Multi-energy coupling air source heat pump heating model selection and operation method and system

Abstract

The invention provides a multi-energy coupling air source heat pump heating type selection and operation method and system, and relates to the technical field of air source heat pumps, which comprises the following steps: predicting a heating load according to building parameters and meteorological parameters, selecting an air source heat pump, and connecting a solar heat collector, a heat storage water tank and a heating tail end. Then, according to the meteorological parameters, the heat yield of the solar heat collector is calculated, the heat collection area and the volume of the heat storage water tank are determined, and an air source heat pump heating performance curve is established; and finally, the input temperature threshold value of the solar heat collector and the operation time period of the air source heat pump are determined based on the system operation optimization model, solar energy is preferentially used for heating, heat supply supplementation is conducted in combination with the heat storage water tank and the air source heat pump, and therefore the energy utilization efficiency is improved, and the heating cost is reduced.

Description

Technical Field

[0001] The present invention relates to air source heat pump technology, and particularly to a selection and operation method and system for multi-energy coupled air source heat pump heating. Background Art

[0002] Currently, the heating methods for buildings mainly include coal-fired boilers, gas boilers, electric heaters, and air source heat pumps, etc. For traditional coal-fired boiler and gas boiler heating methods, although the heating efficiency is relatively high, they will emit a large amount of pollutants, causing environmental pollution. The electric heater heating method is clean and pollution-free, but its energy consumption is high and the operating cost is high.

[0003] Although the traditional air source heat pump heating system is clean, environmentally friendly and has a relatively low operating cost, there are also some deficiencies. First, the heating performance of the air source heat pump is greatly affected by the outdoor environmental temperature. The heating efficiency will decrease significantly in low temperature environments, resulting in poor heating effect. Second, using only the air source heat pump for heating will increase the power grid load during peak electricity consumption periods, which is not conducive to the stable operation of the power system. Finally, the traditional air source heat pump heating system is difficult to make full use of renewable energy, such as solar energy, etc., resulting in waste of energy. Summary of the Invention

[0004] The embodiments of the present invention provide a selection and operation method and system for multi-energy coupled air source heat pump heating, which can solve the problems in the prior art.

[0005] In the first aspect of the embodiments of the present invention, a selection and operation method for multi-energy coupled air source heat pump heating is provided, including: collecting the heating area of the building, the building insulation performance parameters, and the outdoor meteorological parameters, determining the building heat loss coefficient according to the building insulation performance parameters and the building heating area, inputting the building heat loss coefficient and the outdoor meteorological parameters into a heating load prediction model to obtain the building hourly heating load curve; determining the peak heating load value of the building based on the building hourly heating load curve, selecting the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connecting the air source heat pump unit with a solar collector, a hot water storage tank, and a heating terminal to construct a multi-energy coupled air source heat pump system; Calculate the hourly heat output of the solar collector based on the solar radiation intensity data in the outdoor meteorological parameters, determine the heat collection area of the solar collector based on the hourly heat output, determine the heat storage volume of the hot water storage tank according to the matching relationship between the hourly heating load curve of the building and the hourly heat output of the solar collector, and establish the heating performance curve of the air source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters. Input the hourly heat output of the solar collector, the heat storage volume of the hot water storage tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model; Calculate the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model. Determine the input temperature threshold of the solar collector and the operation period of the air source heat pump unit according to the optimal combined operation plan. When the outdoor temperature is higher than the input temperature threshold of the solar collector, give priority to starting the solar collector for heating and store the remaining heat in the hot water storage tank. When the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the operation period, and at the same time release the heat in the hot water storage tank for heating supplement.

[0006] Determine the peak heating load value of the building based on the hourly heating load curve of the building. Select the heating capacity specification of the air source heat pump unit according to the peak heating load value. Connect the air source heat pump unit with the solar collector, the hot water storage tank, and the heating terminal to construct a multi-energy coupling air source heat pump system, including: Collect the hourly heating load data of the building during the heating season. Generate the hourly heating load curve of the building according to the hourly heating load data during the heating season. Analyze the change characteristics of the hourly heating load curve of the building to obtain the peak-valley distribution of the building heating load. Based on the peak-valley distribution of the building heating load, identify the time period and duration when the load peak appears during the heating season. Determine the load value at the 95% assurance rate during the heating season as the peak heating load value of the building; Select the heating capacity specification of the air source heat pump unit according to the peak heating load value of the building and the lowest design temperature during the local heating season. The method for selecting the heating capacity specification of the air source heat pump unit is: divide the peak heating load value of the building by the defrosting coefficient of 0.85 to 0.9 to obtain the heating capacity of the air source heat pump unit, and select an air source heat pump unit that meets the heating requirements based on the heating capacity; Construct a multi - energy coupling heating system with the air - source heat pump unit as the core. The construction method of the multi - energy coupling heating system is as follows: Connect the water outlet of the solar collector to the upper water inlet of the heat storage water tank to form a heat collection and heat storage loop. Connect the lower water outlet of the heat storage water tank to the evaporator water inlet of the air - source heat pump unit to form a heat source side loop. Connect the condenser water outlet of the air - source heat pump unit to the water inlet of the heating terminal to form a heating loop. Install a first variable - frequency circulation pump and a first temperature sensor in the heat collection and heat storage loop, install an electric control valve and a second temperature sensor at the water inlet and outlet of the heat storage water tank, and install a second variable - frequency circulation pump and a flow meter in the heating loop; Adjust the rotation speed of the first variable - frequency circulation pump based on the temperature data detected by the first temperature sensor, control the opening degree of the electric control valve based on the temperature data detected by the second temperature sensor, and adjust the rotation speed of the second variable - frequency circulation pump based on the flow data detected by the flow meter to achieve the coordinated control of the multi - energy coupling heating system.

[0007] Calculate the hourly heat output of the solar collector according to the solar radiation intensity data in the outdoor meteorological parameters, determine the heat collection area of the solar collector based on the hourly heat output data, determine the heat storage volume of the heat storage water tank according to the matching relationship between the building hourly heating load curve and the hourly heat output of the solar collector, and establish the heating performance curve of the air - source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters, including: The outdoor meteorological parameters include solar radiation intensity data and ambient temperature data, and the building heating load data includes the building hourly heating load curve; Calculate the solar altitude angle and azimuth angle according to the solar radiation intensity data, determine the solar radiation incident angle based on the solar altitude angle and the azimuth angle, obtain the incident angle correction coefficient of the solar collector, substitute the incident angle correction coefficient, the optical efficiency of the collector, and the heat loss coefficient of the collector into the collector efficiency calculation formula, and iteratively calculate the hourly heat output of the solar collector; Extract the load data of the period from 9:00 to 15:00 from the building hourly heating load curve, select the heating load value in the range of 40% to 60% during this period as the design condition load, divide the design condition load by the corresponding moment value of the hourly heat output, and determine the heat collection area of the solar collector; Multiply the heat collection area by the hourly heat output to obtain the actual heat output of the solar collector during the heating season. Match the actual heat output with the hourly heating load curve of the building hour by hour, and count the surplus heat when the actual heat output is greater than the building heating load data and the deficit heat when the actual heat output is less than the building heating load data, so as to obtain the cumulative heat storage during continuous heat storage and the cumulative heat release during continuous heat release. By analyzing the data sequence of the difference between the heat storage and heat release during the heating season, select the maximum cumulative heat storage corresponding to the 95% guarantee rate as the theoretical heat storage capacity of the heat storage water tank, and increase the theoretical heat storage capacity by 20% to 30% to obtain the heat storage volume of the heat storage water tank. Based on the ambient temperature data, obtain the heating capacity data and input power data of the air source heat pump unit at intervals of 5 °C in the temperature range from -15 °C to 15 °C through experimental tests. Use the least squares method to establish a quadratic polynomial relationship between the heating capacity and the ambient temperature and a quadratic polynomial relationship between the input power and the ambient temperature respectively, so as to obtain the heating performance curve of the air source heat pump unit.

[0008] Inputting the hourly heat output of the solar collector, the heat storage volume of the heat storage water tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model includes: Construct a heat balance equation of the heat storage water tank according to the surplus heat and the heat storage volume parameter of the heat storage water tank. Substitute the surplus heat into the heat balance equation to calculate the dynamic heat storage of the heat storage water tank, and determine the adjustment coefficient of the heat collection circulation flow based on the dynamic heat storage. Combine the heating performance curve of the air source heat pump unit with the deficit heat, calculate the system energy efficiency ratio when supplementing the deficit heat at different ambient temperatures, and select the temperature range where the system energy efficiency ratio is greater than 3 as the high-efficiency operation range of the air source heat pump unit. Obtain the future 24-hour weather forecast data, substitute the weather forecast data into the heating performance curve to obtain the predicted heating performance, and input the predicted heating performance, the dynamic heat storage, the surplus heat and the deficit heat into the rolling horizon optimization model. Establish an optimization objective function in the rolling horizon optimization model, with the maximum system comprehensive energy efficiency ratio as the goal, and use the heat supply balance constraint and temperature constraint as boundary conditions. The heat supply balance constraint includes that the combination of the hourly heat output data sequence, the dynamic heat storage and the predicted heating performance is greater than the building heating load. Use the mixed integer linear programming method to solve the optimization objective function, obtain the optimal operation parameters of the solar heat collection system, the heat storage water tank and the air source heat pump unit within the 24-hour optimization period, and roll and update the optimal operation parameters to obtain the complete operation strategy for the heating season.

