Thermoelectric decoupling system based on steam-electricity dual-mode energy storage

Abstract

The invention relates to the technical field of energy storage and conversion, and discloses a thermoelectric decoupling system based on steam-electricity dual-mode energy storage, which comprises an energy storage module, a demand analysis module, a regulation and control execution module and an intelligent optimization module. The energy storage module comprises a steam energy storage unit and a battery energy storage unit, and cooperative work is achieved through an energy conversion interface. The demand analysis module collects heat supply and power demand data in real time and judges supply and demand states; the regulation and control execution module dynamically allocates energy storage unit tasks according to requirements; and the intelligent optimization module formulates an optimization strategy through multi-source data analysis. The system flexibly responds when the heat supply or power demand fluctuates, the heat supply demand is preferentially met, meanwhile, the energy utilization efficiency and cost are optimized, and the stability and reliability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the field of energy storage and conversion technology, and in particular to a thermoelectric decoupling system based on steam-electric dual-mode energy storage. Background Art

[0002] With the diversification of energy demand, the role of thermal power systems in heat and electricity supply is becoming increasingly prominent. Traditional thermal power systems usually rely on cogeneration units to achieve heat and power generation, but they face many challenges in terms of efficient energy utilization and stable operation. Lack of flexibility in heating is one of the key issues. Traditional methods mainly rely on synchronous heating modes and lack flexible energy storage and adjustment methods. When the demand for heating changes, the system's response speed is slow, which may lead to a mismatch between heating capacity and demand, thereby affecting user experience or causing energy waste. For example, during peak heating demand periods, the ability of cogeneration units to increase heating capacity is limited; and when heating demand is low, excess heat energy is often not effectively stored and utilized.

[0003] At the same time, as the proportion of renewable energy increases, the volatility of power loads in the power grid is gradually increasing. Traditional thermal power systems rely primarily on starting and stopping power generation equipment or adjusting power levels to regulate power loads. This approach is slow to respond and cannot meet rapidly fluctuating power demands. During peak load periods, power generation equipment may be unable to increase output power in a timely manner, leading to power shortages. During low load periods, there is a lack of effective storage methods for excess energy, resulting in energy waste.

[0004] Furthermore, traditional thermal power systems lack intelligent control strategies, making it difficult to monitor and dynamically analyze energy supply and demand in real time. This limits the system's energy distribution and storage strategies, resulting in low energy efficiency, increased operating costs, and compromised system reliability and stability. For example, when energy supply and demand are unbalanced, excess heat and electricity are often simply discharged or left idle, failing to be fully utilized. Frequent equipment startups and shutdowns, as well as load adjustments, not only increase equipment wear and tear and maintenance costs, but can also reduce system reliability, further impacting users' normal production and daily lives. Summary of the Invention

[0005] The purpose of the present invention is to provide a thermoelectric decoupling system based on steam-electric dual-mode energy storage, aiming to address the shortcomings of thermoelectric systems in the prior art in terms of insufficient heating flexibility, limited power load regulation capabilities, and lack of intelligent control strategies. Traditional thermoelectric systems are difficult to respond quickly to fluctuations in energy demand, resulting in particularly prominent problems such as energy waste and decreased system stability. By introducing a collaborative working mode of steam energy storage and battery energy storage, and combining it with an intelligent optimization algorithm to achieve dynamic control, the present invention provides a practical technical solution to the above-mentioned problems.

[0006] The present invention is implemented as follows: on the one hand, a thermoelectric decoupling system based on steam-electric dual-mode energy storage comprises: An energy storage module is configured to receive externally input thermal energy and electrical energy and store them in a steam energy storage unit and a battery energy storage unit, respectively. The energy storage module comprises at least a steam energy storage unit and a battery energy storage unit capable of operating independently, and energy exchange between the two units is performed via an energy conversion interface. The demand analysis module is used to determine the current energy supply and demand status of the system based on the real-time collected heating demand and electricity demand data; heating demand and electricity demand have different priorities, with heating demand taking precedence over electricity demand; The control execution module is used to: when a peak in heating demand and a valley in electricity demand are detected, give priority to releasing the thermal energy in the steam energy storage unit to meet the heating demand, while using the excess electrical energy to charge the battery energy storage unit; when a peak in electricity demand and a valley in heating demand are detected, give priority to releasing the electrical energy in the battery energy storage unit to meet the electricity demand, while using the excess thermal energy to charge the steam energy storage unit; The intelligent optimization module is used to establish a load forecasting model and formulate an optimization strategy through multi-source data fusion and analysis to regulate the charging and discharging behavior of the energy storage module; the optimization strategy is designed based on the goals of maximizing energy utilization efficiency, minimizing energy costs and ensuring system stability.

