Heat pump dual-combined supply system, operation control method and device thereof and medium

Abstract

The invention discloses a heat pump dual-combined supply system, an operation control method and device of the heat pump dual-combined supply system and a medium. The method comprises the steps that historical operation data of the heat pump dual-combined supply system are obtained; according to the historical operation data and a pre-established parameter generation model, generating candidate control parameters for the off-peak electricity period; the candidate control parameters are at least input into a simulation model, so that the simulation model simulates the future operation process of the heat pump dual-combined supply system to obtain a simulation result, and the simulation model is a physical model of the heat pump dual-combined supply system and the operation environment of the heat pump dual-combined supply system; and according to the simulation result, target control parameters used for heat storage in the off-peak electricity period are determined, and the heat pump dual-combined supply system is controlled to operate according to the target control parameters in the off-peak electricity period so that the heat pump dual-combined supply system can conduct heat storage in the off-peak electricity period. The method is used for solving the technical problems that the operation cost of the heat pump dual-combined supply system is high, and the power grid load in the peak power period is large.

Description

Technical Field

[0001] This invention belongs to the field of home appliance technology, and in particular relates to a heat pump dual-supply system and its operation control method, device and medium. Background Technology

[0002] "Heat-Fluorine-Water System" is a dual-supply heat pump system combining central air conditioning and underfloor heating. The central air conditioning system at the top uses a "fluorine system" for cooling or heating, while the underfloor heating system uses a "water system" for both. In summer, the "fluorine system" is typically activated alone for cooling, while in winter, both systems can be used simultaneously for heating. The "water system" offers better comfort during continuous operation, but its energy consumption is high, leading to higher operating costs. Current underfloor heating systems primarily use feedback control, relying solely on feedback from the "water system" regarding outlet and return water temperatures to control its operation. This results in an unreasonable allocation of operating time for the heat pump system, leading to higher grid loads during peak electricity periods and increased operating costs. Summary of the Invention

[0003] This invention provides a combined heat pump and power system and its operation control method, device and medium to solve the technical problems of high operating cost and high grid load during peak power periods caused by the combined heat pump and power system.

[0004] In a first aspect of the present invention, an operation control method for a combined heat pump and combined power supply system is provided, comprising: acquiring historical operation data of the combined heat pump and combined power supply system; generating candidate control parameters for off-peak electricity periods based on the historical operation data and a pre-established parameter generation model; inputting at least the candidate control parameters into a simulation model to simulate the future operation process of the combined heat pump and combined power supply system to obtain simulation results, wherein the simulation model is a physical model of the combined heat pump and combined power supply system and its operating environment; determining target control parameters for heat storage during off-peak electricity periods based on the simulation results, and controlling the operation of the combined heat pump and combined power supply system according to the target control parameters during off-peak electricity periods to enable the combined heat pump and combined power supply system to store heat during off-peak electricity periods.

[0005] In conjunction with the first aspect, some embodiments further include: acquiring external input parameters, said external input parameters being necessary parameters for solving the simulation model; and inputting at least the candidate control parameters into the simulation model so that the simulation model simulates the future operation of the heat pump combined heat and power system to obtain simulation results, including:

[0006] The candidate control parameters and the external input parameters are input into the simulation model so that the simulation model can simulate the future operation of the heat pump dual-supply system based on the candidate control parameters and the external input parameters, so as to obtain the simulation results.

[0007] In conjunction with the first aspect, in some embodiments, the candidate control parameters include M groups, where M is an integer greater than 1, and the simulation results include simulation data corresponding to each group of candidate control parameters in the M groups; the step of inputting the candidate control parameters and the external input parameters into the simulation model so that the simulation model simulates the future operation process of the heat pump combined heat and power system based on the candidate control parameters and the external input parameters includes: for each group of candidate control parameters in the M groups, inputting the group of candidate control parameters and the external input parameters into the simulation model so that the simulation model simulates the future operation process of the heat pump combined heat and power system based on the group of candidate control parameters and the external input parameters to obtain the simulation data corresponding to the group of candidate control parameters.

[0008] In conjunction with the first aspect, in some embodiments, determining the target control parameters for heat storage during the off-peak electricity period based on the simulation results includes: inputting the simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model can optimize among the M groups of candidate control parameters to obtain the target control parameters.

[0009] In conjunction with the first aspect, in some embodiments, the simulation data corresponding to each group of candidate control parameters includes energy consumption prediction values. The step of inputting the simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model optimizes among the M groups of candidate control parameters to obtain the target control parameter, includes: sorting the M groups of candidate control parameters based on energy consumption prediction values ​​using the parameter generation model to obtain a sorting result; and obtaining, based on the parameter generation model and the sorting result, the group of candidate control parameters with the lowest energy consumption prediction value among the M groups of candidate control parameters, as the target control parameter.