[0009] Calculating the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model, and determining the input temperature threshold of the solar collector and the operation period division of the air source heat pump unit according to the optimal combined operation plan, including: Constructing a time series database with the environmental temperature data, the solar radiation intensity data, the building heat load data, the collector efficiency data, and the heat pump performance data; discretizing the time parameters in the time series database at one-hour intervals and the temperature parameters at one-degree Celsius intervals to obtain discretized parameters; performing piecewise linearization on the relationship curve between the collector efficiency and temperature and the relationship curve between the heat pump performance data and the environmental temperature to obtain the piecewise linearization result; Inputting the discretized parameters and the piecewise linearization result into the system operation optimization model, using the branch and bound method to calculate the feasible solution space for each time step, and obtaining the optimal solution that meets the convergence condition through iterative calculation; Analyzing the corresponding relationship between the solar collector efficiency and the inlet temperature based on the optimal solution, plotting the efficiency-temperature curve, determining the inflection point temperature at which the efficiency drops by more than the preset drop threshold, and setting the inflection point temperature as the input temperature threshold of the solar collector; Analyzing the variation law of the coefficient of performance of the air source heat pump unit with time based on the optimal solution, and combining the environmental temperature distribution and the building heat consumption law to divide the operation period into a priority operation period, a peak shaving operation period, and a restricted operation period.

[0010] When the outdoor temperature is higher than the input temperature threshold of the solar collector, the solar collector is preferentially started for heating, and the remaining heat is stored in the hot water storage tank. When the outdoor temperature is lower than the input temperature threshold of the solar collector, the air source heat pump unit is started for heating according to the operation period division, and at the same time, the heat in the hot water storage tank is released for heating supplement, including: When the outdoor temperature is higher than the input temperature threshold of the solar collector, the solar radiation intensity and the collector panel temperature are detected. When the solar radiation intensity is higher than the preset radiation threshold and the collector panel temperature is higher than the preset temperature difference of the return water temperature, the solar collector is preferentially started for heating; a variable frequency water pump is used to adjust the circulation flow rate of the solar collector, and the circulation flow rate is dynamically adjusted according to the real-time heat collection efficiency to keep the outlet temperature of the solar collector within the optimal temperature range; Calculate the difference between the heat supply of the solar collector and the building heat load in real time. When the heat supply is greater than the building heat load, store the excess heat in the hot water storage tank and control the temperature at the top of the hot water storage tank not to exceed the maximum temperature limit value. When the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the division of the operation period. During the priority operation period, when the outdoor temperature is higher than the first temperature threshold, control the air source heat pump unit to operate at full power. When the outdoor temperature is between the second temperature threshold and the first temperature threshold, control the air source heat pump unit to perform load tracking operation. During the peak shaving operation period, determine the supplementary heating power of the air source heat pump unit according to the user's heat demand, and use the fuzzy control algorithm to optimize the operation frequency of the air source heat pump unit. During the restricted operation period, release the heat stored in the hot water storage tank for heating supplement. When the outdoor temperature is lower than the set temperature value, control the air source heat pump unit to maintain the minimum power operation. While the air source heat pump unit is operating, continuously release the heat in the hot water storage tank for heating supplement.

[0011] In the second aspect of the embodiments of the present invention, Provide an electronic device, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0012] In the third aspect of the embodiments of the present invention, Provide a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0013] The beneficial effects of this application are as follows: 1. Energy-saving effect: Through the combined operation of the solar collector and the air source heat pump, the free heat provided by the solar energy is preferentially utilized, reducing the operation time of the air source heat pump, thereby reducing the overall energy consumption of the system and realizing energy-saving heating.

[0014] 2. Improve heating efficiency: This patent accurately calculates the heating demand of the building through the heating load prediction model, and determines the optimal combined operation plan of the solar collector and the air source heat pump according to the outdoor meteorological parameters and the system operation optimization model, thereby improving the heating efficiency of the system and ensuring the stability and reliability of heating.

[0015] 3. Optimize system operation: By setting the input temperature threshold of the solar collector and the operation period of the air source heat pump, the intelligent operation of the multi-energy coupling system is realized, energy waste is avoided, and the service life of the system is extended. Brief Description of the Drawings

[0016] Figure 1 It is a schematic flow chart of the multi-energy coupling air source heat pump heating selection and operation method according to the embodiment of the present invention; Figure 2 It is a schematic diagram for comparing the building heating load distribution curves according to the embodiment of the present invention; Figure 3 It is a schematic diagram of the change curve of the dynamic heat storage capacity of the heat storage water tank according to the embodiment of the present invention; Figure 4 It is a schematic diagram for analyzing the convergence characteristics of the optimization algorithm according to the embodiment of the present invention; Figure 5 It is a schematic diagram for analyzing the heat supply composition and energy efficiency of the 24-hour system according to the embodiment of the present invention. Detailed Embodiments

[0017] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0019] Figure 1 It is a schematic flow chart of the multi-energy coupling air source heat pump heating selection and operation method according to the embodiment of the present invention, as Figure 1 shown, the method includes: Collect the heating area of the building, the building insulation performance parameters, and the outdoor meteorological parameters. Determine the heat loss coefficient of the building according to the building insulation performance parameters and the building heating area. Input the building heat loss coefficient and the outdoor meteorological parameters into the heating load prediction model to obtain the building hourly heating load curve; Determine the peak heating load value of the building based on the building hourly heating load curve, select the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connect the air source heat pump unit with the solar collector, the heat storage water tank, and the heating terminal to construct a multi-energy coupling air source heat pump system; Calculate the hourly heat output of the solar collector according to the solar radiation intensity data in the outdoor meteorological parameters, determine the heat collection area of the solar collector based on the hourly heat output, determine the heat storage volume of the hot water storage tank according to the matching relationship between the hourly heating load curve of the building and the hourly heat output of the solar collector, and establish the heating performance curve of the air source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters. Input the hourly heat output of the solar collector, the heat storage volume of the hot water storage tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model; Calculate the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model. Determine the input temperature threshold of the solar collector and the operation period of the air source heat pump unit according to the optimal combined operation plan. When the outdoor temperature is higher than the input temperature threshold of the solar collector, give priority to starting the solar collector for heating and store the remaining heat in the hot water storage tank. When the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the operation period, and at the same time release the heat in the hot water storage tank for heating supplement.

[0020] In an optional implementation manner, determine the peak heating load value of the building based on the hourly heating load curve of the building, select the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connect the air source heat pump unit with the solar collector, the hot water storage tank, and the heating terminal to construct a multi-energy coupled air source heat pump system, including: Collect the hourly heating load data of the building during the heating season, generate the hourly heating load curve of the building according to the hourly heating load data during the heating season, analyze the change characteristics of the hourly heating load curve of the building, obtain the peak-valley distribution of the building heating load, identify the time period and duration when the load peak appears during the heating season based on the peak-valley distribution of the building heating load, and determine the load value under 95% guarantee rate during the heating season as the peak heating load value of the building; Select the heating capacity specification of the air source heat pump unit according to the peak heating load value of the building and the lowest design temperature during the local heating season. The method for selecting the heating capacity specification of the air source heat pump unit is: divide the peak heating load value of the building by the defrosting coefficient of 0.85 to 0.9 to obtain the heating capacity of the air source heat pump unit, and select the air source heat pump unit that meets the heating requirements based on the heating capacity; Construct a multi - energy coupling heating system with the air - source heat pump unit as the core. The construction method of the multi - energy coupling heating system is as follows: Connect the water outlet of the solar collector to the upper water inlet of the hot - water storage tank to form a heat - collection and heat - storage loop. Connect the lower water outlet of the hot - water storage tank to the water inlet of the evaporator of the air - source heat pump unit to form a heat - source - side loop. Connect the water outlet of the condenser of the air - source heat pump unit to the water inlet of the heating terminal to form a heating loop. Install a first variable - frequency circulation pump and a first temperature sensor in the heat - collection and heat - storage loop, install an electric control valve and a second temperature sensor at the water inlet and outlet of the hot - water storage tank, and install a second variable - frequency circulation pump and a flowmeter in the heating loop. Adjust the rotation speed of the first variable - frequency circulation pump based on the temperature data detected by the first temperature sensor, control the opening degree of the electric control valve based on the temperature data detected by the second temperature sensor, and adjust the rotation speed of the second variable - frequency circulation pump based on the flow data detected by the flowmeter to achieve the coordinated control of the multi - energy coupling heating system.

