Energy storage type electric heat supply method and system based on multi-period heat storage and load regulation and control

Abstract

The invention discloses an energy storage type electric heat supply method and system based on multi-period heat storage and load regulation, and the method comprises the steps: responding to an operation signal of a power grid in a valley electricity period or a power consumption period, executing an electricity-heat conversion and heat storage process which comprises heat energy grade management operation aiming at a medium in a heat storage device, the availability of high-grade heat energy is improved; before releasing heat to the heat supply network, selectively executing a heat energy grade enhanced heat release mode based on the evaluation of the heat energy grade state in the current heat storage device and the demand of the heat supply network; and when the heat energy grade enhanced heat release mode is selected to be executed, dynamic shunting based on real-time grade judgment is carried out on the heating medium extracted from the heat storage device, and the part which is judged to be a low-grade heating medium is guided into the electric-heat conversion unit to carry out heat energy grade improvement. Through collaborative optimization design of the whole chain of the energy storage type electric heat supply system, the unification of guaranteeing the heat supply quality, improving the operation economy and enhancing the power grid friendliness is realized.

Description

Technical Field

[0001] This application relates to the field of energy storage electric heating technology, and in particular to an energy storage electric heating method and system based on multi-period heat storage and load regulation. Background Technology

[0002] Against the backdrop of current energy structure transformation and improved grid flexibility, utilizing inexpensive off-peak electricity or intermittent renewable energy generation during nighttime hours for electrothermal conversion and storage has become an important development direction in the district heating sector. Energy storage-based electric heating systems, through peak shaving and valley filling, not only reduce operating costs but also provide valuable regulation capabilities to the grid, aligning with the green and low-carbon development trend.

[0003] However, such systems face a common and profound technical bottleneck in practical large-scale applications: the inherent problem of thermal energy grade decay during storage. In actual operation, due to physical processes such as natural mixing of hot and cold water, heat conduction, and dynamic charging and discharging of heat, it is difficult for the thermal storage device to maintain an ideal thermal stratification state, causing the stored thermal energy to diffuse to medium and low grades. The direct consequence of this phenomenon is that during peak heating periods the next day, when high-grade thermal energy is required, the system cannot directly extract a sufficient amount of heat medium that meets the temperature requirements from the thermal storage device.

[0004] Traditional solutions often force the use of high-power electrothermal conversion equipment as the main heat source. This essentially converts the low-cost off-peak electricity into expensive electricity during peak hours for heating, which seriously undermines the original economic purpose of the system. This not only means increased operating costs, but may also exacerbate the power supply pressure during peak electricity demand, causing negative impacts.

[0005] In existing technologies, the management of thermal storage devices relies heavily on passive monitoring and simple start-stop control. There is a lack of proactive shaping and precise regulation of the key state quantity of heat energy quality. During the heat release phase, especially when facing fluctuating heat loads, the system often exhibits slow response and simplistic strategies, making it difficult to achieve a dynamic optimal balance among ensuring heating quality, improving operational economy, and supporting grid stability. Summary of the Invention

[0006] To address the aforementioned problems, embodiments of the present invention provide an energy storage-based electric heating method based on multi-period thermal storage and load regulation, the method comprising: In response to the grid's operating signals during off-peak hours or power consumption periods, the electrothermal conversion and thermal storage process is executed. This process includes thermal energy grade management operations for the internal medium of the thermal storage device to improve the availability of high-grade thermal energy. Before releasing heat into the heating network, a heat release mode with enhanced heat quality is selectively executed based on an assessment of the current internal heat energy grade of the heat storage device and the needs of the heating network. When the heat energy grade enhancement heat release mode is selected, the heat medium extracted from the heat storage device is dynamically diverted based on real-time grade determination, and the portion of heat medium determined to be low grade is introduced into the electrothermal conversion unit to improve the heat energy grade. Based on the system power changes caused by the implementation of the enhanced heat release mode, the terminal load of the heating system is adjusted accordingly to achieve overall system power balance.

[0007] Furthermore, the thermal energy quality management operation during the thermal storage stage is specifically a type of active disturbance thermal storage control, which is achieved through the following steps: A thermal energy grade-capacity distribution model based on a vertical temperature field is constructed; an optimization function representing the desired thermal energy grade distribution target is defined; based on the model and the optimization function, action commands for controlling physical disturbance devices are calculated and output to optimize the thermal stratification structure inside the thermal storage device.

[0008] Furthermore, the construction of the thermal energy grade-capacity distribution model specifically includes: associating the temperature value of each temperature measurement point with the cumulative thermal storage capacity to form a quantitative relationship curve characterizing the temperature of the output heat medium and the corresponding maximum usable capacity under the current state. The shape characteristics of this curve are used as the basis for evaluating the thermal energy grade state.