[0010] In conjunction with the first aspect, in some embodiments, each group of candidate control parameters is a combination of multi-dimensional control parameters, and the simulation data of each group of candidate control parameters includes predicted energy consumption values ​​and predicted values ​​of other dimensions. The step of inputting the simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model optimizes among the M groups of candidate control parameters to obtain the target control parameter, includes the following steps performed by the parameter generation model: removing each group of candidate control parameters whose predicted values ​​of other dimensions do not reach a preset threshold from the M groups of candidate control parameters to obtain the remaining groups of candidate control parameters; sorting the remaining groups of candidate control parameters based on the predicted energy consumption values ​​to obtain a sorting result; and obtaining the group of candidate control parameters with the lowest predicted energy consumption value as the target control parameter based on the sorting result.

[0011] In conjunction with the first aspect, in some implementations, each group of candidate control parameters is a combination of multi-dimensional control parameters, and the simulation data of each group of candidate control parameters includes predicted values ​​of multiple dimensions; the step of inputting the simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model optimizes in the M groups of candidate control parameters to obtain the target control parameter, includes: for each dimension, normalizing the M predicted values ​​of that dimension in the M groups of candidate control parameters to obtain the normalized predicted value of that dimension; for each group of candidate control parameters, performing a weighted sum calculation on the normalized predicted values ​​of that group of candidate control parameters in each dimension to obtain the weighted calculation result corresponding to that group of candidate control parameters; and obtaining the group of candidate control parameters with the smallest weighted sum from the M groups of candidate control parameters through the parameter generation model as the target control parameter.

[0012] In conjunction with the first aspect, in some embodiments, acquiring the historical operating data of the combined heat pump and combined power system includes: acquiring the historical operating data of the combined heat pump and combined power system according to a preset cycle and a preset time window; generating candidate control parameters for off-peak electricity periods based on the historical operating data and a pre-established parameter generation model includes: after each acquisition of the historical operating data of the combined heat pump and combined power system, inputting the currently acquired historical operating data into the parameter generation model; and processing the currently acquired historical operating data through the parameter generation model to generate M sets of candidate control parameters for the off-peak electricity periods, where M is an integer greater than 1.

[0013] In conjunction with the first aspect, in some embodiments, the step of processing the currently acquired historical operating data through the parameter generation model to generate M sets of candidate control parameters for the off-peak electricity period includes: processing the historical operating data through the parameter generation model to obtain a parameter given range; and generating M sets of candidate control parameters within the parameter given range through the parameter generation model.

[0014] In conjunction with the first aspect, in some implementations, the parameter generation model generates M sets of candidate control parameters within the given parameter range, including: after generating the current set of candidate control parameters, determining whether a preset condition is met; if not, feeding back the simulation data corresponding to the current set of candidate control parameters to the parameter generation model; so that the parameter generation model generates the next set of candidate control parameters for the off-peak electricity period based on the historical operating data and the simulation data corresponding to the current set of candidate control parameters and inputs it into the simulation model, so that the simulation model re-simulates the future operation process of the heat pump combined heat and power system using the next set of candidate control parameters; if satisfied, obtaining the target control parameter from the generated sets of candidate control parameters.

[0015] In conjunction with the first aspect, in some embodiments, the heat pump dual-supply system includes an outdoor unit, multiple air conditioning terminals connected to the outdoor unit, and multiple water terminals connected to the outdoor unit via a hydraulic module; the target control parameters include a combination of the following multi-dimensional control parameters: the start / stop control parameters of the hydraulic module in the heat pump dual-supply system, the temperature setpoint of the air conditioning terminals, the outlet water temperature setpoint of the hydraulic module, the compressor operating frequency, the switching time between different temperature setpoints of the air conditioning terminals, and / or the switching time between different outlet water temperature setpoints of the hydraulic module.

[0016] In a second aspect of the invention, an operation control device for a combined heat pump and combined power supply system is provided, comprising: a data acquisition unit for acquiring historical operation data of the combined heat pump and combined power supply system; a parameter generation unit for generating candidate control parameters for off-peak electricity periods based on the historical operation data and a pre-established parameter generation model; a simulation execution unit for inputting at least the candidate control parameters into a simulation model to simulate the future operation of the combined heat pump and combined power supply system to obtain simulation results, wherein the simulation model is a physical model of the combined heat pump and combined power supply system and its operating environment; and a control execution unit for determining target control parameters for heat storage during off-peak electricity periods based on the simulation results, and controlling the operation of the combined heat pump and combined power supply system according to the target control parameters during off-peak electricity periods to enable the combined heat pump and combined power supply system to store heat during off-peak electricity periods.

[0017] In a third aspect of the present invention, a combined heat pump and cooling system is provided, comprising: an outdoor unit; a hydraulic module and a plurality of air conditioning terminals respectively connected to the outdoor unit; a plurality of water terminals connected to the outdoor unit via the hydraulic module; a processor; and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the instructions to implement the operation control method of the combined heat pump and cooling system according to any embodiment of the first aspect.

[0018] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, characterized in that, when executed by a processor, the program implements the operation control method for a combined heat pump and combined heat and power system as described in any embodiment of the first aspect.

