Photovoltaic-thermal pump heating control method based on dynamic load

Abstract

The present application relates to the technical field of photovoltaic and photo-thermal heating, and discloses a photovoltaic and photo-thermal heat pump heating control method based on dynamic load. The method comprises the following steps: measuring and recording the solar radiation intensity, ambient temperature, relative humidity and building heat load demand of a target heating area to form environmental load data; based on the data, the connection topology of a photovoltaic and photo-thermal heat collector array and a heat pump unit is optimized through a multi-source heating component collaborative configuration method to determine a system structure configuration scheme; then, according to the scheme, the output heat power of the heat collector array is dynamically predicted by using a photo-thermal conversion efficiency prediction method to obtain a predicted heat power distribution scheme; finally, based on the predicted heat power distribution scheme, the circulation path of the heat medium between the heat collector array and the heat pump unit is independently controlled by using a heat medium directional scheduling method to generate a heat medium scheduling instruction. The method can realize the dynamic collaboration of the components of the heating system, adapt to the change of the building heat load, and improve the flexibility and adaptability of the system operation.

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:

[0007] 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;

[0008] 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;

[0009] 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;

[0010] 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.

[0011] 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:

[0012] 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;

[0013] 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;

[0014] 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;

[0015] 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.

[0016] 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:

[0017] The real-time heat collector surface temperature data collected by the distributed temperature sensor network is received;

[0018] 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;

[0019] calling the load matching model to calculate a matching relationship between an auxiliary heating power required by the heat pump unit and a predicted thermal power output value of the collector array, to obtain a thermal power gap parameter;

[0020] generating a cooperative working instruction of the photovoltaic-thermal collector array and the heat pump unit based on the thermal power gap parameter.

[0021] Preferably, the 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, and the specific steps are as follows:

[0022] Based on the thermal power gap parameter, the heat exchange priority between the collector array and the heat pump unit is identified;

[0023] According to the heat exchange priority and the building thermal load demand data, a flow direction allocation strategy of the heat medium between the collector array, the auxiliary thermal storage unit and the heat pump unit is determined;

[0024] The flow control valve group is used to execute the flow direction allocation strategy to generate an on-off sequence instruction of the heat medium circulation path.

[0025] Preferably, the method further comprises:

[0026] Based on the auxiliary thermal storage unit temperature gradient data monitored by the distributed temperature sensor network, a gravity-assisted thermal storage method is used to optimize the natural convection path of the heat medium;

[0027] According to the spatial distribution of the high-temperature zone and the low-temperature zone in the auxiliary thermal storage unit temperature gradient data, an optimal heat convection channel in the gravity direction is determined;

[0028] The opening degree of the flow control valve group is adjusted to match the optimal heat convection channel to generate a gravity-assisted heat medium scheduling instruction.

[0029] Preferably, the method further comprises:

[0030] Based on the real-time collected grid load peak-valley data, an intermittent energy adaptation method is used to control the operation period of the heat pump unit;

[0031] According to the grid load peak-valley data and the building thermal load demand data, a time window and a power upper limit allowed for operation of the heat pump unit are calculated;

[0032] In combination with the thermal power gap parameter, the heat pump unit heating function is activated in segments within the time window to generate an intermittent operation control instruction.

[0033] Preferably, the method further comprises:

[0034] adopting a dynamic thermal compensation method to adjust the output of the auxiliary thermal storage unit when it is detected that the predicted thermal power distribution scheme deviates from the actual heating temperature by more than a threshold value;

[0035] calculating an instantaneous compensation heat demand based on the difference between the indoor temperature data fed back by the distributed temperature sensor network and the target heating temperature value;

[0036] calling the heat medium directional scheduling method to correct the release rate of the auxiliary thermal storage unit and generating a compensation heat medium scheduling instruction.

[0037] Preferably, the method further comprises:

[0038] If an external energy interruption occurs, a breakpoint continuation method is used to record the current system state;

[0039] Freezing the execution process of the intermittent operation control instruction and the compensation heat medium scheduling instruction, and synchronously saving the released heat value, the unfinished heating area, and the current cycle state of the heat medium medium;

[0040] After the energy is restored, the remaining heating demand is recalculated based on the released heat value and the building heat load demand data, and a continuation control instruction is generated.

