode that combines power feedforward and pressure feedback. It adjusts the molten salt flow rate in advance according to the load command and coordinates it with the PID adjustment of the main steam pressure. This effectively compensates for the lag in response of the molten salt heat source side, alleviates the timing mismatch problem between the turbine's fast valve adjustment and the slow dynamic of the heat source, and improves the timeliness of system regulation.

[0053] Fourth, the system coupling modeling and coordinated control method of the parabolic trough solar thermal power plant of the present invention, through multi-variable coordinated control, synchronously adjusts the feedwater flow rate, molten salt flow rate and turbine regulating valve opening, realizes coordinated regulation of power, pressure, flow rate and water level, keeps the main steam pressure and temperature stable within a wide load variable operating condition range, reduces parameter fluctuations caused by irradiation fluctuations and load changes, and improves the stability of unit operation.

[0054] Fifth, the parabolic trough solar thermal power plant system coupling modeling and coordinated control method of the present invention builds a dynamic model of the entire system based on the Dymola platform, realizes integrated simulation verification and parameter optimization tuning of the model and control strategy, greatly improves the solar thermal power plant's response capability and grid adaptability to grid load commands, and the model can be directly used for engineering design and on-site commissioning, with good application prospects and promotion value. Attached Figure Description

[0055] Figure 1 This is a flowchart of the system coupling modeling and coordinated control method for a parabolic trough solar thermal power plant according to the present invention. Detailed Implementation

[0056] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0057] See Figure 1 This invention discloses a method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system, the method comprising the following steps:

[0058] A unified, coupled dynamic model of the entire chain of the steam generation system and the steam-water power generation system of a parabolic trough solar thermal power plant is constructed in the Dymola platform. The steam generation system is connected in sequence according to the working fluid flow order, consisting of a preheater, evaporator, steam drum, and superheater; the steam-water power generation system includes a turbine, condenser, and regenerative system in sequence according to the steam's work operation order.

[0059] For the preheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The Colebrook formula is used to smoothly solve for the friction coefficient to calculate the pressure drop along the tube and shell sides. The working fluid heated by the preheater is input into the evaporator. A dynamic model adapted to the drastic changes in the two-phase flow properties is constructed for the evaporator. By automatically switching between the single-phase forced convection heat transfer correlation and the nucleus boiling heat transfer correlation, the movement law of the phase change interface in the evaporator is captured in real time. The steam-water mixture output from the evaporator is input into the steam drum. A non-equilibrium lumped parameter model is used for the steam drum. The method strictly decouples the vapor and liquid phases inside the steam drum, establishing mass conservation equations and energy conservation equations for the vapor and liquid phases respectively. The dryness fraction deviating from the saturated equilibrium state is used as the driving variable to simulate the dynamic process of evaporation and condensation inside the steam drum. The saturated steam separated from the steam drum is input into the superheater, and the heat transfer and pressure drop process of the superheater is calculated using the single-phase flow heat transfer model. Finally, the main steam is output to the steam-water power generation system. The main steam is input into the turbine to expand and do work. The exhaust steam after doing work enters the condenser to condense into condensate. The condensate flows back to the steam generation system through the regenerative system.

[0060] For steam turbines, a turbine stage model is constructed based on the Vlugel formula. An inlet temperature correction term is introduced into the turbine stage model to characterize the flow capacity of the steam turbine under varying operating conditions.

[0061] At the coupling interface between the steam generation system and the steam-water power generation system, the main steam temperature, pressure, and flow rate parameters at the outlet of the steam generation system are used as the turbine inlet boundary conditions, while the feedwater return from the turbine condenser side is used as the working fluid at the steam generation system inlet, thus realizing the dynamic connection of the closed thermodynamic cycle.

[0062] In terms of modeling, this invention uses energy conservation and mass conservation as fundamental constraints, incorporating the heat collection field, heat storage system, steam generation system, and steam turbine power generation system into a unified modeling framework. For the preheater, superheater, and reheater, their single-phase flow heat transfer processes satisfy the following energy conservation relationship:

[0063]

[0064] in, To exchange heat, For the working fluid mass flow rate, For isobaric specific heat capacity, and These are the inlet and outlet temperatures, respectively.

[0065] For evaporators, due to the significant phase change process, energy transfer is expressed in terms of enthalpy difference:

[0066]

[0067] in, and These are the inlet and outlet enthalpies of the working fluid, respectively, used to describe the absorption and release of latent heat during the phase transition.

[0068] Based on this, the distributed parameter modeling method is used to discretize each heat exchanger into multiple calculation units along the flow direction. Local mass and energy conservation equations are established in each unit to describe the spatial distribution characteristics of temperature, pressure and enthalpy, while considering the nonlinear changes in pressure drop and heat transfer coefficient along the flow path.