[0007] As a further solution of the present invention, the energy storage module specifically includes: The steam energy storage unit uses a phase-change thermal storage material as its core energy storage medium. The phase-change thermal storage material has high latent heat and good thermal conductivity. A heat exchange structure is provided inside the steam energy storage unit. The heat exchange structure achieves efficient heat transfer through multiple layers of heat conducting plates and fluid channels. Battery energy storage units use high-performance lithium-ion batteries or flow batteries as energy storage devices. The battery energy storage unit is integrated with a battery management system. The battery management system uses sensors to monitor the battery's power, voltage, and temperature parameters in real time, and implements battery charge and discharge management through control algorithms. The energy conversion interface is used to connect the steam energy storage unit and the battery energy storage unit. A bidirectional energy conversion device is provided inside the energy conversion interface. The bidirectional energy conversion device realizes the mutual conversion between thermal energy and electrical energy through a thermoelectric converter.

[0008] As a further solution of the present invention, the demand analysis module specifically includes: The data acquisition unit is used to collect real-time operating parameters of the thermal power system through a distributed sensor network, including power generation, heat supply, energy consumption, power load, heating load, etc.; it also collects external information such as real-time grid electricity prices, meteorological data, and user energy consumption behavior data; The state judgment unit is used to judge the energy supply and demand status of the current system based on the collected data; when the difference between the heating demand and the electricity demand exceeds the preset threshold, it is determined that the system is in an unbalanced state and the control execution module is triggered to respond.

[0009] As a further solution of the present invention, the control execution module further includes: The task allocation unit is used to reasonably allocate the charging and discharging tasks of the steam energy storage unit and the battery energy storage unit according to the changes in heating demand and electricity demand. The task allocation unit is internally equipped with priority judgment logic, which is dynamically adjusted based on the real-time changes in heating demand and electricity demand. The execution feedback unit is used to monitor the operating status of the control execution module and feed back the execution results to the intelligent optimization module; the execution feedback unit exchanges information with the intelligent optimization module through the data communication interface to ensure real-time updating of the control strategy.

[0010] As a further solution of the present invention, the intelligent optimization module specifically includes: The data processing unit is used to clean, segment, and extract features from collected multi-source data. A deep learning algorithm module is built into the data processing unit. This module analyzes historical and real-time data to discover potential patterns and provide support for the load forecasting model. The load forecasting unit is used to establish power load forecasting models and heating load forecasting models. The load forecasting unit uses an integrated learning method to integrate neural networks, support vector machines, and time series analysis models to improve forecasting accuracy and reliability. The strategy generation unit is used to generate an optimized charging and discharging strategy for the energy storage module based on load forecast results and real-time energy supply and demand conditions. The strategy generation unit is internally provided with an optimization algorithm module, which solves the optimal strategy based on a genetic algorithm or a particle swarm optimization algorithm.

[0011] As a further solution of the present invention, the control execution module also includes the following functions: Acquiring the user terminal's location information and, upon detecting that the user terminal is moving toward a location along the navigation route, determining that the user is about to leave the current area; triggering a recovery operation if the user terminal continues to move along the navigation route and reaches a preset distance; the recovery operation includes re-enabling the output function of the steam energy storage unit or battery energy storage unit and displaying new control options for the user to select; The control options include the estimated time of the user's return and the temperature information of the current area. The temperature information is predicted based on the current temperature change trend and return time, providing a reference for the user.

[0012] As a further embodiment of the present invention, the steam energy storage unit and the battery energy storage unit are physically connected to achieve collaborative operation. The heat exchange structure of the steam energy storage unit is connected to the cooling system of the battery energy storage unit via a heat pipe. The heat pipe is filled with a working fluid with a high thermal conductivity coefficient. The working fluid circulates within the heat pipe to achieve efficient heat transfer. The cooling system of the battery energy storage unit absorbs the waste heat released by the steam energy storage unit through heat pipes, reducing the operating temperature of the battery and improving the battery's operating efficiency and service life.

