Photovoltaic photo-thermal heat pump heating control method based on dynamic load

Abstract

The invention relates to the technical field of photovoltaic photo-thermal heating, and discloses a photovoltaic photo-thermal heat pump heating control method based on a dynamic load. The method comprises the steps that the solar irradiation intensity, the environment temperature, the relative humidity and the building heat load requirement of a target heating area are measured and recorded, and environment load data are formed; on the basis of the data, the connection topology of a photovoltaic photo-thermal heat collector array and a heat pump unit is optimized through a multi-source heat supply assembly collaborative configuration method, and a system structure configuration scheme is determined; according to the scheme, the output thermal power of the heat collector array is dynamically predicted through a photo-thermal conversion efficiency prediction method, and a predicted thermal power distribution scheme is obtained; and finally, on the basis of the predicted heat power distribution scheme, a circulation path of a heat medium between the heat collector array and the heat pump unit is independently controlled through a heat medium directional scheduling method, and a heat medium scheduling instruction is generated. According to the method, dynamic cooperation of all assemblies of the heating system can be achieved, the method adapts to building heat load changes, and the flexibility and adaptability of system operation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic and photo-thermal heating, in particular to a photovoltaic and photo-thermal heat pump heating control method based on dynamic load. BACKGROUND

[0002] In the context of energy transformation and increasing demand for low-carbon heating, the combination of photovoltaic and photo-thermal technology with heat pump technology has become an important direction for solving building heating energy consumption problems. In traditional heating systems, photovoltaic and photo-thermal collectors and heat pump units are usually connected in a fixed topology. This structure cannot be dynamically adjusted according to actual environmental changes and building heat load demands. For example, when the solar irradiance intensity drops sharply, the fixedly connected collector array cannot timely change the coordination mode with the heat pump unit, causing the heat pump unit to have to be in a high-load operation state for a long time, which not only increases power consumption but also easily causes accelerated wear and tear of unit components.

[0003] Existing systems rely on static models to predict the output thermal power of the collector array, and cannot respond in real time to dynamic changes in parameters such as solar irradiance intensity and ambient temperature. This causes deviations between the thermal power distribution scheme and actual demand, resulting in either over-supply leading to energy waste or under-supply affecting heating effectiveness. In addition, traditional heat medium circulation path control usually adopts a unified scheduling mode, i.e., all collectors and heat pump units share one circulation system, which cannot be independently adjusted according to the heat load demand of different regions and the output efficiency of different collectors. When the heat load of some regions increases sharply or the output efficiency of some collectors decreases, the unified scheduling mode will cause the heating stability of the entire system to decrease, making it difficult to meet the differentiated heating demands of different regions.

[0004] In cold northern regions, the ambient temperature is low and the solar irradiance intensity fluctuates greatly in winter. The above problems of traditional photovoltaic and photo-thermal heat pump heating systems are more prominent. In a low-temperature environment, the heating efficiency of the heat pump unit will decrease significantly, and if the output thermal power of the collector array cannot be timely supplemented, it will be difficult to meet the heating temperature standard. When the solar irradiance intensity is high at noon, the fixed connection topology and static power prediction will make it difficult to effectively utilize the excess heat, resulting in energy waste. In addition, with the development of building intelligence, users have increasingly high requirements for the dynamic adjustment capability of heating systems. The traditional system lacks a flexible control mechanism and is difficult to adapt to the diversified heating demands of modern buildings, which restricts the further popularization and application of photovoltaic and photo-thermal heat pump heating technology. SUMMARY

[0005] The present application aims to provide a photovoltaic and photo-thermal heat pump heating control method based on dynamic load to solve the problems raised in the background.

[0006] To achieve the above object, the application provides a photovoltaic-thermal heat pump heating control method based on dynamic load, which comprises the following steps: The initial environmental parameters of the target heating area are measured and recorded to obtain solar radiation intensity, environmental temperature, relative humidity and building heat load demand data, which are marked as environmental load data; Based on the environmental load data, a multi-source heating component collaborative configuration method is used to optimize the connection topology of the photovoltaic-thermal heat collector array and the heat pump unit to obtain a system structure configuration scheme; Based on the system structure configuration scheme, a photothermal conversion efficiency prediction method is used to dynamically predict the output heat power of the heat collector array to obtain a predicted heat power distribution scheme; Based on the predicted heat power distribution scheme, a heat medium directional scheduling method is used to independently control the circulation path of the heat medium between the heat collector array and the heat pump unit to obtain heat medium scheduling instructions.

[0007] Preferably, the multi-source heating component collaborative configuration method is used to optimize the connection topology of the photovoltaic-thermal heat collector array and the heat pump unit, and the specific steps are as follows: Based on the building heat load demand data in the environmental load data, the series or parallel mode of the photovoltaic-thermal heat collector array is selected to obtain a basic connection topology; Based on the solar radiation intensity and environmental temperature in the environmental load data, an auxiliary heat storage unit is added to the basic connection topology to obtain an enhanced connection topology; A distributed temperature sensor network is used to monitor the heat transfer efficiency of each node in the enhanced connection topology in real time to obtain topology performance data; Based on the topology performance data, the access position of the auxiliary heat storage unit is dynamically adjusted to obtain a final system structure configuration scheme.

[0008] Preferably, the photothermal conversion efficiency prediction method is used to dynamically predict the output heat power of the heat collector array, and the specific steps are as follows: The real-time heat collector surface temperature data collected by the distributed temperature sensor network is received; Based on the real-time heat collector surface temperature data and the historical solar radiation intensity variation trend, the heat power output value of the future period is predicted through a preset heat conduction model; The load matching model is called to calculate the matching relationship between the auxiliary heating power required by the heat pump unit and the predicted heat power output value of the heat collector array to obtain a heat power gap parameter; Based on the heat power gap parameter, a collaborative working instruction of the photovoltaic-thermal heat collector array and the heat pump unit is generated.