[0041] Preferably, the method further comprises:

[0042] A safety constraint verification method is used to check the feasibility of all control instructions;

[0043] Obtaining 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 thermal storage release rate parameter in the compensation heat medium scheduling instruction;

[0044] Based on the preset pipeline pressure limit, the maximum power threshold of the heat pump, and the upper limit of the capacity of the thermal storage unit, it is verified whether the control instruction parameters exceed the safety boundary;

[0045] Performing a proportional reduction operation on the parameters that exceed the safety boundary to generate a safety constraint correction instruction.

[0046] Preferably, the method further comprises:

[0047] A closed-loop task management method is used to continuously update the environmental load data;

[0048] Periodically collecting the latest environmental temperature, solar radiation intensity, and building heat load demand data of the distributed temperature sensor network;

[0049] The latest environmental load data is fed back to the photo-thermal conversion efficiency prediction method to start a new round of thermal power distribution calculation, forming a closed-loop control sequence.

[0050] Compared with the prior art, the present application has the beneficial effects of:

[0051] From environmental load data acquisition, system structure configuration, thermal 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-thermal heat pump heating system. At the system structure level, the connection topology of the photovoltaic-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.

[0052] At the thermal power utilization level, the application of the photothermal conversion efficiency prediction method realizes the dynamic prediction of the output thermal power of the collector array. The traditional static prediction model cannot respond to environmental parameter changes in real time, resulting in a disconnection between thermal power distribution and actual demand, while the dynamic prediction method can continuously combine dynamic parameters such as solar irradiance and environmental temperature in the environmental load data to accurately predict the output thermal power change trend of the collector array, and then develop a highly compatible predicted thermal power distribution scheme with actual demand. In this process, it does not rely on fixed prediction parameters or empirical models, but dynamically adjusts the prediction results according to real-time data, so that the thermal power distribution neither wastes energy due to over-supply nor affects the heating effect due to under-supply, so that every bit of heat energy can be reasonably utilized to adapt to the dynamic changes of building heat load.

[0053] At the level 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 the different regional heat load demand and different collector output efficiency, resulting in poor overall heating stability of the system. While the independent control method can plan a dedicated circulation path 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 heat exchange link of the corresponding area; when the output efficiency of a part of the collector decreases, the heat medium circulation path of this part can also 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.

[0054] The method as a whole revolves around dynamic load, which can adapt to changes in building heat load demand and environmental parameters in real time, avoiding the energy waste or heating shortage problems caused by the fixed operation mode of traditional systems. In the environmental changes of different seasons and different time periods, the system can always maintain an efficient operation state through dynamic adjustment of the connection topology, heat power distribution and heat medium path, not only adapting to the demand for flexibility of modern buildings for heating systems, but also providing a more adaptable control idea for the wide application of photovoltaic-thermal heat pump heating technology, which helps to promote the development of heating systems in the direction of intelligence and efficiency, better meeting the heating needs of users while better meeting the trend of energy transformation and low-carbon development. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The working principle diagram of the photovoltaic-thermal heat pump heating control method based on dynamic load described in the present application;

[0056] Figure 2 The flowchart for the collaborative configuration of multi-source heating components;

[0057] Figure 3 The flowchart for the prediction of photothermal conversion efficiency;

[0058] Figure 4 The flowchart for gravity-assisted heat storage;

[0059] Figure 5 The flowchart for dynamic heat compensation. DETAILED DESCRIPTION

[0060] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0061] 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:

[0062] The initial environmental parameters of the target heating area are measured and recorded to obtain the 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 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, and output heat medium scheduling instructions. The whole process realizes dynamic and efficient control of the heating system.

[0063] 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 heating habits of occupants, 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 value 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, so as to obtain a higher outlet temperature to meet the demand for high-temperature heat source in the building preheating stage.

[0064] 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 in 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.

[0065] 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.

[0066] 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.

[0067] 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 thermal 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.

[0068] Based on this performance data, the system dynamically adjusts the connection position of the auxiliary thermal storage unit. The control logic determines that the thermal storage unit should be changed from a parallel branch to a series connection in the main circuit between the collector array and the heat pump unit. This is achieved by switching the path of a three-way electric valve. After the adjustment, all heat medium flowing out of the collector array must first pass through the thermal storage unit before entering the heat pump unit. This change brings multiple effects. First, it eliminates the heat pump inlet temperature fluctuations caused by parallel flow splitting, allowing the heat pump to operate at a more stable inlet temperature, thus improving its COP. Second, all heat medium flows in from the bottom and out from the top of the thermal storage unit. This flow pattern enhances thermal convection within the tank, promotes natural heat stratification, and concentrates the high-temperature zone at the top, making it easier to utilize the high-temperature heat medium for subsequent heating. Finally, this series connection allows the thermal storage unit to more effectively smooth out temperature fluctuations from the collector array, forming a stable buffer heat source for the subsequent heat pump unit.