[0013] The present invention provides a thermoelectric decoupling system based on steam-electric dual-mode energy storage. It has the following beneficial effects: This invention achieves flexible storage and release of thermal and electrical energy by introducing a collaborative working mechanism between steam energy storage units and battery energy storage units, combined with the dynamic control strategy of an intelligent optimization module. When heating demand peaks and electricity demand is low, the steam energy storage unit is prioritized to meet heating demand while the battery energy storage unit is charged. When electricity demand peaks and heating demand is low, the battery energy storage unit is prioritized to meet electricity demand while the steam energy storage unit is charged. This collaborative working mode not only improves energy utilization efficiency and reduces energy costs, but also enhances system stability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is the overall structural block diagram of the system of the present invention; Figure 2 This is a block diagram of the internal structure of the energy storage module of the present invention; Figure 3 This is a flow chart showing the function implementation of the demand analysis module of the present invention; Figure 4 A logic flow chart for allocating tasks to the control execution module of the present invention; Figure 5 This is a functional block diagram of the intelligent optimization module of the present invention; Figure 6 This is a block diagram of the heat exchange structure between the steam energy storage unit and the battery energy storage unit of the present invention; Figure 7 This is a trigger logic flow chart of the user terminal navigation route detection and recovery operation of the present invention; Figure 8 This is a flow chart of the operating status feedback of the system of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] The present invention provides a thermoelectric decoupling system based on steam-electric dual-mode energy storage, the overall structure of which is as follows: Figure 1 As shown in the figure, it includes an energy storage module, a demand analysis module, a control execution module, and an intelligent optimization module. These modules are interconnected through data communication interfaces to form a complete closed-loop control system to achieve flexible storage and release of thermal and electrical energy.

[0017] The energy storage module is the core component of the system, and its internal structure is as follows Figure 2 As shown, it includes a steam energy storage unit, a battery energy storage unit and an energy conversion interface. The steam energy storage unit uses phase change heat storage material as the core energy storage medium. This material has high phase change latent heat and good thermal conductivity, and can effectively store and release thermal energy. The steam energy storage unit is equipped with a heat exchange structure composed of multiple layers of heat conducting plates and fluid channels to achieve efficient heat transfer. The battery energy storage unit uses high-performance lithium-ion batteries or liquid flow batteries as energy storage devices. It has an integrated battery management system that monitors the battery's power, voltage and temperature parameters in real time through sensors, and realizes battery charge and discharge management through control algorithms. The energy conversion interface is located between the steam energy storage unit and the battery energy storage unit. It is equipped with a bidirectional energy conversion device inside. The device realizes the mutual conversion between thermal energy and electrical energy through a thermoelectric converter. The steam energy storage unit and the battery energy storage unit work together through a physical connection. Their cooling systems are connected through a heat pipe. The heat pipe is filled with a working fluid with a high thermal conductivity coefficient, which can circulate in the heat pipe, thereby transferring the waste heat released by the steam energy storage unit to the cooling system of the battery energy storage unit, reducing the battery operating temperature and improving working efficiency.

[0018] The functional implementation process of the demand analysis module is as follows Figure 3 As shown, the system includes a data acquisition unit and a state determination unit. The data acquisition unit uses a distributed sensor network to collect real-time operating parameters of the thermal power system, including power generation, heat supply, energy consumption, power load, and heating load. It also collects external information, such as real-time grid electricity prices, meteorological data, and user energy consumption data. The state determination unit uses the collected data to determine the current energy supply and demand status of the system. When the difference between heating demand and power demand exceeds a preset threshold, the system is deemed unbalanced and triggers the control execution module to respond.

[0019] The task allocation logic of the control execution module is as follows Figure 4As shown, it includes a task allocation unit and an execution feedback unit. The task allocation unit rationally allocates the charging and discharging tasks of the steam energy storage unit and the battery energy storage unit according to the changes in the heating demand and the electricity demand. The task allocation unit is internally provided with a priority judgment logic, which is dynamically adjusted based on the real-time changes in the heating demand and the electricity demand. The priority of the heating demand is higher than the priority of the electricity demand. When a peak in the heating demand is detected and a valley in the electricity demand is low, the thermal energy in the steam energy storage unit is released first to meet the heating demand, and the excess electricity is used to charge the battery energy storage unit; when a peak in the electricity demand is detected and a valley in the heating demand is low, the electricity in the battery energy storage unit is released first to meet the electricity demand, and the excess thermal energy is used to charge the steam energy storage unit. The execution feedback unit monitors the operating status of the control execution module and feeds back the execution results to the intelligent optimization module to ensure real-time updates of the control strategy.

