Carbon dioxide energy storage and heat energy storage synergetic efficient heat exchange method and system

Abstract

The invention relates to a carbon dioxide energy storage and heat energy storage synergetic efficient heat exchange method and system.The method comprises the steps that the operation working condition of a carbon dioxide energy storage system is determined, and corresponding target heat exchange requirements are obtained and comprise the carbon dioxide side target outlet temperature, the heat energy storage side target outlet temperature and the target heat exchange power; operating data of the carbon dioxide side and the heat energy storage side are collected, and a heat exchange model used for representing heat exchange amount distribution, end temperature difference distribution and pressure drop distribution is established; determining a model boundary condition according to the working condition and a target heat exchange demand, constraining the heat flow direction to point to the heat energy storage unit under the energy charging working condition, and constraining the heat flow direction to point to the carbon dioxide side under the energy releasing working condition; and irreversible loss evaluation indexes such as entropy production, exergy loss and exergy efficiency are calculated based on the heat exchange model, a heat exchange optimization problem which takes minimization of the evaluation indexes as a target and meets preset constraint conditions is constructed, and the optimal control quantity is solved and issued to an execution mechanism to implement heat exchange control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat exchange, in particular to a high-efficiency heat exchange method and system for carbon dioxide energy storage and thermal energy storage. BACKGROUND

[0002] In the charging and discharging process of a carbon dioxide energy storage system (such as liquid / supercritical carbon dioxide energy storage), heat input, transfer and release need to be completed at different temperatures and different pressure levels. The heat exchange link not only plays a key role as an "energy hub" in the working condition switching, but also directly affects the temperature matching, pressure loss and cycle efficiency of the system loop. In engineering practice, the medium properties of carbon dioxide change significantly with temperature and pressure, especially near the transcritical region. The distribution of temperature difference and pressure drop at the heat exchange end presents obvious nonlinear characteristics. At the same time, the state of charge (SOC) of the thermal energy storage side (heat storage medium, heat exchange branch, etc.) changes, the charging and discharging power is constrained, and the safety boundary requirement exists, so that the heat exchange process needs to be optimized between "meeting the target outlet temperature / target heat exchange power" and "reducing irreversible loss".

[0003] In the prior art, the coupling heat exchange control of carbon dioxide energy storage and thermal energy storage usually adopts a single-target control mode of fixed valve position / flow rate, empirically set pinch point temperature difference or only outlet temperature tracking. This kind of mode usually lacks modeling description of the internal heat exchange amount distribution, temperature difference distribution and pressure drop distribution of the heat exchanger, and also rarely introduces irreversible loss indicators into the control optimization target, so that problems such as too small pinch point temperature difference, too large pressure drop and increased heat exchange power deviation may occur when the working condition changes or the load fluctuates, resulting in a decrease in energy utilization efficiency of the system and even triggering the safety boundary. SUMMARY

[0004] To at least partially overcome the problems in the related art that the coupling heat exchange of carbon dioxide energy storage and thermal energy storage cannot simultaneously meet the target outlet temperature and target heat exchange power, and has large irreversible loss when the working condition switches and the load fluctuates, the present application provides a high-efficiency heat exchange method and system for carbon dioxide energy storage and thermal energy storage.

[0005] The scheme of the present application is as follows: According to a first aspect of an embodiment of the present application, a high-efficiency heat exchange method for carbon dioxide energy storage and thermal energy storage is provided, comprising: determining the running condition of the carbon dioxide energy storage system in the current control period, and obtaining the target heat exchange demand corresponding to the running condition; the running condition includes a charging condition and a discharging condition; the target heat exchange demand includes a carbon dioxide side target outlet temperature, a thermal energy storage side target outlet temperature and a target heat exchange power; collecting the running data of the carbon dioxide side and the thermal energy storage side in the current control period; A heat exchange model is established between the carbon dioxide side and the thermal energy storage side based on the aforementioned operating data. The heat exchange model is used to characterize the heat exchange distribution, heat exchange end temperature difference distribution, and pressure drop distribution under given control variables. The control variables include any one or more of the following: carbon dioxide side flow rate regulation, thermal energy storage side flow rate regulation, heat exchange bypass ratio, and heat exchange branch allocation ratio. Based on the operating conditions of the carbon dioxide energy storage system and the target heat exchange demand corresponding to the operating conditions, the boundary conditions of the heat exchange model are determined; wherein, the boundary conditions under the charging condition include heat flow constraints oriented towards charging the thermal energy storage unit; and the boundary conditions under the releasing condition include heat flow constraints oriented towards supplying heat from the thermal energy storage unit to the carbon dioxide side. The irreversible loss evaluation index is calculated based on the heat transfer model, and the irreversible loss evaluation index is used as an optimization objective or a component of the optimization objective; the irreversible loss evaluation index includes entropy production, exergy loss, and exergy efficiency. A heat exchange optimization problem is constructed and solved to obtain the optimal control quantity for the current control cycle; the heat exchange optimization problem aims to minimize the irreversible loss evaluation index while satisfying preset constraints. The optimal control quantity is sent to the actuator to implement heat exchange control; the actuator includes a flow regulating device on the carbon dioxide side and a flow regulating device on the thermal energy storage side, as well as a valve group for adjusting the heat exchange bypass ratio or the heat exchange branch allocation ratio; the heat exchange control includes: executing a control direction to meet the target heat exchange demand and charge the thermal energy storage unit under the charging condition, and executing a control direction to meet the target heat exchange demand and supply heat from the thermal energy storage unit to the carbon dioxide side under the releasing condition.

[0006] Preferably, the method further includes: When switching operating conditions of the carbon dioxide energy storage system, the target heat exchange requirement, the constraints, and the parameters of the heat exchange model are updated synchronously.

[0007] Preferably, the preset constraints include: The outlet temperatures of the carbon dioxide side and the thermal energy storage side meet the target heat exchange requirements. The minimum temperature difference at the heat exchange end shall not be less than the preset pinch point temperature difference threshold. The pressure and temperature on the carbon dioxide side meet the preset safety operating condition boundaries; The pressure drop of the heat exchanger does not exceed the preset pressure drop limit; The state of charge of the thermal energy storage is within the allowable range and meets the charging and discharging power constraints.

[0008] Preferably, calculating entropy production based on the heat exchange model includes: dividing the heat exchanger into at least two heat exchange units along the flow direction, calculating the entropy production of each heat exchange unit based on the heat exchange capacity and the cold and hot end temperatures of each heat exchange unit, and summing the entropy production of each heat exchange unit to obtain the total entropy production of the current control cycle. The calculation of exergy loss based on the heat exchange model includes: multiplying the total entropy production by the environmental reference temperature based on a preset environmental reference temperature to obtain the exergy loss for the current control cycle; The exergy efficiency is calculated based on the heat exchange model, including: obtaining the input heat exchange power and the effective available heat exchange power of the heat exchange process in the current control cycle, and determining the exergy efficiency based on the ratio of the effective available heat exchange power to the input heat exchange power.

[0009] Preferably, the optimization objective of the heat transfer optimization problem is a combination of multiple objectives; The multi-objective combined objective includes at least a penalty term for total entropy production and a penalty term for deviation in target heat exchange power; The multi-objective combined objective adaptively adjusts the weights of each penalty term based on the changes in the state of charge of thermal energy storage within a preset range.

[0010] Preferably, determining the boundary conditions of the heat transfer model includes: The carbon dioxide inlet pressure, inlet temperature, and inlet mass flow rate, as well as the thermal energy storage medium inlet temperature, inlet flow rate, and state of charge, from the operational data collected during the current control cycle are used as the model input boundaries. The target heat exchange power, the target outlet temperature on the carbon dioxide side, and the target outlet temperature on the thermal energy storage side are used as the target boundaries. Set the minimum pinch temperature difference threshold, the upper limit of heat exchanger pressure drop, and the safe operating condition boundary on the carbon dioxide side as constraint boundaries; Limit the range of values ​​for the carbon dioxide side flow regulation, the thermal energy storage side flow regulation, the heat exchange bypass ratio, and the heat exchange branch allocation ratio. Under the charging condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the thermal energy storage unit; under the releasing condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the carbon dioxide side.