[0009] Preferably, the heat medium directional scheduling method independently controls the circulation path of the heat medium between the collector array and the heat pump unit, and the specific steps are as follows: Based on the heat power gap parameter, the heat exchange priority between the collector array and the heat pump unit is identified; According to the heat exchange priority and the building heat load demand data, the flow direction allocation strategy of the heat medium between the collector array, the auxiliary heat storage unit and the heat pump unit is determined; The flow control valve group is used to execute the flow direction allocation strategy to generate the start-stop sequence instruction of the heat medium circulation path.

[0010] Preferably, the method further comprises: Based on the auxiliary heat storage unit temperature gradient data monitored by the distributed temperature sensor network, the natural convection path of the heat medium is optimized by using the gravity auxiliary heat storage method; According to the spatial distribution of the high temperature area and the low temperature area in the auxiliary heat storage unit temperature gradient data, the optimal heat convection channel in the gravity direction is determined; The opening of the flow control valve group is adjusted to match the optimal heat convection channel to generate the gravity auxiliary heat medium scheduling instruction.

[0011] Preferably, the method further comprises: Based on the real-time collected peak-valley data of the power grid load, the intermittent energy adaptation method is used to control the operation period of the heat pump unit; According to the peak-valley data of the power grid load and the building heat load demand data, the time window and the power upper limit allowed for the operation of the heat pump unit are calculated; In combination with the heat power gap parameter, the heating function of the heat pump unit is activated in segments within the time window to generate the intermittent operation control instruction.

[0012] Preferably, the method further comprises: When it is detected that the deviation between the predicted heat power distribution scheme and the actual heating temperature exceeds a threshold value, the dynamic heat compensation method is used to adjust the output of the auxiliary heat storage unit; Based on the difference between the indoor temperature data fed back by the distributed temperature sensor network and the target heating temperature value, the instantaneous compensation heat demand is calculated; The heat medium directional scheduling method is called to correct the release rate of the auxiliary heat storage unit to generate the compensation heat medium scheduling instruction.

[0013] Preferably, the method further comprises: If an external energy interruption occurs, the breakpoint continuation method is used to record the current system state; The execution processes of the intermittent operation control instruction and the compensation heat medium scheduling instruction are frozen, and the released heat value, the unfinished heating area and the current circulation state of the heat medium are saved synchronously. After the energy is recovered, the residual heating demand is recalculated based on the released heat value and the building heat load demand data, and a continuation control instruction is generated.

[0014] Preferably, the method further comprises: The feasibility of all control instructions is checked by using a safety constraint verification method; The flow control valve group opening parameter in the heat medium directional scheduling instruction, the heat pump power parameter in the intermittent operation control instruction, and the heat storage release rate parameter in the compensation heat medium scheduling instruction are obtained; Based on the preset pipeline pressure limit, the maximum power threshold of the heat pump, and the upper limit of the capacity of the heat storage unit, it is verified whether the control instruction parameters exceed the safety boundary; The parameters that exceed the safety boundary are subjected to an equal proportion reduction operation to generate a safety constraint correction instruction.

[0015] Preferably, the method further comprises: The environmental load data is continuously updated by using a closed-loop task management method; The latest environmental temperature, solar irradiance, and building heat load demand data of the distributed temperature sensor network are periodically collected; The latest environmental load data is fed back to the photo-thermal conversion efficiency prediction method to start a new round of heat power allocation calculation, forming a closed-loop control sequence.

[0016] Compared with the prior art, the beneficial effects of the present application are: From environmental load data collection, system structure configuration, heat power prediction to heat medium scheduling, a complete dynamic control system is formed, which provides a new solution for the efficient operation of the photovoltaic photo-thermal heat pump heating system. At the system structure level, the connection topology of the photovoltaic photo-thermal collector array and the heat pump unit is optimized by the multi-source heating component collaborative configuration method, breaking the limitations of the traditional fixed connection topology. The traditional fixed topology cannot adjust the collaborative relationship according to environmental parameters and heat load demand, while the method can flexibly adjust the connection mode of the two according to the real-time collected environmental load data, so that the system can maintain the optimal collaborative state under different environmental conditions. For example, when the solar irradiance is high, the collector array can be optimized to bear more heating load and reduce the operating pressure of the heat pump unit; when the environmental temperature is low and the solar irradiance is insufficient, the topology can be adjusted to enhance the collaborative heating capacity of the heat pump unit and the collector array, avoiding the problem of high load operation of a single component, so that the system structure always matches the actual operation demand.

[0017] In the aspect of heat power utilization, the application of the light-heat conversion efficiency prediction method realizes the dynamic prediction of the output heat power of the collector array. The traditional static prediction model cannot respond to changes in environmental parameters in real time, leading to a disconnection between heat power distribution and actual demand. However, the dynamic prediction method can continuously combine dynamic parameters such as solar radiation intensity and environmental temperature in the environmental load data to accurately predict the output heat power trend of the collector array, and then develop a predicted heat power distribution scheme that closely matches the actual demand. In this process, there is no need to rely on fixed prediction parameters or empirical models, but to dynamically adjust the prediction results based on real-time data, so that the heat power distribution neither wastes energy due to oversupply nor affects the heating effect due to undersupply, allowing every bit of heat energy to be reasonably utilized to adapt to the dynamic changes in building heat load.

