Multi-source collaborative heat storage system based on electromagnetic induction and solid waste gradient packaging

Abstract

The embodiment of the invention provides a multi-source collaborative heat storage system based on electromagnetic induction and solid waste gradient packaging. The system comprises a heat storage module and a control module. The heat storage module sequentially comprises a heat storage layer, a heat conduction layer and a flexible electrode layer from inside to outside. Wherein the heat conduction layer is of a solid waste-based nanometer network structure; the control module comprises an electromagnetic induction heating unit, a temperature measuring unit and a heat exchange unit and is used for executing an off-peak electricity dynamic optimization algorithm to control the heat storage module and the clean energy equipment to conduct collaborative heat storage or heat release. The valley electricity dynamic optimization algorithm comprises the following steps: collecting historical data; based on historical data, predicting a future valley electricity period and a load change trend of the system by using a deep learning model; and according to the prediction result, the heat storage module and the clean energy equipment are controlled to conduct collaborative heat storage or heat release. Through fusion of electromagnetic induction heating, solid waste gradient packaging and a deep learning algorithm, the heat storage performance is improved, the operation cost is reduced, the system intelligence is enhanced, and multi-source complementation of the energy storage system is promoted.

Description

Technical Field

[0001] The embodiments disclosed herein belong to the technical field of thermal storage systems, specifically relating to a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation. Background Technology

[0002] Against the backdrop of the current energy transition, efficient and intelligent thermal energy storage systems have become a research hotspot.

[0003] For example, patent CN118517941A proposes a separate high-temperature thermal storage device, which improves heat exchange efficiency by designing the thermal storage channel and the heat release channel separately. However, its thermal storage material is expensive, the system is complex, and the requirements for the thermal conductivity of the material are extremely strict, which limits its promotion in large-scale applications.

[0004] Another patent, CN112361429A, provides an electrically heated fluidized bed solid thermal storage system that effectively improves energy utilization efficiency by storing sensible heat from solid particles. However, this system suffers from uneven solid thermal storage and low precision in heat release control, especially when dealing with dynamic load changes, where its response speed and efficiency need improvement.

[0005] In addition, existing technologies focus on heat storage / release itself and fail to achieve intelligent collaboration with clean energy to promote the synergistic development of multiple objectives such as heating, power generation, and hydrogen production. As a result, they fail to effectively utilize the low-priced electricity during off-peak hours for efficient heat storage, and the overall energy efficiency and benefits of the system are not optimal. Summary of the Invention

[0006] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation.

[0007] The system includes a thermal storage module and a control module; The thermal storage module comprises, from the inside out, a thermal storage layer, a thermally conductive layer, and a flexible electrode layer; wherein, the thermally conductive layer is a solid waste-based nano-network structure. The control module includes an electromagnetic induction heating unit, a temperature measuring unit, and a heat exchange unit, which is used to execute a valley electricity dynamic optimization algorithm to control the thermal storage module and the clean energy equipment to perform collaborative thermal storage or heat release.

[0008] Furthermore, the solid waste-based nanonetwork structure is composed of carbon nanotubes modified from solid waste materials.

[0009] Furthermore, the heat storage layer includes solid paraffin wax and a metal fiber reinforced ceramic shell encapsulating the solid paraffin wax.

[0010] Furthermore, the flexible electrode layer comprises a copper foil paper aluminum-plastic composite plate and an insulating polyimide film and a highly thermally conductive nanofluid within it.

[0011] Furthermore, the electromagnetic induction heating unit is disposed on the flexible electrode layer and includes an iron core and an inductor coil surrounding the iron core, for heating the heat storage module through the eddy current effect.

[0012] Furthermore, the temperature measuring unit includes a K-type thermocouple inserted into the heat-conducting layer.

[0013] Furthermore, the thermal storage module can perform collaborative thermal storage or release with clean energy equipment through the heat exchange unit.

[0014] Furthermore, the clean energy equipment includes at least a biomass combustion boiler and an electrolysis water hydrogen production device.

[0015] Furthermore, the off-peak electricity dynamic optimization algorithm includes: Collect historical data, including meteorological data, heat production data, power grid price data, and user energy consumption data; Based on the historical data, a deep learning model is used to predict the future off-peak electricity periods and load change trends of the system. Based on the predicted off-peak electricity periods and load change trends, the thermal storage module is controlled to perform coordinated thermal storage or release with clean energy equipment.