[0069] Specifically, for the preheater, superheater, and reheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The Colebrook formula is used to smoothly solve for the friction coefficient to calculate the pressure drop along the tube and shell sides. The specific steps include:

[0070] A1 establishes a framework of governing equations for a single-phase flow heat transfer process based on the principles of mass conservation, energy conservation, and momentum conservation.

[0071] A2, assuming the fluid is compressible, introduce partial derivatives of density with respect to pressure and enthalpy into the mass balance equation to construct a dynamic mass balance equation considering the compressibility of the working fluid:

[0072] ;

[0073] In the formula, A represents the flow cross-sectional area of ​​the heat exchanger; This indicates the density of the working fluid. and t represents the working fluid pressure and specific enthalpy of the (i+1)th micro-element segment, respectively; t represents time, and i represents the sequence number of the discrete micro-element segment along the flow direction. This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th micro-element segment and the (i+1)-th micro-element segment, respectively.

[0074] A3 introduces flow convection, wall heat transfer, and axial property diffusion terms into the energy balance equation, dividing the equipment into multiple micro-segments along the flow direction. The temperature gradient and heat transfer in each micro-segment are calculated, resulting in a micro-segment energy balance equation corrected for axial non-uniform temperature distribution.

[0075]

[0076] In the formula: This represents the axial diffusion heat flow, which is the superposition of inlet diffusion and outlet diffusion; A represents the flow cross-sectional area of ​​the heat exchanger. Let represent the working fluid density of the i-th micro-element, t represent time, and i represent the index of the discrete micro-element along the flow direction; This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th and (i+1)-th infinitesimal segments, respectively. This represents the specific enthalpy of the working fluid at the entrance of the (i+1)th infinitesimal segment; This represents the heat exchange between the working fluid and the pipe wall within the i-th micro-element segment.

[0077] A4. To accurately solve for the transient response of flow pressure drop and flow rate, the following momentum conservation equation is established, and the Colebrook formula is used to solve for the friction coefficients on the pipe side and shell side, respectively, and the pressure drop along the pipe side and shell side is calculated:

[0078]

[0079] In the formula: This represents the frictional pressure drop within the i-th infinitesimal segment. This represents the gravitational pressure drop within the i-th infinitesimal segment. This represents the local pressure drop within the i-th infinitesimal segment; This represents the cross-sectional area of ​​the micro-element. This represents the axial length of the infinitesimal segment; the friction coefficient on the pipe side is smoothly solved using the Colebrook formula. The pressure drop is Considering the number of baffles on the shell side The pressure drop is Gravitational pressure drop is expressed as .

[0080] A5 combines the mass balance equation, energy balance equation, and momentum conservation equation for a coupled solution, thus completing the construction of a single-phase flow heat transfer model.

[0081] The evaporator in a parabolic trough solar thermal power plant is the core phase change heat transfer device. Inside the tubes, the working fluid gradually undergoes a phase change from subcooled water to a steam-water mixture. The flow process sequentially experiences two stages: forced convection heat transfer with single-phase subcooled water and saturated nucleus boiling phase change heat transfer. The working fluid properties and heat transfer coefficient exhibit dramatic nonlinear jumps, making it impossible for traditional single-phase heat transfer models to accurately describe the phase change interface migration and dynamic heat transfer process. This example specifically constructs a dynamic evaporator model adapted to the dramatic jumps in two-phase flow properties. Through multi-condition heat transfer correlation automatic switching, it achieves accurate dynamic capture of the phase change interface. The specific implementation process and principle are as follows:

[0082] First, using molten salt as the heat source, an overall energy balance relationship for the evaporator is established. Under steady-state and dynamic operating conditions, the total heat transfer from the heat transfer medium to the pipe wall is equal to the total energy change corresponding to the enthalpy difference between the inlet and outlet of the working fluid inside the pipe. This forms the overall energy balance equation for the evaporator, determining the fundamental calculation relationships for overall heat transfer, energy accumulation, and dissipation, providing global energy constraints for subsequent refined modeling of micro-element units. Second, the evaporator heat exchange pipes are uniformly discretized along the working fluid flow axis into several independent computational micro-element units, each representing an independent heat transfer calculation region. For each micro-element unit, instantaneous mass conservation equations and energy conservation equations are established to accurately describe the changes in working fluid mass accumulation, energy accumulation, and inlet / outlet mass and energy transfer processes within a single micro-element, achieving a transition from overall evaporator modeling to local refined modeling. Third, the operating temperature, operating pressure, and real-time specific enthalpy parameters of the working fluid inside each discrete micro-element unit are collected in real time. Combined with the saturation temperature and saturation enthalpy value corresponding to the current pressure, the heat transfer state of the micro-element is determined. When the enthalpy of the working fluid in a micro-element is lower than the saturated liquid enthalpy and no phase change occurs, it is determined to be a single-phase forced convection heat transfer state, and the real-time heat transfer coefficient of the micro-element is calculated using the single-phase forced convection heat transfer correlation. When the enthalpy of the working fluid in a micro-element reaches the saturated enthalpy and phase change vaporization occurs, it automatically switches to the nucleus boiling heat transfer correlation to calculate the high-efficiency heat transfer coefficient under the phase change state, achieving dynamic adaptation of the heat transfer mechanism across the entire pipe section. Next, based on the heat transfer coefficient obtained from the real-time switching of each micro-element and combined with the pipe wall heat transfer temperature difference, the wall heat flux density of each micro-element is calculated in real time to obtain the instantaneous heat transfer of each micro-element. Based on the heat transfer of the micro-element, the real-time enthalpy of the working fluid in each unit is iteratively updated, correcting the enthalpy distribution across the entire pipe section and reconstructing the dynamic process of the working fluid gradually absorbing heat, heating up, and undergoing phase change along the flow direction. Finally, the real-time enthalpy distribution of all discrete micro-element units is traversed, and the critical threshold of the working fluid enthalpy of each micro-element unit and the saturation enthalpy value under the corresponding pressure is compared to define the boundary between the single-phase heat exchange section and the two-phase boiling section. The forward and backward movement dynamics of the phase change interface are captured in real time, and finally the construction of the evaporator two-phase flow dynamic model that adapts to the drastic jump in the two-phase flow properties and can accurately characterize the phase change dynamic process is completed.

[0083] This embodiment completely solves the problems of traditional evaporator models being unable to adapt to the abrupt changes in physical properties during phase change intervals, the fuzzy positioning of phase change interfaces, and the low accuracy of heat transfer calculations by combining distributed parameter discrete modeling with heat transfer correlation adaptive switching. It accurately restores the dynamic heat transfer and phase change migration characteristics of the evaporator under variable irradiance and variable load conditions of solar thermal power plants, providing accurate model support for subsequent stable control of system parameters and dynamic adjustment of operating conditions.

[0084] The steam drum of a trough solar thermal power plant, as the core equipment for steam-water separation and two-phase energy storage buffering, receives the high-temperature steam-water mixture output from the evaporator. Internally, a two-way phase change process occurs simultaneously: liquid water evaporation and saturated steam condensation. The vapor phase pressure and liquid phase level are constantly fluctuating dynamically. Traditional equilibrium modeling of the steam drum assumes that the vapor and liquid phases are always in thermodynamic saturation equilibrium, ignoring the dynamic deviations of the two-phase parameters under varying operating conditions. This fails to accurately simulate the hysteresis characteristics of evaporation and condensation and the non-equilibrium dynamic processes under irradiance fluctuations and load disturbances. This embodiment, based on the concept of non-equilibrium lumped parameter modeling, decouples and independently models the vapor and liquid two-phase regions of the steam drum. Relying on dryness deviation to drive the phase change process, it accurately reproduces the dynamic operating characteristics inside the steam drum. The specific implementation process and principle are as follows:

[0085] First, using the normal operating water level of the steam drum as the dividing line, the internal cavity of the steam drum is completely divided into two independent sealed spaces: the upper space is defined as the pure vapor phase flow and accumulation region, and the lower space is defined as the pure liquid phase storage and phase change region. It is assumed that the operating parameters such as temperature, pressure, and density are uniform within each independent space, and that mass exchange and energy transfer between the two phases occur only through the phase interface, providing clear spatial boundary conditions for the subsequent establishment of the two-phase independent conservation equations. Specifically, an instantaneous mass conservation equation is established for the upper independent vapor phase space of the steam drum, accurately calculating the difference between the mass of steam input to the evaporator, the mass of steam consumed by condensation at the phase interface, and the mass of steam overflowing from the steam drum outlet, characterizing the dynamic change in steam accumulation within the vapor phase space. Simultaneously, an energy conservation equation for the vapor phase space is established, combining the enthalpy carried by the input steam, the heat release from condensation phase change, and the enthalpy loss of the outlet steam, to calculate the internal energy change of the vapor phase space, fully reconstructing the dynamic pressure fluctuation characteristics corresponding to the entire process of steam generation, loss, and overflow in the steam drum. Furthermore, an instantaneous mass conservation equation is established for the independent liquid phase space at the bottom of the steam drum. The difference between the input mass of the system feedwater, the mass of the vapor phase condensate return water, and the mass loss from interface evaporation and steam generation is statistically analyzed to characterize the changes in the liquid phase space's water storage in real time, enabling dynamic calculation of the steam drum's liquid level. Simultaneously, an energy conservation equation for the liquid phase space is established, combining the enthalpy of the feedwater, the heat absorption and release during phase change, and the changes in the water's own heat storage to accurately describe the heating, cooling, and boiling evaporation processes of the liquid phase water, fully covering the dynamic characteristics of water phase inflow, phase change evaporation, and liquid level rise and fall. Next, the actual operating dryness of the working fluid inside the steam drum is collected in real time, and combined with the saturated equilibrium dryness corresponding to the current steam drum pressure, the dynamic deviation between the two is calculated. Using this dryness deviation as the phase change driving variable at the vapor-liquid interface, a dynamic coupling calculation relationship between evaporation and condensation is constructed, quantifying the evaporation rate and condensation rate under different operating conditions. This achieves a refined characterization of the bidirectional phase change process inside the steam drum, breaking through the limitations of traditional equilibrium models that lack bias and hysteresis. Finally, the mass and energy conservation equations in the vapor and liquid phases are coupled together and substituted into the dynamic phase change calculation formula based on dryness deviation to construct a complete set of non-equilibrium calculation equations for the steam drum. By iteratively solving the equations, core operating parameters such as steam pressure, liquid level, and evaporation-condensation rate in the steam drum are updated in real time. This accurately simulates the mass migration and energy transfer processes inside the steam drum under varying irradiance and load conditions, ultimately completing the construction of a non-equilibrium two-phase dynamic model of the steam drum adapted to the dynamic operating characteristics of the solar thermal power plant.