[0021] Collect the building heating load data and determine the peak heating load value. First, during the heating season, collect the building heating load data once an hour and continuously collect the data for the entire heating season. For example, the hourly heating load data of the building can be obtained from the building's building automation system or measured by a heat meter installed on the return pipe of the heating system. Plot the collected data as a building hourly heating load curve. By analyzing this curve, the peak - valley distribution of the building heating load can be observed. For example, it can be found that the load is higher during the day on weekdays and lower at night; the load on weekends is generally lower than on weekdays. According to the load curve, identify the time period and duration when the load peak occurs during the heating season. For example, it may be found that 18:00 - 21:00 every day is the load peak period, with a duration of 3 hours. To determine the building peak heating load value, use the percentile method and select the load value at the 95% confidence level during the heating season. For example, sort all the collected hourly heating load data from smallest to largest, and take the value corresponding to the 95% position after sorting as the building's peak heating load value. Suppose this value is 100 kW.

[0022] Select an air source heat pump unit according to the peak heating load value of the building. Based on the peak heating load value of the building determined in the previous step, such as 100 kW, and the lowest design temperature during the local heating season, such as -10°C, select a suitable air source heat pump unit. Considering that the air source heat pump unit needs to defrost in a low-temperature environment, it is necessary to divide the peak heating load value of the building by a defrosting coefficient to determine the heating capacity specification of the air source heat pump unit. The defrosting coefficient usually ranges from 0.85 to 0.9. For example, dividing 100 kW by 0.85 gives 117.6 kW. Then, select an air source heat pump unit that meets the heating requirements according to the calculated heating capacity. An air source heat pump unit with a heating capacity of 120 kW can be selected to ensure that the heating demand of the building can be met even in a low-temperature environment.

[0023] Construct a multi-energy coupled air source heat pump heating system. With the selected air source heat pump unit as the core, construct a multi-energy coupled heating system. Connect the outlet of the solar collector to the upper inlet of the hot water storage tank to form a heat collection and storage loop. Connect the lower outlet of the hot water storage tank to the inlet of the evaporator of the air source heat pump unit to form a heat source side loop. Connect the outlet of the condenser of the air source heat pump unit to the inlet of the heating terminal to form a heating loop. Install a first variable-frequency circulation pump and a first temperature sensor in the heat collection and storage loop. Install an electric control valve and a second temperature sensor at the inlet and outlet of the hot water storage tank. Install a second variable-frequency circulation pump and a flow meter in the heating loop.

[0024] Achieve the coordinated control of the multi-energy coupled heating system. Through various sensors and controllers, achieve the coordinated control of the system. The first temperature sensor detects the temperature at the outlet of the solar collector and transmits the data to the controller. The controller adjusts the speed of the first variable-frequency circulation pump according to the set temperature threshold to control the flow rate of the heat collection cycle. For example, when the temperature at the outlet of the collector is higher than the set value, increase the speed of the circulation pump to accelerate the heat collection speed; otherwise, decrease the speed of the circulation pump. The second temperature sensor detects the temperature at the inlet and outlet of the hot water storage tank and transmits the data to the controller. The controller controls the opening of the electric control valve according to the set temperature threshold to adjust the temperature of the hot water entering the evaporator of the air source heat pump. For example, when the temperature of the hot water storage tank is relatively high, increase the opening of the electric control valve to allow more hot water to enter the evaporator; otherwise, decrease the opening. The flow meter detects the flow rate of the heating loop and transmits the data to the controller. The controller adjusts the speed of the second variable-frequency circulation pump according to the heating demand of the building to control the flow rate of the heating water. For example, when the heating demand increases, increase the speed of the circulation pump to increase the flow rate of the heating water; otherwise, decrease the speed of the circulation pump.

[0025] Figure 2 Schematic diagram for comparing the building heating load distribution curves of the embodiments of the present invention: A detailed analysis and comparison of the building heating load distribution curves show that in a continuous 24-hour operation cycle, this technical solution (an intelligent control solution using a multi-energy coupled air source heat pump system, combined with a solar-air source heat pump combined energy supply mode and an intelligent hierarchical heat storage control strategy) demonstrates the optimal load adaptation ability. This solution can maintain a maximum heating capacity of 52 kW during the peak load period from 10:00 to 14:00, and still maintain a basic heating output of 36 - 37 kW during the low load period from 3:00 to 6:00 at night. In contrast, the load curve of the traditional control solution (an air source heat pump system using conventional PID control) is generally about 3 - 4 kW lower than this technical solution as a whole, with a maximum load of only 48 kW and a minimum load dropping to 34 kW; while the fixed-frequency control solution (a traditional heat pump system using a fixed-frequency compressor) has the worst load adaptation ability due to insufficient regulation flexibility, with a peak load of only 46 kW and a significant attenuation of capacity during the load trough period, with a minimum of only 32 kW. The data proves that this technical solution has the best load adaptation ability throughout the whole period, with a small load fluctuation rate, and better meets the dynamic heating demand of the building.

[0026] Regarding the problem of building heating load distribution, the existing technologies generally use a single air source heat pump system with conventional PID control or a traditional heat pump system with a fixed-frequency compressor for heating regulation. These technical solutions have the following problems: on the one hand, the single heat pump system has limited regulation ability when the load fluctuates greatly and is difficult to respond to the dynamic heating demand of the building in a timely manner; on the other hand, due to the fixed operating frequency of the compressor in the fixed-frequency system, it lacks operating flexibility and will cause the system to start and stop frequently during the load trough period, affecting the heating effect and the service life of the equipment.

[0027] This solution first collects the hourly heating load data of the building during the heating season, analyzes the change characteristics of the load curve, accurately identifies the time period and duration of the load peak during the heating season, and thus scientifically determines the peak heating load value of the building. On this basis, the solar collector is coupled and integrated with the air source heat pump to construct a multi-energy complementary energy supply system, and an intelligent hierarchical heat storage control strategy is innovatively introduced to realize the cascade utilization of heat.

[0028] First of all, the accurate selection based on load data analysis and the design of the multi-energy coupled system significantly improve the system's response ability to the dynamic heating demand of the building; secondly, through the combined energy supply mode of solar energy and heat pump, the problem of insufficient regulation ability of a single heat source system is effectively solved; thirdly, the application of the intelligent hierarchical heat storage control strategy realizes the smooth transition of the system operation and avoids frequent start and stop of the equipment. Generally speaking, this application significantly improves the load adaptation ability and operation stability of the heating system, realizes the accurate satisfaction of the building heating demand, and at the same time improves the overall energy efficiency level of the system.

[0029] In an alternative embodiment, the hourly heat output of the solar collector is calculated based on the solar radiation intensity data in the outdoor meteorological parameters, the collector area of the solar collector is determined based on the hourly heat output data, the heat storage volume of the hot water storage tank is determined according to the matching relationship between the hourly heating load curve of the building and the hourly heat output of the solar collector, and the heating performance curve of the air source heat pump unit is established based on the ambient temperature data in the outdoor meteorological parameters, including: The outdoor meteorological parameters include solar radiation intensity data and ambient temperature data, and the building heating load data includes the hourly heating load curve of the building; The solar altitude angle and azimuth angle are calculated based on the solar radiation intensity data, the solar radiation incident angle is determined based on the solar altitude angle and the azimuth angle, the incident angle correction coefficient of the solar collector is obtained, and the incident angle correction coefficient, the optical efficiency of the collector, and the heat loss coefficient of the collector are substituted into the collector efficiency calculation formula to iteratively calculate the hourly heat output of the solar collector; The load data in the time period from 9:00 to 15:00 is extracted from the hourly heating load curve of the building, and the heating load value in the range of 40% to 60% in this time period is selected as the design condition load, and the collector area of the solar collector is determined by dividing the design condition load by the corresponding moment value of the hourly heat output; The collector area is multiplied by the hourly heat output to obtain the actual heat output of the solar collector during the heating season. The actual heat output is matched with the hourly heating load curve of the building hour by hour, and the surplus heat when the actual heat output is greater than the building heating load data and the deficit heat when the actual heat output is less than the building heating load data are statistically obtained to obtain the cumulative heat storage during continuous heat storage and the cumulative heat release during continuous heat release; By analyzing the data sequence of the difference between the heat storage and heat release during the heating season, the maximum cumulative heat storage corresponding to the 95% guarantee rate is selected as the theoretical heat storage capacity of the hot water storage tank, and the heat storage volume of the hot water storage tank is obtained by increasing the theoretical heat storage capacity by 20% to 30%; Based on the ambient temperature data, the heating capacity data and input power data of the air source heat pump unit at intervals of 5 °C in the temperature range from -15 °C to 15 °C are obtained through experimental tests. The quadratic polynomial relationship between the heating capacity and the ambient temperature and the quadratic polynomial relationship between the input power and the ambient temperature are established by using the least square method to obtain the heating performance curve of the air source heat pump unit.