[0020] The functional components of the intelligent optimization module are as follows: Figure 5 As shown, the system includes a data processing unit, a load forecasting unit, and a strategy generation unit. The data processing unit cleans, segments, and extracts features from collected multi-source data. It incorporates a deep learning algorithm module that analyzes historical and real-time data to uncover underlying patterns and support the load forecasting model. The load forecasting unit establishes power and heating load forecasting models, employing an ensemble learning approach to integrate neural networks, support vector machines, and time series analysis models to improve forecast accuracy and reliability. The strategy generation unit generates optimized charging and discharging strategies for the energy storage module based on load forecast results and real-time energy supply and demand. It incorporates an optimization algorithm module that solves for the optimal strategy using either a genetic algorithm or a particle swarm optimization algorithm.

[0021] When the user terminal moves to a certain location along the navigation route, the control execution module obtains the location information of the user terminal and determines that the user is about to leave the current area. If the user terminal continues to move along the navigation route and reaches the preset distance, the recovery operation is triggered. The recovery operation includes re-enabling the output function of the steam energy storage unit or the battery energy storage unit, and displaying new control options for the user to choose. The control options include the estimated time of the user's return and the temperature information of the current area. The temperature information is predicted based on the current temperature change trend and the return time to provide a reference for the user. The user terminal navigation route detection and recovery operation triggering logic is as follows Figure 7 shown.

[0022] System operation status feedback process is as follows Figure 8 As shown in the figure, the information exchange process between the control execution module and the intelligent optimization module is realized through the execution feedback unit. The execution feedback unit provides real-time feedback of the operating status of the control execution module to the intelligent optimization module. The intelligent optimization module adjusts the optimization strategy based on the feedback information and re-issues it to the control execution module, thus forming a closed-loop control.

[0023] In a practical application scenario, consider an industrial zone with significant fluctuations in heating and electricity demand. Electricity demand is high during the day, while heating demand is low, while the opposite is true at night. The system first uses the demand analysis module to collect real-time data and determine the current energy supply and demand status. For example, during peak daytime electricity demand, the system prioritizes releasing electricity from the battery energy storage unit to meet electricity demand, while simultaneously using excess heat energy to charge the steam energy storage unit. During peak nighttime heating demand, the system prioritizes releasing heat from the steam energy storage unit to meet heating demand, while simultaneously using excess electricity to charge the battery energy storage unit. Through this collaborative mechanism, the system achieves flexible scheduling of heat and electricity, improving energy efficiency and reducing energy costs.

[0024] In addition, the heat exchange structure between the steam energy storage unit and the battery energy storage unit is as follows Figure 6 As shown, heat pipes achieve efficient heat transfer. A high-thermal-conductivity working fluid circulates within the heat pipes, transferring waste heat from the steam energy storage unit to the battery energy storage unit's cooling system. This reduces the battery's operating temperature and improves its efficiency and lifespan. This design not only enhances system stability and reliability but also further optimizes energy efficiency.

[0025] The above describes in detail the specific embodiments of the present invention, and combines the specific drawings in the accompanying drawings and the individual Arabic numerals of each component in the drawings to provide a detailed description, ensuring that the technical solution is fully disclosed and can be implemented by those skilled in the art.

[0026] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is further supplemented below with reference to a specific application scenario.

[0027] In actual operation at a specific industrial park, the system first activates the data acquisition unit in the demand analysis module. Using a distributed sensor network, it collects real-time operating parameters of the thermal power system, including power generation, heat supply, energy consumption, power load, and heating load. Simultaneously, external information such as real-time grid electricity prices, meteorological data, and user energy usage behavior is also acquired. The state determination unit analyzes the collected data. When the difference between heat demand and power demand exceeds a preset threshold, the system is deemed unbalanced and the control execution module is triggered to respond.

[0028] The task allocation unit of the control execution module dynamically adjusts the task priority according to the changes in heating demand and electricity demand. For example, during the peak period of electricity demand during the day, the system detects a significant increase in electricity load, while the heating demand is low. At this time, the task allocation unit prioritizes releasing the electrical energy in the battery energy storage unit to meet the electricity demand, while using the excess thermal energy to charge the steam energy storage unit. Specifically, the battery energy storage unit efficiently outputs the stored electrical energy to the power grid through the bidirectional energy conversion device in the energy conversion interface. At the same time, the steam energy storage unit stores the excess thermal energy in the phase change thermal storage material through the heat exchange structure. This process ensures a rapid response to electricity demand while avoiding the waste of thermal energy.