[0086] This embodiment employs a non-equilibrium lumped parameter decoupling modeling approach, overcoming the inherent limitations of traditional steam drum equilibrium modeling. Through independent modeling of the vapor-liquid two-phase space and a core design using dryness deviation-driven phase change, it accurately reproduces the bidirectional dynamic hysteresis characteristics of steam drum evaporation and condensation, effectively improving the calculation accuracy of steam drum pressure and liquid level dynamic parameters. This model can accurately adapt to the complex operating conditions of concentrated solar power plants, including frequent fluctuations in solar irradiance and rapid load disturbances, providing reliable model support for subsequent stable control of steam-water system parameters and safe and stable unit operation.

[0087] The turbine in a parabolic trough solar thermal power plant is the core equipment for power-to-heat conversion, relying on steam expansion to generate mechanical energy. Its flow characteristics and efficiency are highly susceptible to fluctuations in inlet steam temperature, pressure, and flow rate. Solar thermal power plants are characterized by unstable solar irradiance and frequent load adjustments, resulting in the turbine operating under variable parameters and loads for extended periods. Traditional Flueger's basic turbine model only considers the impact of inlet and outlet pressures on flow capacity, neglecting deviations in steam specific volume and density caused by dynamic changes in inlet steam temperature. This model cannot accurately characterize the turbine's true flow characteristics and power response under wide-load variable operating conditions. This embodiment constructs a dynamic model of the turbine stage based on an improved Flueger's formula. By adding an inlet temperature correction term, it compensates for flow errors caused by temperature disturbances, accurately adapting to the unit's variable operating characteristics. The specific implementation process and principle are as follows:

[0088] First, based on the turbine's factory design parameters, the design flow area, design pressure ratio, design operating flow rate, and ideal gas fundamental parameters of each turbine stage are determined. A benchmark model for turbine stage flow calculation is constructed using the classical Flueger formula, establishing a fundamental mapping relationship between pressure ratio and steam flow rate. This clarifies the flow characteristics of the turbine stage under rated operating conditions, providing benchmark constraints for variable operating condition correction calculations. Second, dynamic operating parameters such as stage inlet pressure, outlet pressure, inlet steam temperature, and real-time steam flow rate are collected in real time during turbine operation. Simultaneously, the unit's rated design temperature parameters are acquired, and the deviation between real-time operating temperature and design temperature is compared to quantify the impact of temperature fluctuations on steam properties. Third, based on the deviation relationship between inlet temperature and design temperature, a temperature correction coefficient adapted to variable operating conditions is constructed to specifically compensate for the dynamic shifts in steam specific volume and fluid density caused by temperature changes. This overcomes the modeling shortcomings of the traditional Flueger formula, which assumes a constant temperature and only applies to rated operating conditions. Next, the constructed inlet temperature correction coefficient is embedded into the basic Flueger formula for flow calculation, replacing the original constant temperature assumption. This forms a turbine stage flow calculation model with temperature adaptive correction, enabling accurate calculation of turbine flow rate under different inlet temperatures and pressure ratios, and accurately reflecting the suppression or enhancement effect of temperature disturbances on the unit's flow capacity. Finally, by combining the corrected real-time flow rate, steam enthalpy drop characteristics, and stage work efficiency, the real-time turbine output power is solved simultaneously, fully characterizing the dynamic coupling relationship between turbine flow rate, pressure, temperature, and output power under varying operating conditions. This completes the construction of a dynamic turbine stage model adaptable to wide load and multi-disturbance operating conditions.