[0018] In the aspect of heat medium control, the heat medium directional scheduling method realizes independent control of the heat medium circulation path, changing the disadvantages of the traditional unified scheduling mode. In the traditional mode, all collectors and heat pump units share the circulation system, which cannot be adjusted differently according to different regional heat load demand and different collector output efficiency, leading to poor overall heating stability of the system. However, the independent control method can plan exclusive circulation paths for different collector arrays and heat pump unit combinations according to the predicted heat power distribution scheme. When the heat load of a certain area increases sharply, more heat medium can be directed to the corresponding heat exchange link in that area. When the output efficiency of a part of the collector decreases, the heat medium circulation path of that part can be adjusted in time to avoid affecting the heat energy delivery of other high-efficiency collectors. This fine scheduling method ensures that each heating area can obtain stable and demand-adapted heat energy supply, improving the overall heating stability and reliability of the system.

[0019] This method is based on dynamic load and can adapt to changes in building heat load demand and environmental parameters in real time, avoiding the problems of energy waste or insufficient heating caused by fixed operation mode in traditional systems. In different seasons and time periods, the system can dynamically adjust the connection topology, heat power distribution, and heat medium path to maintain high-efficiency operation at all times, not only adapting to the demand for flexibility of modern buildings, but also providing a more adaptable control idea for the widespread application of photovoltaic-thermal heat pump heating technology, contributing to the development of heating systems towards intelligence and efficiency, meeting user heating demand while better adapting to the trend of energy transformation and low-carbon development. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The working principle diagram of the photovoltaic-thermal heat pump heating control method based on dynamic load described in the present application; Figure 2 Flowchart for collaborative configuration of multi-source heating components; Figure 3Flowchart for photothermal conversion efficiency prediction Figure 4 Flowchart for gravity-assisted heat storage Figure 5 Flowchart for dynamic thermal compensation. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0022] Please refer to Figure 1 The present application provides a photovoltaic-thermal heat pump heating control method based on dynamic load, which comprises the following steps: The initial environmental parameters of the target heating area are measured and recorded to obtain solar irradiance, ambient temperature, relative humidity, and building heat load demand data, which are marked as environmental load data. Based on the environmental load data, a multi-source heating component collaborative configuration method is used to optimize the connection topology of the photovoltaic-thermal collector array and the heat pump unit, forming a system structure configuration scheme. Then, based on the system structure configuration scheme, a photothermal conversion efficiency prediction method is used to dynamically predict the output thermal power of the collector array, generating a predicted thermal power distribution scheme. Based on the predicted thermal power distribution scheme, a heat medium directional scheduling method is used to independently control the circulation path of the heat medium between the collector array and the heat pump unit, outputting heat medium scheduling instructions. The whole process realizes dynamic and efficient control of the heating system.

[0023] Embodiment 1: Please refer to Figure 2 The building heat load demand data is derived from building energy consumption simulation software and temperature and energy consumption monitoring nodes deployed in the target heating area. These nodes continuously record indoor temperature changes, building envelope thermal performance, and occupants' heating habits, forming a dynamic demand curve. For example, an office building located in a cold region has a heat load demand that presents a feature of high during the day and low at night, and a steep peak in the morning preheating stage. Based on this feature, the system determines that a high-temperature heat medium is needed for rapid temperature rise, so it chooses to use a series mode of photovoltaic-thermal collector array. The series mode makes the heat medium flow through multiple collector units in sequence, absorbing heat step by step, thereby obtaining a higher outlet temperature to meet the demand for high-temperature heat source in the building preheating stage.

[0024] Solar irradiance and ambient temperature data were collected by a weather station installed on the roof of the building. The weather station was equipped with a pyranometer and a platinum resistance temperature sensor to record data at a frequency of once per minute. Historical data analysis showed that the region often experienced intermittent cloudy weather in winter, resulting in a sharp fluctuation in solar irradiance within a short period of time. The ambient temperature also had a large difference between day and night. This unstable energy input characteristic indicates that it is difficult to meet the continuous heating demand by relying solely on instantaneous solar conversion. Therefore, in the basic connection topology based on the series mode, an auxiliary thermal storage unit must be added between the collector array and the heat pump unit. This thermal storage unit is a pressurized water tank, and its capacity is configured according to thirty percent of the daily heat load of the building. The enhanced connection topology allows excess heat to be stored in the water tank when the irradiance is sufficient, and the stored heat can be released to supplement the heating when the irradiance is insufficient.

[0025] The deployment of the distributed temperature sensor network is a key step in the implementation. In the photovoltaic-thermal collector array, PT1000 temperature sensors are installed on the inlet and outlet pipes of each collector unit. Inside the auxiliary thermal storage unit, three temperature sensors are arranged vertically to monitor the temperatures of the upper, middle, and lower layers. The same type of sensors are also installed at the inlet and outlet of the evaporator and the inlet and outlet of the condenser of the heat pump unit. All sensors are connected to the data collector through the RS-485 bus, and the collector acquires temperature readings at a sampling frequency of once every ten seconds and transmits them to the central controller through the Modbus TCP protocol.

[0026] Based on real-time monitoring data, the system calculates the heat transfer efficiency of each node, for example, the instantaneous efficiency of the collector array is calculated by the ratio of the inlet temperature difference, flow rate, and solar irradiance. The heat loss efficiency of the thermal storage unit is estimated by the difference between its shell temperature and the ambient temperature. The COP value of the heat pump unit is obtained by the ratio of the condenser output heat to the compressor power consumption. These real-time calculated topology performance data reveal the thermodynamic state of the system in operation.

[0027] The analysis of the topology performance data shows that in the initial enhanced connection topology, the thermal storage unit is connected in parallel to the main circuit of the collector array. When the collector outlet temperature is high, the heat medium preferentially flows to the thermal storage unit for storage. However, sensor data shows that during the storage process in the thermal storage unit, due to the shunt effect of the parallel circuit, the flow rate of the heat medium to the heat pump unit decreases, resulting in a decrease in the heat pump inlet temperature and a decrease in the COP value of the unit. At the same time, the temperature sensor at the lower part of the thermal storage unit shows that the heat medium flowing into the unit does not fully mix due to its density difference, forming a clear thermal stratification, with high temperature at the upper part and low temperature at the lower part, affecting the effective use of the thermal storage capacity.