[0009] Furthermore, the judgment logic selectively executed during the heat release decision-making stage includes: based on the heat energy grade-capacity distribution model, querying the effective capacity that meets the predicted highest grade demand of the heating network within a future set time period; when the effective capacity is lower than the set proportion threshold of the predicted total heat load for that time period, then deciding to execute the heat energy grade enhanced heat release mode.

[0010] Furthermore, the dynamic diversion based on real-time grade determination in the heat release and grade enhancement stage is manifested at the system level as follows: a branch line leading to the electrothermal conversion unit is set in parallel on the main outlet pipeline of the thermal storage device, which is controlled by a valve; the on / off state of the controllable valve is determined by a controller based on the comparison result of the real-time temperature value of the heat medium and a dynamically set grade requirement threshold.

[0011] Furthermore, the logic for setting and dynamically adjusting the grade requirement threshold includes: its base value is the predicted maximum temperature of the heat medium required by the heating network in the future period, obtained from historical data and weather forecasts, and an adaptive offset for optimizing system energy efficiency can be superimposed based on the evaluation results of the grade status of the thermal storage device.

[0012] Furthermore, the terminal load of the heating system is regulated accordingly. The specific implementation strategy includes: according to the additional electrical power value generated by the improvement in quality, and in accordance with the preset priority order based on the regional thermal inertia and the allowable fluctuation range of comfort, temperature setpoint adjustment instructions are sent to the selected terminal heating units instead of directly cutting off the heat source supply.

[0013] Furthermore, the optimization process of the active disturbance thermal storage control includes a closed-loop self-learning mechanism. This mechanism records and analyzes the relationship between historical disturbance parameters and the final thermal energy grade-capacity distribution curve, and uses an optimization algorithm to iteratively update the calculation rules of the action command in order to approximate the desired target.

[0014] The energy storage electric heating method based on multi-period thermal storage and load regulation also includes: A dynamic threshold control strategy based on feedforward-feedback composite is introduced.

[0015] An energy storage electric heating system based on multi-period thermal storage and load regulation, the system includes: The thermal energy storage calculation module responds to the grid's operating signals during off-peak hours or power consumption periods and performs electrothermal conversion and thermal energy storage processes. This process includes thermal energy grade management operations for the internal medium of the thermal energy storage device to improve the availability of high-grade thermal energy. The heat release decision module, before releasing heat to the heating network, selectively executes a heat release mode that enhances the heat energy quality based on an assessment of the current internal heat energy quality of the heat storage device and the needs of the heating network. When the heat energy quality enhancement release mode is selected, the quality enhancement module dynamically diverts the heat medium extracted from the heat storage device based on real-time quality determination, and introduces the portion of heat medium determined to be low-grade heat medium into the electrothermal conversion unit to improve the heat energy quality. The load coordination module adjusts the terminal load of the heating system according to the system power changes caused by the execution of the enhanced heat release mode, so as to achieve overall system power balance.

[0016] The technical effects and advantages of the energy storage electric heating method based on multi-time period heat storage and load regulation provided by this invention are as follows: This invention achieves a balance between ensuring heating quality, improving operational economy, and enhancing grid friendliness through the coordinated optimization design of the entire "heat storage-heat release-electricity consumption" chain of an energy storage electric heating system. In the heat storage stage, the invention optimizes internal thermal stratification using active disturbance control, maximizing the conversion of off-peak electricity into high-grade heat energy for storage. In the heat release stage, only a small amount of substandard heat medium is precisely upgraded to meet demand, significantly reducing peak-hour electricity consumption compared to traditional solutions and fundamentally improving overall operational economy. By constructing a heat energy grade distribution model and implementing dynamic diversion control, the system can accurately and promptly ensure that the temperature of the heat medium supplied to the heating network always meets requirements, thus providing stable and reliable high-quality heating services to users. Furthermore, by precisely controlling its own increased power consumption during peak hours and flexibly adjusting in conjunction with end-load, the system transforms itself from a simple electricity consumer into a flexible load with demand response capabilities, providing positive regulation capabilities for the stable operation of the power grid. Attached Figure Description

[0017] Figure 1 This is a flowchart of the energy storage electric heating method based on multi-time period thermal storage and load regulation in Example 1; Figure 2 This is a flowchart of the energy storage electric heating method based on multi-time period thermal storage and load regulation in Example 2; Figure 3 This is a schematic diagram of the connection of the energy storage electric heating system based on multi-time period heat storage and load regulation in Example 3. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Please see Figure 1 As shown, embodiments of the present invention provide an energy storage-based electric heating method based on multi-period thermal storage and load regulation, including the following steps: S1: In response to the grid's operating signal during off-peak hours or power consumption periods, perform electrothermal conversion and thermal storage processes. This process includes thermal energy grade management operations for the internal medium of the thermal storage device to improve the availability of high-grade thermal energy. S2: Before releasing heat to the heating network, based on the assessment of the current internal thermal energy grade of the thermal storage device and the needs of the heating network, selectively execute the thermal energy grade enhancement heat release mode. S3: When the heat energy grade enhancement heat release mode is selected, the heat medium extracted from the heat storage device is dynamically diverted based on real-time grade determination, and the part of the heat medium determined to be low grade is introduced into the electrothermal conversion unit to improve the heat energy grade. S4: Based on the system power change caused by the execution of the enhanced heat release mode, the terminal load of the heating system is adjusted accordingly to achieve overall system power balance.