[0016] Furthermore, the step of controlling the thermal storage module and clean energy equipment to perform coordinated thermal storage or release based on predicted off-peak electricity periods and load change trends includes: During off-peak electricity hours, the thermal storage module is controlled to store heat. The operating status of the clean energy equipment is dynamically adjusted according to the load change trend.

[0017] This disclosure discloses a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation. It employs high-frequency electromagnetic induction heating technology to replace traditional resistance wire heating, utilizing the eddy current effect to achieve high-efficiency electrothermal conversion, overcoming the shortcomings of low thermal efficiency and slow response of traditional methods. Using solid waste-based thermal storage as the core material, it achieves high-value utilization of urban solid waste, is environmentally friendly, and produces no pollutants. Through gradient encapsulation technology, it not only improves the thermal conductivity of the storage body and shortens the heat storage / release time, but also triggers latent heat storage within a specific temperature range, significantly increasing the heat storage density and capacity. Through the aforementioned off-peak electricity dynamic optimization method, it constructs a closed-loop feedback and precise matching relationship between clean energy, the energy storage system, and load demand, realizing intelligent energy scheduling of the system and achieving the coupling and synergistic supply of multiple energy forms such as "electricity-heat-hydrogen," thereby improving the overall system efficiency and flexibility. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a heat storage module according to another embodiment of the present disclosure; Figure 3 This is a flowchart illustrating another embodiment of the off-peak electricity dynamic optimization algorithm of this disclosure. Detailed Implementation

[0019] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0020] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0021] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any and more of the associated listed items.

[0023] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0024] like Figure 1 and Figure 2As shown, embodiments of this disclosure provide a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation, including a thermal storage module 100 and a control module 200. The thermal storage module 100, from the inside out, includes a thermal storage layer 110, a thermally conductive layer 120, and a flexible electrode layer 130; wherein the thermally conductive layer 120 is a solid waste-based nano-network structure. The control module 200 includes an electromagnetic induction heating unit 210, a temperature measuring unit 220, and a heat exchange unit 230, used to execute a valley electricity dynamic optimization algorithm to control the thermal storage module 100 and the clean energy device 300 to perform synergistic thermal storage or heat release.

[0025] Specifically, the thermal storage module 100 comprises an outer flexible electrode layer 130, a middle thermally conductive layer 120, and an inner thermal storage layer 110. The flexible electrode layer 130 is made of a 0.2mm thick copper foil aluminum-plastic composite board. The composite board contains two layers of insulating polyimide (PI) films and is filled with highly thermally conductive nanofluid, used to connect the electromagnetic induction heating unit 210 and withstand pressure. The thermally conductive layer 120 is mainly composed of solid waste materials such as steel slag or fly ash, with uniformly mixed modified highly thermally conductive carbon nanotubes inside, forming a highly thermally conductive solid waste-based nanonetwork structure responsible for transferring heat to the thermal storage layer 110. The thermal storage layer 110 uses solid paraffin as a phase change medium, which, after melting and crystallization, is encapsulated in an inorganic ceramic shell reinforced with metal fibers, forming a sealed solid paraffin phase change microcapsule.

[0026] The preparation process of the thermal storage module 100 includes: in-situ mixing and solidification of solid waste (at least one of steel slag, fly ash, slag, tailings, ceramic waste, and sandstone) at medium and low temperatures (300℃~500℃) into a block shape, and placing it in a high-frequency electromagnetic field to rapidly heat it to a predetermined temperature using the Joule heating effect of the induced current, forming a high-temperature thermal storage body; wrapping the high-temperature thermal storage body particles in a high-temperature resistant inert shell to form a hollow box-shaped structure with cubic corners; installing the hollow box-shaped structure into a vacuum tank, sealing it with a hinged cap, and wrapping phase change microcapsules on the outside of the vacuum tank to obtain a solid thermal storage unit with high latent heat density; connecting multiple solid thermal storage units to each other through a high thermal conductivity bridge structure to form a multi-source complementary thermal storage unit, which is an integration of the thermal storage layer 110 and the thermally conductive layer 120; and finally encapsulating the integration body in a flexible electrode layer 130 to form the thermal storage module 100.

[0027] In some embodiments, the solid-state thermal storage unit is prepared by crushing sufficient steel slag into powder, adding a certain amount of curing agent, and sintering it into brick-like blocks at 310°C. The tested density is 2700 kg / m³. 3The thermal conductivity is 1.35 W / (m·K). The prepared brick-shaped steel slag is placed in a mold and cast. After molding, it is dried in a drying chamber at 120℃. The dried brick-shaped steel slag is placed in a vacuum tank, and the tank opening is sealed with a hinged cover. A near-vacuum state is achieved by evacuating the tank. The encapsulated brick-shaped steel slag particles are then placed into the vacuum tank, and the tank opening is sealed with a hinged cover. Finally, phase change microcapsules are wrapped around the outside of the vacuum tank to obtain a solid-state thermal storage unit.