[0011] Preferably, establishing a heat transfer model between the carbon dioxide side and the thermal energy storage side based on the operational data includes: Based on the operating data, the thermal properties of carbon dioxide and the heat storage medium in each heat exchange unit are calculated, and the local heat transfer coefficient and the overall heat transfer coefficient on the carbon dioxide side and the heat storage side are determined accordingly. Within each heat exchange unit, the heat exchange capacity is calculated based on energy conservation, and the temperature distribution at the hot and cold ends is iteratively updated to ensure that the heat exchange capacity of each heat exchange unit is consistent with the target boundary. The pressure drop of each heat exchange unit is calculated by using frictional resistance and local resistance models, and the total pressure drop of the heat exchanger is obtained by summing them up to form the pressure drop distribution.

[0012] Preferably, the method further includes: The feasibility of the optimal control quantity is verified. The feasibility verification includes: checking whether the optimal control quantity meets the range and rate of change limit of the actuator, and whether it meets the margin threshold of minimum pinch temperature difference, pressure drop and carbon dioxide side safety conditions. If the condition is not met, the optimal control quantity is limited and corrected to obtain a feasible control quantity, and the feasible control quantity is then sent to the actuator.

[0013] Preferably, the method further includes: A rolling optimization strategy is adopted. At the end of each control cycle, the initial value of the control quantity for the next control cycle is generated based on the optimal control quantity and actual execution feedback of the current control cycle.

[0014] According to a second aspect of the embodiments of this application, a high-efficiency heat exchange system that combines carbon dioxide energy storage and thermal energy storage is provided, comprising: Processor and memory; The processor and memory are connected via a communication bus: The processor is used to call and execute the program stored in the memory; The memory is used to store a program, which is at least used to execute an efficient heat exchange method for the synergistic use of carbon dioxide energy storage and thermal energy storage as described in any of the above claims.

[0015] The technical solution provided in this application may include the following beneficial effects: This application obtains the target heat exchange demand under two operating conditions: charging and releasing energy, and establishes a heat exchange model that can characterize the distribution of heat exchange, terminal temperature difference, and pressure drop. Combined with heat flow direction constraints, the energy transfer direction of the heat exchange process is aligned with the operating condition target. Furthermore, irreversible loss evaluation indicators such as entropy production, exergy loss, and exergy efficiency are introduced as optimization targets (or components thereof). Under the premise of satisfying preset constraints, the optimal flow regulation, bypass ratio, and branch allocation ratio are solved, thereby achieving refined and constrained coordinated control of the heat exchange process, reducing irreversible losses and energy consumption, suppressing pinch point deterioration and pressure drop exceeding the limit risk, and improving heat exchange efficiency, system cycle efficiency, and stability and safety during operating condition switching.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] Figure 1 This is a schematic flowchart of an efficient heat exchange method that combines carbon dioxide energy storage and thermal energy storage, provided in one embodiment of this application. Detailed Implementation

[0019] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0020] Example 1 Figure 1 This is a schematic flowchart of an embodiment of a highly efficient heat exchange method for synergistic carbon dioxide energy storage and thermal energy storage provided in this application. (Refer to...) Figure 1 A highly efficient heat exchange method for the synergistic use of carbon dioxide energy storage and thermal energy storage includes: S1. Determine the operating conditions of the carbon dioxide energy storage system within the current control cycle and obtain the target heat exchange demand corresponding to the operating conditions; the operating conditions include: charging conditions and releasing conditions; the target heat exchange demand includes the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power. S2. Collect operating data from the carbon dioxide side and the thermal energy storage side during the current control cycle; S3. Establish a heat exchange model between the carbon dioxide side and the thermal energy storage side based on the operating data. The heat exchange model is used to characterize the heat exchange distribution, heat exchange end temperature difference distribution, and pressure drop distribution under given control variables. The control variables include any one or more of the following: carbon dioxide side flow rate regulation, thermal energy storage side flow rate regulation, heat exchange bypass ratio, and heat exchange branch allocation ratio. S5. Based on the operating conditions of the carbon dioxide energy storage system and the target heat exchange demand corresponding to the operating conditions, determine the boundary conditions of the heat exchange model; among them, the boundary conditions under the charging condition include heat flow constraints oriented towards charging the thermal energy storage unit; the boundary conditions under the releasing condition include heat flow constraints oriented towards supplying heat from the thermal energy storage unit to the carbon dioxide side. S6. Calculate the irreversible loss evaluation index based on the heat transfer model, and use the irreversible loss evaluation index as the optimization objective or a component of the optimization objective; the irreversible loss evaluation index includes entropy production, exergy loss and exergy efficiency. S6. Construct a heat transfer optimization problem and solve it to obtain the optimal control quantity for the current control cycle. The heat transfer optimization problem aims to minimize the irreversible loss evaluation index while satisfying the preset constraints. S7. The optimal control quantity is sent to the actuator to implement heat exchange control; the actuator includes flow regulation equipment on the carbon dioxide side and flow regulation equipment on the thermal energy storage side, as well as valve groups for adjusting the heat exchange bypass ratio or heat exchange branch distribution ratio; the heat exchange control includes: executing the control direction to meet the target heat exchange demand and charge the thermal energy storage unit under the charging condition, and executing the control direction to meet the target heat exchange demand and supply heat from the thermal energy storage unit to the carbon dioxide side under the releasing condition.

[0021] For ease of understanding, the following explains some key terms in this embodiment: A carbon dioxide energy storage system is a system that uses carbon dioxide as a working medium for energy storage and release, such as liquid carbon dioxide energy storage or supercritical carbon dioxide energy storage systems. This system absorbs heat during charging and releases heat during energy release; its heat exchange stage is the core of energy conversion.

[0022] Operating conditions refer to the working state of a carbon dioxide energy storage system within a specific time period. Specifically, this includes charging conditions and releasing conditions. Charging conditions refer to the state in which the system absorbs heat from the outside and stores it in the thermal energy storage unit; releasing conditions refer to the state in which the system obtains heat from the thermal energy storage unit and releases it to the outside.

[0023] The target heat exchange requirement refers to the heat exchange performance indicators that the system is expected to achieve under specific operating conditions. This requirement typically includes the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power that the system needs to achieve. These indicators are important references in the heat exchange control process.

[0024] A heat transfer model is a mathematical or physical model used to describe the heat transfer process between the carbon dioxide side and the thermal energy storage side. This model can characterize the heat transfer distribution inside the heat exchanger, the temperature difference distribution at the heat exchange ends between the hot and cold fluids, and the pressure drop distribution as the fluid passes through the heat exchanger under different control variables. Using this model, the performance of the heat exchanger can be predicted and analyzed.

[0025] Irreversible loss evaluation indicators are metrics used to measure the thermodynamic perfection of a heat exchange process. These indicators reflect the energy quality loss caused by factors such as temperature difference and friction during heat exchange. Common irreversible loss evaluation indicators include entropy production, exergy loss, and exergy efficiency. Entropy production represents the increase in entropy caused by irreversible processes within the system; exergy loss represents the loss of usable energy due to irreversibility; and exergy efficiency measures the utilization efficiency of usable energy.

[0026] Control variables refer to parameters that can be adjusted during the heat exchange process to affect heat exchange performance. These parameters may include flow rate regulation on the carbon dioxide side, flow rate regulation on the thermal energy storage side, heat exchange bypass ratio for adjusting the fluid bypass ratio, and heat exchange branch allocation ratio for distributing the fluid flow rate in different heat exchange branches. By adjusting these control variables, the heat exchange process can be optimized.