[0030] Obtain outdoor meteorological parameters and building heating load data. The outdoor meteorological parameters include hourly solar radiation intensity data and ambient temperature data throughout the year. The building heating load data includes the hourly building heating load curve throughout the year. For example, the solar radiation intensity data for a certain day in a certain area is the value in watts per square meter recorded per hour, and the ambient temperature data is the value in degrees Celsius recorded per hour; the heating load curve of a certain building in this area is the value in kilowatts recorded per hour.

[0031] Calculate the solar altitude angle and azimuth angle based on the solar radiation intensity data. Using the known date, time, and geographical location information, calculate the solar altitude angle and azimuth angle at each moment through astronomical algorithms. For example, at 9 am on January 1, 2024, the solar altitude angle in this area is 15 degrees, and the azimuth angle is 120 degrees.

[0032] Determine the solar radiation incident angle and calculate the incident angle correction coefficient. According to the collector installation tilt angle and azimuth angle, combined with the calculated solar altitude angle and azimuth angle, calculate the solar radiation incident angle on the collector surface. Then, according to the type and structure of the collector, consult relevant literature or conduct experimental tests to obtain the incident angle correction coefficient of the collector, which represents the change in the heat collection efficiency under different incident angles. For example, when the solar radiation incident angle is 30 degrees, the incident angle correction coefficient of this collector is 0.95.

[0033] Iteratively calculate the hourly heat output of the solar collector. Substitute parameters such as the collector optical efficiency, collector heat loss coefficient, incident angle correction coefficient, and solar radiation intensity into the collector efficiency calculation formula, and finally obtain the heat output of the solar collector at each moment through an iterative calculation method. For example, the solar radiation intensity at 9 am is 500 watts per square meter. After calculation, the heat output of this solar collector at this moment is 300 watts.

[0034] Extract the design condition load. Extract the load data from 9 am to 3 pm from the building hourly heating load curve. Select the heating load values in the range of 40% to 60% during this period as the design condition load. For example, the heating load data during this period is 40 kW, 45 kW, 50 kW, 55 kW, 60 kW, and 65 kW, then select 50 kW as the design condition load.

[0035] Determine the heat collection area of the solar collector. Divide the design condition load by the hourly heat output at the corresponding moment to obtain the heat collection area of the solar collector. For example, the design condition load is 50 kW, and the hourly heat output at the corresponding moment is 0.3 kW, then the heat collection area is 166.67 square meters.

[0036] Calculate the actual heat output of the solar collector during the heating season. Multiply the collector area by the hourly heat output at each moment to obtain the data sequence of the actual heat output of the solar collector during the heating season. For example, if the hourly heat output at 10 am is 0.35 kW, the actual heat output at this moment is 58.33 kW.

[0037] Statistically calculate the cumulative heat storage and cumulative heat release. Match the actual heat output with the building hourly heating load curve hour by hour. When the actual heat output is greater than the building heating load, accumulate the surplus heat to obtain the cumulative heat storage during the continuous heat storage period; when the actual heat output is less than the building heating load, accumulate the insufficient heat to obtain the cumulative heat release during the continuous heat release period.

[0038] Determine the heat storage volume of the heat storage water tank. Analyze the data sequence of the difference between heat storage and heat release during the heating season, and select the maximum cumulative heat storage corresponding to the 95% confidence level as the theoretical heat storage capacity of the heat storage water tank. Increase the theoretical heat storage capacity by 20% to 30% to obtain the heat storage volume of the heat storage water tank. For example, if the maximum cumulative heat storage corresponding to the 95% confidence level is 1000 kWh, the heat storage volume of the heat storage water tank is 1200 kWh to 1300 kWh.

[0039] Establish the heating performance curve of the air source heat pump unit. Obtain the heating capacity data and input power data of the air source heat pump unit at intervals of 5 °C in the temperature range from -15 °C to 15 °C through experimental tests. Use the least squares method to establish the quadratic polynomial relationship between the heating capacity and the ambient temperature, and the quadratic polynomial relationship between the input power and the ambient temperature, respectively, to obtain the heating performance curve of the air source heat pump unit. For example, when the ambient temperature is 0 °C, the measured heating capacity of the air source heat pump unit is 10 kW, and the input power is 3 kW.

[0040] Improve energy utilization efficiency: Make full use of solar energy resources, reduce dependence on traditional energy sources, and reduce heating costs. Improve indoor thermal comfort: Provide a stable heating heat source, improve the stability of indoor temperature, and enhance living comfort. Reduce carbon emissions: Use clean energy for heating, reduce emissions of greenhouse gases such as carbon dioxide, and protect the environment.

[0041] In an alternative embodiment, inputting the hourly heat output of the solar collector, the heat storage volume of the heat storage water tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model includes: Construct a heat balance equation for the heat storage water tank according to the surplus heat and the heat storage volume parameter of the heat storage water tank, substitute the surplus heat into the heat balance equation to calculate the dynamic heat storage of the heat storage water tank, and determine the adjustment coefficient of the heat collection circulation flow rate based on the dynamic heat storage. Combine the heating performance curve of the air source heat pump unit with the insufficient heat quantity, calculate the system energy efficiency ratio when supplementing the insufficient heat quantity at different ambient temperatures, and select the temperature range where the system energy efficiency ratio is greater than three as the high-efficiency operation range of the air source heat pump unit; Obtain the weather forecast data for the next 24 hours, substitute the weather forecast data into the heating performance curve to obtain the predicted heating performance, and input the predicted heating performance, the dynamic heat storage quantity, the surplus heat quantity, and the insufficient heat quantity into the rolling horizon optimization model; Establish an optimization objective function in the rolling horizon optimization model, aiming at the maximum system comprehensive energy efficiency ratio, and use the heat supply balance constraint and the temperature constraint as boundary conditions. The heat supply balance constraint includes that the combination of the hourly heat production data sequence, the dynamic heat storage quantity, and the predicted heating performance is greater than the building heating load; Use the mixed integer linear programming method to solve the optimization objective function, obtain the optimal operation parameters of the solar collector system, the hot water storage tank, and the air source heat pump unit within the 24-hour optimization period, and roll and update the optimal operation parameters to obtain the complete operation strategy for the heating season.

[0042] A solar-air source heat pump combined heating system and its control method based on predicted weather and heat balance can achieve the high-efficiency operation of the system during the heating season.

[0043] The system includes a solar collector, a hot water storage tank, and an air source heat pump unit. The core of the control method lies in predicting the system operation condition for the next 24 hours and optimizing the operation parameters of each device accordingly.

[0044] First, it is necessary to obtain the hourly heat production data of the solar collector, which can be measured in real time by sensors. For example, the heat production from 10:00 to 11:00 is 5 kWh. At the same time, obtain the heat storage volume parameter of the hot water storage tank. For example, the total volume of the hot water storage tank is 1 ton. Assuming that 4.2 kJ of heat is required to raise the water temperature by 1 °C, the heat storage volume of the hot water storage tank is 4200 kWh / °C. It is also necessary to obtain the heating performance curve of the air source heat pump unit, which describes the relationship between the heat production and power consumption of the heat pump at different ambient temperatures. For example, when the ambient temperature is 0 °C, the heat production of the heat pump is 10 kW and the power consumption is 3 kW; when the ambient temperature is -5 °C, the heat production of the heat pump is 8 kW and the power consumption is 4 kW.

[0045] Next, based on the heat production of the solar collector and the heat storage volume parameter of the hot water storage tank, establish the heat balance equation of the hot water storage tank. Substitute the surplus heat of the solar collector (i.e., the heat exceeding the current heating demand) into the heat balance equation to calculate the heat storage of the hot water storage tank at the next moment. For example, at the current moment, the water temperature of the hot water storage tank is 40°C, and the stored heat is 40°C * 4200 kWh / °C = 168000 kWh. The heat production of the solar collector is 5 kWh, and the current heating demand is 3 kWh. Then the surplus heat is 2 kWh, and the heat storage of the hot water storage tank at the next moment is 168000 kWh + 2 kWh = 168002 kWh, and the corresponding water temperature is approximately 40.0005°C. Based on the calculated dynamic heat storage of the hot water storage tank, determine the adjustment coefficient of the heat collection circulation flow rate. For example, when the heat storage of the hot water storage tank is high, reduce the heat collection circulation flow rate; otherwise, increase the heat collection circulation flow rate.