[0019] The one or more technical solutions provided in the embodiments of the present invention achieve at least the following technical effects or advantages:

[0020] By acquiring historical operating data of the combined heat pump and combined power system (CHP) and generating candidate control parameters for off-peak electricity periods based on the historical operating data and a pre-established parameter generation model, the candidate control parameters are input into a simulation model to simulate the future operation of the CHP system and obtain simulation results. The simulation model is a physical model of the CHP system and its operating environment. Based on the simulation results, target control parameters for heat storage during off-peak electricity periods are determined. This achieves the goal of finding suitable control parameters for heat storage during off-peak electricity periods by providing candidate control parameters and using simulation. This allows the CHP system to utilize the heat storage characteristics of its water system and the floor during off-peak electricity periods, accumulating as much heat as possible in the water system and floor, thereby reducing the operating time of the CHP system during peak electricity periods. Therefore, it can reduce the grid load during peak electricity periods and also reduce the operating cost of the CHP system. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 The structure of a combined heat pump and power supply system in some embodiments of the present invention is shown;

[0023] Figure 2 The flowchart of the operation control method of the heat pump dual-supply system in some embodiments of the present invention is shown;

[0024] Figure 3 Simulation models are shown in some embodiments of the present invention;

[0025] Figure 4 The following are application scenarios of the operation control method of the heat pump dual-supply system in some embodiments of the present invention;

[0026] Figure 5 This shows the typical daily meteorological parameters for a certain city in a certain month;

[0027] Figure 6 A schematic diagram comparing energy consumption and operating costs using related technologies and using the present invention is shown;

[0028] Figure 7 The structure of the operation control device of the heat pump dual-supply system in some embodiments of the present invention is shown;

[0029] Figure 8 The control structure of a heat pump dual-supply system in some embodiments of the present invention is shown. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0032] This invention provides an operation control method for a combined heat pump and combined heat and power system, such as... Figure 1 As shown, Figure 1The diagram illustrates the structure of a heat pump dual-supply system in some embodiments of the present invention. This system includes an outdoor unit, a hydraulic module connected to the outdoor unit, and multiple air conditioning terminals, as well as multiple water terminals connected to the outdoor unit via the hydraulic module. The water terminals can be coils installed under the floor. The number of water terminals and air conditioning terminals is determined by the number of rooms in the installation environment. At least the outdoor unit and each air conditioning terminal constitute a refrigerant system, where each air conditioning terminal is also an indoor air conditioning unit. The refrigerant system directly transfers the energy stored in the refrigerant of the outdoor unit to the indoor space. The outdoor unit, hydraulic module, and water terminals constitute a water system. The hydraulic module transfers the energy stored in the refrigerant of the outdoor unit to the water, and the water with stored energy is then released to the indoor space through the water terminals. It is understood that there is a one-to-one correspondence between the air conditioning terminals and the water terminals; that is, in multiple rooms, each room has one air conditioning terminal and one water terminal installed.

[0033] The operation control method of the heat pump dual-supply system provided by the present invention can be executed by a cloud device or edge device that has established communication with the heat pump dual-supply system. Of course, in scenarios where the heat pump dual-supply system has sufficient computing power, it can also be executed directly on the heat pump dual-supply system.

[0034] like Figure 2 As shown, Figure 2 The flowchart of the operation control method of a heat pump dual-supply system in some embodiments of the present invention is shown. The operation control method of the heat pump dual-supply system includes the following steps S101 to S104.

[0035] S101: Obtain historical operating data of the heat pump dual-supply system.

[0036] It is understandable that the historical operating data of a heat pump combined cooling, heating, and power (CCHP) system refers to the data generated by the system during the most recent historical period. In scenarios where the operation control method of a heat pump CCHP system is applied to cloud or edge devices, the system collects various operating data generated during its operation within the most recent historical period and uploads it to the cloud or edge devices.

[0037] In some implementations, the historical operating data of the heat pump dual-supply system includes all or some of the following types: the operating frequency of the outdoor unit's compressor, the temperature setpoint of the air conditioning terminal, the on / off signals of each valve in the heat pump dual-supply system, the energy consumption of the heat pump dual-supply system during a historical period within a preset time window, and the historical operating data also includes the outlet water temperature, return water temperature and water flow rate of the hydraulic module.

[0038] It should be understood that a 24-hour day is divided into at least peak and off-peak periods based on grid load changes. In some implementations, step S101 involves acquiring historical operating data of the combined heat pump and power system according to a preset time window before entering N off-peak periods (i.e., the off-peak periods of each day within N days). This historical operating data is then input into a pre-established parameter generation model, allowing the parameter generation module to generate candidate control parameters for the upcoming N off-peak periods based on the historical operating data. N is a positive integer, meaning it can predict candidate control parameters for off-peak periods in the next day or multiple days. Different prediction durations can be set as required, ranging from one or two days to up to 30 days. For example, N can be 1, meaning that the target control parameters for the next off-peak period are determined once through simulation before entering the next off-peak period, and the target control parameters for the off-peak period are not updated with the operation of the combined heat pump and power system.