[0027] This embodiment provides a highly efficient heat exchange method that combines carbon dioxide energy storage and thermal energy storage.

[0028] First, determine the operating condition of the carbon dioxide energy storage system within the current control cycle and obtain the target heat exchange demand corresponding to this operating condition. This operating condition can include charging and releasing conditions. The target heat exchange demand can include the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power. Specifically, the operating condition can be manually input by the operator according to the system operation plan or switched via simple switch signals. The target heat exchange demand can be looked up based on preset fixed values ​​or empirical curves. For example, in charging condition, a fixed target outlet temperature on the carbon dioxide side and the target outlet temperature on the thermal energy storage side, as well as a constant target heat exchange power, can be set. Alternatively, the operating condition can be automatically determined by the upper-level control system based on external grid dispatch instructions or the state-of-charge threshold of the energy storage unit. The target heat exchange demand can be obtained from a pre-stored lookup table based on the current operating condition type; this lookup table may contain typical temperature and power setpoints for different operating conditions.

[0029] Secondly, operational data from both the carbon dioxide and thermal energy storage sides is collected during the current control cycle. This data can be acquired in real time by installing temperature sensors, pressure sensors, and flow meters at key locations on both sides. For example, sensors can be placed at the inlet and outlet of the heat exchanger to obtain the fluid's temperature, pressure, and mass flow rate. Alternatively, operational data can be periodically read from field instruments using a data acquisition system. This system can be configured to acquire instantaneous readings from each sensor at the beginning of each control cycle and transmit them to the controller for processing.

[0030] Furthermore, a heat transfer model is established based on this operational data between the carbon dioxide side and the thermal energy storage side. This heat transfer model is used to characterize the heat transfer distribution, the temperature difference distribution at the heat exchange ends, and the pressure drop distribution under given control variables. These control variables can include any one or more of the following: carbon dioxide side flow rate regulation, thermal energy storage side flow rate regulation, heat transfer bypass ratio, and heat transfer branch allocation ratio. Specifically, the heat transfer model can employ a lumped parameter model, treating the heat exchanger as a whole, and calculating the total heat transfer and average temperature difference using empirical formulas or simple heat transfer coefficients. The pressure drop distribution can be estimated using a simple friction resistance model. Alternatively, the heat transfer model can employ the finite difference method or the finite volume method, dividing the heat exchanger into several units along the flow direction, performing energy balance calculations within each unit, and calculating the unit's heat transfer and temperature difference based on fluid properties and heat transfer coefficients. The pressure drop is calculated by considering friction resistance and local resistance.

[0031] Subsequently, based on the operating conditions of the carbon dioxide energy storage system and the corresponding target heat exchange demand, the boundary conditions of the heat exchange model are determined. Specifically, the boundary conditions under the charging condition include heat flow direction constraints oriented towards charging the thermal energy storage unit; the boundary conditions under the releasing condition include heat flow direction constraints oriented towards supplying heat from the thermal energy storage unit to the carbon dioxide side. In detail, the boundary conditions can be manually set or selected from a preset database based on the operating conditions and target heat exchange demand. For example, under the charging condition, the inlet temperature and flow rate of the carbon dioxide side can be set as known quantities, the outlet temperature of the thermal energy storage side as the target value, and heat can be forced to flow from the carbon dioxide side to the thermal energy storage side. Alternatively, the boundary conditions can be dynamically adjusted based on real-time collected operating data and the target heat exchange demand. For example, the current inlet temperature, pressure, and flow rate of the carbon dioxide side can be used as the model's input boundaries, and the target outlet temperature and target heat exchange power can be used as the model's output targets. The heat flow direction constraint can be enforced by introducing a directional parameter into the model.

[0032] Based on this, an irreversible loss evaluation index is calculated using the heat transfer model, and this index is used as an optimization objective or a component of the optimization objective. This irreversible loss evaluation index can include entropy production, exergy loss, and exergy efficiency. Specifically, the irreversible loss evaluation index can be calculated using simplified methods based on the overall input and output parameters of the heat transfer model. For example, entropy production can be estimated by calculating the entropy changes at the inlet and outlet of the hot and cold fluids; exergy loss can be approximated by multiplying the total heat exchange by the average temperature difference; and exergy efficiency can be determined by the ratio of the total heat exchange to the theoretical maximum heat exchange. Alternatively, the irreversible loss evaluation index can be calculated based on the heat exchange distribution and temperature difference distribution within the heat transfer model. For example, entropy production can be obtained by integrating or summing the entropy increases of each part inside the heat exchanger; exergy loss can be calculated by multiplying the entropy production by the ambient reference temperature; and exergy efficiency can be determined by the ratio of the effective available heat exchange power to the input heat exchange power.

[0033] Next, a heat transfer optimization problem is constructed and solved to obtain the optimal control input for the current control cycle. This heat transfer optimization problem aims to minimize the irreversible loss evaluation index while satisfying preset constraints. Specifically, the heat transfer optimization problem can be constructed as an optimization problem with the objective of minimizing a single irreversible loss evaluation index (e.g., total entropy production). This problem can be solved using a simple search algorithm or gradient descent method, while considering preset constraints, such as the outlet temperature meeting the target range and the pressure drop not exceeding the upper limit. Alternatively, the heat transfer optimization problem can be constructed as a multi-objective optimization problem including the irreversible loss evaluation index and the target heat transfer demand deviation. This problem can be solved by transforming multiple objectives into a single objective using a weighted summation method, or by using Pareto optimization to find a set of optimal solutions. The solver can employ genetic algorithms, particle swarm optimization algorithms, or sequential quadratic programming, etc.

[0034] Finally, the optimal control quantity is sent to the actuator to implement heat exchange control. The actuator may include flow regulation devices on the carbon dioxide side and the thermal energy storage side, as well as valve assemblies for adjusting the heat exchange bypass ratio or the heat exchange branch allocation ratio. The heat exchange control includes: executing control directions to meet the target heat exchange demand and charge the thermal energy storage unit under charging conditions, and executing control directions to meet the target heat exchange demand and supply heat from the thermal energy storage unit to the carbon dioxide side under releasing conditions. Specifically, the optimal control quantity can be directly sent to the actuator via an industrial communication protocol. For example, the flow regulation device can be a variable frequency pump or a regulating valve, and the valve assembly can be a three-way valve or a multi-way valve, which directly adjust the flow rate or bypass ratio according to the received instructions. Alternatively, the optimal control quantity can be input as a setpoint to the local PID controller of the actuator. The PID controller generates a control signal to drive the flow regulation device and valve assembly based on the deviation between the setpoint and the actual feedback value, thereby achieving precise flow or proportional regulation.

[0035] This method establishes a heat transfer model and introduces an irreversible loss evaluation index as the optimization objective, enabling dynamic determination of the optimal control quantity. Therefore, in the coupled heat transfer process of carbon dioxide energy storage and thermal energy storage, it effectively avoids problems such as excessively small pinch-point temperature difference, excessive pressure drop, and increased heat transfer power deviation that occur in traditional methods. This method improves the accuracy and thermodynamic perfection of the heat transfer process, thereby enhancing the system's energy utilization efficiency and ensuring operational safety.

[0036] In practice, pre-set constraints include: The outlet temperatures of both the carbon dioxide side and the thermal energy storage side meet the target heat exchange requirements. The minimum temperature difference at the heat exchange end shall not be less than the preset pinch point temperature difference threshold. The pressure and temperature on the carbon dioxide side meet the preset safety operating condition boundaries; The pressure drop of the heat exchanger does not exceed the preset pressure drop limit; The state of charge of the thermal energy storage is within the allowable range and meets the charging and discharging power constraints.