[0046] Then, combine the heating performance curve of the air source heat pump unit with the insufficient heat (i.e., the heating demand that cannot be met by the solar collector and the hot water storage tank) to calculate the system energy efficiency ratio (heating capacity / electricity consumption) when supplementing the insufficient heat at different ambient temperatures. For example, when the ambient temperature is 0°C and the insufficient heat is 2 kW, the heat pump needs to run for an additional period of time to supplement this 2 kW of heat. Assume that the heat pump runs for 0.2 hours at this time, then the electricity consumption is 0.2 hours * 3 kW / hour = 0.6 kWh, and the system energy efficiency ratio is 2 kWh / 0.6 kWh = 3.33. Select the temperature range where the system energy efficiency ratio is greater than 3 as the high-efficiency operation range of the air source heat pump unit.

[0047] Obtain the weather forecast data for the next 24 hours. For example, the temperatures for the next 24 hours are -2°C, -1°C, 0°C... Substitute the weather forecast data into the heating performance curve of the air source heat pump unit to obtain the predicted heating performance. Input the predicted heating performance, the dynamic heat storage of the hot water storage tank, the surplus heat of the solar collector, and the insufficient heat into the rolling horizon optimization model.

[0048] In the rolling horizon optimization model, with the maximum system comprehensive energy efficiency ratio as the goal, establish the optimization objective function. Take the heat supply balance constraint and the temperature constraint as boundary conditions. The heat supply balance constraint means that the combination of the heat production of the solar collector, the heat storage of the hot water storage tank, and the heating capacity of the air source heat pump unit needs to be greater than the building heating load. The temperature constraint means that it is necessary to ensure that the indoor temperature of the building is within a comfortable range.

[0049] The optimization objective function is solved using the mixed-integer linear programming method to obtain the optimal operating parameters of the solar thermal collector system, the hot water storage tank, and the air source heat pump unit within a 24-hour optimization period. For example, in the time period from 10:00 to 11:00, the heat collection circulation flow rate of the solar collector is 10 L / min, the set temperature value of the hot water storage tank is 45 °C, and the air source heat pump unit is turned off. The optimal operating parameters are updated iteratively to obtain the complete operating strategy for the entire heating season.

[0050] Figure 3 Schematic diagram of the dynamic heat storage capacity change curve of the hot water storage tank in the embodiment of the present invention: The circular data points in the figure represent the technical solution of the present invention. By adopting a dynamic optimization heat storage capacity regulation strategy, integrating a heat balance prediction model, a dynamic capacity optimization algorithm, and a multi-variable coordinated control model, the heat storage capacity of the system can be flexibly adjusted within the range of 65 - 92 kWh. Especially during the high-load period from 10:00 to 14:00, it can be maintained at a high level of 85 - 92 kWh, while during the low-load period from 3:00 to 6:00, the basic heat storage capacity remains at 65 - 75 kWh. The triangular data points represent the conventional constant volume control scheme, which uses a fixed capacity setting model and a simple temperature stratification control algorithm, resulting in a limited range of heat storage capacity change between 55 - 80 kWh. The square data points represent the fixed heat storage strategy, which uses a basic timing control model and a single charge-discharge switching algorithm, and the heat storage capacity only changes between 50 - 75 kWh. By comparison, it can be seen that the technical solution of the present invention significantly improves the heat storage regulation ability and energy utilization efficiency of the system.

[0051] In the field of dynamic regulation of hot water storage tanks, the prior art mainly adopts two implementation schemes: one is the conventional constant volume control scheme that uses a fixed capacity setting model and a simple temperature stratification control algorithm. Due to the fixed control strategy, it is unable to flexibly adjust the heat storage capacity according to the actual heating demand, resulting in difficulty for the system to respond in a timely manner when the load fluctuates greatly; the other is the fixed heat storage strategy that uses a basic timing control model and a single charge-discharge switching algorithm. This scheme only performs simple charge-discharge switching based on the preset time and lacks the ability to optimize the real-time operating state of the system, resulting in low energy utilization efficiency of the hot water storage tank.

[0052] This strategy first predicts the future operating state of the system through a heat balance prediction model, then uses a dynamic capacity optimization algorithm to calculate the optimal heat storage capacity in real time, and combines a multi-variable coordinated control model to precisely adjust the system. This innovative technical solution not only considers the dynamic heating demand of the building but also makes full use of the regulation potential of the system to achieve intelligent management of the heat storage capacity.

[0053] First, based on the real-time calculation of the heat balance prediction model, the response speed of the system to changes in heating demand has been significantly improved. Secondly, through the application of the dynamic capacity optimization algorithm, the heat storage regulation range of the system has been significantly expanded, and the utilization efficiency of the heat storage water tank has been improved. Thirdly, the introduction of the multi-variable coordinated control model has achieved a smooth transition of the system operation and avoided the violent fluctuations in the traditional control scheme. Overall, this application has greatly improved the regulation flexibility and energy utilization efficiency of the heat storage water tank, providing a reliable guarantee for the stable operation of the building heating system.

[0054] In an alternative embodiment, based on the system operation optimization model, calculate the optimal combined operation plan of the solar collector and the air source heat pump unit. Determining the input temperature threshold of the solar collector and the operation period division of the air source heat pump unit according to the optimal combined operation plan includes: Construct a time series database with the environmental temperature data, the solar radiation intensity data, the building heat load data, the collector efficiency data, and the heat pump performance data; discretize the time parameters in the time series database at one-hour intervals and the temperature parameters at one-degree Celsius intervals to obtain discretized parameters; perform piecewise linearization on the relationship curve between the collector efficiency and temperature and the relationship curve between the heat pump performance data and the environmental temperature to obtain the piecewise linearization result; Input the discretized parameters and the piecewise linearization result into the system operation optimization model, use the branch and bound method to calculate the feasible solution space for each time step, and obtain the optimal solution that meets the convergence condition through iterative calculation; Based on the optimal solution, analyze the corresponding relationship between the solar collector efficiency and the inlet temperature, draw the efficiency-temperature curve, determine the inflection point temperature at which the efficiency drops by more than the preset drop threshold, and set the inflection point temperature as the input temperature threshold of the solar collector; Based on the optimal solution, analyze the variation law of the performance coefficient of the air source heat pump unit with time, and combine the environmental temperature distribution and the building heat consumption law to divide the operation period into a priority operation period, a peak shaving operation period, and a restricted operation period.

[0055] Based on the operation optimization requirements of the multi-energy coupled heating system, a combined operation optimization method for a solar collector and an air source heat pump unit is proposed. First, establish a time series database and collect key operation parameters such as environmental temperature, solar radiation intensity, building heat load, collector efficiency, and heat pump performance. The environmental temperature data is collected in real time by a weather station with a data collection period of one hour; the solar radiation intensity data is measured by a standard pyranometer with a measurement accuracy of ±2%; the building heat load data is obtained by calculating the supply and return water temperature difference and flow rate; the collector efficiency data comes from the laboratory calibration results; the heat pump performance data is based on the performance curve provided by the manufacturer.

[0056] Preprocess the collected data, and discretize the time parameter at one-hour intervals. Taking the winter heating in a certain city as an example, record the hourly data from 6:00 am to 10:00 pm. Discretize the temperature parameter at one-degree Celsius intervals, and the test temperature range is from -20 degrees Celsius to 20 degrees Celsius. Perform piecewise linearization on the relationship curve between the collector efficiency and temperature, divide the curve into three segments: high-efficiency zone, transition zone, and low-efficiency zone, and fit each segment with a linear equation. Similarly, divide the relationship curve between the coefficient of performance of the heat pump and the ambient temperature into three segments: high-efficiency zone, normal zone, and defrosting zone for linearization processing.

[0057] Input the preprocessed parameters into the system operation optimization model, and use the branch and bound method to solve the optimal operation plan. First, set the initial solution space, including decision variables such as the inlet temperature of the collector and the start time of the heat pump. Through iterative calculation, when the relative change rate of the objective function value is less than one percent, it is determined to converge. The calculation results of a certain engineering example show that a stable solution can be obtained after about 20 iterations.

[0058] Based on the optimal solution, analyze the corresponding relationship between the collector efficiency and the inlet temperature, and draw the efficiency-temperature curve. By analyzing the change of the curve slope, it is found that when the inlet temperature rises to 45 degrees Celsius, there is an obvious inflection point in the efficiency, which rapidly drops from 65% to 40%. Accordingly, 45 degrees Celsius is determined as the input temperature threshold of the collector, and it is not suitable to be put into operation when the temperature is higher than this temperature.

[0059] Combine the optimal solution to divide the operation period of the heat pump. The period when the ambient temperature is higher than 5 degrees Celsius and the building heat load is large is designated as the priority operation period, and at this time, the coefficient of performance of the heat pump can reach more than 4; the period when the ambient temperature is between -5 degrees Celsius and 5 degrees Celsius is designated as the peak shaving operation period, which is used to make up for the shortage of solar heating; the period when the ambient temperature is lower than -5 degrees Celsius is designated as the restricted operation period, and at this time, the defrosting load is large and it is not suitable for frequent startup.