[0028] The control module 200 includes an electromagnetic induction heating unit 210, a temperature measuring unit 220, and a heat exchange unit 230 based on a heat transfer liquid. The electromagnetic induction heating unit 210 is an inductor coil with an iron core, tightly attached to the copper foil of the flexible electrode layer 130. Multiple coils can be used. By connecting a power source with a frequency of 20kHz to 50kHz, an alternating magnetic field is generated. The magnetic lines of force pass through the copper foil and the PI isolation layer, cutting through the nanofluid in the heat-conducting layer 120, generating an eddy current effect that heats the coil. The temperature measuring unit 220 is a K-type thermocouple inserted deep into the solid waste-based nano-network structure of the heat-conducting layer 120. The heat exchange unit 230 is a heat exchanger that transfers heat to the heat storage module 100 via the heat transfer liquid for heating, or extracts heat from the heat storage module 100 for power generation, cooling, hydrogen production, etc. It is connected to a biomass combustion boiler and an electrolytic water hydrogen production device, enabling the heat storage module 100 to collaboratively store or release heat with the biomass combustion boiler and the electrolytic water hydrogen production device. The biomass combustion boiler can directly transfer heat energy to the thermal storage module 100; the water electrolysis hydrogen production equipment relies on the heat released by the thermal storage module 100 to generate electricity, and uses the electrical energy to electrolyze water to produce hydrogen.

[0029] In addition to biomass combustion boilers and water electrolysis hydrogen production equipment, clean energy equipment 300 may also include photovoltaic power generation equipment, molten salt tank solar thermal power generation equipment, wind power generation equipment, etc. Photovoltaic power generation equipment, molten salt tank solar thermal power generation equipment, wind power generation equipment, etc. can be connected to electromagnetic induction heating unit 210 to provide the required electrical energy for heat storage module 100.

[0030] like Figure 3 As shown, the off-peak electricity dynamic optimization algorithm includes: Step S1: Collect historical data, including meteorological data, heat generation data, power grid price data, and user energy consumption data.

[0031] Specifically, meteorological data includes, but is not limited to, historical time-series data of ambient temperature, relative humidity, solar irradiance, wind speed, and wind direction. High-precision data can be obtained directly from meteorological stations in the area where the thermal storage system is located, or through an application programming interface (API) to access authoritative regional meteorological data centers or public service platforms (such as the National Meteorological Information Center) to obtain gridded historical meteorological data corresponding to the address coordinates of the thermal storage system. The data time resolution should be no less than 1 hour, and the historical time span should cover at least one complete annual cycle in order to capture seasonal variation patterns.

[0032] Heat production data refers to the historical heat or electricity production data of clean energy equipment operating in conjunction with the heat storage system disclosed herein. Examples include the historical heat output curve of biomass combustion boilers, the historical power generation data of photovoltaic and wind power generation equipment, and the historical start-up / shutdown status and input power data of water electrolysis hydrogen production equipment. Data from each clean energy device can be collected in real time via IoT gateways or industrial communication protocols (such as Modbus TCP) and timestamped uniformly.

[0033] Electricity grid price data consists of historical time-of-use (TOU) or real-time price data, clearly distinguishing between peak, flat, and valley periods and their corresponding prices. This data can be obtained through the power company's data interface or the electricity market trading platform's API. The data's time resolution can be consistent with the electricity billing cycle, typically 15 minutes, 30 minutes, or 1 hour.

[0034] User energy consumption data refers to the historical heat demand and electricity load data of the end users served by the disclosed thermal storage system. Heat demand data can be obtained by installing temperature and flow sensors at key nodes in the heating network to collect historical supply / return water temperatures and flow rates, and then calculating historical heat load curves. Electricity load data can be obtained through user-side smart meters or the plant's energy management system (EMS) to acquire historical power consumption curves for the target user or the internal busbar side.

[0035] Step S2: Based on the historical data, use a deep learning model to predict the future off-peak electricity periods and load change trends of the system.

[0036] Specifically, using the historical data collected in step S1 above, a deep learning neural network model is established to analyze and predict the supply and demand relationship of thermal energy in future time periods. The deep learning neural network model has the ability to capture long-term dependencies and nonlinear characteristics in the data, thus handling multivariate time series forecasting problems. By using historical data as training samples, multi-step forward-looking forecasts of the supply and demand relationship of thermal energy in future time periods are ultimately achieved.