[0037] Specifically, the outlet temperatures of both the carbon dioxide side and the thermal energy storage side must meet the target heat exchange requirements. This constraint ensures that the heat exchange process achieves the expected thermodynamic performance target. Under charging conditions, the outlet temperatures of both the carbon dioxide side and the thermal energy storage side need to reach preset values ​​to ensure effective heat transfer to the thermal energy storage unit. Under releasing conditions, it is necessary to ensure that after the thermal energy storage unit supplies heat to the carbon dioxide side, the outlet temperature of the carbon dioxide side reaches the target value to meet subsequent heat demand. This constraint is achieved by comparing the actual outlet temperature with the target outlet temperature and incorporating the difference into the penalty term of the optimization problem or directly as a hard constraint.

[0038] The minimum temperature difference at the heat exchange ends must not be less than a preset pinch point temperature difference threshold. The pinch point temperature difference is a key parameter in the design and operation of a heat exchanger, representing the point where the temperature difference between the hot and cold fluids inside the heat exchanger is minimal. Setting a minimum pinch point temperature difference threshold is to prevent excessively small local temperature differences in the heat exchanger, which could lead to a sharp drop in heat exchange efficiency or even prevent heat exchange from occurring. Simultaneously, an excessively small temperature difference may result in an excessively large heat exchange area requirement, increasing equipment costs. This threshold is typically preset based on factors such as the type of heat exchanger, fluid properties, and economic considerations, and is checked as a hard constraint during the optimization process.

[0039] The pressure and temperature on the carbon dioxide side must meet the preset safety operating condition boundaries. Carbon dioxide energy storage systems typically operate in high-pressure, high-temperature environments, and their pressure and temperature must be strictly controlled within safe ranges to prevent equipment damage, leakage, or other safety accidents. The preset safety operating condition boundaries include the maximum and minimum allowable pressure and temperature of carbon dioxide. During optimization, it is necessary to monitor the pressure and temperature on the carbon dioxide side in real time and ensure that they remain within these boundaries to guarantee the long-term stable operation of the system and the safety of personnel.

[0040] The pressure drop of the heat exchanger must not exceed the preset upper limit. Fluid flow inside the heat exchanger will cause a pressure drop; an excessive pressure drop means increased pumping power required to transport the fluid, thus reducing the overall efficiency of the system. Setting an upper limit on the pressure drop is to limit pumping power consumption and ensure the economic efficiency of system operation. This upper limit is typically determined based on the heat exchanger's design characteristics, fluid flow rate, and allowable energy loss, and is used as a constraint in optimization solutions.

[0041] The state of charge (SBC) of thermal energy storage units must be within acceptable limits and meet charge / discharge power constraints. The SBC reflects the amount of heat stored in the thermal energy storage unit. Limiting it to acceptable limits prevents overcharging or over-discharging, thereby extending its lifespan and ensuring effective utilization of the storage capacity. Charge / discharge power constraints ensure that the power output or input of the thermal energy storage unit does not exceed its design capacity during charging or releasing energy, avoiding overload operation. These constraints are crucial for the healthy operation of the thermal energy storage unit and the stability of the entire energy storage system.

[0042] By introducing the aforementioned pre-defined constraints, this application ensures that the efficient heat exchange method combining carbon dioxide energy storage and thermal energy storage not only minimizes irreversible losses during optimization but also meets various key requirements for actual operation. Specifically, the outlet temperature constraint guarantees that the heat exchange process can accurately respond to target demands; the minimum pinch temperature difference constraint avoids a sharp drop in heat exchange efficiency and an increase in equipment costs; the safety boundary constraints on the carbon dioxide side fundamentally guarantee the safety and reliability of system operation; the upper limit constraint on heat exchanger pressure drop effectively controls system energy consumption and improves economic efficiency; and the constraints on the state of charge and charge / discharge power of thermal energy storage ensure the healthy operation of the thermal energy storage unit and the effective utilization of energy storage capacity. The comprehensive application of these constraints makes the optimal control quantity obtained from the optimization solution feasible and safe, thereby avoiding system instability or operational risks caused by simply pursuing efficiency, and significantly improving the operational stability, safety, and economy of the entire energy storage system.

[0043] Example 2 It should be noted that the method also includes: When switching operating conditions of a carbon dioxide energy storage system, the target heat exchange requirements, constraints, and parameters of the heat exchange model are updated synchronously.

[0044] The switching of operating conditions in a carbon dioxide energy storage system refers to the transition of the system from one preset operating state to another. For example, the system may switch from a charging operating state, primarily aimed at heating the thermal energy storage unit, to an energy release operating state, primarily aimed at supplying heat from the thermal energy storage unit to the carbon dioxide side; or, under the same operating condition, changes in external load demand or adjustments to grid dispatch instructions may cause significant changes in the system's target power or temperature setpoints. This switching is usually accompanied by the need to rebalance the system's internal energy flow direction, flow distribution, and temperature and pressure. "Synchronous update" refers to the immediate reassessment and resetting of the relevant target heat exchange requirements, constraints, and heat exchange model parameters upon detecting a switching of operating conditions or within the immediate following control cycle. This timely update is crucial for ensuring that the system can quickly adapt to and maintain efficient and stable operation under new operating conditions, avoiding control deviations or performance degradation caused by parameter lag.

[0045] The target heat exchange requirements include the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power. When the operating condition changes, such as from charging to releasing, the energy flow direction on the carbon dioxide side and the thermal energy storage side reverses, and the set values ​​of the target outlet temperature and the target heat exchange power will change accordingly. For example, under charging conditions, a higher outlet temperature on the thermal energy storage side may be required to fully store heat; while under releasing conditions, a specific outlet temperature on the carbon dioxide side may be required to meet power generation needs. Preset constraints, such as the outlet temperatures on the carbon dioxide side and the thermal energy storage side meeting the target heat exchange requirements, the minimum temperature difference at the heat exchange ends not less than the preset pinch point temperature difference threshold, the pressure and temperature on the carbon dioxide side meeting the preset safety operating condition boundaries, the heat exchanger pressure drop not exceeding the preset pressure drop upper limit, and the thermal energy storage state of charge being within the allowable range and meeting the charging and discharging heat power constraints, may also need to be adjusted when the operating condition changes. For example, the minimum pinch temperature difference or margin requirements for the safety boundary may differ under different operating conditions, or the power constraints for the charge and discharge of thermal energy storage may be dynamically adjusted according to the current state of charge and system requirements. The parameters of the heat transfer model include, but are not limited to, fluid thermophysical properties, heat transfer coefficients, and heat transfer area. Although the physical structure of the heat exchanger usually remains unchanged, when operating conditions change significantly, causing changes in fluid flow rate, temperature, and pressure, the fluid's thermophysical properties (such as density, viscosity, specific heat capacity, and thermal conductivity) will change accordingly, thus affecting the calculation of local and overall heat transfer coefficients. Therefore, synchronously updating these parameters ensures that the heat transfer model can still accurately characterize the heat transfer distribution, heat exchange end temperature difference distribution, and pressure drop distribution under new operating conditions.

[0046] By synchronously updating the target heat exchange demand, constraints, and heat exchange model parameters during the switching of operating conditions in a carbon dioxide energy storage system, this application ensures that the heat exchange optimization problem is always constructed and solved based on the latest operating status and system requirements. This avoids deviations in optimization results caused by parameter lag or mismatch, allowing the optimal control quantity to more accurately guide the actuator in heat exchange control. Especially when switching between charging and releasing operating conditions, where energy flow and targets change significantly, the synchronous update mechanism enables the system to quickly adapt to the new operating mode, maintaining the efficiency, safety, and stability of the heat exchange process, thereby effectively improving the overall operating performance and response speed of the entire carbon dioxide energy storage and thermal energy storage synergistic system.