[0060] Figure 4 Schematic diagram for analyzing the convergence characteristics of the optimization algorithm in the embodiment of the present invention: The circular data points in the figure represent the measured data of the collector efficiency. This data is obtained by using an adaptive sampling algorithm based on dynamic response characteristics, combined with the multi-point temperature field reconstruction technology and the instantaneous efficiency measurement method, achieving high-precision performance characterization. The solid line represents the efficiency curve after piecewise linear fitting, based on the piecewise optimization algorithm of the least squares method and dynamic weighting. The data shows that the interval of 20 - 35 °C is the high-efficiency zone, where the efficiency linearly decreases from 0.75 to 0.70, with a slope of -0.005 / °C; the interval of 35 - 45 °C is the transition zone, where the efficiency decreases from 0.70 to 0.62, and the slope increases to -0.008 / °C; above 45 °C is the low-efficiency zone, where the efficiency rapidly decreases, and the slope reaches -0.012 / °C. The triangular data points (△) represent the measured values of the heat pump coefficient of performance. The comprehensive performance test method based on energy balance is used, combined with the analysis of transient response characteristics. The solid line is the fitting result of the performance curve based on adaptive segmentation. In the defrosting interval from -20 °C to 0 °C, the COP increases from 2.0 to 2.8, with a slope of 0.04 / °C; in the normal operation interval of 0 - 10 °C, the COP increases from 2.8 to 3.4, and the slope increases to 0.06 / °C; in the high-efficiency interval above 10 °C, the COP continues to increase but the slope decreases to 0.04 / °C. This linearization processing method based on multi-level piecewise optimization not only retains the key characteristics of the system response characteristics but also significantly reduces the computational complexity of subsequent optimization and solution.

[0061] In the field of system performance characteristic characterization, there are mainly two implementation methods in the existing technology: one is to use the single-point measurement method with a fixed sampling interval to obtain performance data. Due to the fixed sampling strategy, it is difficult to accurately capture the dynamic response characteristics of the system, resulting in insufficient performance characterization accuracy when the operating conditions change greatly; the other is to use a simple linear fitting method to process the measured data. This method does not consider the performance differences in different operating condition intervals and uses a unified fitting strategy, resulting in the distortion of characteristics near the key operating points and affecting the accuracy of subsequent optimal control.

[0062] This method first realizes the accurate monitoring of the system state through the multi-point temperature field reconstruction technology, then uses the adaptive sampling algorithm to focus on collecting key operating points, and combines the instantaneous efficiency measurement method to obtain high-precision performance data. In terms of data processing, the dynamic weighted piecewise optimization algorithm based on the least squares method is innovatively introduced, and the piecewise parameters and weight coefficients are adaptively adjusted according to the performance characteristics of different operating condition intervals, realizing the accurate fitting of the performance curve.

[0063] First, the adaptive sampling strategy based on dynamic response characteristics significantly improves the accuracy of system performance characterization, especially the characterization accuracy during drastic changes in operating conditions. Second, by combining the multi-point temperature field reconstruction and the instantaneous efficiency measurement method, the reliability of performance data is significantly improved. Third, the application of the segmented optimization algorithm based on dynamic weighting not only retains the key characteristics of the system in different operating condition intervals but also reduces the complexity of subsequent optimization calculations. Overall, the technical solution provided in this application greatly improves the accuracy of system performance characterization and computational efficiency, laying a solid foundation for the optimal control of multi-energy coupling systems.

[0064] In an alternative embodiment, when the outdoor temperature is higher than the solar collector input temperature threshold, the solar collector is preferentially started for heating, and the remaining heat is stored in the hot water storage tank. When the outdoor temperature is lower than the solar collector input temperature threshold, the air source heat pump unit is started for heating according to the division of the operating period, and at the same time, the heat stored in the hot water storage tank is released for heating supplement, including: When the outdoor temperature is higher than the input temperature threshold of the solar collector, the solar radiation intensity and the collector panel temperature are detected. When the solar radiation intensity is higher than the preset radiation threshold and the collector panel temperature is higher than the preset temperature difference of the return water temperature, the solar collector is preferentially started for heating; a variable-frequency water pump is used to adjust the circulation flow rate of the solar collector, and the circulation flow rate is dynamically adjusted according to the real-time heat collection efficiency so that the outlet temperature of the solar collector is maintained within the optimal temperature range. The difference between the heat supply of the solar collector and the building heat load is calculated in real time. When the heat supply is greater than the building heat load, the excess heat is stored in the hot water storage tank, and the temperature at the top of the hot water storage tank is controlled not to exceed the maximum temperature limit; when the outdoor temperature is lower than the input temperature threshold of the solar collector, the air source heat pump unit is started for heating according to the division of the operating period. During the priority operation period, when the outdoor temperature is higher than the first temperature threshold, the air source heat pump unit is controlled to operate at full power. When the outdoor temperature is between the second temperature threshold and the first temperature threshold, the air source heat pump unit is controlled to perform load tracking operation; during the peak shaving operation period, the supplementary heat power of the air source heat pump unit is determined according to the user's heat demand, and the fuzzy control algorithm is used to optimize the operating frequency of the air source heat pump unit. During the restricted operation period, the heat stored in the hot water storage tank is released for heating supplement. When the outdoor temperature is lower than the set temperature value, the air source heat pump unit is controlled to maintain the minimum power operation; while the air source heat pump unit is operating, the heat stored in the hot water storage tank is continuously released for heating supplement.

[0065] A hybrid heating system based on solar energy and air source heat pump and its control method, aiming to improve energy utilization efficiency and reduce heating costs. The system preferentially uses solar energy for heating, and when solar energy is insufficient, it intelligently controls the operation of the air source heat pump according to the outdoor temperature and operation time period, and combines a hot water storage tank for energy storage and release to achieve efficient and energy-saving heating.

[0066] First, the system performs initialization settings, including setting the input temperature threshold of the solar collector, the preset radiation threshold, the preset temperature difference of the return water temperature, the maximum temperature limit of the hot water storage tank, the temperature thresholds of each operation time period of the air source heat pump, the set temperature value, and the parameters of the fuzzy control algorithm, etc. For example, set the input temperature threshold of the solar collector to 10°C, the preset radiation threshold to 200 W / m², the preset temperature difference of the return water temperature to 5°C, the maximum temperature limit of the hot water storage tank to 80°C, the first temperature threshold to 5°C, the second temperature threshold to 0°C, and the set temperature value to -5°C.

[0067] When the outdoor temperature is higher than the set input temperature threshold of the solar collector (such as 10°C), the system starts to detect the solar radiation intensity and the temperature of the collector panel. Suppose at a certain moment, the detected solar radiation intensity is 300 W / m², the temperature of the collector panel is 25°C, and the return water temperature is 20°C. Since the solar radiation intensity is higher than the preset radiation threshold (200 W / m²) and the temperature of the collector panel is higher than the preset temperature difference of the return water temperature (5°C), the system preferentially starts the solar collector for heating.

[0068] After starting the solar collector, the system uses a variable frequency water pump to adjust the circulation flow rate of the collector. For example, the initial circulation flow rate is set to 1 m³ / h. The system dynamically adjusts the circulation flow rate according to the real-time heat collection efficiency to keep the outlet temperature of the collector within the optimal temperature range (such as 50°C - 60°C). Suppose the current heat collection efficiency is relatively high, the system will appropriately increase the circulation flow rate to 1.2 m³ / h to increase the heat collection amount; otherwise, it will reduce the circulation flow rate.

[0069] The system calculates the difference between the heat supply of the solar collector and the building heat load in real time. Suppose the current heat supply of the solar collector is 10 kW and the building heat load is 8 kW. Since the heat supply is greater than the building heat load, the system stores the excess 2 kW of heat in the hot water storage tank and controls the temperature at the top of the hot water storage tank not to exceed the maximum temperature limit (80°C).

[0070] When the outdoor temperature is lower than the input temperature threshold of the solar collector (e.g., 10°C), the system starts the air source heat pump unit for heating according to the pre-set operation period division. Assume that the current is in the priority operation period and the outdoor temperature is 2°C. Since the outdoor temperature is between the second temperature threshold (0°C) and the first temperature threshold (5°C), the system controls the air source heat pump unit to perform load tracking operation and adjusts the output power according to the real-time heat load demand of the building.

[0071] Assume that the current is in the peak shaving operation period and the user's heat demand is 6 kW. The system uses a fuzzy control algorithm to optimize the operation frequency of the air source heat pump unit to meet the user's heat demand and reduce energy consumption.

[0072] Assume that the current is in the restricted operation period and the outdoor temperature is -8°C. Since the outdoor temperature is lower than the set temperature value (-5°C), the system controls the air source heat pump unit to maintain the minimum power operation, e.g., 2 kW, and at the same time releases the heat stored in the hot water storage tank for heating supplement to make up for the insufficient heat supply of the air source heat pump.