[0020] The thermal energy quality management operation in the S1 thermal storage stage is actually a complete set of active disturbance thermal storage control logic. This scheme aims to optimize the internal temperature distribution pattern of the thermal storage device through controllable intervention. Its technical implementation path includes three progressive stages, including: First, a dynamic thermal energy grade-capacity distribution model needs to be constructed. The system acquires temperature field data through a vertically arranged array of temperature sensors, rearranges these temperature values ​​from high to low, and establishes a mapping relationship between the sorted temperature sequence and its corresponding cumulative thermal storage capacity, forming a thermal energy grade-capacity distribution curve that reflects the current thermal storage quality. The shape of this curve directly reflects the degree of thermal energy grade decay. When the curve shows a gradual downward trend, it indicates that the heat in the high-temperature zone has undergone irreversible diffusion and mixing. For example, in an ideal state, the distribution curve of a device containing phase change thermal storage material should be close to a steep downward step curve, indicating that most of the thermal storage capacity is concentrated in the designed high-temperature range. A gradual downward curve means that the high-temperature heat and the low-temperature heat have undergone significant mixing, that is, "thermal energy grade decay" has occurred.

[0021] After understanding the current state characteristics, the system will set an optimization function based on the heating demand characteristics. This function defines the mathematical expression of the ideal heat energy distribution. For example, the optimization direction is to maximize the heat storage capacity above the target temperature. The target temperature is usually determined based on the heating network operation parameters. Its core objective is to quantify the ideal state of heat energy quality through mathematical methods.

[0022] Finally, the system enters the control command generation stage. The system compares and analyzes the real-time distribution model with the preset optimization function, and calculates the optimal disturbance parameters through the built-in control algorithm. These parameters are converted into action commands that can directly drive the physical disturbance device, including but not limited to adjusting the speed of the stirrer, setting the working cycle of the jet pump, or controlling the bubbling intensity.

[0023] In a typical application scenario, when the system detects an abnormally flat section in the distribution curve near 70 degrees Celsius, it determines that a moderate grade decay has occurred. At this time, the controller may generate a command to require the jet pump to run intermittently at a specific flow rate. By actively breaking the stable thermal stratification structure, it promotes the redistribution of the restricted high-temperature heat energy. This controlled, brief mixing is different from the natural convection process. Its essence is to reset the thermodynamic state of the thermal storage device through external energy input, reshaping the originally gradually deteriorating temperature distribution into an ideal form that is conducive to efficient heat release.

[0024] The active disturbance thermal storage control in this embodiment is not an isolated mechanical action, but a closed-loop control process that integrates state perception, target setting, and intelligent decision-making. It actively and purposefully intervenes in the thermal storage physical process, which is the key technology for solving the core problem of thermal energy quality degradation. Through this control, the system can make full use of off-peak electricity periods, storing not only sufficient quantity of thermal energy, but more importantly, high-quality thermal energy, laying a solid foundation for flexibly responding to high-grade heat demand in the future.

[0025] The construction of a thermal grade-capacity distribution model is a key step in transforming discrete temperature field observation data into a continuous quantitative tool for decision-making. Its core lies in establishing a deterministic relationship between the "output temperature" and the "maximum heat that can be provided at this temperature."

[0026] Specifically, the system constructs the model according to the following process: The system reads the temperature data from all temperature measuring points along the vertical direction of the thermal storage device at the current moment. Then, the data processing unit sorts these temperature values ​​from high to low to form a temperature sequence. ,in, Highest, At the very least, this sorting process is essentially a logical reconstruction of the thermal energy within the thermal storage device according to its "grade".

[0027] The model associates each sorted temperature point with its corresponding thermal storage capacity. The thermal storage capacity represented by each temperature point is determined by the volume or mass of the thermal storage medium corresponding to its installation location; the system starts from the highest temperature point... Initially, the corresponding heat storage capacity is accumulated point by point. For example, suppose the cumulative heat storage capacity corresponding to the first three temperature measurement points starting from the top is respectively... And so on, until the total capacity is accumulated.

[0028] Finally, by plotting the sorted temperature values ​​on the vertical axis and the corresponding cumulative thermal storage capacity on the horizontal axis, a thermal energy grade-capacity distribution curve can be drawn. Any point on this curve ( , The physical meaning of ) is: under the current state, the system can continuously provide a temperature not lower than The maximum theoretical capacity of the heat medium is .