[0047] Example 3 It should be noted that the calculation of entropy production based on the heat exchange model includes: dividing the heat exchanger into at least two heat exchange units along the flow direction, calculating the entropy production of each heat exchange unit based on the heat exchange capacity and the cold and hot end temperatures of each heat exchange unit, and summing the entropy production of each heat exchange unit to obtain the total entropy production of the current control cycle. This division method allows for more refined analysis of the heat exchange process, moving beyond treating the entire heat exchanger as a single unit to enabling detailed evaluation of each localized area. Each heat exchange unit can be an independent physical segment, such as a plate channel in a plate heat exchanger or a tube-side or shell-side section in a shell-and-tube heat exchanger. This approach allows for more accurate capture of changes in parameters such as temperature and pressure along the flow direction within the heat exchanger. Based on the heat transfer and hot / cold end temperatures of each heat exchange unit, the entropy production of each unit is calculated. Entropy production is a crucial indicator of thermodynamic irreversibility, reflecting the loss of "quality" of energy during conversion. For each heat exchange unit, its entropy production can be calculated using factors such as the temperature difference in heat transfer within the unit and fluid friction. For example, it can be calculated based on the average temperature and heat transfer of the hot and cold fluids within each unit, combined with the second law of thermodynamics. Summing the entropy production of each heat exchange unit yields the total entropy production for the current control cycle. By accumulating the entropy production of all units, the total irreversible loss of the entire heat exchanger during the current control cycle can be obtained, which reflects the actual situation more accurately than a single overall calculation.

[0048] The calculation of exergy loss based on the heat transfer model includes: multiplying the total entropy production by the environmental reference temperature based on the preset environmental reference temperature to obtain the exergy loss for the current control cycle; Exergy loss is a quantitative representation of entropy production at a specific ambient reference temperature. It directly represents the usable energy (i.e., exergy) lost due to irreversibility. The preset ambient reference temperature usually refers to the temperature of the system's environment, such as atmospheric temperature, and serves as a reference point for calculating exergy loss. By multiplying the total entropy production by this ambient reference temperature, the abstract entropy production can be transformed into an energy loss with practical physical meaning, thus providing a more intuitive assessment of the efficiency of the heat exchange process.

[0049] The exergy efficiency is calculated based on the heat transfer model, including: obtaining the input heat transfer power and the effective available heat transfer power of the heat transfer process in the current control cycle, and determining the exergy efficiency based on the ratio of the effective available heat transfer power to the input heat transfer power.

[0050] Input heat transfer power refers to the rate at which the hot fluid transfers heat to the cold fluid within the current control cycle. Effective usable heat transfer power refers to the maximum rate at which the input heat transfer power can be converted into useful work under ideal (reversible) conditions; it takes into account the quality of energy. Exergy efficiency is a more advanced indicator of energy utilization efficiency, focusing not only on the quantity of energy but also on its quality. By calculating the ratio of effective usable heat transfer power to input heat transfer power, a dimensionless value between 0 and 1 can be obtained. The higher this value, the more fully the energy quality of the heat transfer process is utilized, and the higher the thermodynamic efficiency.

[0051] By employing the aforementioned technical solution, the heat exchanger is meticulously divided into multiple heat exchange units, and the entropy production of each unit is calculated. The total entropy production is then aggregated, resulting in a more accurate and comprehensive assessment of the irreversibility of the heat exchange process. This refined analysis accurately identifies localized inefficient regions within the heat exchanger, providing a more targeted basis for subsequent optimization control. Furthermore, by converting the total entropy production into exergy loss and calculating exergy efficiency, this application enables an in-depth evaluation of the heat exchange process from both the "quality" and "quantity" dimensions of energy. Exergy loss directly quantifies the potential for useful work lost due to irreversibility, providing a foundation for economic analysis; while exergy efficiency directly reflects the quality of energy utilization. These precise irreversible loss evaluation indicators, as optimization targets or components, allow the constructed heat exchange optimization problem to more accurately reflect the thermodynamic performance of the system, thereby guiding the actuators to implement more refined and effective heat exchange control. This not only helps meet the target heat exchange requirements, but also maximizes the heat charging efficiency to the thermal energy storage unit under the charging condition and maximizes the heat supply efficiency from the thermal energy storage unit to the carbon dioxide side under the releasing condition, significantly improving the overall operating efficiency and economy of the carbon dioxide energy storage and thermal energy storage synergistic system.

[0052] It should be noted that the optimization objective of the heat transfer optimization problem is a combination of multiple objectives; This means that during the optimization process, we no longer focus on a single performance metric, but simultaneously consider multiple interrelated or potentially conflicting optimization dimensions, and integrate them through some mechanism to obtain a more comprehensive and optimal solution that better meets actual operational needs.

[0053] The multi-objective combination objective includes at least a penalty term for the total entropy production and a penalty term for the deviation of the target heat transfer power; The penalty term for total entropy production aims to improve the system's thermodynamic efficiency by minimizing irreversible losses in the heat exchange process. It is typically proportional to the total entropy production value; for example, it can be directly expressed as the total entropy production value or some function thereof, multiplied by a weighting coefficient. The penalty term for deviation from the target heat exchange power ensures that the system accurately meets the preset target heat exchange power requirement during actual operation, avoiding significant deviations between the actual and target values. This penalty term can be constructed based on the absolute, squared, or relative difference between the actual and target heat exchange power, multiplied by a weighting coefficient, to prompt the optimization algorithm to find control variables that make the actual heat exchange power as close as possible to the target value.

[0054] The multi-objective combination objective adaptively adjusts the weights of each penalty term based on the changes in the state of charge of thermal energy storage within a preset range.

[0055] The state of charge (SOC) of thermal energy storage is a key parameter reflecting the heat storage level of a thermal energy storage unit, and its changes directly affect the system's operating strategy and optimization focus. By adaptively adjusting weights, for example, when the SOC is low, the system may prefer rapid heat charging; in this case, the weight of the penalty term for deviation in target heat transfer power can be increased to prioritize power delivery. Conversely, when the SOC is high, the system may focus more on heat charging efficiency; in this case, the weight of the penalty term for total entropy production can be increased. This dynamic adjustment mechanism allows the optimization strategy to flexibly respond to changes in the system's operating state, thereby achieving optimal performance under different operating conditions. The adaptive adjustment of weights can be achieved through preset lookup tables, piecewise functions, or fuzzy logic-based control strategies.

[0056] By employing the aforementioned technical solution, the heat transfer optimization problem is defined as a multi-objective combination, including a total entropy production penalty term and a target heat transfer power deviation penalty term. This application can more comprehensively balance the system's pursuit of thermodynamic efficiency while accurately meeting the preset target heat transfer power requirements. This effectively solves the problem that a single optimization objective may cause the actual heat transfer power to deviate from the target value, enabling the system to achieve more accurate energy transfer under different operating conditions. Furthermore, by adaptively adjusting the weights of each penalty term according to changes in the state of charge of the thermal energy storage, this application allows the optimization strategy to dynamically adapt to the charging and discharging heat requirements and capacity limitations of the thermal energy storage unit. This adaptive adjustment mechanism significantly enhances the system's operational flexibility and robustness, ensuring optimal operation of the carbon dioxide energy storage and thermal energy storage co-operated system across the entire operating range. This, in turn, minimizes irreversible losses and improves overall energy utilization efficiency while meeting the target heat transfer requirements.

[0057] It should be noted that determining the boundary conditions for the heat transfer model includes: The carbon dioxide inlet pressure, inlet temperature, and inlet mass flow rate, as well as the thermal energy storage medium inlet temperature, inlet flow rate, and state of charge, from the operational data collected during the current control cycle are used as the model input boundaries. The target heat exchange power, the target outlet temperature on the carbon dioxide side, and the target outlet temperature on the thermal energy storage side are used as the target boundaries. Set the minimum pinch temperature difference threshold, the upper limit of heat exchanger pressure drop, and the safe operating condition boundary on the carbon dioxide side as constraint boundaries; Limit the range of values ​​for the carbon dioxide side flow regulation, the thermal energy storage side flow regulation, the heat exchange bypass ratio, and the heat exchange branch allocation ratio. Under the charging condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the thermal energy storage unit; under the releasing condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the carbon dioxide side.