[0089] This embodiment introduces an inlet temperature correction mechanism based on the traditional Frugel stage model, effectively solving the problems of large flow calculation errors and low power response fitting accuracy under fluctuating steam temperature conditions. It accurately corrects the modeling deviations caused by steam property shifts during varying operating conditions, significantly improving the simulation accuracy of the turbine's dynamic characteristics across wide loads. This model can accurately adapt to the complex operating scenarios of solar thermal power plants with irradiance fluctuations and load jumps, providing a reliable model foundation for precise unit power control, variable operating condition characteristic analysis, and the design of integrated unit control strategies.

[0090] In handling the system coupling relationship, the steam parameters at the outlet of the steam generation system are used as the boundary conditions at the turbine inlet, while the feedwater return from the turbine condenser side is used as the working fluid at the SGS inlet, thus realizing the dynamic connection of the closed thermodynamic cycle. Specifically, firstly, multi-source operating parameters are collected in real time during the operation of the trough solar thermal power plant, including grid load commands, main steam pressure, main steam temperature, evaporator liquid level, heat sink water level, instantaneous main steam flow rate, and molten salt circulation flow rate. Then, in response to the dynamic changes in real-time grid load commands, the turbine's main steam regulating valve opening is adjusted to change the unit's steam intake and correct the turbine's output power in real time. Simultaneously, the deviation between the actual evaporator liquid level and the set liquid level is calculated in real time, and the instantaneous main steam consumption flow rate signal is collected synchronously. A cascade three-impulse feedforward feedback composite control logic is adopted, outputting the basic feedwater regulation amount based on the liquid level deviation. Combined with the main steam flow feedforward, the feedwater pump speed is corrected in advance to ensure that the feedwater flow rate matches the system's instantaneous evaporation rate in real time, suppressing dynamic disturbances in the liquid level. Finally, the deviation between the actual heat sink water level and the rated set water level is acquired in real time, and a PID control algorithm is used to close the loop. The system regulates the operating speed of the condensate pump and dynamically adjusts the condensate output flow rate to match the turbine exhaust condensation rate in real time, locking the heat sink operating water level and stabilizing the unit's cycle boundary conditions. Based on real-time changes in grid load commands, a load-molten salt flow mapping relationship is established, and a feedforward compensation amount for molten salt flow to adapt to load fluctuations is calculated in advance. The main steam pressure deviation is collected and pressure feedback regulation is obtained through PID calculations. The feedforward compensation amount and feedback regulation amount are linearly superimposed to synchronously adjust the molten salt pump frequency and molten salt regulating valve opening, dynamically correcting the heat supply flow of the collector loop to match the changing operating conditions of the power generation side. Finally, a collector field model, a segmented discrete model of the steam generation system, and a closed-loop power generation model of the turbine are built in the Dymola simulation platform. The control parameters of each control loop are iteratively tuned through dynamic simulation under all operating conditions, completing the deployment and application of the system's collaborative control strategy.

[0091] This invention treats the deaerator interior as a coupled system of steam and water spaces, establishing a dynamic balance equation for mass and energy to calculate the pressure and water level fluctuations in the deaerator in real time. The model aims to simulate the migration of the working fluid between the gas and liquid phases under varying operating conditions, providing an accurate simulation object for the coordinated control of the feedwater system. Regarding control strategies, this invention constructs a multivariable coordinated control system, using main steam temperature, main steam pressure, and evaporator liquid level and pressure as the main control objectives, and adjusting the flow rate of the heat transfer medium and the feedwater flow rate to achieve system state regulation. To enhance the system's adaptability to fluctuations in solar irradiance, a control mechanism combining feedforward and feedback is introduced, employing a cascade three-impulse feedwater control strategy. This modeling approach avoids the interface inconsistencies inherent in traditional segmented modeling, resulting in good physical consistency during dynamic simulation. By introducing an inlet temperature correction term, the flow capacity of the turbine under deviating conditions can be accurately characterized, providing data support for evaluating the overall energy conversion efficiency.