[0028] Based on these performance data, the system performs dynamic adjustment of the auxiliary thermal storage unit access position. The control logic determines to adjust the thermal storage unit from the parallel branch to the series access between the collector array and the main trunk of the heat pump unit. The specific implementation is realized by switching the passage of the three-way electric valve. After adjustment, all the heat medium flowing out of the collector array must first pass through the thermal storage unit and then enter the heat pump unit. This change has multiple effects. First, it eliminates the temperature fluctuations of the heat pump inlet caused by parallel shunt, enabling the heat pump to operate at a more stable inlet temperature, and improving the COP value. Second, all heat medium flows into the bottom and out of the top of the thermal storage unit, which enhances the heat convection in the water tank and promotes the natural stratification of heat, making the high-temperature zone concentrated at the top, which is more conducive to subsequent use of high-temperature heat medium for heating. Finally, this series position enables the thermal storage unit to more effectively smooth the temperature fluctuations of the collector array output, forming a stable buffer heat source for the subsequent heat pump unit.

[0029] The optimization algorithm built-in the central controller evaluates the topology performance data every fifteen minutes, and its core logic is to maximize the overall thermal efficiency of the system. The algorithm weighs the collector efficiency, thermal storage unit utilization rate and heat pump COP value, and continuously fine-tunes the access position and connection method of the thermal storage unit in the system, so as to output the final system structure configuration scheme. This scheme is not fixed, but evolves adaptively with changes in external environmental conditions and internal load demand, ultimately forming an efficient, stable and responsive multi-source heating system structure.

[0030] Embodiment 2: Referring to Figure 3 The distributed temperature sensor network continuously collects the surface temperature data of the photovoltaic-thermal collector array. The network includes PT1000 temperature sensors installed at the center of each collector unit's heat absorption plate, recording temperature values at a sampling frequency of once per minute. For example, in an array consisting of 12 collector plates, the sensors provide real-time feedback on the temperature distribution of each plate, and the data shows that the collector plates located at the edge of the array are usually about 3-5°C lower in temperature than those at the center due to wind effects. These real-time temperature data are transmitted to the central controller through the CAN bus, forming a thermal map of the collector surface temperature field.

[0031] The preset heat conduction model is constructed based on the principle of energy conservation. The model inputs include the real-time collector surface temperature matrix, the current solar radiation intensity value, and the historical radiation intensity change curve in the past two hours. Historical data reveals that the radiation intensity has periodic fluctuation characteristics, such as the sudden drop in radiation intensity caused by thin cloud cover during the morning period. The model simulates the heat conduction process inside the collector plate through a differential equation, combined with the cloud movement trend provided by the weather forecast, to predict the thermal power output of the collector array within the next 60 minutes. The output results are 12 groups of prediction values divided by 5-minute intervals, including the expected thermal power range of each time slice. For example, at the 25th minute of the prediction period, the model outputs a thermal power interval of 8.2-9.7 kW, which reflects the uncertainty of cloud changes.

[0032] The load matching model calls the building envelope thermal parameters and indoor temperature set value, combined with the current outdoor environment temperature, to calculate the theoretical heating amount required to maintain the target room temperature. The model divides the building into multiple thermal zones and calculates the heat load of each region. For example, the south-facing office area of the building is heated by solar radiation, and its instantaneous heat load is 15% lower than that of the north-facing area. At the same time, the model receives the operating state parameters of the heat pump unit, including the condenser outlet water temperature and the compressor frequency, and calculates the actual heating capacity of the unit in real time. By comparing the predicted thermal power output value of the collector array with the total heating amount required by the heat pump unit, the system calculates the thermal power gap parameter. When the predicted value of the collector output at the 25th minute of the prediction period is 8.9 kW (taking the interval median), and the total heat load demand of the building is 12.5 kW, the heat pump needs to supplement 3.6 kW of heat, and the thermal power gap parameter is recorded as +3.6 kW.

[0033] The collaborative work instruction generation module formulates the control strategy according to the positive and negative characteristics of the thermal power gap parameter. When the parameter is positive, the system prioritizes the dispatch of the collector array to output heat, while activating the heat pump unit to supplement the gap heat. For example, for a +3.6 kW gap, the instruction requires the collector array to run at full load, and the heat pump unit to run at 75% of the rated power. When the parameter is negative (such as the collector predicted output of 10.2 kW and the building demand of only 8.0 kW), the instruction turns off the heat pump unit and directs the excess heat into the auxiliary heat storage unit.

[0034] The heat exchange priority determination is based on the numerical value and duration of the thermal power gap parameter. The system sets three levels of priority: for a first-level gap (>5 kW), the heat pump unit has the highest scheduling priority; for a second-level gap (2-5 kW), the collector array and the heat pump run in parallel; for a third-level gap (<2 kW), the collector array is prioritized for heating. For example, if the +3.6 kW gap persists for more than 10 minutes, the system determines it as a second-level gap and starts the parallel running mode.

[0035] The flow distribution strategy is realized by a heat medium dispatching matrix, which defines the connection relationship and flow weight between heat source nodes (collector array, thermal storage unit, heat pump unit) and heat consumption nodes (heating terminal, thermal storage unit). When in the secondary gap state, the matrix is configured as follows: 70% of the heat medium output by the collector array is directly supplied to the heating terminal, and 30% flows to the thermal storage unit; at the same time, the heat pump unit outputs all the heat medium to the heating terminal. The distribution ratio is adjusted in real time according to the heat load of each region of the building, for example, when it is detected that the temperature of the south region approaches the set value, the heat medium distribution weight of this region is automatically reduced.