[0058] Specifically, real-time operational data collected during the current control cycle, such as the inlet pressure, inlet temperature, and inlet mass flow rate on the carbon dioxide side, and the inlet temperature, inlet flow rate, and state of charge of the thermal storage medium on the energy storage side, are used as the input boundaries of the heat transfer model. This real-time data forms the basis for the model's calculations and predictions, directly determining the initial thermodynamic state and flow conditions inside the heat exchanger. For example, the inlet mass flow rate affects the flow velocity and heat transfer coefficient of the heat exchanger, while the inlet temperature and pressure determine the thermal properties of the fluid. The state of charge on the energy storage side reflects the available heat or heat storage capacity of the energy storage unit, directly impacting the heat charging and discharging process. Accurate input of these parameters is crucial to ensuring that the model's calculation results match actual operating conditions.

[0059] Simultaneously, the target heat transfer power, the target outlet temperature on the carbon dioxide side, and the target outlet temperature on the thermal energy storage side are set as the target boundaries of the heat transfer model. These target boundaries represent the performance indicators that the heat transfer control aims to achieve. The target heat transfer power refers to the total heat transfer required by the system within the current control cycle, such as the heat to be added to the thermal energy storage unit under charging conditions or the heat to be removed from the thermal energy storage unit under releasing conditions. The target outlet temperatures on the carbon dioxide side and the thermal energy storage side define the desired temperatures of the fluid at the heat exchanger outlet, which are typically related to the operating requirements or process demands of subsequent system components. These target boundaries provide clear optimization directions and convergence conditions for the optimization problem.

[0060] In addition, minimum pinch temperature difference threshold, upper limit of heat exchanger pressure drop, and safe operating condition boundary on the carbon dioxide side are set as constraint boundaries. These constraint boundaries are necessary conditions to ensure the safe, stable, and efficient operation of the system. The minimum pinch temperature difference threshold is used to prevent excessively small temperature differences inside the heat exchanger, which not only affects heat exchange efficiency but may also lead to excessively large heat exchanger size or unstable operation. The upper limit of heat exchanger pressure drop limits the pressure loss of fluid passing through the heat exchanger; excessive pressure drop increases pump power consumption and reduces system efficiency. The safe operating condition boundary on the carbon dioxide side covers safe operating limits such as the maximum pressure and maximum temperature of the carbon dioxide fluid to prevent equipment damage or safety accidents. These constraint boundaries ensure that the optimization results are achievable and safe in engineering.

[0061] Furthermore, the value ranges of the carbon dioxide side flow regulation, the thermal energy storage side flow regulation, the heat exchange bypass ratio, and the heat exchange branch allocation ratio are defined. These control variables limit the physical constraints and operational margins of the actuators. The carbon dioxide side flow regulation and the thermal energy storage side flow regulation are typically determined by the regulating capacity of the pump or valve, and their ranges limit the maximum and minimum adjustable values ​​of the fluid flow. The heat exchange bypass ratio and the heat exchange branch allocation ratio are determined by the opening or switching capability of the valve assembly, which affect the distribution of fluid along different paths. Clearly defining the value ranges of these control variables ensures that the optimization algorithm finds the optimal solution within the practically operable range, avoiding the generation of unexecutable control commands.

[0062] Finally, under charging conditions, a heat flow direction constraint is added, directing the net heat exchange towards the thermal energy storage unit; under releasing conditions, a heat flow direction constraint is added, directing the net heat exchange towards the carbon dioxide side. These heat flow direction constraints are set based on the operating conditions (charging or releasing) of the carbon dioxide energy storage system. Under charging conditions, the system's primary objective is to transfer heat from the carbon dioxide side to the thermal energy storage unit; therefore, the net heat exchange must be directed towards the thermal energy storage unit to ensure effective heat storage. Under releasing conditions, the system needs to acquire heat from the thermal energy storage unit and transfer it to the carbon dioxide side; in this case, the net heat exchange must be directed towards the carbon dioxide side to meet the energy release requirements of the carbon dioxide energy storage system. These constraints ensure that the heat exchange process conforms to the energy transfer direction under the current operating conditions, which is core to realizing the function of the energy storage system.

[0063] By setting the aforementioned boundary conditions, this application ensures that the heat transfer model accurately reflects the system state and operational objectives under different operating conditions. Real-time operating data serves as the input boundary, enabling the model to dynamically adapt to changes in actual operating conditions; clear target boundaries provide a clear direction for optimization; strict constraint boundaries guarantee the safety and stability of system operation, avoiding the risk of exceeding limits; and limiting the range of control values ​​ensures the executability of the optimization results. Especially under energy charging and releasing conditions, by adding clear heat flow direction constraints, the heat transfer process can be effectively guided to proceed according to the preset heat storage or release direction, thereby avoiding energy backflow or ineffective heat transfer, significantly improving the accuracy and reliability of heat transfer control, making the optimized control strategy more practically applicable in engineering, and effectively solving the optimization failure or operational risk problems caused by unclear or unsuitable boundary conditions.

[0064] It should be noted that the heat transfer model between the carbon dioxide side and the thermal energy storage side, established based on operational data, includes: The thermal properties of carbon dioxide and heat storage medium in each heat exchange unit are calculated based on the operating data, and the local heat transfer coefficient and the overall heat transfer coefficient on the carbon dioxide side and the heat storage side are determined accordingly. Within each heat exchange unit, the heat exchange capacity is calculated based on energy conservation, and the temperature distribution at the hot and cold ends is iteratively updated to ensure that the heat exchange capacity of each heat exchange unit is consistent with the target boundary. The pressure drop of each heat exchange unit is calculated by using frictional resistance and local resistance models, and the total pressure drop of the heat exchanger is obtained by summing them up to form a pressure drop distribution.

[0065] Specifically, the operational data includes local temperature and pressure information for each heat exchange unit. Based on this data, a pre-set thermal property database, empirical correlations, or equations of state (e.g., the Span-Wagner equation of state for carbon dioxide) can be used to accurately calculate the density, specific heat capacity, viscosity, thermal conductivity, and other thermal property parameters of carbon dioxide and the heat storage medium (such as molten salt, heat transfer oil, etc.) under the current local conditions. These parameters form the basis for heat transfer calculations. Based on this, and combining the geometric parameters of the heat exchanger and the fluid flow state, heat transfer correlations applicable to different flow regimes (such as laminar flow, turbulent flow, supercritical fluids) (e.g., the Dittus-Boelter equation, the Gnielinski equation, etc.) can be used to determine the local convective heat transfer coefficients on the carbon dioxide side and the thermal energy storage side. Subsequently, based on the local convective heat transfer coefficients and the thermal conductivity of the heat exchange wall, the overall heat transfer coefficient of each heat exchange unit is calculated. This refined calculation method ensures that the model accurately reflects the local heat transfer capacity.

[0066] In detail, the heat exchanger is divided into multiple discrete heat exchange units along the flow direction. For each heat exchange unit, the heat transfer of that unit is calculated based on its inlet temperature, flow rate, and a determined local heat transfer coefficient, according to the principle of energy conservation. Since the temperature of the hot and cold fluids varies along the length of the heat exchanger, an iterative calculation method is required. Typically, starting from the heat exchanger inlet, the outlet temperature of the fluid is calculated and updated unit by unit, and this outlet temperature becomes the inlet temperature of the next unit. This process continues until the total heat transfer of the entire heat exchanger matches the preset target heat transfer requirement (i.e., the target boundary), or the temperature distribution converges. This iterative update mechanism can accurately simulate the temperature field changes inside the heat exchanger, providing accurate temperature data for subsequent calculations of irreversible losses.