[0069] The central controller's built-in optimization algorithm evaluates the topology performance data every fifteen minutes, with its core logic being to maximize the overall thermal efficiency of the system. The algorithm weighs three key indicators: collector efficiency, thermal storage unit utilization, and heat pump COP value, continuously fine-tuning the access location and connection method of the thermal storage units within the system to output the final system structure configuration. This configuration is not static but adaptively evolves with changes in external environmental conditions and internal load demands, ultimately forming a highly efficient, stable, and responsive multi-source heating system structure.

[0070] Example 2: See Figure 3 A distributed temperature sensor network continuously collects surface temperature data from the photovoltaic thermal collector array. This network includes PT1000 temperature sensors installed at the center of the absorber plate in each collector unit, recording temperature values ​​at a sampling frequency of once per minute. For example, in an array of 12 collector plates, the sensors provide real-time feedback on the temperature distribution of each plate. The data shows that the collector plates at the array edges are typically about 3-5°C cooler than those at the center due to wind influence. This real-time temperature data is transmitted to the central controller via a CAN bus, forming a thermal map of the collector surface temperature field.

[0071] The preset heat conduction model is built on the principle of energy conservation. Model inputs include the real-time collector surface temperature matrix, the current solar irradiance value, and historical irradiance variation curves from the past two hours. Historical data reveals periodic fluctuations in irradiance, such as sudden drops in irradiance due to thin cloud cover often occurring in the morning. The model simulates the heat conduction process within the collector plate using differential equations, and combines this with cloud movement trends provided by weather forecasts to predict the thermal power output of the collector array over the next 60 minutes. The output consists of 12 sets of predicted values ​​divided into 5-minute intervals, including the expected thermal power range for each time segment. For example, at the 25th minute of the prediction period, the model outputs a thermal power range of 8.2-9.7 kW, reflecting the uncertainty of cloud changes.

[0072] The load matching model uses the building envelope's thermal parameters and indoor temperature setpoints, combined with the current outdoor ambient temperature, to calculate the theoretical heat supply required to maintain the target room temperature. The model divides the building into multiple thermal zones and calculates the heat load for each zone separately. For example, the south-facing office area receives heat from solar radiation, resulting in an instantaneous heat load 15% lower than the north-facing area. Simultaneously, the model receives operating status parameters of the heat pump unit, including condenser outlet water temperature and compressor frequency, and calculates the unit's actual heating capacity in real time. By comparing the predicted heat power output of the collector array with the total heat capacity required by the heat pump unit, the system calculates the heat power deficit parameter. When the predicted collector output at the 25th minute of the forecast period is 8.9kW (the midpoint of the interval), and the building's total heat load demand is 12.5kW, the heat pump needs to supplement 3.6kW of heat; in this case, the heat power deficit parameter is recorded as +3.6kW.

[0073] The collaborative work instruction generation module formulates a control strategy based on the positive and negative characteristics of the heat power deficit parameter. When the parameter is positive, the system prioritizes scheduling the heat output of the solar collector array while activating the heat pump unit to supplement the deficit heat. For example, for a deficit of +3.6kW, the instruction requires the solar collector array to operate at full load, and the heat pump unit to operate at 75% of its rated power. When the parameter is negative (e.g., the solar collector is predicted to output 10.2kW while the building demand is only 8.0kW), the instruction shuts down the heat pump unit and directs the excess heat to the auxiliary heat storage unit.

[0074] Heat exchange priority is determined based on the magnitude and duration of the heat power shortage parameter. The system has three priority levels: for a level 1 shortage (>5kW), the heat pump unit receives the highest scheduling priority; for a level 2 shortage (2-5kW), the collector array and heat pump operate in parallel; for a level 3 shortage (<2kW), the collector array is used for heating first. For example, if a +3.6kW shortage persists for more than 10 minutes, the system determines it to be a level 2 shortage and initiates parallel operation mode.