[0029] The shape of this curve serves as a direct basis for assessing the thermal energy quality of the system. An ideal and healthy distribution curve should exhibit a clear "high plateau-slope" shape: a plateau with considerable capacity is formed in the high-temperature zone, which means that a large amount of thermal energy is concentrated in the high-grade range. After the plateau ends, the curve should drop rapidly, indicating that the transition range from high-grade thermal energy to low-grade thermal energy is very narrow and the energy quality is concentrated. Conversely, a gently sloping curve indicates that high, medium and low-grade thermal energy are seriously mixed and the thermal energy quality has significantly deteriorated.

[0030] For example: Imagine a thermal energy storage device with a storage capacity of 1000 kWh. After a perfect nighttime off-peak electricity storage cycle, its distribution curve might show that it still has 800 kWh of capacity at 85°C, but the capacity drops sharply to 200 kWh at 80°C. This pattern indicates that most of the thermal energy is concentrated in a narrow high-temperature range above 85°C, with extremely high quality. After a period of rest or improper operation, the curve might be reconstructed as follows: only 300 kWh at 85°C, 500 kWh at 80°C, and 700 kWh at 75°C. This gently declining curve clearly indicates the occurrence of "thermal energy quality decay," that is, the loss of high-temperature heat through diffusion, and the system stores more medium- and low-temperature heat that is difficult to meet peak demand. Therefore, this quantitative curve constructed by this method is not only a "thermometer" describing the state, but also a "navigation map" for subsequent heat release decisions and active disturbance control.

[0031] The core task of the S2 heat release decision-making phase is to proactively determine whether additional electrical energy needs to be activated to reinforce the reliability of heating, based on an accurate assessment of the current energy storage quality. This decision is not based on simple temperature levels, but rather on a quantitative assessment of whether the available high-grade thermal energy reserves are sufficient. Its judgment logic follows a clear decision tree, including: The decision-making system first calls the latest thermal energy grade-capacity distribution model. On this curve, it uses the highest predicted temperature of the heat medium required by the heating network within a set future time period as a baseline to query the corresponding effective capacity. This effective capacity specifically refers to the total amount of thermal energy that can output a temperature no lower than the predicted highest required temperature under the current thermal storage state. For example, if it is predicted that some high-end users or specific processes in the heating network will require a heat medium of 75°C in the next few hours, the system will find the capacity coordinate corresponding to the temperature coordinate of 75°C on the distribution curve. This value is the currently available effective capacity that can meet the highest grade requirement.

[0032] The system compares this effective capacity with the predicted total heat load for the same period; here, the total heat load refers to the total heat demand without considering temperature grade differences.

[0033] The key to the decision lies in a preset proportional threshold, which is an empirical or optimized value used to define the boundary between sufficient and insufficient reserves. When the calculated effective capacity is lower than the set proportion of the predicted total heat load, the system decides to execute the heat energy grade enhancement heat release mode.

[0034] The underlying logic for setting this percentage threshold is that even if the effective capacity is numerically less than the total load, as long as its proportion is high enough, the system can still operate smoothly by prioritizing the high-quality demand and supplementing it with other peak-shaving methods; while when the proportion is too low, it means that the system cannot guarantee the requirements of both the quantity and quality of heating without starting supplementary heating.

[0035] For example: Suppose that on a certain morning, the system predicts a total heat load of 500 kWh for the next 3 hours, with the highest grade demand at 78°C. The system queries the current distribution curve and finds that the effective capacity corresponding to 78°C is only 150 kWh. If the set percentage threshold is 40%, then 150 kWh / 500 kWh = 30%, which is below the threshold. In this case, the system will decisively generate an instruction to activate the enhanced heat release mode. This is because if it doesn't, the system will exhaust all the high-grade 78°C heat energy in the early stages of the 3-hour heating period, and can only provide low-temperature heat afterwards, failing to meet the high-grade demand, potentially leading to decreased heating quality or complaints. Conversely, if the queried effective capacity at 78°C is 250 kWh (50%), which is above the 40% threshold, the system may choose the conventional heat release mode to save energy.

[0036] The essence of this decision-making mechanism is to transform the thermal storage system from a passive energy warehouse into an intelligent agent that can foresee risks and take proactive measures. This ensures that the heating quality felt by users is stable and reliable under most operating conditions. Even if there is a significant decline in the heat energy quality inside the thermal storage device, it can be compensated for through subsequent quality enhancement processes.

[0037] The dynamic diversion based on real-time quality determination in S3 is physically based on specific configurations in the system pipeline, while its intelligent core lies in a fast-response closed-loop control strategy. At the system level, this function is implemented in the following way: a controllable bypass branch is added in parallel on the main heat medium outlet pipeline of the thermal storage device; the inlet of this branch is connected to the main pipeline through a tee interface, and its outlet is connected to the inlet of the electrothermal conversion unit (such as the high-temperature side of an electric boiler or heat pump). A controllable valve, such as an electric regulating valve or a switching valve, is installed on this branch. The opening and closing of this valve and the degree of opening are precisely controlled by the central controller of the system or a dedicated diversion controller.