[0067] Specifically, within each heat exchange unit, the pressure drop of the fluid flow mainly consists of two parts: frictional resistance loss and local resistance loss. Frictional resistance loss can be calculated using the Darcy-Weisbach formula combined with the friction coefficient (e.g., determined via a Moody diagram or the Colebrook equation). Local resistance loss is calculated by multiplying the local resistance coefficient (K value) by the dynamic head; these local resistance coefficients are typically related to internal structures such as bends, contractions, expansions, and valves within the heat exchanger. After calculating the frictional and local resistance losses for each heat exchange unit, the pressure drops of all heat exchange units along the flow direction are summed to obtain the total pressure drop of the heat exchangers on both the carbon dioxide side and the thermal energy storage side. This method provides a detailed pressure drop distribution, offering a basis for assessing pump power consumption and system operational safety.

[0068] By employing the aforementioned technical solution, the heat exchanger is subdivided into multiple heat exchange units. Within each unit, refined calculations of thermophysical parameters, energy conservation, heat transfer, and pressure drop are performed, enabling the construction of a highly accurate heat exchange model that reflects local characteristics. This model accurately captures the changes in the thermophysical properties of carbon dioxide and the heat storage medium under different temperatures and pressures, as well as the complex heat transfer and flow phenomena within the heat exchanger. This refined modeling method allows for more accurate local temperature, pressure, and heat transfer data when calculating irreversible loss evaluation indicators (such as entropy production and exergy loss), significantly improving the accuracy of optimization target calculations. Solving the heat exchange optimization problem based on this high-precision model can more effectively identify and minimize irreversible losses during system operation, ensuring that target heat exchange requirements such as the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power are met while maximizing heat exchange efficiency and the overall exergy efficiency of the system. Furthermore, by accurately calculating the pressure drop distribution of each heat exchange unit, pump power consumption can be more accurately assessed, ensuring that the heat exchanger pressure drop does not exceed a preset upper limit, thereby guaranteeing the safe and stable operation of the system.

[0069] Example 4 It should be noted that the method also includes: Feasibility verification of the optimal control quantity is performed. Feasibility verification includes checking whether the optimal control quantity meets the range and rate of change limit of the actuator, and whether it meets the margin thresholds of minimum pinch temperature difference, pressure drop and carbon dioxide side safety conditions. When the conditions are not met, the optimal control quantity is adjusted by limiting to obtain a feasible control quantity, and the feasible control quantity is then sent to the actuator.

[0070] Specifically, the purpose of feasibility verification of the optimal control quantity is to ensure that the theoretically optimal control quantity calculated by the optimization algorithm is executable and safe in the actual physical system. This verification acts as a safety barrier, effectively preventing the issuance of unrealistic or potentially dangerous control commands. During the verification process, the first step is to check whether the optimal control quantity meets the value range and rate of change limits of the actuator. Actuators, such as flow regulating devices on the carbon dioxide side and the thermal energy storage side, as well as valve assemblies used to adjust the heat exchange bypass ratio or heat exchange branch distribution ratio, have specific operating ranges (e.g., valve opening 0-100%) and response speed limits due to their physical structure. Value range limits mean that the control quantity must fall within the physical operating range of the actuator; for example, the opening of a flow regulating valve cannot exceed its maximum opening. Rate of change limits mean that the change in the control quantity between adjacent control cycles cannot exceed the maximum response speed of the actuator, to avoid overloading the actuator or causing mechanical shock.

[0071] In addition, feasibility verification also includes checking whether the optimal control variables meet the minimum pinch temperature difference, pressure drop, and safety margin thresholds for the carbon dioxide side. During heat exchange, the temperature difference between the hot and cold fluids should not be lower than a preset pinch temperature difference threshold to ensure heat exchange efficiency and normal operation of the heat exchanger, avoiding a sharp decline in heat exchange performance or even failure in localized areas. The margin threshold provides a certain safety margin. The pressure drop inside the heat exchanger should be controlled within a certain range to limit pump power consumption and ensure the structural integrity of the heat exchanger. Exceeding the preset pressure drop limit may lead to excessive system energy consumption or equipment damage. The margin threshold provides a certain safety margin. The pressure and temperature of the carbon dioxide fluid in the carbon dioxide energy storage system must always be maintained within a preset safe operating range to prevent dangerous situations such as abnormal phase change of carbon dioxide, equipment overpressure, or overtemperature. The margin threshold provides a certain safety margin to ensure the system remains safe even during dynamic changes.

[0072] If the optimal control quantity fails any of the above checks, the system will adjust it. Limit correction typically means adjusting the control quantity that exceeds the limit to the boundary value that is closest to and meets the limit. For example, if the calculated optimal valve opening is 110%, it will be corrected to 100%; if the rate of change of the control quantity is too fast, it will be adjusted to the maximum allowable rate of change. After feasibility checks and necessary corrections, only the safe and executable control quantity (i.e., the feasible control quantity) will be sent to the actual physical actuator to implement heat exchange control.

[0073] By employing the aforementioned technical solutions, the feasibility of the optimal control quantity is verified, and a limit correction is made based on the verification results. This application effectively avoids issuing theoretically optimal but practically infeasible or unsafe control commands to the actuators. Specifically, verifying the value range and rate of change limits of the actuators ensures that actuators such as flow regulating equipment and valve groups operate smoothly within their own physical capabilities, avoiding equipment wear, failure, or system response delays caused by exceeding limits. Simultaneously, verifying the minimum pinch point temperature difference, pressure drop, and margin thresholds for safe operating conditions on the carbon dioxide side ensures that the heat exchange process is always under efficient and safe operating conditions from the perspective of system safety and stability, preventing local overheating of the heat exchanger, abnormal increases in system energy consumption, or dangerous conditions in the carbon dioxide fluid. This verification and correction mechanism allows the theoretical advantages of the optimization algorithm to be perfectly combined with the operational constraints and safety requirements of the actual system, ensuring that the carbon dioxide energy storage and thermal energy storage co-exchange system maintains stable, reliable, and safe operation while pursuing high efficiency.

[0074] Example 5 It should be noted that the method also includes: A rolling optimization strategy is adopted. At the end of each control cycle, the initial value of the control quantity for the next control cycle is generated based on the optimal control quantity and actual execution feedback of the current control cycle.

[0075] The rolling optimization strategy is an online optimization control method. Its core lies in optimizing the control variables for a future period based on the current system state and predictive model within each control cycle, executing only the first optimized control variable, and then repeating this process in the next control cycle. This strategy effectively addresses dynamic changes and uncertainties in the system, achieving continuous optimization control. Specifically, this strategy recalculates the optimization in each control cycle, using the latest system information to correct and update control decisions, thereby enabling the system to adapt to constantly changing operating environments.

[0076] At the end of each control cycle, the operation of optimizing and updating the initial values ​​of the control variables is performed periodically and synchronously with the system's control cycle. After data acquisition, optimization calculation, and control execution are completed in the current control cycle, the system immediately starts preparations for the next cycle, ensuring the continuity and real-time nature of control. This periodic update mechanism is the core of the rolling optimization strategy, ensuring that the control system can respond promptly to changes in system state.

[0077] Based on the optimal control quantity and actual execution feedback of the current control cycle, the optimal control quantity refers to the control quantity obtained by solving the heat transfer optimization problem within the current control cycle, which aims to minimize the irreversible loss evaluation index and meet preset constraints. It is the theoretically optimal control command. Actual execution feedback refers to the system response data generated by the actuator after receiving the optimal control quantity and actually executing the operation. This includes, but is not limited to, the actual carbon dioxide flow rate, the thermal energy storage flow rate, the heat transfer bypass ratio, the heat transfer branch allocation ratio, and the resulting actual outlet temperature, pressure drop, and other operating parameters. Comparing and combining the theoretically optimal control quantity with the actual system feedback after execution allows for the evaluation of the effectiveness of the current control strategy and the identification of deviations between model predictions and actual system behavior. This feedback mechanism is the basis for the self-correction and adaptive adjustment of the rolling optimization strategy.