[0039] Because the actual operating state of the combined heat pump and power system deviates somewhat from the predicted state of the simulation model, determining the target control parameters for each off-peak electricity period through simulation in a single instance is not very accurate. Therefore, in some implementations, after the combined heat pump and power system is turned on, historical operating data of the system is periodically acquired according to a preset cycle and preset time window. The duration of the off-peak electricity period is multiple times the preset cycle, so that multiple preset cycles pass through a single off-peak electricity period. For example, the off-peak electricity period is from 21:00 to 8:00, and the preset cycle can be 1 hour, 2 hours, or 3 hours. The size of the preset time window determines the amount of historical operating data acquired each time.

[0040] S102: Generate candidate control parameters for off-peak electricity periods based on historical operating data and a pre-established parameter generation model.

[0041] It should be noted that the floor (tile or wood floor) where the water terminal is located has heat storage characteristics. The pre-generated parameter generation model aims to optimize heat storage during off-peak hours and generates candidate control parameters for controlling the operation of the heat pump dual-supply system during off-peak hours, so as to store as much heat as possible in the water terminal and the floor where it is located during off-peak hours.

[0042] In some implementations, if step S101 involves periodically acquiring historical operating data of the heat pump dual-supply system according to a preset cycle and a preset time window, step S102 includes: after each acquisition of historical operating data of the heat pump dual-supply system, inputting the currently acquired historical operating data into a parameter generation model; processing the currently acquired historical operating data through the parameter generation model to generate M sets of candidate control parameters for off-peak electricity periods, where M is an integer greater than 1, and each set of candidate control parameters is a combination of multi-dimensional control parameters. In other words, at regular intervals, the target control parameters for heat storage during the same off-peak electricity period are redefined, thereby continuously updating and optimizing the target control parameters for subsequent off-peak electricity periods, continuously progressing forward to make the target control parameters more accurate. This allows the heat pump dual-supply system to operate with higher energy efficiency during off-peak electricity periods and makes the room relatively more comfortable, thus achieving the overall comfort, energy saving, and cost-saving effect of the system.

[0043] In some implementations, each set of candidate control parameters is a combination of the following multi-dimensional control parameters: start-stop control parameters of the combined heat pump and cooling system (including at least the start-stop control parameters of the hydraulic module, and possibly the start-stop control parameters of the heat pump unit in the outdoor unit), temperature setpoint of the air conditioning terminal, outlet water temperature setpoint of the hydraulic module, compressor operating frequency, switching time between different temperature setpoints of the air conditioning terminal, and / or switching time between different outlet water temperature setpoints of the hydraulic module. Therefore, the target control parameter is also a combination of the following multi-dimensional control parameters: start-stop control parameters of the combined heat pump and cooling system (including the start-stop control parameters of the hydraulic module), temperature setpoint of the air conditioning terminal, outlet water temperature setpoint of the hydraulic module, switching time between different temperature setpoints of the air conditioning terminal, and / or switching time between different outlet water temperature setpoints of the hydraulic module, and compressor operating frequency. Equipment-level protections and alarms are not included in this scope but are determined by the system's built-in logic.

[0044] In some implementations, processing the currently acquired historical operating data using a parameter generation model to generate M sets of candidate control parameters for off-peak electricity periods may include: processing the historical operating data using the parameter generation model to obtain parameter setpoint ranges applicable to the upcoming N off-peak electricity periods; and generating M sets of candidate control parameters within the parameter setpoint range using the parameter generation model. It is understood that the parameter setpoint range includes at least the setpoint ranges of the following two control parameters: the setpoint range of the temperature setting value of the air conditioning terminal and the setpoint range of the outlet water temperature setting value of the hydraulic module. Furthermore, considering the different heat states of people after sleep, the air conditioning terminal and / or hydraulic module may require different temperature setpoints at different times during off-peak electricity periods. Therefore, the parameter setpoint range may also include the time setpoint range for switching between different temperature setpoints.

[0045] It should be noted that the M groups of candidate control parameters represent all possible combinations of candidate control parameters generated using the currently acquired historical operating data. Different groups of candidate control parameters have the same parameter type, differing only in parameter values. In some implementations, step S103 can be executed after the parameter generation model generates all possible combinations of candidate control parameters using the currently acquired historical operating data, resulting in simulation data for all possible candidate control parameters. In other implementations, step S103 is executed after the parameter generation model generates one or more groups of candidate control parameters from all possible combinations using the currently acquired historical operating data. The simulation results obtained in step S103 then guide the parameter generation model in generating the next group or more new groups of candidate control parameters using historical operating data.

[0046] S103: At least the candidate control parameters are input into the simulation model so that the simulation model can simulate the future operation of the heat pump combined heat and power system to obtain simulation results. The simulation model is a physical model of the heat pump combined heat and power system and its operating environment.

[0047] In some embodiments, before step S103, the following steps may be included: obtaining external input parameters, wherein the external input parameters are necessary parameters for solving the simulation model; step S103 includes: inputting candidate control parameters and external input parameters into the simulation model, so that the simulation model simulates the future operation process of the heat pump dual-supply system according to the candidate control parameters and external input parameters, so as to obtain simulation results.