[0036] The flow control valve group adopts pulse width modulation technology to control electric valves, and the system generates opening and closing sequence instructions containing valve address, target opening degree and execution time length. For example, the instruction "V3 electric regulating valve opens to 65% opening degree at t+15s and lasts for 300 seconds", which is issued to the valve actuator through PROFINET industrial Ethernet. The opening change rate of the valve is limited within 2% per second to avoid hydraulic impact. During the heat medium switching process, the system uses cross-fading control: when it is necessary to switch the heat source from the collector to the thermal storage unit, the target valve is first gradually opened to 30% opening degree, while the original valve opening degree is reduced, and after the flow stabilizes, the full opening switching is completed, the whole process takes about 45 seconds.

[0037] In the update cycle, the controller monitors the temperature change rate of each node in real time, and when it is detected that the temperature change rate of the heating terminal exceeds 0.5°C / min, the emergency adjustment mechanism is triggered: the heat power gap parameter is recalculated, and a new valve control sequence is generated within 10 seconds. This dynamic adjustment mechanism ensures that the system can quickly respond to changes in heat supply and demand when solar radiation suddenly increases or decreases, and maintains the stability of the heating temperature. The whole control process forms a closed loop of "monitoring-prediction-decision-execution-feedback", so that the heat medium dispatching is always synchronized with the dynamic heat load.

[0038] Example 3: refer to Figure 4 The thermal storage unit is a vertical cylindrical water storage tank with a height of 2.5 meters and a volume of 2000 liters. Five PT100 temperature sensors are arranged at equal intervals along the vertical direction of the tank, labeled T1 to T5, where T1 is located 0.5 meters from the bottom of the tank and T5 is located 0.5 meters from the top of the tank. The sensors collect temperature data at a frequency of twice per minute, forming a temperature gradient distribution curve. Typical data shows that during the thermal storage process, the T5 sensor reading can reach 65°C, while the T1 sensor reading is only 42°C, with a vertical temperature difference of 23°C, indicating that there is a significant thermal stratification phenomenon.

[0039] The gravity-assisted thermal storage method is based on the principle of natural convection in thermodynamics. When the temperature difference between the high-temperature zone (T4, T5) and the low-temperature zone (T1, T2) in the temperature gradient data exceeds 15°C, the system determines that there is an effective thermal stratification structure. The determination of the optimal thermal convection channel is based on the principle of buoyancy-driven flow: high-temperature low-density medium naturally rises, and low-temperature high-density medium naturally sinks. The system calculates the stability index of the temperature gradient by analyzing the numerical distribution of the five temperature measurement points:

[0040] wherein: represents the temperature gradient stability index, represents the temperature value of the i-th sensor (i = 1 to 5, corresponding to the 5 temperature sensors arranged from bottom to top in the thermal storage unit), represents the temperature value of the i-1th temperature sensor, is the vertical distance between adjacent sensors (0.5 meters), is the temperature value of the uppermost layer sensor, is the temperature value of the lowermost layer sensor. When the value is greater than 0.85, it is considered that the thermal stratification is stable, and the optimal thermal convection channel is the natural circulation path in the vertical direction.

[0041] The system includes three electrically adjustable valves: V1 electrically adjustable valve located at the inlet pipeline of the thermal storage unit (bottom connection), V2 electrically adjustable valve located at the outlet pipeline (top connection), and V3 electrically adjustable valve located at the bypass pipeline. When the value meets the standard, the gravity-assisted heat medium scheduling instruction is generated: the valve opening of V1 electrically adjustable valve is increased to 80%, the valve opening of V2 electrically adjustable valve is set to 70%, and the valve of V3 electrically adjustable valve is completely closed. This configuration strengthens the natural flow of the heat medium from bottom to top, and utilizes the density difference to promote the aggregation of high-temperature medium to the top and the sinking of low-temperature medium to the bottom. The precise control of valve opening is achieved through PID adjustment, with T3 sensor temperature as the intermediate point set value, and when T3 temperature fluctuation exceeds ±2°C, the opening ratio of V1 electrically adjustable valve and V2 electrically adjustable valve is automatically adjusted.

[0042] The collection of peak and valley data of power grid load is realized through the data interface between intelligent electric meter and power grid dispatching center. The system obtains the division of peak and valley periods and the corresponding electricity price information for the next 24 hours, wherein the low valley period is from 23:00 at night to 7:00 the next day, and the peak period is from 9:00 to 11:00 in the morning and from 18:00 to 21:00 in the evening. The building heat load demand data comes from the building energy management system, including the hourly predicted heat load value. For example, in a typical winter day, the minimum heat load at night is 8kW, and the maximum heat load during the day reaches 22kW.

[0043] The time window calculation of the heat pump unit allows operation based on grid constraints and heat load demand. The system sets two operation windows: the main window is located in the low electricity price period (23:00-7:00), and the auxiliary window is located in the flat electricity price period (12:00-14:00). The upper limit of power is set according to the grid supply capacity: the maximum power is 18kW in the low valley period, and the maximum power is 12kW in the flat period. The boundary conditions of the time window are expressed by inequality constraints to ensure that the heat pump operation period does not exceed the range allowed by the grid.