[0073] While the air source heat pump unit is operating, the system continuously releases the heat in the hot water storage tank for heating supplement to ensure the stability of the indoor temperature.

[0074] Figure 5 Schematic diagram of the 24-hour system heat supply composition and energy efficiency analysis for the embodiment of the present invention: This figure shows the heat supply composition and performance changes of the hybrid heating system during a 24-hour operation cycle. The data shows that solar heating plays a dominant role during 8:00 - 16:00. The heat supply gradually rises from 2 kW in the morning, reaches a peak of 15 kW at 12:00, and then gradually decreases as the solar radiation intensity weakens. The heat supply of the heat pump shows a complementary characteristic to solar heating, maintaining a basic heat supply of 6 - 7 kW at night and in the early morning, and dropping to a minimum load of 0 - 1 kW during the day. The heat release from heat storage shows a double-peak characteristic throughout the day, reaching peaks of 3 kW at 6:00 - 8:00 in the morning and 16:00 - 18:00 in the evening, which corresponds to the peak heat consumption period of the building. The system's comprehensive COP changes significantly with the outdoor temperature, reaching a maximum value of 5.2 at 12:00 noon and dropping to a minimum of 2.8 at night, and maintaining an average of over 3.8 throughout the day, reflecting the high-efficiency operation characteristics of the system.

[0075] Improve energy utilization efficiency: Give priority to using clean energy such as solar energy for heating, reduce reliance on traditional energy, and thus improve energy utilization efficiency. Reduce heating costs: By intelligently controlling the operation of the air source heat pump and combining with the hot water storage tank for energy storage and release, the operation cost of the system is effectively reduced. Improve heating comfort: The system can intelligently adjust the heating power according to the outdoor temperature and user demand to ensure the stability of the indoor temperature, thereby improving heating comfort.

[0076] The embodiments of the present invention further include: Firstly, a real-time power demand monitoring module during the heating period is set up. A distributed sensor network is adopted, and power acquisition devices are arranged at key nodes such as heat pump units, circulation pumps, and terminal devices to record the operating power of each device in real time. The acquisition frequency is set to 1 minute / time, and the recorded data includes: device number, timestamp, active power, reactive power, power factor, etc. The original data is preprocessed by an edge computing unit to screen out outliers and supplement missing values.

[0077] Next, a heat demand prediction model for the heating period is established based on historical temperature and humidity data. The outdoor temperature and humidity data for the past 3 heating seasons are collected, and the time granularity is at the hourly level. Combining building physical characteristic parameters (building area, insulation level, orientation, etc.), a multi-dimensional analysis model is established. Taking a certain building as an example: the building area is 20,000 square meters, the thermal conductivity of the exterior wall insulation material is 0.035 W / mK, and the heating period is from November 15th to March 15th of the following year. Through data analysis, it is obtained that under the conditions of outdoor temperature of -12°C and relative humidity of 60%, the heating load of the building is 1.8 MW.

[0078] Then, the heat pump efficiency correction calculation is carried out. The operating parameters such as the inlet and outlet water temperatures, flow rate, and input power of the heat pump are monitored in real time. Taking a certain heat pump as an example: the rated heating capacity is 2000 kW, the rated input power is 400 kW, and the performance coefficient is 5.0 under the design conditions. Considering the influence of the actual operating conditions, a correction model is established. When the inlet water temperature drops by 1°C, the performance coefficient drops by 2%; when the outdoor temperature drops by 1°C, the performance coefficient drops by 3%.

[0079] The system automatically calculates the supply power of the heat pump in the coupling system. According to the corrected heat pump efficiency and combined with the heating load demand, the operating states of each heat pump are dynamically adjusted. High-efficiency devices are preferentially started, and the load is reasonably distributed. When the outdoor temperature is -5°C, the load distribution ratio of 3 heat pumps in a certain system is: 40%, 35%, 25%, and the total heating power is 1500 kW.

[0080] Subsequently, the heating cost of the system is calculated. The electricity price per unit (peak-valley time-of-use pricing), equipment maintenance cost, labor cost, etc. are counted. The peak period electricity price is 1.2 yuan / kWh, and the valley period electricity price is 0.4 yuan / kWh. Through intelligent scheduling, 70% of the operating time is arranged in the valley period, and the average heating cost is 0.25 yuan / kWh.

[0081] Finally, the return on investment of the system is evaluated. The initial investment of the equipment, annual operating cost, energy-saving benefits, etc. are calculated. The initial investment of a certain project is 8 million yuan, the annual operating cost is 1.2 million yuan, and the cost is saved by 1.8 million yuan / year compared with the traditional heating method. The payback period is 4.5 years.

[0082] Implement intelligent management throughout the entire life cycle of the heating system, automatically collect and analyze system operation data, accurately predict heating load requirements, dynamically optimize equipment operation strategies, and form a complete operation and maintenance management closed-loop. Significantly improve the system operation efficiency, with the heat pump operation efficiency increased by 15%, the system failure rate reduced by 30%, the maintenance response time shortened by 50%, and the work efficiency of operation and maintenance personnel increased by 40%. Create significant economic benefits, with the annual operation cost reduced by 25%, the equipment service life extended by 20%, the investment payback period shortened by 1.5 years, and the overall system return rate increased by 35%.

[0083] In the second aspect of the embodiments of the present invention, A kind of electronic device is provided, including: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method described above.

[0084] In the third aspect of the embodiments of the present invention, A computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method described above is implemented.

[0085] The present invention can be a method, a device, a system and / or a computer program product. The computer program product can include a computer-readable storage medium, on which computer-readable program instructions for executing various aspects of the present invention are carried.

[0086] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. Selection and operation method of multi - energy coupled air - source heat pump for heating, characterized in that Including: Collect the heating area of the building, the building insulation performance parameters, and the outdoor meteorological parameters, determine the building heat loss coefficient according to the building insulation performance parameters, input the building heat loss coefficient and the outdoor meteorological parameters into the heating load prediction model to obtain the building hourly heating load curve; determine the peak heating load value of the building based on the building hourly heating load curve, select the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connect the heating capacity specification with the heat storage water tank to construct a multi-energy coupled air source heat pump system; Calculate the hourly heat output of the solar collector according to the solar radiation intensity data in the multi-energy coupled air source heat pump system, determine the heat collection area of the solar collector based on the hourly heat output, determine the heat storage volume of the heat storage water tank according to the matching relationship between the building hourly heating load curve and the hourly heat output, and establish the heating performance curve of the air source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters, and input the hourly heat output of the solar collector, the heat storage volume of the heat storage water tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model; Calculate the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model, determine the input temperature threshold of the solar collector and the operation period division of the air source heat pump unit according to the optimal combined operation plan, give priority to starting the solar collector for heating when the outdoor temperature is higher than the input temperature threshold of the solar collector, and when the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the operation period division, and at the same time release the heat in the heat storage water tank for heating supplement.

2. The method according to claim 1, wherein Determining the peak heating load value of the building based on the building hourly heating load curve, selecting the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connecting the heating capacity specification with the heat storage water tank to construct a multi-energy coupled air source heat pump system includes: Analyze the change characteristics of the building hourly heating load curve, obtain the peak-valley distribution of the building heating load, identify the time period and duration when the load peak appears during the heating season based on the peak-valley distribution of the building heating load, and determine the load value at the 95% guarantee rate during the heating season as the building peak heating load value; Select the heating capacity specification of the air source heat pump unit according to the building peak heating load value and the lowest design temperature during the local heating season, divide the building peak heating load value by the defrosting coefficient of 0.85 to 0.9 to obtain the heating capacity of the air source heat pump unit, and select the air source heat pump unit that meets the heating requirements based on the heating capacity; Construct a multi - energy coupling heating system with the air - source heat pump unit as the core. Connect the outlet of the solar collector to the upper inlet of the hot - water storage tank to form a heat - collection and heat - storage loop. Connect the lower outlet of the hot - water storage tank to the inlet of the evaporator of the air - source heat pump unit to form a heat - source - side loop. Connect the outlet of the condenser of the air - source heat pump unit to the inlet of the heating terminal to form a heating loop. Install a first variable - frequency circulation pump and a first temperature sensor in the heat - collection and heat - storage loop, install an electric control valve and a second temperature sensor at the inlet and outlet of the hot - water storage tank, and install a second variable - frequency circulation pump and a flowmeter in the heating loop; Adjust the rotation speed of the first variable - frequency circulation pump based on the temperature data detected by the first temperature sensor, control the opening degree of the electric control valve based on the temperature data detected by the second temperature sensor, and adjust the rotation speed of the second variable - frequency circulation pump based on the flow data detected by the flowmeter to achieve the coordinated control of the multi - energy coupling heating system.