[0048] In some implementations, the candidate control parameters include M groups, and the simulation results include simulation data corresponding to each group of candidate control parameters in the M groups. The candidate control parameters and external input parameters are input into the simulation model so that the simulation model can simulate the future operation of the heat pump combined heat and power system based on the candidate control parameters and external input parameters. This includes: for each group of candidate control parameters in the M groups, inputting that group of candidate control parameters and external input parameters into the simulation model so that the simulation model can simulate the future operation of the heat pump combined heat and power system based on that group of candidate control parameters and external input parameters, thereby obtaining the simulation data corresponding to that group of candidate control parameters.

[0049] Understandably, a simulation model is a physical model that simulates a combined heat pump and cooling system and the residence in which it is located. It is a system of equations and a solver based on first principles, and has the characteristics of clear physical meaning and wide applicability. It can be built on various platforms (such as Energyplus, Trnsy, Modelica, Python, etc.). As long as the input of the simulation model is given, the simulation model can simulate the operation of the combined heat pump and cooling system in the residence to obtain a reasonable output response.

[0050] like Figure 3 As shown, Figure 3 The simulation model is shown in some embodiments of the present invention. Taking a house with four rooms as an example, the candidate control parameters input to the simulation model include the temperature setpoint of the air conditioning terminal in the four rooms, the start and stop signals of the water terminal in the four rooms, the water outlet temperature setpoint of the hydraulic module, and the defrost start and stop signals of the outdoor unit, etc. The simulation model simulates the operation of the heat pump dual-supply system in the real environment according to the input candidate control parameters and external input parameters.

[0051] In some implementations, the external input parameters include at least the outdoor temperature. In other implementations, in addition to the outdoor temperature, the external input parameters may also include the actual electricity price and / or other necessary inputs, wherein other necessary inputs may include: the number of people in the room, the power of lights and equipment, etc., the actual electricity price is the electricity price of the city, and the outdoor temperature may be detected by a sensor or obtained from meteorological parameters via a network, and the meteorological parameters may be the predicted value of the weather forecast or the daily typical meteorological parameters of the city.

[0052] S104: Determine the target control parameters for heat storage during off-peak hours based on the simulation results, and control the operation of the combined heat pump system according to the target control parameters during off-peak hours so that the combined heat pump system can store heat during off-peak hours.

[0053] In some implementations, if step S103 is executed after the parameter generation model generates all possible combinations of candidate control parameters using the historical operating data acquired in the current iteration, then the target control parameters for heat storage during off-peak hours are determined based on the simulation results, including: inputting the simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model so that the parameter generation model can optimize among the M groups of candidate control parameters to obtain the target control parameters.

[0054] Understandably, the simulation data for each set of candidate control parameters includes predicted values ​​in K dimensions, where K is a positive integer, such as 1, 2, 3, or 4, etc.

[0055] In some implementations, the simulation data for each group of candidate control parameters in the M groups only includes the predicted energy consumption value. The parameter generation model can optimize the M groups of candidate control parameters using only the predicted energy consumption value: the parameter generation model sorts the M groups of candidate control parameters based on the predicted energy consumption value to obtain the sorting result; based on the parameter generation model and the sorting result, the group of candidate control parameters with the lowest predicted energy consumption value in the M groups of candidate control parameters is obtained as the target control parameter. That is, the parameter generation model obtains the group of candidate control parameters with the lowest predicted energy consumption value in the M groups of candidate control parameters based on the sorting result, thereby achieving the purpose of energy saving by storing heat with lower energy consumption during off-peak electricity periods.

[0056] In other implementations, the simulation data for each set of candidate control parameters includes predicted values ​​across multiple dimensions. That is, in addition to predicted energy consumption, the simulation data for each set of candidate control parameters may also include predicted values ​​for one or more of the following dimensions: the predicted operating cost (e.g., electricity cost) required to operate according to that set of candidate control parameters, the predicted room temperature of each room where the air conditioning terminal is located, and the predicted room PMV (Predicted Mean Vote). The parameter generation model then optimizes among the M sets of candidate control parameters based on the predicted values ​​for each dimension in each of the M sets of simulation data that correspond one-to-one with the M sets of candidate control parameters, to obtain the target control parameters.

[0057] In some implementations, the parameter generation model optimizes the M sets of candidate control parameters based on the predicted values ​​of each dimension in each set of simulation data that corresponds one-to-one with the M sets of candidate control parameters. This optimization includes the following steps performed by the parameter generation model: removing candidate control parameters whose predicted values ​​for other dimensions do not reach a preset threshold, thus obtaining the remaining candidate control parameters; sorting the remaining candidate control parameters based on their predicted energy consumption values ​​to obtain a sorting result; and obtaining the candidate control parameters with the lowest predicted energy consumption value as the target control parameters based on the sorting result. Understandably, from the M groups of candidate control parameters, we can eliminate those whose predicted operating costs do not reach the preset cost threshold, those whose predicted room temperatures do not reach the preset temperature threshold, and those whose predicted room PMV does not reach the preset PMV value, thus obtaining the remaining groups of candidate control parameters. We can then sort the remaining groups of candidate control parameters based on their predicted energy consumption values ​​to obtain a sorting result. Based on the sorting result, we can obtain the group of candidate control parameters with the lowest predicted energy consumption value as the target control parameter.