[0037] The model's predictive outputs mainly include forecasts for future off-peak electricity periods and load change trends. For the off-peak electricity period forecast, the model combines grid electricity price data, user energy consumption data, and potential holiday information to identify and predict specific "off-peak electricity periods" with low electricity prices in the future (e.g., the next day), such as "21:00~23:00" and "02:00~05:00". For the load change trend forecast, the model analyzes the correlation between meteorological data, heat production data, and user energy consumption data to predict the change curves of the heat load and electrical load that the thermal storage system needs to bear in the future, such as quantitative trends like "estimated electricity consumption of 1000 kWh / d from 22:00 to 24:00, and estimated electricity consumption of 3300 kWh / d from 24:00 to 2:00 the next day".

[0038] Step S3: Based on the predicted off-peak electricity period and load change trend, control the thermal storage module and clean energy equipment to perform coordinated thermal storage or release.

[0039] Specifically, based on the prediction results of the previous step S2, the control module performs a forward-looking analysis and generates a dynamic collaborative control strategy for multi-objective optimization of the thermal storage system.

[0040] Forward-looking analysis includes: comparing the predicted load change trend with the predicted clean energy output trend to analyze the energy gap or surplus in each period; combining the predicted off-peak electricity period with the grid electricity price change trend to evaluate the economics of using grid electricity for electromagnetic induction thermal storage in different periods; determining the "optimal time period for high-intensity induction heating thermal storage" (e.g., 22:10~23:30) and determining the start-up and shutdown sequence of clean energy equipment and thermal storage modules.

[0041] The specific dynamic cooperative control strategy for multi-objective optimization is as follows: 1. Based on predicted off-peak electricity periods and load variation trends, effectively coordinate and control the thermal storage system and related equipment in a timely manner, including: During the predicted optimal off-peak electricity hours (e.g., 22:10~23:30), the electromagnetic induction heating unit is connected to a low-frequency alternating power supply to start eddy current heating. At the same time, based on the predicted load change trend, the electromagnetic induction heating unit is controlled to perform high-intensity induction heating at the corresponding power to store heat for the thermal storage module. During the predicted high electricity price period or peak energy consumption period, the control system stops electromagnetic induction heating and extracts heat from the thermal storage module through the heat exchange unit according to the load change trend. This heat is used to supply heat to users or provide thermal / electrical support for equipment such as water electrolysis hydrogen production equipment.

[0042] Based on the changing trends of electricity prices and the characteristics of user energy consumption, the start-up and shutdown of biomass combustion boilers, wind power generation equipment, solar power generation equipment, and electromagnetic induction heating units are planned in a coordinated manner, so as to achieve the best overall economic benefits while ensuring reliable energy supply.

[0043] By rationally arranging the start-up sequence and timing control, clean energy can obtain the best price. For example, during peak photovoltaic output and higher electricity prices in the daytime, photovoltaic power can be used first or combined with thermal storage modules to power the electrolysis hydrogen production equipment; during off-peak hours at night, the electromagnetic induction thermal storage is mainly driven by grid power.

[0044] 2. Based on the predicted trends and durations of user energy consumption changes, formulate and implement differentiated energy consumption combination strategies, including: For daytime energy needs that last for a long period of time (such as basic heating throughout the day), priority is given to using off-peak electricity to indirectly supply energy through thermal storage modules; For short-duration instantaneous energy demand or load peaks, energy storage devices can be used for real-time response, such as directly utilizing the heat storage modules or quickly starting biomass boilers as a supplement to smooth the load curve.

[0045] For example, when using an electromagnetic induction heating unit to heat the thermal storage module, the phase change state of the solid waste can also be monitored in real time using an infrared monitoring device.

[0046] The above control strategies are based on the following: selecting appropriate times to activate the electromagnetic induction heating unit during off-peak hours based on the predicted difference between daytime peak load and nighttime average load, thereby reducing the capital investment and land area required for additional facility construction; judging based on the comparison between nighttime off-peak electricity price and daytime electricity price, if the former is lower than the latter, then selecting to activate the electromagnetic induction heating unit for off-peak production; if the latter is higher than the former, then environmental factors (such as the availability of clean energy) need to be comprehensively considered to select appropriate times for production, in order to balance economic efficiency and low-carbon goals; the control module comprehensively considers the energy supply situation and regional characteristics of various clean energy sources such as molten salt trough solar thermal power generation, wind power, and photovoltaic power generation, explores multi-source complementary energy supply modes and the complementarity between various energy sources, and dynamically constructs and executes a multi-source complementary-coupled-coordinated overall energy optimization model.