[0075] The heat transfer distribution strategy is implemented through a heat transfer media scheduling matrix, which defines the connection relationships and flow weights between heat source nodes (collector arrays, thermal storage units, and heat pump units) and heat consumption nodes (heating terminals and thermal storage units). When in a secondary deficit state, the matrix is ​​configured as follows: 70% of the heat transfer media output from the collector arrays is directly supplied to the heating terminals, and 30% flows to the thermal storage units; simultaneously, all the heat transfer media output from the heat pump units is supplied to the heating terminals. This distribution ratio is adjusted in real time according to the heat load of each area of ​​the building. For example, when the temperature in the south-facing area is detected to be close to the set value, the heat transfer media distribution weight for that area is automatically reduced.

[0076] The flow control valve assembly uses pulse width modulation (PWM) technology to control the electric valves. The system generates an opening and closing sequence command that includes the valve address, target opening degree, and execution duration. For example, the command "V3 electric regulating valve opens to 65% opening degree at t+15s and lasts for 300 seconds" is sent to the valve actuator via PROFINET industrial Ethernet. The valve opening change rate is limited to within 2% per second to avoid hydraulic shock. During heat medium switching, the system uses cross-gradual control: when switching the heat source from the collector to the storage unit, the target valve is gradually opened to 30% opening degree while the original valve opening degree is reduced. After the flow stabilizes, the full opening switch is completed, and the entire process takes about 45 seconds.

[0077] During the update cycle, the controller monitors the temperature change rate of each node in real time. When the temperature change rate at the heating terminal exceeds 0.5°C / minute, an emergency adjustment mechanism is triggered: the heat power deficit parameters are 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 and maintain stable heating temperatures when solar irradiance suddenly increases or decreases. The entire control process forms a closed loop of "monitoring-prediction-decision-execution-feedback," ensuring that the heat medium scheduling is always synchronized with the dynamic heat load.

[0078] Example 3: See Figure 4 The thermal storage unit is a vertical cylindrical water tank, 2.5 meters high, with a volume of 2000 liters. Five PT100 temperature sensors, labeled T1 to T5, are evenly spaced along the vertical direction of the tank, with T1 located 0.5 meters from the bottom and T5 located 0.5 meters from the top. The sensors collect temperature data twice per minute, generating a temperature gradient distribution curve. Typical data shows that during thermal storage, the T5 sensor reading can reach 65°C, while the T1 sensor reading is only 42°C, resulting in a vertical temperature difference of 23°C, indicating a significant thermal stratification phenomenon.

[0079] Gravity-assisted thermal storage is based on the principle of thermodynamic natural convection. When the temperature difference between the high-temperature region (T4, T5) and the low-temperature region (T1, T2) in the temperature gradient data exceeds 15°C, the system determines that an effective thermal stratification structure exists. The optimal thermal convection channel is determined based on the principle of buoyancy-driven flow: high-temperature, low-density media naturally rise, and low-temperature, high-density media naturally sink. The system calculates the stability index of the temperature gradient by analyzing the numerical distribution of five temperature measurement points.

[0080]

[0081] in: Indicates the temperature gradient stability index. This 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 heat storage unit). This represents the temperature value of the (i-1)th temperature sensor. The vertical distance between adjacent sensors is 0.5 meters. The temperature value is from the topmost sensor. This is the temperature value of the bottommost sensor. When... When the value is greater than 0.85, the thermal stratification is considered stable, and the optimal thermal convection channel is the natural circulation path in the vertical direction.

[0082] The system includes three electrically operated regulating valves: V1 is located on the inlet pipe of the thermal storage unit (bottom connection), V2 is located on the outlet pipe (top connection), and V3 is located on the bypass pipe. When the values ​​meet the standard, a gravity-assisted heat medium dispatching command is generated: the opening of the V1 electric regulating valve is increased to 80%, the opening of the V2 electric regulating valve is set to 70%, and the V3 electric regulating valve is completely closed. This configuration enhances the natural flow of the heat medium from bottom to top, utilizing density differences to promote the accumulation of high-temperature media at the top and the settling of low-temperature media at the bottom. Precise control of the valve opening is achieved through PID regulation, using the T3 sensor temperature as the intermediate setpoint. When the T3 temperature fluctuates beyond ±2°C, the opening ratio of the V1 and V2 electric regulating valves is automatically fine-tuned.