[0058] In other implementations, the parameter generation model optimizes the M sets of candidate control parameters based on the predicted values ​​of each dimension in the M sets of simulation data corresponding to the M sets of candidate control parameters. This includes: for each dimension, normalizing the M predicted values ​​of that dimension in the M sets of candidate control parameters to obtain the normalized predicted value of that dimension; for each set of candidate control parameters, performing a weighted sum calculation on the normalized predicted values ​​of that set of candidate control parameters in each dimension to obtain the weighted calculation result corresponding to that set of candidate control parameters; and using the parameter generation model to obtain the set of candidate control parameters with the smallest weighted sum from the M sets of candidate control parameters as the target control parameters. Understandably, when normalizing the M predicted values ​​for each dimension: for each air conditioning terminal, the temperature difference between the predicted room temperature of the room where the air conditioning terminal is located and the temperature setpoint of the air conditioning terminal is normalized; the difference between the predicted room PMV (Predicted Mean Vote) of the room where the air conditioning terminal is located and the optimal PMV value is normalized; the difference between the predicted energy consumption of the heat pump dual-supply system and the preset energy consumption value is normalized; and the difference between the predicted operating cost of the heat pump dual-supply system and the preset operating cost is normalized.

[0059] Using the established simulation model, the calculated specific speed (physical time / simulation time) can reach 1000 times, and 3 days can be simulated within 3 minutes. Therefore, the overall optimization process will be completed in a short time.

[0060] In some implementations, a parameter generation model generates M sets of candidate control parameters within a given parameter range. This includes: after the parameter generation model generates a set of candidate control parameters from all possible combinations of candidate control parameters using the historical operating data acquired in the current iteration (i.e., after generating the current set of candidate control parameters), determining whether a preset condition is met; if not, feeding back the simulation data corresponding to the current set of candidate control parameters to the parameter generation model; so that the parameter generation model generates the next set of candidate control parameters for off-peak electricity periods based on the historical operating data and the simulation data corresponding to the current set of candidate control parameters and inputs it into the simulation model, so that the simulation model re-simulates the future operation of the heat pump combined heat and power system based on the next set of candidate control parameters; if the condition is met, no further generation of the next set of candidate control parameters is performed, and the target control parameter is obtained from the already generated sets of candidate control parameters. In this case, the last generated set of candidate control parameters can be used as the target control parameter.

[0061] Understandably, the preset conditions could be that the number of generated candidate control parameter groups reaches a preset group number threshold M, or that the energy consumption prediction value in the simulation data of the current group of candidate control parameters is lower than a preset energy consumption threshold.

[0062] Taking the execution by cloud or edge devices that have established communication with the heat pump combined heat and power system as an example, such as... Figure 4 As shown, Figure 4 This paper illustrates application scenarios of the operation control method for a combined heat pump and combined cooling, heating, and power (CHP) system according to some embodiments of the present invention. These scenarios include a CHP system, a predictive model deployed in a cloud or edge device, and a parameter generation model and a simulation model. The parameter generation model generates candidate control parameters based on historical operating data of the CHP system and inputs these parameters into the simulation model. The simulation model uses the candidate control parameters and external input parameters to simulate the operation of the CHP system in its installation environment, thereby obtaining simulation results such as predicted room temperature, predicted room PMV, and predicted energy consumption. The simulation model feeds the simulation results back to the parameter generation model. The parameter generation model optimizes the target control parameters from M groups of candidate control parameters based on the simulation results. The prediction model is modular and has configurable parameters. Since it is necessary to determine multiple parameters such as when the heat pump dual-supply system will store heat, the temperature setpoint of the air conditioning terminal in the room during operation, the outlet water temperature setpoint of the hydraulic module, and the switching time point of different setpoints, it is impossible to perform simple calculations for such a complex nonlinear system as the heat pump dual-supply system. Therefore, the prediction model is used to give all possible combinations to find the best combination, thereby achieving overall optimization and enabling intelligent optimization control of the heat pump dual-supply system with natural refrigerant and ground water.

[0063] In some implementations, the operation of the combined heat pump and cooling system is controlled according to target control parameters during off-peak hours. This includes: cloud devices or edge devices sending the target control parameters to the controller of the combined heat pump and cooling system, so that after entering the off-peak hours, the controller executes the received target control parameters to control the operation of the combined heat pump and cooling system during the off-peak hours, so as to store as much heat as possible in the water system and floor during the off-peak hours, thereby reducing the operating time of the combined heat pump and cooling system during peak hours.

[0064] In some implementations, the operation of the heat pump dual-supply system is controlled according to preset control parameters during all periods of the day except for off-peak hours. The division between off-peak and peak hours is determined based on actual conditions. For example, the off-peak hours are from 21:00 to 8:00 and the peak hours are from 8:00 to 21:00.