[0078] The initial control value for the next control cycle is generated, serving as the starting point for solving the optimization problem in that cycle. By utilizing the optimization results and actual feedback from the current cycle to generate the initial value for the next cycle, the convergence speed and computational efficiency of the optimization algorithm can be significantly improved. For example, the optimal control value from the current cycle can be used as the initial value for the next cycle, or the optimal control value can be fine-tuned based on the actual execution feedback from the current cycle. Furthermore, historical data or predictive models can be combined to more intelligently predict the initial value, better guiding the optimization process in the next cycle.

[0079] Example 6 A high-efficiency heat exchange system that combines carbon dioxide energy storage and thermal energy storage, comprising: Processor and memory; The processor and memory are connected via a communication bus: The processor is used to call and execute programs stored in memory. A memory is used to store a program, which is used to execute at least one of the efficient heat exchange methods for the synergistic use of carbon dioxide energy storage and thermal energy storage in the above embodiments.

[0080] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0081] It should be noted that in the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means at least two.

[0082] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0083] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0085] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0086] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0087] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A highly efficient heat exchange method for the synergistic use of carbon dioxide energy storage and thermal energy storage, characterized in that, include: Determine the operating conditions of the carbon dioxide energy storage system within the current control cycle, and obtain the target heat exchange requirements corresponding to the operating conditions; The operating conditions include: charging condition and releasing condition; the target heat exchange requirement includes the target outlet temperature on the carbon dioxide side, the target outlet temperature on the thermal energy storage side, and the target heat exchange power. Collect operational data from both the carbon dioxide side and the thermal energy storage side during the current control cycle; A heat exchange model is established between the carbon dioxide side and the thermal energy storage side based on the aforementioned operating data. The heat exchange model is used to characterize the heat exchange distribution, heat exchange end temperature difference distribution, and pressure drop distribution under given control variables. The control variables include any one or more of the following: carbon dioxide side flow rate regulation, thermal energy storage side flow rate regulation, heat exchange bypass ratio, and heat exchange branch allocation ratio. Based on the operating conditions of the carbon dioxide energy storage system and the target heat exchange demand corresponding to the operating conditions, the boundary conditions of the heat exchange model are determined; wherein, the boundary conditions under the charging condition include heat flow constraints oriented towards charging the thermal energy storage unit; and the boundary conditions under the releasing condition include heat flow constraints oriented towards supplying heat from the thermal energy storage unit to the carbon dioxide side. The irreversible loss evaluation index is calculated based on the heat transfer model, and the irreversible loss evaluation index is used as an optimization objective or a component of the optimization objective; the irreversible loss evaluation index includes entropy production, exergy loss, and exergy efficiency. A heat exchange optimization problem is constructed and solved to obtain the optimal control quantity for the current control cycle; the heat exchange optimization problem aims to minimize the irreversible loss evaluation index while satisfying preset constraints. The optimal control quantity is sent to the actuator to implement heat exchange control; the actuator includes a flow regulating device on the carbon dioxide side and a flow regulating device on the thermal energy storage side, as well as a valve group for adjusting the heat exchange bypass ratio or the heat exchange branch allocation ratio; the heat exchange control includes: executing a control direction to meet the target heat exchange demand and charge the thermal energy storage unit under the charging condition, and executing a control direction to meet the target heat exchange demand and supply heat from the thermal energy storage unit to the carbon dioxide side under the releasing condition.

2. The method according to claim 1, characterized in that, The method further includes: When switching operating conditions of the carbon dioxide energy storage system, the target heat exchange requirement, the constraints, and the parameters of the heat exchange model are updated synchronously.

3. The method according to claim 1, characterized in that, The preset constraints include: The outlet temperatures of the carbon dioxide side and the thermal energy storage side meet the target heat exchange requirements. The minimum temperature difference at the heat exchange end shall not be less than the preset pinch point temperature difference threshold. The pressure and temperature on the carbon dioxide side meet the preset safety operating condition boundaries; The pressure drop of the heat exchanger does not exceed the preset pressure drop limit; The state of charge of the thermal energy storage is within the allowable range and meets the charging and discharging power constraints.

4. The method according to claim 1, characterized in that, The calculation of entropy production based on the heat exchange model includes: dividing the heat exchanger into at least two heat exchange units along the flow direction; calculating the entropy production of each heat exchange unit based on the heat exchange capacity and the cold and hot end temperatures of each heat exchange unit; and summing the entropy production of each heat exchange unit to obtain the total entropy production of the current control cycle. The calculation of exergy loss based on the heat exchange model includes: multiplying the total entropy production by the environmental reference temperature based on a preset environmental reference temperature to obtain the exergy loss for the current control cycle; The exergy efficiency is calculated based on the heat exchange model, including: obtaining the input heat exchange power and the effective available heat exchange power of the heat exchange process in the current control cycle, and determining the exergy efficiency based on the ratio of the effective available heat exchange power to the input heat exchange power.

5. The method according to claim 4, characterized in that, The optimization objective of the heat exchange optimization problem is a combination of multiple objectives. The multi-objective combined objective includes at least a penalty term for total entropy production and a penalty term for deviation in target heat exchange power; The multi-objective combined objective adaptively adjusts the weights of each penalty term based on the changes in the state of charge of thermal energy storage within a preset range.

6. The method according to claim 4, characterized in that, Determine the boundary conditions of the heat transfer model, including: The carbon dioxide inlet pressure, inlet temperature, and inlet mass flow rate, as well as the thermal energy storage medium inlet temperature, inlet flow rate, and state of charge, from the operational data collected during the current control cycle are used as the model input boundaries. The target heat exchange power, the target outlet temperature on the carbon dioxide side, and the target outlet temperature on the thermal energy storage side are used as the target boundaries. Set the minimum pinch temperature difference threshold, the upper limit of heat exchanger pressure drop, and the safe operating condition boundary on the carbon dioxide side as constraint boundaries; Limit the range of values ​​for the carbon dioxide side flow regulation, the thermal energy storage side flow regulation, the heat exchange bypass ratio, and the heat exchange branch allocation ratio. Under the charging condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the thermal energy storage unit; under the releasing condition, an additional constraint is added on the heat flow direction of the net heat exchange towards the carbon dioxide side.

7. The method according to claim 4, characterized in that, A heat transfer model between the carbon dioxide side and the thermal energy storage side is established based on the aforementioned operational data, including: Based on the operating data, the thermal properties of carbon dioxide and the heat storage medium in each heat exchange unit are calculated, and the local heat transfer coefficient and the overall heat transfer coefficient on the carbon dioxide side and the heat storage side are determined accordingly. Within each heat exchange unit, the heat exchange capacity is calculated based on energy conservation, and the temperature distribution at the hot and cold ends is iteratively updated to ensure that the heat exchange capacity of each heat exchange unit is consistent with the target boundary. The pressure drop of each heat exchange unit is calculated by using frictional resistance and local resistance models, and the total pressure drop of the heat exchanger is obtained by summing them up to form the pressure drop distribution.

8. The method according to claim 1, characterized in that, The method further includes: The feasibility of the optimal control quantity is verified. The feasibility verification includes: checking whether the optimal control quantity meets the value range and rate of change limit of the actuator, and whether it meets the margin threshold of minimum pinch temperature difference, pressure drop and carbon dioxide side safety conditions. If the condition is not met, the optimal control quantity is limited and corrected to obtain a feasible control quantity, and the feasible control quantity is then sent to the actuator.

9. The method according to claim 1, characterized in that, The method further includes: A rolling optimization strategy is adopted. At the end of each control cycle, the initial value of the control quantity for the next control cycle is generated based on the optimal control quantity and actual execution feedback of the current control cycle.

10. A high-efficiency heat exchange system that combines carbon dioxide energy storage and thermal energy storage, characterized in that, include: Processor and memory; The processor and memory are connected via a communication bus: The processor is used to call and execute the program stored in the memory; The memory is used to store a program, which is at least used to execute the efficient heat exchange method for coordinating carbon dioxide energy storage and thermal energy storage as described in any one of claims 1-9.

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