[0083] The collection of peak and valley load data for the power grid is achieved through the data interface between smart meters and the power grid dispatch center. The system obtains the peak and valley time periods and corresponding electricity price information for the next 24 hours, with the valley period being from 23:00 to 7:00 the next day, and the peak period being from 9:00 to 11:00 in the morning and from 18:00 to 21:00 in the evening. Building heat load demand data comes from the building energy management system and includes hourly predicted heat load values. For example, on a typical winter day, the minimum heat load at night is 8kW, and the maximum heat load during the day reaches 22kW.

[0084] The time window for the heat pump unit's permissible operation is calculated based on grid constraints and heat load demand. The system sets two operating windows: a primary window during off-peak electricity prices (23:00-7:00) and an auxiliary window during peak electricity prices (12:00-14:00). The power limit is set according to the grid's supply capacity: a maximum allowable power of 18kW during off-peak hours and a maximum allowable power of 12kW during peak hours. The boundary conditions of the time windows are expressed through inequality constraints to ensure that the heat pump's operating period does not exceed the grid's permissible range.

[0085] The generation of intermittent operation control commands is optimized in segments based on heat power deficit parameters. The system divides the 8-hour off-peak period into 16 30-minute intervals, allocating operating power to each interval according to the predicted heat load curve. For example, in the 1:00-1:30 AM interval, the building heat load is 9kW, the collector has no output, and the heat power deficit is +9kW. During this period, the heat pump operates at 50% of its rated power (9kW). In the 6:00-6:30 AM interval, the heat load rises to 15kW, and the heat pump power is correspondingly increased to 83% of its rated power (15kW). At the beginning of each interval, the system recalculates the heat power deficit parameters and dynamically adjusts the operating power. During peak electricity price periods, the heat pump remains in standby mode unless the heat power deficit exceeds a safety threshold (e.g., >8kW). The execution of control commands is achieved through a smart circuit breaker, which can receive 0-10V analog signals to control the output power. When the command requires the heat pump to operate at 75% power, the system outputs a 7.5V control signal, adjusting the compressor frequency and condenser fan speed accordingly. The power regulation process adopts a ramp-up method, with the power change not exceeding 10% of the rated power per minute to avoid impacting the power grid.

[0086] The entire control process employs a dual optimization mechanism: on the one hand, it maximizes the utilization rate of natural energy through gravity-assisted heat storage; on the other hand, it optimizes the grid's energy consumption structure through an intermittent operation strategy. The system recalculates the temperature gradient stability index and grid load status every 15 minutes, dynamically updating valve control commands and heat pump operating strategies. This implementation allows the system to adaptively adjust its operating mode, achieving efficient energy utilization while ensuring heating performance.

[0087] Example 4: See Figure 5The system continuously monitors the real-time temperature of each heating zone through a distributed temperature sensor network. This network includes temperature sensors placed in the center of each room, collecting data once per minute. During one operation, the system detected an actual temperature of 18.2°C in the office on the north side of the building, while the target heating temperature was set at 20.0°C, a deviation of 1.8°C, exceeding the set threshold of 1.5°C. Simultaneously, the actual temperature in the east conference room was 19.8°C, the west office area was 19.5°C, and the south open office area was 20.3°C. This uneven temperature distribution indicates a deviation in the predicted heat power distribution scheme, and the system immediately activates a dynamic heat compensation method.

[0088] The dynamic heat compensation method first calculates the instantaneous heat demand for each area. Taking the north office as an example, this area has a volume of 120 cubic meters. Based on the air's heat capacity characteristics, the heat required to raise the temperature by 1.8°C is calculated as: area volume multiplied by the product of air density and specific heat capacity, then multiplied by the temperature difference. The system calculates that this area needs to be immediately supplemented with 85 kJ of heat. The calculation results for each area are summarized in Table 1.

[0089] Table 1: Calculation of Compensation Heat Demand for Each Heating Area

[0090]

[0091] Based on this calculation data, the system determined that the north office area was the priority compensation area, followed by the west office area, with the east conference room having lower demand, while the south office area required reduced heating due to excessively high temperatures. The corresponding compensation heat medium dispatch command was generated: increasing the heat medium output flow rate of the auxiliary heat storage unit to 85% of the rated flow rate, with 60% of the heat directed to the north office area and 25% to the west office area, while simultaneously reducing the heat medium valve opening in the south office area from 70% to 45%.