3. The method according to claim 1, wherein Calculate the hourly heat output of the solar collector according to the solar radiation intensity data in the multi - energy coupling air - source heat pump system. Determine the heat - collection area of the solar collector based on the hourly heat output. Determine the heat - storage volume of the hot - water storage tank according to the matching relationship between the building hourly heating load curve and the hourly heat output. And establish the heating performance curve of the air - source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters, including: The outdoor meteorological parameters include solar radiation intensity data and ambient temperature data, and the building heating load data includes the building hourly heating load curve; Calculate the solar altitude angle and azimuth angle according to the solar radiation intensity data. Determine the solar radiation incident angle based on the solar altitude angle and the azimuth angle to obtain the incident - angle correction coefficient of the solar collector. Substitute the incident - angle correction coefficient, the optical efficiency of the collector, and the heat - loss coefficient of the collector into the collector efficiency calculation formula, and iteratively calculate the hourly heat output of the solar collector; Extract the load data in the time period from 9:00 to 15:00 from the building hourly heating load curve. Select the heating load value in the range of 40% to 60% in this time period as the design - condition load. Divide the design - condition load by the corresponding value of the hourly heat output at that moment to determine the heat - collection area of the solar collector; Multiply the heat - collection area by the hourly heat output to obtain the actual heat output of the solar collector during the heating season. Match the actual heat output with the building hourly heating load curve hour by hour. Statistically analyze the surplus heat when the actual heat output is greater than the building heating load data and the insufficient heat when the actual heat output is less than the building heating load data to obtain the cumulative heat - storage amount during continuous heat - storage periods and the cumulative heat - release amount during continuous heat - release periods; By analyzing the data sequence of the difference between the heat - storage and heat - release amounts during the heating season, select the maximum cumulative heat - storage amount corresponding to the 95% guarantee rate as the theoretical heat - storage capacity of the hot - water storage tank, and increase the theoretical heat - storage capacity by 20% to 30% to obtain the heat - storage volume of the hot - water storage tank; Based on the ambient temperature data, the heating capacity data and input power data of the air source heat pump unit at intervals of five degrees Celsius in the temperature range from minus fifteen degrees Celsius to fifteen degrees Celsius are obtained through experimental tests. The quadratic polynomial relationships between the heating capacity and the ambient temperature, and between the input power and the ambient temperature are established respectively by using the least square method, and the heating performance curve of the air source heat pump unit is obtained.

4. The method according to claim 1, characterized in that, Inputting the hourly heat output of the solar collector, the heat storage volume of the hot water storage tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model includes: Constructing a heat balance equation of the hot water storage tank according to the surplus heat and the heat storage volume parameter of the hot water storage tank, substituting the surplus heat into the heat balance equation to calculate the dynamic heat storage of the hot water storage tank, and determining the adjustment coefficient of the heat collection cycle flow rate based on the dynamic heat storage; Combining the heating performance curve of the air source heat pump unit with the insufficient heat, calculating the system energy efficiency ratio when supplementing the insufficient heat at different ambient temperatures, and selecting the temperature range where the system energy efficiency ratio is greater than three as the high-efficiency operation range of the air source heat pump unit; Obtaining the weather forecast data for the next twenty-four hours, substituting the weather forecast data into the heating performance curve to obtain the predicted heating performance, and inputting the predicted heating performance, the dynamic heat storage, the surplus heat and the insufficient heat into the rolling horizon optimization model; Establishing an optimization objective function in the rolling horizon optimization model, aiming at the maximum system comprehensive energy efficiency ratio, and taking the heat supply balance constraint and the temperature constraint as boundary conditions. The heat supply balance constraint includes that the combination of the hourly heat output data sequence, the dynamic heat storage and the predicted heating performance is greater than the building heating load; Using the mixed integer linear programming method to solve the optimization objective function, obtaining the optimal operation parameters of the solar collector system, the hot water storage tank and the air source heat pump unit within the twenty-four-hour optimization period, and rolling and updating the optimal operation parameters to obtain the complete operation strategy for the heating season.

5. The method according to claim 1, wherein Calculating the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model, and determining the input temperature threshold of the solar collector and the operation period division of the air source heat pump unit according to the optimal combined operation plan, including: Constructing a time series database with the ambient temperature data, the solar radiation intensity data, the building heat load data, the collector efficiency data and the heat pump performance data; discretizing the time parameters in the time series database at one-hour intervals, discretizing the temperature parameters at one-degree Celsius intervals, and performing piecewise linearization processing on the relationship curves between the collector efficiency and the temperature, and between the heat pump performance coefficient and the ambient temperature; Inputting the discretized parameters and the results of the piecewise linearization processing into the system operation optimization model, using the branch and bound method to calculate the feasible solution space at each time step, and obtaining the optimal solution that meets the convergence condition through iterative calculation; Analyze the corresponding relationship between the efficiency of the solar collector and the inlet temperature based on the optimal solution, draw the efficiency-temperature curve, determine the inflection point temperature at which the efficiency drops sharply, and set the inflection point temperature as the input temperature threshold of the solar collector; Analyze the variation law of the performance coefficient of the air source heat pump unit with time based on the optimal solution, and combine the ambient temperature distribution and the heat consumption law of the building to divide the operation period into a priority operation period, a peak shaving operation period, and a restricted operation period.

6. The method according to claim 1, characterized in that When the outdoor temperature is higher than the input temperature threshold of the solar collector, the solar collector is preferentially started for heating. When the outdoor temperature is lower than the input temperature threshold of the solar collector, the air source heat pump unit is started for heating according to the operation period division, and at the same time, the heat in the hot water storage tank is released for heating supplement, including: Detect the outdoor temperature and judge whether the outdoor temperature is higher than the input temperature threshold of the solar collector, and the input temperature threshold of the solar collector is determined based on the solar collector operation efficiency curve; When the outdoor temperature is higher than the input temperature threshold of the solar collector, detect the solar radiation intensity and the collector panel temperature. When the solar radiation intensity is higher than the preset radiation threshold and the collector panel temperature is higher than the preset temperature difference of the return water temperature, preferentially start the solar collector for heating; use a variable frequency water pump to adjust the circulation flow rate of the solar collector, and dynamically adjust the circulation flow rate according to the real-time heat collection efficiency to keep the outlet temperature of the solar collector within the optimal temperature range; Calculate the difference between the heat supply of the solar collector and the building heat load in real time. When the heat supply is greater than the building heat load, store the excess heat in the hot water storage tank and control the top temperature of the hot water storage tank not to exceed the maximum temperature limit; when the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the operation period division; During the priority operation period, when the outdoor temperature is higher than the first temperature threshold, control the air source heat pump unit to operate at full power. When the outdoor temperature is between the second temperature threshold and the first temperature threshold, control the air source heat pump unit to perform load tracking operation; during the peak shaving operation period, determine the heat supplement power of the air source heat pump unit according to the user's heat consumption demand, and use a fuzzy control algorithm to optimize the operation frequency of the air source heat pump unit; During the restricted operation period, release the heat stored in the hot water storage tank for heating supplement. When the room temperature is lower than the set temperature value, control the air source heat pump unit to maintain the minimum power operation; while the air source heat pump unit is operating, continuously release the heat in the hot water storage tank for heating supplement.

7. Multi-functional coupled air source heat pump heating selection and operation system, used to implement the method described in any one of the foregoing claims 1-6, characterized in that, Including: The first unit is used to collect the heating area of the building, the building insulation performance parameters, and the outdoor meteorological parameters, determine the building heat loss coefficient according to the building insulation performance parameters, input the building heat loss coefficient and the outdoor meteorological parameters into the heating load prediction model to obtain the building hourly heating load curve; determine the peak heating load value of the building based on the building hourly heating load curve, select the heating capacity specification of the air source heat pump unit according to the peak heating load value, and connect the heating capacity specification with the heat storage water tank to construct a multi-energy coupling air source heat pump system; The second unit is used to calculate the hourly heat production of the solar collector according to the solar radiation intensity data in the multi-energy coupling air source heat pump system, determine the heat collection area of the solar collector based on the hourly heat production, determine the heat storage volume of the heat storage water tank according to the matching relationship between the building hourly heating load curve and the hourly heat production, and establish the heating performance curve of the air source heat pump unit based on the ambient temperature data in the outdoor meteorological parameters, and input the hourly heat production of the solar collector, the heat storage volume of the heat storage water tank, and the heating performance curve of the air source heat pump unit into the system operation optimization model; The third unit is used to calculate the optimal combined operation plan of the solar collector and the air source heat pump unit based on the system operation optimization model, determine the input temperature threshold of the solar collector and the operation time period division of the air source heat pump unit according to the optimal combined operation plan, preferentially start the solar collector for heating when the outdoor temperature is higher than the input temperature threshold of the solar collector, and when the outdoor temperature is lower than the input temperature threshold of the solar collector, start the air source heat pump unit for heating according to the operation time period division, and at the same time release the heat in the heat storage water tank for heating supplement.

8. An electronic device, characterized in that, Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

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