[0029] During the peak heating demand period at night, the system detects a significant increase in heating load, while electricity demand is low. At this time, the task allocation unit prioritizes releasing the thermal energy in the steam energy storage unit to meet heating demand, while using excess electrical energy to charge the battery energy storage unit. The steam energy storage unit efficiently transfers the stored thermal energy to the heating network through the multi-layer heat conduction plates and fluid channels in the heat exchange structure, meeting the user's heating needs. At the same time, the battery energy storage unit converts excess electrical energy into chemical energy through the bidirectional energy conversion device in the energy conversion interface and stores it in high-performance lithium-ion batteries or liquid flow batteries. This collaborative working mechanism not only realizes the flexible scheduling of thermal energy and electrical energy, but also significantly improves energy utilization efficiency.

[0030] The intelligent optimization module plays a key role in the entire process. The data processing unit cleans, segments, and extracts features from the collected multi-source data, and uses a deep learning algorithm module to mine potential patterns. The load forecasting unit, based on an ensemble learning method, integrates neural networks, support vector machines, and time series analysis models to establish power load forecasting models and heating load forecasting models to provide support for the strategy generation unit. Based on the load forecast results and real-time energy supply and demand, the strategy generation unit uses genetic algorithms or particle swarm optimization algorithms to solve the optimal strategy and generate an optimized charging and discharging plan for the energy storage module. For example, during peak electricity demand periods, the strategy generation unit will prioritize the discharge depth and discharge rate of the battery energy storage unit to ensure the stability of the power supply; during peak heating demand periods, it will prioritize the thermal energy release efficiency and thermal energy storage capacity of the steam energy storage unit to meet heating needs.

[0031] In addition, when the user terminal moves along the navigation route, the control execution module obtains the user's location information through the execution feedback unit and determines that the user is about to leave the current area. If the user terminal continues to move along the navigation route and reaches the preset distance, the recovery operation is triggered. The recovery operation includes re-enabling the output function of the steam energy storage unit or the battery energy storage unit, and displaying new control options for the user to choose. The control options include the estimated time of the user's return and the temperature information of the current area. The temperature information is predicted based on the current temperature change trend and the return time to provide a reference for the user. This function ensures that when the user leaves and returns to the area, the system can adjust the energy allocation strategy in time to avoid energy waste.

[0032] In the system operation status feedback process, the execution feedback unit will feed back the operation status of the control execution module to the intelligent optimization module in real time. The intelligent optimization module adjusts the optimization strategy based on the feedback information and re-sends it to the control execution module to form a closed-loop control. For example, when the temperature monitoring data of the battery energy storage unit shows that its operating temperature is close to the upper limit, the intelligent optimization module will adjust the strategy and give priority to using the waste heat released by the steam energy storage unit to cool the battery energy storage unit. Specifically, the steam energy storage unit transfers the waste heat to the cooling system of the battery energy storage unit through a heat pipe, reducing the battery operating temperature and improving its working efficiency and service life.

[0033] Through the above steps, the thermoelectric decoupling system of the present invention achieves efficient scheduling of thermal and electrical energy in practical applications. For example, during peak daytime electricity demand, the system prioritizes releasing electrical energy from the battery energy storage unit to meet electricity demand, while simultaneously using excess thermal energy to charge the steam energy storage unit. During peak nighttime heating demand, the system prioritizes releasing thermal energy from the steam energy storage unit to meet heating demand, while simultaneously using excess electrical energy to charge the battery energy storage unit. This collaborative working mechanism not only significantly improves energy utilization efficiency and reduces energy costs, but also enhances system stability and reliability.

[0034] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A thermoelectric decoupling system based on steam-electric dual-mode energy storage, characterized in that: include: An energy storage module is used to receive external input heat energy and electrical energy and store them in a steam energy storage unit and a battery energy storage unit respectively. The energy storage module includes at least a steam energy storage unit and a battery energy storage unit that can operate independently. Energy exchange between the two units is carried out through an energy conversion interface. The demand analysis module is used to determine the energy supply and demand status of the current system based on the real-time collected heating demand and electricity demand data, where heating demand has a higher priority than electricity demand; A control execution module is configured to, when a peak in heating demand and a valley in electricity demand are detected, give priority to releasing the thermal energy in the steam energy storage unit to meet the heating demand, while using the excess electrical energy to charge the battery energy storage unit; when a peak in electricity demand and a valley in heating demand are detected, give priority to releasing the electrical energy in the battery energy storage unit to meet the electricity demand, while using the excess thermal energy to charge the steam energy storage unit; The intelligent optimization module is used to establish a load forecasting model and formulate an optimization strategy through multi-source data fusion and analysis to regulate the charging and discharging behavior of the energy storage module.