[0038] The controller's decision logic is direct and efficient; its core is to perform a real-time temperature comparison, including: Input signal: A temperature sensor installed on the main outlet line of the thermal storage device continuously measures the real-time temperature of the heat medium that is about to enter the heating cycle.

[0039] Judgment Criteria: A dynamically set grade requirement threshold; this threshold is not a fixed value, and its setting is based on the heating strategy determined in the heat release decision stage (S2). For example, when the system decides that it needs to ensure that the output heat medium temperature is not lower than 75°C, this threshold is set to 75°C.

[0040] Decision rule: The controller will instantly compare the collected real-time temperature value with the currently set quality requirement threshold.

[0041] When the real-time temperature value is lower than the threshold, the controller determines that the heat medium is low-grade heat medium and then sends an opening command (or an increase in opening degree command) to the controllable valve. At this time, the unqualified heat flow flowing out of the heat storage device will no longer be directly sent to the heat user, but will be introduced into the branch leading to the electrothermal conversion unit for reprocessing to improve its grade.

[0042] When the real-time temperature value reaches or exceeds the threshold, the controller determines that the heat medium has met the requirements and then keeps the controllable valve in the closed state (or at the minimum opening) so that the qualified heat medium continues to be transported to the heating network along the main pipeline.

[0043] For example: On a cold day, the system sets a dynamic quality requirement threshold of 80°C to ensure heating quality. In the early stage of heat release, the outlet temperature of the heat storage device may be stable at 82°C. At this time, the temperature sensor reading (82°C) is higher than the threshold (80°C), and the controller keeps the valve on the bypass branch closed, so that high-quality heat energy is directly output.

[0044] As heat release proceeds, the high-temperature energy stored in the upper part of the thermal storage device is gradually depleted, and the outlet temperature begins to fluctuate and may decrease. At a certain moment, when the temperature sensor measures the temperature of the heat medium to be 78°C (below the 80°C threshold), the controller responds within milliseconds and immediately opens the valve of the bypass branch. This 78°C heat flow is then guided to the electrothermal conversion unit, which consumes additional electrical energy (such as taking advantage of the preferential electricity price during the off-peak period after the off-peak electricity period) to rapidly heat it to 85°C or higher, and then injects it into the main pipeline to mix with the qualified heat medium flowing directly from the thermal storage device, thereby always keeping the temperature of the heat medium sent to the heating network above 80°C.

[0045] This dynamic diversion mechanism refines the macroscopic heat quality enhancement heat release mode decision into real-time evaluation and handling of each heat flow, ensuring that the final output heat energy meets 100% of the preset quality requirements, and realizing a fundamental transformation of energy supply quality from passively relying on energy storage to actively controlling and guaranteeing it.

[0046] During the dynamic diversion process, the quality demand threshold used as the judgment criterion is not static, but a dynamic variable that is optimized online based on external demand, internal status, and system energy efficiency. Its setting logic reflects the system's unremitting pursuit of economical operation while ensuring heating quality.

[0047] The calculation of the taste requirement threshold follows a compound formula: final threshold = base value + adaptive offset.

[0048] The baseline value is directly derived from the actual demand of the heating network. The system integrates historical heating data (such as user heating patterns under similar outdoor temperatures) and real-time weather forecast information (especially temperature, wind speed, and solar radiation intensity for the next few hours) to predict the maximum temperature of the heat medium required by the heating network in the future period through a predictive algorithm. For example, during the peak heating period in the cold morning, considering the heat loss of the pipeline network and the highest demand at the user end, the predictive model may conclude that "83°C heat medium is needed". In this case, the baseline value of the threshold is set to 83°C. This ensures that the basic goal of the system operation is always aligned with the highest quality demand on the user side.

[0049] The introduction and calculation of adaptive offset is the essence of intelligent optimization in this scheme. The adaptive offset is not a fixed value. Its magnitude and sign (i.e., whether it is adjusted upward or downward) depend on the evaluation results of the current thermal energy grade status of the thermal storage device. The most important evaluation basis is the real-time updated thermal energy grade-capacity distribution model and its curve shape. The controller will analyze the current distribution curve. When the evaluation finds that the high-grade thermal energy reserves in the thermal storage device are very abundant (manifested as a long and flat high-temperature plateau on the curve), the system may introduce a small positive offset.

[0050] For example: Assuming a base value of 80°C, but the thermal energy grade-capacity distribution model shows that the thermal storage capacity above 85°C is still considerable, in order to save the expensive electrical energy consumed by electrothermal conversion, the system may raise the threshold by 1 to 2°C from 80°C, setting it to 81°C or 82°C. This means that the shunt controller will implement stricter standards, allowing less heat medium to be judged as "low grade" and enter the bypass, and the system will rely more on the high-quality reserves of the thermal storage device itself to meet the demand, thereby improving the overall system energy efficiency.