[0047] This disclosure discloses a multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation. It employs high-frequency electromagnetic induction heating technology to replace traditional resistance wire heating, utilizing the eddy current effect to achieve high-efficiency electrothermal conversion, overcoming the shortcomings of low thermal efficiency and slow response of traditional methods. It uses solid waste-based thermal storage materials (such as steel slag and fly ash) as the core material, achieving high-value utilization of urban solid waste, which is environmentally friendly and produces no pollutants. Through gradient encapsulation technology, it not only improves the thermal conductivity of the storage material and shortens the heat storage / release time, but also triggers latent heat storage within a specific temperature range, significantly increasing the heat storage density and capacity. By introducing deep learning neural networks to predict future off-peak electricity periods and user load trends, it performs high-intensity heat storage during off-peak electricity periods with the lowest electricity prices, and stores heat during periods of high electricity prices or negative loads. The system releases heat during peak load periods, maximizing the use of off-peak electricity price differences and significantly reducing system operating costs. Through the aforementioned off-peak electricity dynamic optimization method, a closed-loop feedback and precise matching relationship is established between clean energy, energy storage systems, and load demand, enabling intelligent energy scheduling and the coupling and coordinated supply of multiple energy forms such as electricity, heat, and hydrogen, thus improving the system's overall efficiency and flexibility. Based on user energy consumption trends and durations, differentiated energy combination strategies are formulated to effectively smooth load fluctuations and improve energy utilization efficiency. Combined with multi-stage closed-loop temperature control technology and real-time monitoring (such as K-type thermocouples and infrared monitoring), the system can quickly respond to dynamic load changes, reducing the response lag problem of traditional thermal storage systems and achieving precise and efficient control of thermal energy storage and release.

[0048] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A multi-source synergistic thermal storage system based on electromagnetic induction and solid waste gradient encapsulation, characterized in that, The system includes a thermal storage module and a control module; The thermal storage module comprises, from the inside out, a thermal storage layer, a thermally conductive layer, and a flexible electrode layer; wherein, the thermally conductive layer is a solid waste-based nano-network structure. The control module includes an electromagnetic induction heating unit, a temperature measuring unit, and a heat exchange unit, which is used to execute a valley electricity dynamic optimization algorithm to control the thermal storage module and the clean energy equipment to perform collaborative thermal storage or heat release.

2. The system according to claim 1, characterized in that, The solid waste-based nanonetwork structure is composed of carbon nanotubes modified from solid waste materials.

3. The system according to claim 1, characterized in that, The heat storage layer includes solid paraffin wax and a metal fiber reinforced ceramic shell that encapsulates the solid paraffin wax.

4. The system according to claim 1, characterized in that, The flexible electrode layer comprises a copper foil paper aluminum-plastic composite plate and an insulating polyimide film and a highly thermally conductive nanofluid inside it.

5. The system according to claim 1, characterized in that, The electromagnetic induction heating unit is disposed on the flexible electrode layer and includes an iron core and an inductor coil surrounding the iron core, used to heat the heat storage module through the eddy current effect.

6. The system according to claim 1, characterized in that, The temperature measuring unit includes a K-type thermocouple inserted into the heat-conducting layer.

7. The system according to claim 1, characterized in that, The thermal storage module works in synergy with clean energy equipment to store or release heat through the heat exchange unit.

8. The system according to claim 7, characterized in that, The clean energy equipment includes at least a biomass combustion boiler and an electrolysis water hydrogen production device.

9. The system according to any one of claims 1 to 7, characterized in that, The off-peak electricity dynamic optimization algorithm includes: Collect historical data, including meteorological data, heat production data, power grid price data, and user energy consumption data; Based on the historical data, a deep learning model is used to predict the future off-peak electricity periods and load change trends of the system. Based on the predicted off-peak electricity periods and load change trends, the thermal storage module is controlled to perform coordinated thermal storage or release with clean energy equipment.

10. The system according to claim 9, characterized in that, The method of controlling the thermal storage module and clean energy equipment to perform coordinated thermal storage or release based on predicted off-peak electricity periods and load change trends includes: During off-peak electricity hours, the thermal storage module is controlled to store heat. The operating status of the clean energy equipment is dynamically adjusted according to the load change trend.

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