[0044] The generation of intermittent operation control instructions is combined with the heat power gap parameter for segmented optimization. The system divides the 8-hour low valley period into 16 30-minute intervals, and allocates the operation power of each interval according to the predicted heat load curve. For example, in the 1:00-1:30 interval, the building heat load is 9kW, the collector has no output, and the heat power gap is +9kW. The heat pump operates at 50% rated power (9kW) during this period; in the 6:00-6:30 interval, the heat load rises to 15kW, and the heat pump power is increased to 83% rated power (15kW). At the beginning of each interval, the system recalculates the heat power gap parameter and dynamically adjusts the operation power. During the peak electricity price period, unless the heat power gap exceeds the safety threshold (such as >8kW), the heat pump remains in standby state. The execution of the control instruction is realized through an intelligent circuit breaker, which can receive a 0-10V analog signal to control the output power. When the instruction requires the heat pump to operate at 75% power, the system outputs a 7.5V control signal, which adjusts the compressor frequency and condenser fan speed accordingly. The power adjustment process uses a ramp change method, with a power change of no more than 10% of the rated power per minute, to avoid impacting the grid.

[0045] The entire control process forms a double optimization mechanism: on the one hand, it maximizes the utilization of natural energy through gravity-assisted heat storage, and on the other hand, it optimizes the grid energy consumption structure through intermittent operation strategy. The system recalculates the temperature gradient stability index and grid load state every 15 minutes, and dynamically updates the valve control instructions and heat pump operation strategy. This implementation enables the system to adaptively adjust the operation mode, achieving efficient use of energy while ensuring heating effect.

[0046] Example 4: see Figure 5The system continuously monitors the real-time temperature of each heating zone through a distributed temperature sensor network. The network contains temperature sensors placed in the center of each room, collecting data at a frequency of once per minute. In one run, the system detected that the actual temperature in the office on the north side of the building was 18.2°C, while the target heating temperature was set to 20.0°C, with a deviation of 1.8°C, exceeding the set threshold of 1.5°C. At the same time, the actual temperature in the conference room on the east side was 19.8°C, in the office area on the west side was 19.5°C, and in the open office area on the south side was 20.3°C. This uneven distribution of temperature indicates that the predicted heat power distribution scheme is biased, and the system immediately starts the dynamic heat compensation method.

[0047] The dynamic heat compensation method first calculates the instantaneous compensation heat demand of each zone. Taking the north office as an example, the volume of this area is 120 cubic meters. According to the characteristics of air heat capacity, the heat required to raise the temperature by 1.8°C is calculated as follows: the volume of the area multiplied by the product of air density and specific heat capacity, and then multiplied by the temperature difference. The system calculates that this area needs to immediately supplement 85 kilojoules of heat. The results of each zone are summarized in Table 1.

[0048] Table 1: Calculation of compensation heat demand for each heating zone.

[0049]

[0050] Based on these calculation data, the system determines that the north office is the priority compensation zone, followed by the west office area, the east conference room has smaller demand, and the south office area needs to reduce heating due to the high temperature. The compensation heat medium dispatching instruction is generated accordingly: the heat medium output flow of the auxiliary heat storage unit is increased to 85% of the rated flow, of which 60% of the heat is directed to the north office and 25% to the west office area, while the heat medium valve opening degree of the south office area is reduced from 70% to 45%.

[0051] During the execution of the instructions, the system continuously monitors the temperature changes. After 15 minutes, the temperature in the north office rises to 19.1°C, the west office to 19.7°C, but the south office drops to 19.9°C due to the excessive adjustment of the valve. The system immediately makes corrections: the north office's thermal medium distribution ratio is reduced to 50%, the west office's remains at 25%, and the south office's valve opening is adjusted back to 55%. This dynamic adjustment makes the temperatures in each region tend to balance after 30 minutes, with the north reaching 19.6°C, the west remaining at 19.8°C, and the south stabilizing at 20.0°C. When an external energy interruption occurs, such as a sudden power outage, the system immediately activates the breakpoint supply method. All ongoing intermittent operation control instructions and compensation thermal medium scheduling instructions are immediately frozen. The system records the current state parameters, including the amount of heat released, the areas with unfulfilled heating needs, and the current circulation state of the thermal medium. The specific recorded data are: 42 kilojoules of heat have been released to the north office and 18 kilojoules to the west office; there are still 43 kilojoules of demand unmet in the north office and 27 kilojoules in the west office; the thermal medium is currently in the pipeline from the heat storage unit to the north office, with a flow rate of 0.8 m / s and a temperature of 58°C. These state data are written to non-volatile memory, and at the same time, the system switches to backup power to maintain the power supply of critical sensors. During the power outage, temperature sensors continue to monitor temperature changes in each region and record temperature decay curves. The data show that the temperature in the north office decreases at a rate of 0.8°C per hour, and the temperature in the west office decreases at a rate of 0.5°C per hour.

[0052] When power supply is restored, the system reads the saved state data and recalculates the remaining heating demand. Considering the temperature decay during the power outage, the current temperature in the north office is 18.9°C, which is 0.3°C lower than before the power outage, so the total demand is adjusted to 46 kilojoules; the current temperature in the west office is 19.5°C, and the demand is adjusted to 31 kilojoules. The supply control instructions are generated accordingly: the heating demand of the north office is prioritized, with 70% of the output flow from the heat storage unit being directed to this area, and the remaining 30% being directed to the west office. At the same time, the operating parameters of the heat pump unit are adjusted, with its output power being increased to 90% of the rated power to accelerate the heat supplement process. During the supply process, the system adopts a gradual recovery strategy. In the initial stage, the thermal medium flow is controlled at 60% of the normal level, and after 5 minutes, it is gradually increased to 85% to avoid system instability caused by sudden large flow shocks. The temperature sensor monitors the changes in real time, and when the temperature in the north office reaches 19.8°C, the flow is automatically adjusted back to the normal level. The entire recovery process takes 25 minutes, and the temperatures in all regions stabilize within the range of ±0.3°C of the target value.