2. A thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The energy storage module specifically includes: The steam energy storage unit uses a phase-change thermal storage material as its core energy storage medium. The phase-change thermal storage material has high latent heat and good thermal conductivity. The steam energy storage unit is internally provided with a heat exchange structure that achieves efficient heat transfer through multiple layers of heat conducting plates and fluid channels. Battery energy storage units use high-performance lithium-ion batteries or flow batteries as energy storage devices. The battery energy storage unit is integrated with a battery management system. The battery management system uses sensors to monitor the battery's power, voltage, and temperature parameters in real time, and implements battery charge and discharge management through control algorithms. The energy conversion interface is used to connect the steam energy storage unit and the battery energy storage unit. A bidirectional energy conversion device is provided inside the energy conversion interface. The bidirectional energy conversion device realizes the mutual conversion between thermal energy and electrical energy through a thermoelectric converter.

3. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The demand analysis module specifically includes: The data acquisition unit is used to collect real-time operating parameters of the thermal power system through a distributed sensor network, including power generation, heat supply, energy consumption, power load and heating load, as well as external information, including real-time grid electricity prices, meteorological data and user energy consumption behavior data; The state judgment unit is used to judge the energy supply and demand status of the current system based on the collected data. When the difference between the heating demand and the electricity demand exceeds the preset threshold, it is determined that the system is in an unbalanced state and the control execution module is triggered to respond.

4. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The control execution module also includes: The task allocation unit is used to reasonably allocate the charging and discharging tasks of the steam energy storage unit and the battery energy storage unit according to the changes in heating demand and electricity demand. The task allocation unit is internally equipped with priority judgment logic, which is dynamically adjusted based on the real-time changes in heating demand and electricity demand; The execution feedback unit is used to monitor the running status of the control execution module and feed back the execution results to the intelligent optimization module.

5. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The intelligent optimization module specifically includes: The data processing unit is used to clean, segment, and extract features from the collected multi-source data. It is equipped with a deep learning algorithm module that analyzes historical and real-time data to discover potential patterns and provide support for the load forecasting model. The load forecasting unit is used to establish power load forecasting models and heating load forecasting models. The load forecasting unit uses an integrated learning method to integrate neural networks, support vector machines, and time series analysis models. The strategy generation unit is used to generate an optimized charging and discharging strategy for the energy storage module based on the load forecast results and the real-time energy supply and demand situation. The strategy generation unit is internally provided with an optimization algorithm module, which solves the optimal strategy based on the genetic algorithm or the particle swarm optimization algorithm.

6. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The control execution module is also used to obtain the location information of the user terminal. When it is detected that the user terminal is moving toward a certain location along the navigation route, it is determined that the user is about to leave the current area; if the user terminal continues to move along the navigation route and reaches a preset distance, a recovery operation is triggered. The recovery operation includes re-enabling the output function of the steam energy storage unit or the battery energy storage unit and displaying new control options for the user to select.

7. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 6, characterized in that: The control options include the estimated time of the user's return and the temperature information of the current area, and the temperature information is predicted based on the current temperature change trend and the return time.

8. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The steam energy storage unit and the battery energy storage unit are physically connected to achieve collaborative work. The heat exchange structure of the steam energy storage unit is connected to the cooling system of the battery energy storage unit through a heat pipe. The heat pipe is filled with a working fluid with a high thermal conductivity coefficient. The working fluid circulates in the heat pipe to achieve efficient heat transfer.

9. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 8, characterized in that: The cooling system of the battery energy storage unit absorbs the waste heat released by the steam energy storage unit through the heat pipe, thereby reducing the operating temperature of the battery.

10. The thermoelectric decoupling system based on steam-electric dual-mode energy storage according to claim 1, characterized in that: The execution feedback unit exchanges information with the intelligent optimization module through a data communication interface to ensure real-time updating of the control strategy.

Images