[0051] Conversely, when the assessment finds that high-grade thermal energy is nearing depletion (manifested as a steep decline in the high-temperature zone of the curve), in order to ensure that the core heating quality does not decline, the system may not only not raise the threshold, but will also maintain the base value unchanged. In some emergency situations, a small negative offset may be considered as a preventive measure to ensure that there is a sufficient flow of heat medium entering the electrothermal conversion unit for grade reinforcement. At this time, the system prioritizes reliability rather than economy.

[0052] Through this dynamic adjustment mechanism, the grade demand threshold becomes an intelligent hub connecting the three dimensions of demand forecasting, energy storage status, and energy efficiency optimization. This allows the system to no longer mechanically execute a fixed temperature target, but to flexibly adjust the external purchase (electric-thermal conversion) strategy according to the quality of the inventory (thermal storage status), ultimately optimizing the company's own operational economy while meeting the social heating service needs.

[0053] In S4, when the system activates the electrothermal conversion unit to improve heat quality, especially during peak electricity consumption periods, it creates additional power demand on the local power grid. To mitigate this impact, the terminal load of the heating system is regulated accordingly. The core of this regulation is not a drastic "heat cut-off" to users, but rather a refined energy management approach based on building thermal characteristics and human comfort. Its specific execution strategy includes a logically clear decision-making and instruction issuance process, including: The system first estimates the instantaneous power increase required to enhance the power quality. For example, if a 100 kW rated electric heater is started, the additional power required to be coordinated is 100 kW.

[0054] The controller does not simply order a building to stop heating; instead, it formulates a control strategy based on a preset priority list. The criteria for determining this priority list primarily consider two key factors: Regional thermal inertia: refers to the rate at which the indoor temperature of different types of buildings drops after heating is stopped or reduced. Buildings with high thermal inertia (such as large concrete structures) have a slower temperature change, providing users with a longer comfort buffer time, and are therefore more suitable as priority targets for regulation.

[0055] Allowable fluctuation range of comfort level: Set an acceptable range for lowering indoor temperature based on the usage function of different areas (such as office areas, residential areas) and time periods, for example, temporarily lowering it from 22°C to 20°C.

[0056] Based on the above strategy, the system sends temperature setpoint adjustment commands to one or more selected terminal heating units (such as heat exchange stations or building automation systems in a certain area).

[0057] For example: Suppose that the system adds 200 kW of electrical load during the evening peak hours due to enhanced heating. To match this load, the system needs to save an equivalent heat load at the end of the line. Traditionally, this might involve directly shutting down a heat exchange station that is exactly carrying 200 kW of heat load, causing a heating outage in that area. In this approach, the system selects an office area with high thermal inertia and a residential area with medium thermal inertia from a priority list as the control targets. Then, it sends a command to the heat exchange station in the office area to temporarily lower its secondary side temperature setpoint from 45°C to 41°C. At the same time, it sends a command to the heat exchange station in the residential area to temporarily lower its temperature setpoint from 43°C to 40.5°C. The room temperature in these areas will not drop suddenly, but will decrease very slowly by 1-1.5°C over one or two hours. This change is usually within a comfortable range that is almost imperceptible to the human body.

[0058] Through this collaborative mechanism of front-end power replenishment and end-end fine-tuning, the system achieves global balance in the energy form conversion process, making it appear as a "friendly" load on the grid side, actively adjusting its own power consumption behavior to absorb fluctuations; on the user side, by utilizing the building's thermal inertia as a natural battery, it achieves flexible load shifting while ensuring basic comfort.

[0059] As described in the S1 active disturbance thermal storage control logic, the active disturbance thermal storage control guides the thermal storage device to reach the desired temperature stratification state by executing a series of calculated action commands. However, for systems with different initial states and different external environments, the optimal disturbance strategy is not static. Therefore, this method embeds a closed-loop self-learning mechanism into the active disturbance thermal storage control, enabling it to continuously optimize itself and thus more accurately and efficiently approach the desired target.

[0060] This self-learning mechanism operates as a continuously running background process, and its workflow is as follows: After each active disturbance control operation (e.g., after a period of off-peak electricity) and the generation of a new thermal grade-capacity distribution curve, the system stores the complete control case in the historical database. Each case contains at least three key data sets: Initial state: Temperature distribution of the thermal storage device before the disturbance begins.

[0061] Disturbance parameters: The set of specific action commands to be executed, such as the opening sequence, duration, and flow rate of each disturbance valve.

[0062] Final result: Key characteristics of the thermal energy grade-capacity distribution curve formed after the disturbance ends, such as the capacity of its high-temperature plateau and the steepness of the curve's descent segment.

[0063] The system periodically calls historical data and uses built-in optimization algorithms (such as gradient descent and genetic algorithms) to mine this data. The core task of the algorithm is to find the hidden pattern between the perturbation parameters and the final result, that is, how to adjust the calculation rules of the action command in order to achieve a better temperature stratification effect (manifested as a distribution curve with a more ideal high-platform-slope shape).