[0053] When the grid voltage fluctuation is detected to exceed 10% of the normal value, the heat medium flow rate is automatically reduced to a safe level, while the heat storage capacity of the heat storage unit is increased, to prepare for possible interruptions. In the case of unstable energy supply, the system will prioritize the heating of the core area, appropriately reducing the heating standard of the secondary area, to ensure that the basic heating function can be maintained to the maximum extent in the event of an unexpected interruption. This implementation enables the system to respond to various emergencies and maintain the continuity and stability of the heating system.

[0054] In Example 5, the system performs a safety constraint verification process on all generated control instructions. This process first intercepts the instruction set to be executed, including the valve opening parameter in the heat medium directional scheduling instruction, the heat pump power setting value in the intermittent operation control instruction, and the heat storage release rate parameter in the compensation heat medium scheduling instruction. For example, a certain instruction set contains: V3 electric regulating valve valve opening 85%, heat pump power setting 16.5kW, and heat storage unit release rate 220L / min.

[0055] The safety boundary database stores the physical limit parameters of the system hardware. The pipe pressure limit value is derived from the engineering design specification, and different pipe sections use differentiated threshold values: the upper limit of the main pipe pressure is 1.6MPa, and the upper limit of the branch pipe is 1.0MPa. The maximum power threshold of the heat pump is set to 18kW according to the equipment nameplate parameters, and considering the electrical protection requirements, the instantaneous overload capacity is limited to 110% of the rated value for 5 minutes. The upper limit of the heat storage unit capacity is determined by combining the physical volume and the heat medium expansion coefficient, with an effective volume of 2000L and a safe liquid level fluctuation range of ±50L.

[0056] The verification engine compares the instruction parameters with the safety boundary one by one. For the valve opening parameter, the system calculates the expected pressure value of the corresponding pipe section. When the V3 electric regulating valve valve opening is 85%, the flow sensor feedbacks that the current system pressure has reached 1.52MPa, close to the 1.6MPa limit of the main pipe. Although the heat pump power setting is 16.5kW, which is lower than the 18kW threshold, combined with the operation log, it is found that this unit has been running for 3 hours, and according to the temperature rise curve model, the actual maximum allowed power should be reduced to 17kW. The heat storage unit release rate of 220L / min corresponds to a liquid level drop speed of 12cm / min, and the monitoring shows that the current liquid level is only 15cm away from the safe lower limit. At this rate, it will trigger the low liquid level protection in 75 seconds.

[0057] The parameter reduction operation adopts a constraint priority strategy, and the system identifies three over-limit risks: pipeline pressure close to the limit (high risk level), heat pump power margin insufficient (medium risk level), and heat storage unit release rate too fast (high risk level). First, perform proportional reduction on high-risk items: reduce the V3 electric regulating valve opening to 75%, corresponding to a pressure estimate of 1.42 MPa; adjust the heat storage release rate to 180 L / min, and extend the time to reach the lower limit of the liquid level to 125 seconds. The heat pump power is medium risk level and not directly over-limit, so it is temporarily maintained at 16.5 kW.

[0058] After the safety constraint correction instruction is generated, the system re-evaluates the overall coordination. The combination of reduced valve opening and release rate results in a decrease in total heat medium flow, which requires a corresponding extension of heat pump operation time to compensate for the heat gap. The control algorithm automatically adjusts the intermittent operation period: the original 45-minute operation cycle is extended to 52 minutes, and the power curve is changed to maintain 16.5 kW for the first 30 minutes and reduce to 15 kW for the last 22 minutes. This adjustment meets the heat demand and avoids the risk of heat pump overload.

[0059] The closed-loop task management system activates the update process every 5 minutes, and at the beginning of each cycle, the distributed sensor network uploads the latest data set: the rooftop weather station provides the current solar irradiance value (e.g., 685 W / m²) and the ambient temperature (-3.2°C); the indoor network feeds back the temperature of each region (north side 19.7°C, south side 20.1°C); the heat meter records the real-time data of building heat load (12.8 kW). These data are marked as a new version of environmental load data. The data preprocessing module performs outlier filtering and environmental compensation, and when a temperature sensor reading is detected to have a sudden change of more than 3°C, the adjacent sensor data interpolation is automatically enabled to replace it. The solar irradiance value is weighted according to the cloud observation, and the fog data is corrected with a confidence coefficient of 0.8. The processed data packet is pushed to the solar-thermal conversion efficiency prediction module, triggering a new round of calculation cycle.

[0060] The prediction module receives the updated environmental load data, resets the calculation initial conditions. The current measured collector surface temperature 42°C replaces the previous cycle's prediction value, the latest irradiance 685 W / m² refreshes the heat conduction model input. The model outputs a 60 minutes thermal power prediction curve, the first 5 minutes prediction value is corrected from the original 9.1 kW to 8.7 kW. This change causes the thermal power gap parameter to be adjusted from +3.1 kW to +3.5 kW, which in turn triggers a cascading update of control commands: the heat pump start time is advanced by 2 minutes, the thermal storage unit release rate is increased by 5%. The command execution monitoring unit tracks the response delay of the closed-loop control sequence. The system records the time interval from data collection completion to new command issuance, the typical value is 8-12 seconds. When the interval exceeds 15 seconds, automatically start performance optimization: compress data transmission packet size, simplify control logic calculation steps. At the same time, monitor the command execution deviation, such as the actual opening degree of the valve and the command value difference continuously exceeding 3%, trigger the actuator calibration program.

[0061] The system maintains a self-consistent operation log, generates a data packet containing time stamp, environmental data snapshot, prediction result, command version and security verification record for each control cycle. When the same type of security constraint alarm appears for three consecutive cycles, start the deep diagnosis mode: analyze historical data trends, identify potential device degradation, generate maintenance warning notifications. This implementation architecture enables the system to continuously optimize the accuracy and adaptability of heating control under the premise of ensuring safety.