[0092] Furthermore, this invention constructs a global collaborative control system encompassing "load command feedforward—process variable feedback—actuator coordination" in its control strategy, utilizing multivariate decoupling logic to achieve precise regulation of power, pressure, flow rate, and water level. By coordinating four core loops—turbine power, feedwater flow rate, molten salt heat source, and heat trap water level—it achieves cross-media dynamic closed-loop regulation from source-end heat storage to terminal power output. The design logic of the entire architecture follows the decoupling and collaborative principle of "load command driven, mass flow rate matching, and heat source feedforward compensation." When the external power grid load command changes abruptly, the turbine power control loop instantly adjusts the opening of the main steam regulating valve, fully mobilizing the inherent internal energy storage of the steam generation system to achieve second-level rapid follow-up of output power. While rapidly responding to the load, the valve action disrupts the original thermodynamic balance, causing wide fluctuations in main steam pressure and main steam consumption flow rate. In the dynamic balance dimension of the material flow in the steam-water cycle, the feedwater pump and condensate pump control loops each perform their respective functions, jointly anchoring the boundary conditions of the Rankine cycle. The feedwater pump flow control loop breaks away from the limitations of conventional "lagging regulation" that relies solely on liquid level feedback. It incorporates the current main steam consumption flow rate as a feedforward guiding signal into the control law, establishing a direct mapping relationship between feedwater pump speed and instantaneous evaporation. This feedforward-feedback composite design utilizes the fast dynamic characteristics of the flow signal, enabling it to respond ahead of liquid level deviation at the linear measurement level. This effectively suppresses nonlinear interference from "false water levels" caused by intense flash evaporation or condensation within the heating surface, ensuring a constant subcritical working fluid holding capacity. Simultaneously, the condensate pump level control loop, located in the low-pressure zone at the end of the cycle, uses the rated set water level of the heat sink as a reference and adjusts the condensate pump speed using a PID algorithm to match the exhaust steam condensation rate. Precise locking of the actual water level in the heat sink not only prevents condensate pump cavitation but also ensures extreme stability of the turbine exhaust back pressure and vacuum.

[0093] This invention, based on the existing main steam pressure closed-loop feedback loop, introduces a "power-flow mapping" feedforward regulation stage for the power signal. When load fluctuations occur, the feedforward channel calculates and outputs the required molten salt mass flow compensation amount in advance according to the changing trend of the power command. This compensation amount is then linearly and algebraically superimposed with the adjustment amount obtained from the pressure deviation after PID calculation, jointly driving the molten salt pump or regulating valve to generate a linked hot molten salt flow. This design transforms the slow-scale heat source-side response into a predictive regulation dominated by "power feedforward," making the molten salt loop and the fast-response turbine valve regulation complementary on a time scale. The entire control strategy is implemented and verified in the Dymola environment, and the control parameters can be optimized and tuned through dynamic simulation to obtain better system performance.

[0094] In one specific embodiment, a complete dynamic model of a parabolic trough solar thermal power plant is established based on the Dymola platform. The collector field adopts a dynamic heat flow model based on solar irradiance input to simulate the temperature change of the heat transfer medium over time. The steam generation system adopts a segmented modeling method, dividing the preheater, evaporator, and superheater into several discrete units along the flow direction to improve the accuracy of the description of temperature gradients and phase change processes. The steam-water power generation system adopts a turbine model based on isentropic efficiency and forms a closed loop with the condenser and feedwater pump.

[0095] Regarding parameter settings, the main steam temperature is set to 540℃, the main steam pressure is set to 16.7MPa, and the feedwater flow rate and heat transfer medium flow rate are dynamically adjusted according to the operating conditions.

[0096] In terms of control implementation, the system is adjusted through the feedforward-feedback control structure, wherein the feedforward signal comes from the real-time command of the power grid load change, and the feedback signal comes from the steam temperature and pressure sensor.

[0097] During the dynamic simulation verification process, a disturbance condition was set where the solar irradiance intensity decreased from 800 W / m² to 600 W / m². The simulation results showed that the main steam temperature fluctuation range was controlled within ±5℃, and the system pressure could recover to stability within 300s without significant oscillation, thus verifying the effectiveness of the method.

[0098] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0099] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system, characterized in that, The method includes the following steps: A unified, coupled dynamic model of the entire link between the steam generation system and the steam-water power generation system of a parabolic trough solar thermal power plant was constructed in the Dymola platform. The steam generation system is internally connected in sequence according to the working fluid flow order: preheater, evaporator, steam drum, and superheater. For the preheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The Colebrook formula is used to smoothly solve for the friction coefficient to calculate the pressure drop along the tube and shell sides. The working fluid heated by the preheater is input into the evaporator. A dynamic model adapted to the evaporator for drastic changes in two-phase flow properties is constructed. This model automatically switches between single-phase forced convection heat transfer correlations and nucleus boiling heat transfer correlations to capture real-time changes. The movement law of the phase change interface in the evaporator; the steam-water mixture output from the evaporator is input into the steam drum. For the steam drum, the non-equilibrium lumped parameter method is used to strictly decouple the steam and liquid phases inside the steam drum. Mass conservation equations and energy conservation equations for the steam phase and liquid phase are established respectively. The dryness fraction deviating from the saturated equilibrium state is used as the driving variable to simulate the dynamic process of evaporation and condensation in the steam drum; the saturated steam separated from the steam drum is input into the superheater. The heat transfer and pressure drop process of the superheater is calculated using the single-phase flow heat transfer model. Finally, the main steam is output to the steam-water power generation system. The steam-water power generation system, in the order of steam doing work, includes a steam turbine, a condenser, and a regenerative system. Main steam is input into the steam turbine for expansion and work; the exhaust steam after work enters the condenser and condenses into condensate. The condensate then flows back to the steam generation system via the regenerative system. For the steam turbine, a turbine stage model is constructed based on the Flueger formula, introducing an inlet temperature correction term to characterize the turbine's flow capacity under varying operating conditions. At the coupling interface between the steam generation system and the steam-water power generation system, the main steam temperature, pressure, and flow rate parameters at the steam generation system outlet are used as the turbine inlet boundary conditions. Simultaneously, the feedwater return from the turbine condenser side is used as the working fluid at the steam generation system inlet, achieving a dynamic connection of the closed-loop thermodynamic cycle.

2. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 1, characterized in that, For the preheater, a single-phase flow heat transfer model considering axial diffusion and the compressibility of the working fluid is established. The process of using the Colebrook formula to smoothly solve for the friction coefficient and calculate the pressure drop along the tube and shell sides includes: A1. Based on the conservation of mass, energy and momentum, a framework of governing equations for a single-phase flow heat transfer process is established. A2. By introducing partial derivatives of density with respect to pressure and enthalpy into the mass balance equation, a dynamic mass balance equation considering the compressibility of the working fluid is constructed. A3 introduces flow convection, wall heat transfer and axial property diffusion terms into the energy balance equation, divides the equipment into multiple micro-segments along the flow direction, calculates the temperature gradient and heat transfer of each micro-segment, and forms the micro-segment energy balance equation with axial non-uniform temperature distribution correction. A4. Establish the momentum conservation equation, use the Colebrook formula to solve for the friction coefficients of the tube side and the shell side, and calculate the friction drop along the tube side and the shell side respectively. A5 combines the mass balance equation, energy balance equation, and momentum conservation equation for a coupled solution, thus completing the construction of a single-phase flow heat transfer model.

3. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 2, characterized in that, In step A2, the dynamic mass balance equation is: ； In the formula, A represents the flow cross-sectional area of ​​the heat exchanger; Indicates the density of the working fluid. and t represents the working fluid pressure and specific enthalpy of the (i+1)th micro-element segment, respectively; t represents time, and i represents the sequence number of the discrete micro-element segment along the flow direction. This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th micro-element segment and the (i+1)-th micro-element segment, respectively.

4. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 2, characterized in that, In step A3, the energy balance equation, which incorporates the flow convection term, wall heat transfer term, and axial property diffusion term, is as follows: ； In the formula: This represents the axial diffusion heat flow, which is the superposition of inlet diffusion and outlet diffusion; A represents the flow cross-sectional area of ​​the heat exchanger. Let represent the working fluid density of the i-th micro-element, t represent time, and i represent the index of the discrete micro-element along the flow direction; This represents the axial length of the infinitesimal segment. and Let represent the working fluid mass flow rates at the inlet of the i-th and (i+1)-th infinitesimal segments, respectively. This represents the specific enthalpy of the working fluid at the entrance of the (i+1)th infinitesimal segment; This represents the heat exchange between the working fluid and the pipe wall within the i-th micro-element segment.

5. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 2, characterized in that, In step A4, the momentum conservation equation is: ； In the formula: This represents the frictional pressure drop within the i-th infinitesimal segment. This represents the gravitational pressure drop within the i-th infinitesimal segment. This represents the local pressure drop within the i-th infinitesimal segment; This represents the cross-sectional area of ​​the micro-element. This represents the axial length of the infinitesimal segment; the friction coefficient on the pipe side is smoothly solved using the Colebrook formula. The pressure drop is Considering the number of baffles on the shell side The pressure drop is Gravitational pressure drop is expressed as .

6. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 1, characterized in that, The process of inputting the preheated working fluid into the evaporator and constructing a dynamic model adapted to the drastic changes in the two-phase flow properties, and automatically switching between single-phase forced convection heat transfer correlation and nucleus boiling heat transfer correlation to capture the movement law of the phase change interface in the evaporator in real time, includes the following steps: B1. Based on the energy conservation relationship based on enthalpy difference, the overall energy balance equation of the evaporator is established. B2. Using the distributed parameter modeling method, the evaporator is discretized into multiple computational units along the flow direction, and local mass and energy conservation equations are established in each computational unit. B3 determines the flow heat transfer type of the current section based on the real-time temperature, pressure, and enthalpy of the working fluid in each calculation unit; when it is determined to be a single-phase forced convection state, it calls the single-phase forced convection heat transfer correlation to calculate the heat transfer coefficient; when it is determined to be a nucleus boiling state, it switches to the nucleus boiling heat transfer correlation to calculate the heat transfer coefficient. B4 updates the heat flux density and enthalpy distribution of each calculation unit based on the real-time calculation results of the heat transfer coefficient; B5. Based on the enthalpy distribution of each unit, determine and track the dynamic position of the phase change interface in the evaporator to complete the construction of the two-phase flow dynamic model.

7. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 1, characterized in that, For the steam drum, firstly, based on the concept of lumped parameters, the internal region of the steam drum is divided into independent vapor phase space and liquid phase space. Secondly, mass conservation equations and energy conservation equations are established for the vapor phase space to characterize the generation, overflow, and dynamic changes in pressure of steam. Mass conservation equations and energy conservation equations are also established for the liquid phase space to characterize the inflow, evaporation, and dynamic changes in liquid level of the water phase. Then, the deviation between the actual dryness fraction and the saturated equilibrium dryness fraction is used as the driving force for phase change to construct the dynamic coupling relationship between evaporation and condensation within the steam drum. Finally, the conservation equations for the vapor and liquid phases are solved simultaneously to simulate the dynamic process of evaporation and condensation within the steam drum, thus completing the construction of the steam drum model.

8. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 1, characterized in that, For steam turbines, a turbine stage model is constructed based on the Vlugel formula. The process of introducing an inlet temperature correction term into the turbine stage model to characterize the flow capacity of the turbine under varying operating conditions includes the following steps: D1. Based on the design operating parameters of the turbine stage, determine the stage design flow rate, design pressure ratio, and corresponding ideal gas constant, and establish the benchmark calculation form of the Vlugel formula. D2 collects real-time operating parameters of the steam turbine, including stage inlet pressure, outlet pressure, inlet temperature, and current steam flow rate; D3. Based on the difference between the inlet temperature and the design temperature, an inlet temperature correction coefficient is constructed. This correction coefficient is then introduced into the flow calculation term of the Flugel formula to form a stage flow calculation model with temperature correction. D4. The temperature correction factor is embedded into the Flueger formula to form a cascade flow calculation model with temperature correction. D5, based on the real-time pressure, pressure ratio and the corrected flow rate calculation relationship, solve the actual flow rate of the turbine stage under variable operating conditions; D6. Based on the matching relationship between actual flow rate and operating parameters, a turbine stage model adapted to varying operating conditions was constructed.

9. The method for coupled modeling and coordinated control of a parabolic trough solar thermal power plant system according to claim 1, characterized in that, At the coupling interface between the steam generation system and the steam-water power generation system, the main steam temperature, pressure, and flow rate parameters at the outlet of the steam generation system are used as the turbine inlet boundary conditions, while the feedwater return from the turbine condenser side is used as the working fluid at the steam generation system inlet. The process of achieving dynamic connection of the closed-loop thermodynamic cycle includes the following steps: E1 collects multi-source operating parameters in real time during the operation of the parabolic trough solar thermal power plant, including grid load commands, main steam pressure, main steam temperature, evaporator liquid level, heat trap water level, instantaneous main steam flow rate, and molten salt circulation flow rate. E2 responds to the dynamic changes in real-time grid load commands by adjusting the opening degree of the turbine's main steam regulating valve to change the unit's steam intake and correct the turbine's output power in real time. E3 calculates the deviation signal between the actual liquid level and the set liquid level of the evaporator in real time, and synchronously collects the instantaneous consumption flow signal of the main steam. It adopts a cascade three-impulse feedforward feedback composite control logic, outputs the basic feedwater adjustment amount according to the liquid level deviation, and combines the feedforward amount of the main steam flow to correct the feedwater pump speed in advance, so that the feedwater flow matches the instantaneous evaporation of the system in real time and suppresses the dynamic disturbance of the liquid level. E4: Real-time acquisition of the deviation between the actual water level of the heat sink and the rated set water level; use PID control algorithm to close-loop adjust the operating speed of the condensate pump; dynamically adjust the condensate output flow rate; make the condensate flow rate match the turbine exhaust steam condensation rate in real time; lock the operating water level of the heat sink; and stabilize the unit's cycle boundary conditions. E5: Based on the real-time changes in the grid load command, establish a load-molten salt flow mapping relationship, calculate in advance the molten salt flow feedforward compensation amount to adapt to load fluctuations; collect the main steam pressure deviation and obtain the pressure feedback regulation amount through PID calculation; after linearly superimposing the feedforward compensation amount and the feedback regulation amount, synchronously adjust the molten salt pump frequency and the molten salt regulating valve opening, dynamically correct the heat supply flow of the heat collection circuit, and match the changing operating conditions of the power generation side. E6: Build a thermal field model, a segmented discrete model of the steam generation system, and a closed-loop power generation cycle model of the steam turbine in the Dymola simulation platform. Iterate and tune the control parameters of each control loop through dynamic simulation under all operating conditions to complete the deployment and application of the system's coordinated control strategy.

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