[0064] By analyzing a large number of historical cases, the algorithm can identify which disturbance strategies are effective and which are ineffective or even harmful under a specific initial state. Based on these analyses, the system generates an updated and more accurate set of control rules or parameters and applies them to the next active disturbance control.

[0065] For example: Assume that in the initial stage, the system's perturbation strategy is calculated based on a general physical model. During a week of continuous operation, it recorded seven different perturbation parameters and corresponding distribution curves. Analysis revealed that on Monday and Wednesday, when the system adopted a strategy of "perturbing the upper layer at a high rate first, then the middle layer at a low rate," the resulting distribution curves showed that the high-grade capacity above 85°C was approximately 8% lower than the expected target. However, on Tuesday and Thursday, when the system accidentally adjusted the valve timing due to external factors, adopting a strategy of "alternating short-pulse perturbations between the upper and middle layers," the final high-grade capacity was actually closer to the target. Therefore, the self-learning mechanism extracted this pattern through optimization algorithms and strengthened the weight of the "alternating short-pulse" strategy in subsequent rules. After several rounds of such iterations, the system gradually learned the optimal perturbation method for specific thermal storage devices and their common operating conditions.

[0066] This closed-loop self-learning mechanism enables the control system to adapt to long-term drift factors such as equipment aging and changes in media characteristics. Its control effect will continue to improve with the accumulation of operating time, and finally realize the leap from control based on theoretical models to optimized control based on practical experience data. This ensures that the active disturbance method can be perfected over time and has long-term vitality.

[0067] Example 2: like Figure 2As shown, this embodiment further improves upon the design of Embodiment 1. The difference is that in actual operation of Embodiment 1, it was found that during periods of rapid rise and fall in heating load, a single grade demand threshold could not simultaneously balance response speed and economy. This caused the system to frequently switch between two states when determining "whether diversion is needed," which not only exacerbated valve wear but also resulted in energy waste, failing to achieve smooth and optimized control of the dynamic process. Therefore, the energy storage electric heating method based on multi-period heat storage and load regulation further includes: S3.1: Introducing a dynamic threshold control strategy based on a feedforward-feedback composite approach. Specifically, the dynamic threshold control strategy includes: The system no longer relies solely on real-time feedback of the outlet temperature, but instead synchronously incorporates the instantaneous rate of change of the heating network load as a feedforward signal. When a rapid increase in load is detected, the system will proactively and appropriately lower the current grade demand threshold. The intention of this strategy is to anticipate that a sudden increase in load will accelerate the consumption of high-grade thermal energy within the thermal storage device, thereby proactively and in advance increasing the bypass flow rate to allow the electrothermal conversion unit to intervene earlier, offsetting the impending grade decrease and preventing excessive adjustment lag and oscillations after the system triggers the threshold.

[0068] To avoid frequent valve movements (i.e., "chatter") near the threshold point due to measurement noise or minor fluctuations, the system sets a reasonable hysteresis range for the comparison logic of the grade requirement threshold.

[0069] For example: Assume the basic quality requirement threshold is 80°C, and set a hysteresis interval of 2°C.

[0070] When the system begins to cool down from the target temperature (≥80°C), the valve does not open immediately when the temperature reaches 79.9°C. Instead, it waits until the temperature remains consistently below 79°C (i.e., 80°C - 1°C) before executing the opening command. Conversely, when heating from the bypass state, the valve does not close immediately when the temperature rises back to 79°C. The system waits until the temperature remains consistently above 81°C (i.e., 80°C + 1°C) before executing the closing command. This effectively protects the equipment and improves control stability.

[0071] By integrating the predictability of feedforward with the robustness of feedback, the method in this embodiment significantly improves the control quality of the system under dynamic operating conditions, making the start-up and shutdown of the electrothermal conversion unit smoother and the energy flow of the entire system smoother. While ensuring the quality of heating, it further reduces the overall operating energy consumption and equipment wear of the system.

[0072] Example 3: like Figure 3As shown, based on the same inventive concept as the energy storage electric heating method based on multi-period heat storage and load regulation in the foregoing embodiments, this application provides an energy storage electric heating system based on multi-period heat storage and load regulation. The system and method embodiments in this application are based on the same inventive concept. The system includes: Thermal energy storage calculation module: The thermal energy storage calculation module responds to the grid's operating signals during off-peak hours or power consumption periods and performs electrothermal conversion and thermal energy storage processes. This process includes thermal energy quality management operations for the internal medium of the thermal energy storage device to improve the availability of high-grade thermal energy. Heat release decision module: Before releasing heat into the heating network, the heat release decision module selectively executes a heat release mode that enhances the heat energy quality based on the assessment of the current internal heat energy quality of the heat storage device and the needs of the heating network. Grade Enhancement Module: When the heat energy grade enhancement heat release mode is selected, the grade enhancement module performs dynamic diversion of the heat medium extracted from the heat storage device based on real-time grade determination, and introduces the portion of heat medium determined to be low grade into the electrothermal conversion unit to improve the heat energy grade. Load Coordination Module: The load coordination module adjusts the terminal load of the heating system according to the system power changes caused by the execution of the enhanced heat release mode, so as to achieve overall system power balance.