[0062] It should be noted that the relational terms, such as first and second, and the like, are used solely to distinguish one from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0063] While the embodiments of the present application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and alterations can be made hereto without departing from the spirit and scope of the application in its broadest form. The scope of the application should be limited only by the appended claims and their equivalents.

Claims

1. A photovoltaic-thermal heat pump heating control method based on dynamic load, characterized in that, include: The initial environmental parameters of the target heating area are measured and recorded to obtain data on solar irradiance, ambient temperature, relative humidity, and building heat load demand, which are then labeled as environmental load data. Based on the environmental load data, the connection topology of the photovoltaic thermal collector array and the heat pump unit is optimized by adopting a multi-source heating component collaborative configuration method to obtain a system structure configuration scheme. Based on the system structure configuration scheme, the output thermal power of the collector array is dynamically predicted using the photothermal conversion efficiency prediction method to obtain the predicted thermal power distribution scheme. Based on the predicted heat power distribution scheme, a heat medium directional scheduling method is used to independently control the circulation path of the heat medium between the collector array and the heat pump unit, thereby obtaining heat medium scheduling instructions.

2. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 1, characterized in that, The method of coordinating the configuration of multi-source heating components to optimize the connection topology between the photovoltaic thermal collector array and the heat pump unit involves the following steps: Based on the building heat load demand data in the environmental load data, the series or parallel connection mode of the photovoltaic thermal collector array is selected to obtain the basic connection topology. Based on the solar irradiance and ambient temperature in the environmental load data, an auxiliary heat storage unit is added to the basic connection topology to obtain an enhanced connection topology; A distributed temperature sensor network is used to monitor the heat transfer efficiency of each node in the enhanced connection topology in real time to obtain topology performance data; Based on the aforementioned topology performance data, the access location of the auxiliary thermal storage unit is dynamically adjusted to obtain the final system structure configuration scheme.

3. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 2, characterized in that, The method for dynamically predicting the output thermal power of the solar collector array using a photothermal conversion efficiency prediction method includes the following steps: Receive real-time collector surface temperature data collected by the distributed temperature sensor network; Based on the real-time surface temperature data of the solar collector and the historical trend of solar irradiance, the thermal power output value for future periods is predicted by a preset heat conduction model. The load matching model is called to calculate the ratio between the auxiliary heating power required by the heat pump unit and the predicted heat power output value of the collector array, and the heat power gap parameter is obtained. Based on the aforementioned thermal power gap parameters, a collaborative working instruction is generated for the photovoltaic thermal collector array and the heat pump unit.

4. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 3, characterized in that, The method of using a heat medium directional scheduling to independently control the circulation path of the heat medium between the solar collector array and the heat pump unit involves the following steps: Based on the aforementioned thermal power gap parameters, the heat exchange priority between the solar collector array and the heat pump unit is identified; Based on the heat exchange priority and building heat load demand data, determine the flow distribution strategy of the heat medium among the collector array, auxiliary heat storage unit and heat pump unit. The flow direction allocation strategy is executed by a flow control valve group, generating an opening and closing sequence command for the heat medium circulation path.

5. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 4, characterized in that, Also includes: Based on the temperature gradient data of the auxiliary heat storage unit monitored by the distributed temperature sensor network, the natural convection path of the heat medium is optimized by the gravity-assisted heat storage method. Based on the spatial distribution of high-temperature and low-temperature regions in the temperature gradient data of the auxiliary heat storage unit, the optimal heat convection channel in the direction of gravity is determined. Adjust the opening of the flow control valve group to match the optimal heat convection channel, and generate a gravity-assisted heat medium scheduling command.

6. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 5, characterized in that, Also includes: Based on real-time collected peak and valley data of power grid load, an intermittent energy adaptation method is used to control the operating period of the heat pump unit. Based on the power grid load peak and valley data and building heat load demand data, calculate the allowable operating time window and power limit of the heat pump unit; Based on the aforementioned thermal power gap parameters, the heating function of the heat pump unit is activated in segments within the time window, generating intermittent operation control commands.

7. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 6, characterized in that, Also includes: When the deviation between the predicted heat power distribution scheme and the actual heating temperature exceeds a threshold, a dynamic heat compensation method is used to adjust the output of the auxiliary heat storage unit. The instantaneous compensation heat demand is calculated based on the difference between the indoor temperature data fed back by the distributed temperature sensor network and the target heating temperature value. The release rate of the auxiliary heat storage unit is corrected by invoking the heat medium directional scheduling method, and a compensating heat medium scheduling command is generated.

8. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 7, characterized in that, Also includes: If an external power outage occurs, the current system status is recorded using a breakpoint resume method. Freeze the execution process of the intermittent operation control command and the compensation heat medium scheduling command, and simultaneously save the released heat value, the unfinished heating area and the current circulation status of the heat medium; After energy is restored, the remaining heating demand is recalculated based on the released heat value and building heat load demand data, and a continued supply control command is generated.

9. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 8, characterized in that, Also includes: The feasibility of all control commands was verified using a safety constraint verification method. Obtain the flow control valve group opening parameters from the heat medium directional scheduling command, the heat pump power parameters from the intermittent operation control command, and the heat storage release rate parameters from the compensation heat medium scheduling command; Based on the preset pipeline pressure limit, heat pump maximum power threshold and thermal storage unit capacity limit, verify whether the control command parameters exceed the safety boundary. Perform a proportional reduction operation on parameters that exceed the safety boundary to generate a safety constraint correction instruction.

10. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 9, characterized in that, Also includes: A closed-loop task management method is used to continuously update environmental load data; The latest ambient temperature, solar irradiance, and building heat load demand data are periodically collected from the distributed temperature sensor network. The latest environmental load data is fed back to the photothermal conversion efficiency prediction method to initiate a new round of heat power allocation calculation, forming a closed-loop control sequence.

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