[0065] To facilitate understanding of the energy-saving and cost-reducing effects of the heat pump dual-supply system operation control method provided by this invention, a comparison of the effects of various implementation methods of this invention and related technologies is given below, using a 7A-modified environmental laboratory as an example. It should be noted that the outlet water temperature setpoint Tw1 of the hydraulic module and the temperature setpoint Tsp of the air conditioning terminal in Table 1 below are merely exemplary and not intended to limit the invention. Related technology is shown in Example 1 of Table 1 below: the outlet water temperature setpoint of the hydraulic module is 45℃, i.e., a fixed outlet water temperature of 45℃, and the temperature setpoint of the air conditioning terminal is 22℃, so that the room temperature is within the range of 22℃±1℃. Examples 2 and 3 in Table 1 below are control strategies for heat storage during off-peak electricity hours based on this invention. In Examples 2 and 3, the outlet water temperature setpoint Tw1 of the hydraulic module and the temperature setpoint Tsp of the air conditioning terminal during off-peak electricity hours are obtained through parameter generation models and simulation models.

[0066] Table 1. Comparison of related technologies and embodiments of the present invention

[0067]

[0068] Taking the outdoor temperature of a city as an example, using typical meteorological parameters for the current month in that city (e.g.) Figure 5 As shown, the energy consumption comparison of Examples 1, 2, and 3 above during peak and off-peak hours is for reference. Figure 6 As shown in Figure (1), the comparison of operating costs of Examples 1, 2, and 3 above during peak and off-peak hours is for reference. Figure 6 As shown in (2). It can be seen that the energy consumption of Example 2 and 3 is reduced by 17% to 20% compared with Example 1. Taking the electricity price during peak hours as 0.5583 and the electricity price during off-peak hours as 0.3583 as an example, the operating cost of Example 2 and 3 is reduced by 22% to 25% compared with Example 1.

[0069] Based on the same inventive concept, the present invention also provides an operation control device for a combined heat pump and combined heat and power system, such as... Figure 7 As shown, Figure 7The structure of an operation control device for a heat pump combined heat and power system in some embodiments of the present invention is shown. The operation control device includes: a data acquisition unit 701 for acquiring historical operation data of the heat pump combined heat and power system; a parameter generation unit 702 for generating candidate control parameters for off-peak electricity periods based on the historical operation data and a pre-established parameter generation model; a simulation execution unit 703 for inputting at least the candidate control parameters into a simulation model to simulate the future operation of the heat pump combined heat and power system and obtain simulation results, wherein the simulation model is a physical model of the heat pump combined heat and power system and its operating environment; and a control execution unit 704 for determining target control parameters suitable for heat storage during off-peak electricity periods based on the simulation results, and controlling the operation of the heat pump combined heat and power system during off-peak electricity periods according to the target control parameters.

[0070] In some embodiments, the operation control device may further include: a parameter acquisition unit for acquiring external input parameters, wherein the external input parameters are necessary parameters for solving the simulation model; and a simulation execution unit 703 for: inputting the candidate control parameters and the external input parameters into the simulation model, so that the simulation model simulates the future operation process of the heat pump dual-supply system based on the candidate control parameters and the external input parameters, so as to obtain the simulation results.

[0071] In some embodiments, the candidate control parameters include M groups, where M is an integer greater than 1, and the simulation results include simulation data corresponding to each group of candidate control parameters in the M groups; the simulation execution unit 703 is used to: for each group of candidate control parameters in the M groups, input the group of candidate control parameters and the external input parameters into the simulation model, so that the simulation model simulates the future operation process of the heat pump dual-supply system based on the group of candidate control parameters and the external input parameters, so as to obtain the simulation data corresponding to the group of candidate control parameters.

[0072] In some implementations, the control execution unit 704 includes an optimization subunit, configured to: input simulation data corresponding to each group of candidate control parameters in the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model performs optimization in the M groups of candidate control parameters to obtain the target control parameter.

[0073] In some implementations, the simulation data corresponding to each group of candidate control parameters includes energy consumption prediction values. The optimization subunit includes: a sorting module, used to sort the M groups of candidate control parameters based on energy consumption prediction values ​​using the parameter generation model to obtain a sorting result; and a first screening module, used to obtain the group of candidate control parameters with the lowest energy consumption prediction value among the M groups of candidate control parameters according to the parameter generation model and the sorting result, as the target control parameter.

[0074] In some implementations, each group of candidate control parameters is a combination of multi-dimensional control parameters, and the simulation data of each group of candidate control parameters includes predicted values ​​in multiple dimensions; the optimization subunit includes: a normalization module, used to normalize the M predicted values ​​of each dimension in the M groups of candidate control parameters to obtain the normalized predicted value of that dimension; a weighting module, used to perform a weighted sum calculation on the normalized predicted values ​​of each group of candidate control parameters in each dimension to obtain the weighted calculation result corresponding to the group of candidate control parameters; and a second screening module, used to obtain the group of candidate control parameters with the smallest weighted sum from the M groups of candidate control parameters through the parameter generation model as the target control parameter.

[0075] In some implementations, the data acquisition unit is used to: acquire historical operating data of the heat pump combined heat and power system according to a preset cycle and a preset time window; the parameter generation unit is used to: after each acquisition of historical operating data of the heat pump combined heat and power system, input the currently acquired historical operating data into the parameter generation model; and process the currently acquired historical operating data through the parameter generation model to generate M sets of candidate control parameters for the off-peak electricity period, where M is an integer greater than 1.