[0073] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0074] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present application, based on the technical solution and concept of the present application, should be covered within the scope of protection of the present application.

Claims

1. A storage-based electric heating method based on multi-period thermal storage and load regulation, characterized in that, The methods include: In response to the grid's operating signals during off-peak hours or power consumption periods, the electrothermal conversion and thermal storage process is executed. This process includes thermal energy grade management operations for the internal medium of the thermal storage device to improve the availability of high-grade thermal energy. Before releasing heat into the heating network, a heat release mode with enhanced heat quality is selectively executed based on an assessment of the current internal heat energy grade of the heat storage device and the needs of the heating network. When the heat energy grade enhancement heat release mode is selected, the heat medium extracted from the heat storage device is dynamically diverted based on real-time grade determination, and the portion of heat medium determined to be low grade is introduced into the electrothermal conversion unit to improve the heat energy grade. Based on the system power changes caused by the implementation of the enhanced heat release mode, the terminal load of the heating system is adjusted accordingly to achieve overall system power balance.

2. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 1, characterized in that, The thermal energy quality management operation during the thermal storage stage is specifically a type of active disturbance thermal storage control, which is achieved through the following steps: A thermal energy grade-capacity distribution model based on a vertical temperature field is constructed; an optimization function representing the desired thermal energy grade distribution target is defined; based on the model and the optimization function, action commands for controlling physical disturbance devices are calculated and output to optimize the thermal stratification structure inside the thermal storage device.

3. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 2, characterized in that, The construction of the thermal energy grade-capacity distribution model specifically includes: associating the temperature value of each temperature measurement point with the cumulative thermal storage capacity to form a quantitative relationship curve characterizing the temperature of the output heat medium and the corresponding maximum usable capacity under the current state. The morphological characteristics of this curve are used as the basis for evaluating the thermal energy grade state.

4. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 1, characterized in that, The selectively executed judgment logic in the heat release decision-making stage includes: based on the heat energy grade-capacity distribution model, querying the effective capacity that meets the predicted highest grade demand of the heating network within a set future time period; when the effective capacity is lower than the set proportion threshold of the predicted total heat load for that time period, then deciding to execute the heat energy grade enhanced heat release mode.

5. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 1, characterized in that, The dynamic diversion based on real-time grade determination is manifested at the system level as follows: a branch line leading to the electrothermal conversion unit is set in parallel on the main outlet pipeline of the thermal storage device, which is controlled by a controllable valve; the on / off state of the controllable valve is determined by a controller based on the comparison result of the real-time temperature value of the heat medium and a dynamically set grade requirement threshold.

6. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 5, characterized in that, The logic for setting and dynamically adjusting the grade requirement threshold includes: its base value is the predicted maximum temperature of the heat medium required by the heating network in the future period, obtained from historical data and weather forecasts, and an adaptive offset for optimizing system energy efficiency can be superimposed based on the evaluation results of the grade status of the thermal storage device.

7. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 1, characterized in that, The specific implementation strategy for regulating the terminal load of the heating system includes: sending temperature setpoint adjustment commands to selected terminal heating units according to the additional electrical power value generated by the improvement in quality, based on a preset priority order that is based on the regional thermal inertia and the allowable fluctuation range of comfort, instead of directly cutting off the heat source supply.

8. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 2, characterized in that, The optimization process of the active disturbance thermal storage control includes a closed-loop self-learning mechanism. This mechanism records and analyzes the relationship between historical disturbance parameters and the final thermal energy grade-capacity distribution curve, and uses an optimization algorithm to iteratively update the calculation rules of the action command in order to approximate the desired target.

9. The energy storage electric heating method based on multi-time period thermal storage and load regulation according to claim 1, characterized in that, The method also includes: A dynamic threshold control strategy based on feedforward-feedback composite is introduced.

10. An energy storage electric heating system based on multi-period thermal storage and load regulation, characterized in that, The system includes: The thermal energy storage calculation module responds to the grid's operating signals during off-peak hours or power consumption periods and performs electrothermal conversion and thermal energy storage processes. This process includes thermal energy grade management operations for the internal medium of the thermal energy storage device to improve the availability of high-grade thermal energy. The heat release decision module, before releasing heat to the heating network, selectively executes a heat release mode that enhances the heat energy quality based on an assessment of the current internal heat energy quality of the heat storage device and the needs of the heating network. When the heat energy quality enhancement release mode is selected, the quality enhancement module dynamically diverts the heat medium extracted from the heat storage device based on real-time quality determination, and introduces the portion of heat medium determined to be low-grade heat medium into the electrothermal conversion unit to improve the heat energy quality. The load coordination module adjusts the terminal load of the heating system according to the system power changes caused by the execution of the enhanced heat release mode, so as to achieve overall system power balance.

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