[0092] During command execution, the system continuously monitored temperature changes. After 15 minutes, the temperature in the north office rose to 19.1°C, and the west office area rose to 19.7°C. However, the temperature in the south office dropped to 19.9°C due to excessive valve adjustment. The system immediately corrected this: reducing the heat medium distribution ratio in the north office to 50%, maintaining it at 25% in the west office area, and adjusting the valve opening in the south office area back to 55%. This dynamic adjustment allowed the temperatures in each area to reach equilibrium after 30 minutes, with the north office reaching 19.6°C, the west office maintaining 19.8°C, and the south office stabilizing at 20.0°C. In the event of an external energy interruption, such as a sudden power outage, the system immediately activated the breakpoint recovery method. All ongoing intermittent operation control commands and compensating heat medium scheduling commands were immediately frozen. The system recorded current status parameters, including the released heat value, areas not yet heated, and the current circulation status of the heat medium. Specific recorded data is as follows: 42 kJ of heat has been released to the north office and 18 kJ of heat to the west office area; the north office still has an unmet heat demand of 43 kJ, and the west office area has a remaining demand of 27 kJ; the heat medium is currently located 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. This status data is written to non-volatile memory, and the system switches to backup power to maintain power supply to critical sensors. During the power outage, temperature sensors continue to monitor temperature changes in each area and record temperature decay curves. Data shows that the temperature in the north office is decreasing at a rate of 0.8°C per hour, and the temperature in the west office area is decreasing at a rate of 0.5°C per hour.

[0093] Once power is restored, the system reads the saved status data and recalculates the remaining heating demand. Considering the temperature drop during the power outage, the current temperature in the north office is 18.9°C, a decrease of 0.3°C from before the outage, therefore the total demand is adjusted to 46 kJ; the current temperature in the west office area is 19.5°C, and the demand is adjusted to 31 kJ. The corresponding recovery control command is generated: prioritize the heating demand of the north office, directing 70% of the heat storage unit's output flow to that area, and the remaining 30% to the west office area. Simultaneously, the operating parameters of the heat pump unit are adjusted, increasing its output power to 90% of its rated power to accelerate the heat replenishment process. During the recovery process, the system adopts a gradual recovery strategy. Initially, the heat medium flow is controlled at 60% of the normal level, gradually increasing to 85% after 5 minutes of operation to avoid system instability caused by sudden large flow surges. Temperature sensors monitor 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 took 25 minutes, stabilizing the temperature in all areas within the target range of ±0.3°C.

[0094] When a grid voltage fluctuation exceeding 10% of the normal value is detected, the system automatically reduces the heat transfer medium flow to a safe level and increases the heat storage capacity of the thermal storage units to prepare for potential outages. In cases of unstable energy supply, the system prioritizes heating the core areas and appropriately reduces heating standards in secondary areas to ensure that basic heating functions are maintained to the maximum extent possible in the event of an unexpected interruption. This implementation method enables the system to cope with various emergencies and maintain the continuity and stability of the heating system.

[0095] Example 5: The system executes a safety constraint verification process for all generated control commands. This process first extracts the set of commands to be executed, including valve opening parameters in the heat medium directional scheduling command, heat pump power setting values ​​in the intermittent operation control command, and heat storage release rate parameters in the compensating heat medium scheduling command. For example, a certain command set includes: V3 electric regulating valve opening 85%, heat pump power setting 16.5kW, and heat storage unit release rate 220L / min.

[0096] The safety boundary database stores the physical limit parameters of the system hardware. Pipeline pressure limits are derived from engineering design specifications, with differentiated thresholds for different pipe sections: the maximum pressure limit for main pipelines is 1.6 MPa, and for branch pipelines, it is 1.0 MPa. The maximum power threshold for the heat pump is set at 18 kW based on the equipment nameplate parameters. Considering electrical protection requirements, the instantaneous overload capacity is limited to 110% of the rated value for 5 minutes. The maximum capacity of the heat storage unit is determined by a combination of physical volume and the coefficient of thermal expansion of the heat transfer medium, with an effective volume of 2000 L and a safe liquid level fluctuation range of ±50 L.

[0097] The verification engine compares each instruction parameter with the safety boundary. For the valve opening parameter, the system calculates the expected pressure value for the corresponding pipe section. When the V3 electric regulating valve is open to 85%, the flow sensor reports that the current system pressure has reached 1.52 MPa, close to the main pipeline's 1.6 MPa limit. Although the heat pump power setting of 16.5 kW is lower than the 18 kW threshold, the operating log shows that the unit has been running continuously for 3 hours. According to the temperature rise curve model, the actual maximum allowable power should be reduced to 17 kW at this point. The heat storage unit's release rate of 220 L / min corresponds to a liquid level drop rate of 12 cm / min. Monitoring shows that the current liquid level is only 15 cm away from the safety lower limit. At this rate, low liquid level protection will be triggered in 75 seconds.

[0098] The parameter reduction operation employs a constraint priority strategy. The system identified three risks exceeding limits: pipeline pressure approaching the limit (high risk level), insufficient heat pump power margin (medium risk level), and excessively rapid heat storage unit release rate (high risk level). First, the high-risk item was reduced proportionally: the opening of the V3 electric regulating valve was reduced to 75%, corresponding to an estimated pressure reduction of 1.42 MPa; the heat storage release rate was adjusted to 180 L / min, and the time for the liquid level to reach the lower limit was extended to 125 seconds. The heat pump power, due to its medium risk level and lack of direct exceedance, was temporarily maintained at 16.5 kW.

[0099] After the safety constraint correction command is generated, the system reassesses overall coordination. The reduced valve opening and release rate combination leads to a decrease in the total flow rate of the heat medium, requiring a corresponding extension of the heat pump operating time to compensate for the heat shortfall. The control algorithm automatically adjusts the intermittent operating period: the original 45-minute operating cycle is extended to 52 minutes, and the power curve is changed to maintain 16.5kW for the first 30 minutes and then decrease to 15kW for the last 22 minutes. This adjustment satisfies the heat demand while avoiding the risk of heat pump overload.

[0100] The closed-loop task management system activates the update process every 5 minutes. At the start of each cycle, the distributed sensor network uploads the latest dataset: the rooftop weather station provides the current solar irradiance (e.g., 685 W / m²) and ambient temperature (-3.2°C); the indoor network reports the temperatures of each area (19.7°C on the north side, 20.1°C on the south side); and the heat meter records real-time building heat load data (12.8 kW). This data is labeled as the new version of the environmental load data. The data preprocessing module performs outlier filtering and environmental compensation. When a temperature sensor reading changes by more than 3°C, it automatically uses interpolation from adjacent sensor data. Solar irradiance values ​​are weighted according to cloud cover observations, and foggy 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.

[0101] The prediction module receives updated environmental load data and resets the initial calculation conditions. The current measured collector surface temperature of 42°C replaces the previous cycle's prediction, and the latest irradiance of 685 W / m² is used to refresh the heat conduction model input. The model outputs a heat power prediction curve for the next 60 minutes, with the prediction value for the first 5 minutes revised from 9.1 kW to 8.7 kW. This change causes the heat power deficit parameter to adjust from +3.1 kW to +3.5 kW, triggering a chain reaction of control command updates: the heat pump start-up time is advanced by 2 minutes, and the heat 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 acquisition completion to the issuance of a new command, typically 8-12 seconds. When the interval exceeds 15 seconds, performance optimization is automatically initiated: compressing the data transmission packet size and simplifying the control logic calculation steps. Simultaneously, command execution deviations are monitored; if the difference between the actual valve opening and the command value consistently exceeds 3%, an actuator calibration procedure is triggered.

[0102] The system maintains a self-consistent operational log, generating a data packet for each control cycle that includes a timestamp, environmental data snapshot, prediction results, command version, and security verification records. When the same type of safety constraint alarm occurs for three consecutive cycles, a deep diagnostic mode is activated: historical data trends are analyzed to identify potential equipment degradation and maintenance warnings are generated. This implementation architecture enables the system to continuously optimize the accuracy and adaptability of heating control while ensuring safety.

[0103] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0104] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined 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, the 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 to obtain the heat medium scheduling command. 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.

2. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 1, 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.

3. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 2, 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.

4. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 3, 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.

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 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.

6. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 5, 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.

7. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 6, 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.

8. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 7, 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 dispatching command, the heat pump power parameters from the intermittent operation control command, and the heat storage release rate parameters from the compensation heat medium dispatching 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.

9. The photovoltaic-thermal heat pump heating control method based on dynamic load as described in claim 8, 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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