[0076] In some implementations, the parameter generation unit includes: a range determination subunit, used to process the historical operating data through the parameter generation model to obtain a given range of parameters; and a parameter generation subunit, used to generate M sets of candidate control parameters within the given range of parameters through the parameter generation model.

[0077] In some implementations, the parameter generation subunit is used to: after generating the current group of candidate control parameters, determine whether a preset condition is met; if not, feed back the simulation data corresponding to the current group of candidate control parameters to the parameter generation model; so that the parameter generation model generates the next set of candidate control parameters for the off-peak electricity period based on the historical operating data and the simulation data corresponding to the current group of candidate control parameters and inputs it into the simulation model, so that the simulation model re-simulates the future operation process of the heat pump combined heat and power system using the next set of candidate control parameters; if the condition is met, obtain the target control parameter from the generated groups of candidate control parameters.

[0078] In some embodiments, the combined heat pump and cooling system includes an outdoor unit, multiple air conditioning terminals connected to the outdoor unit, and multiple water terminals connected to the outdoor unit via a hydraulic module; the target control parameters include a combination of the following multi-dimensional control parameters: the start / stop control parameters of the hydraulic module in the combined heat pump and cooling system, the temperature setpoint of the air conditioning terminals, the outlet water temperature setpoint of the hydraulic module, the switching time between different temperature setpoints of the air conditioning terminals, and / or the switching time between different outlet water temperature setpoints of the hydraulic module.

[0079] The specific functions of each functional unit in the above-mentioned device have been described in detail in the operation control method of the heat pump dual-supply system provided in some embodiments of the present invention, and will not be elaborated here.

[0080] Based on the same inventive concept, this invention also provides a combined heat pump and cooling system, such as... Figure 1 As shown, it includes: an outdoor unit; a hydraulic module and multiple air conditioning terminals, each connected to the outdoor unit; and multiple water terminals connected to the outdoor unit via the hydraulic module; as shown. Figure 8 As shown, Figure 8 The control structure of a heat pump combined heat and power system in some embodiments of the present invention is shown. The heat pump combined heat and power system further includes: a processor 802; and a memory 804 for storing executable instructions of the processor 802, wherein the processor 802 is configured to execute the instructions to implement the above-described operation control method of the heat pump combined heat and power system.

[0081] Among them, Figure 8 In this document, a bus architecture (represented by bus 800) is used. Bus 800 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 802 and memory represented by memory 804. Bus 800 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 805 provides an interface between bus 800 and receiver 801 and transmitter 803. Receiver 801 and transmitter 803 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 802 is responsible for managing bus 800 and general processing, while memory 804 can be used to store data used by processor 802 during operation.

[0082] Based on the same inventive concept, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described operation control method for a combined heat pump and dual power supply system.

[0083] According to one or more embodiments of the present invention, the operation of the heat pump combined heat and power system is controlled according to target control parameters during off-peak electricity hours. This allows the heat pump combined heat and power system to utilize the heat storage characteristics of its water system and the floor to accumulate as much heat as possible in the water system and the floor during off-peak electricity hours, thereby reducing the operating time of the heat pump combined heat and power system during peak electricity hours. Therefore, it can reduce the grid load during peak electricity hours and also reduce the operating cost of the heat pump combined heat and power system. Furthermore, the present invention is an active predictive control, rather than a passive feedback response, which can effectively respond to various scenarios such as changes in user demand and has good flexibility. It also improves the intelligence level of the combined heat pump combined heat and power system. Moreover, according to one or more embodiments of the present invention, all possible candidate control parameters are sorted based on energy consumption, and the set of candidate control parameters with the lowest energy consumption is selected, thereby achieving energy saving.

[0084] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 Devices that specify the functions in one or more boxes.

[0085] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction device, which is implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0088] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0089] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for operating and controlling a heat pump dual-supply system, characterized in that, include: Obtain the historical operating data of the heat pump dual-supply system; Based on the historical operating data and the pre-established parameter generation model, candidate control parameters for off-peak electricity periods are generated; The candidate control parameters are at least input into the simulation model so that the simulation model can simulate the future operation of the heat pump combined heat and power system to obtain simulation results. The simulation model is a physical model of the heat pump combined heat and power system and its operating environment. Based on the simulation results, target control parameters for heat storage during off-peak electricity periods are determined, and the heat pump dual-supply system is controlled to operate according to the target control parameters during off-peak electricity periods so that the heat pump dual-supply system can store heat during off-peak electricity periods.

2. The method as described in claim 1, characterized in that, Also includes: Obtain external input parameters, which are necessary parameters for solving the simulation model; The step involves inputting at least the candidate control parameters into the simulation model to simulate the future operation of the heat pump combined heat and power system, thereby obtaining simulation results, including: The candidate control parameters and the external input parameters are input into the simulation model so that the simulation model can simulate the future operation of the heat pump dual-supply system based on the candidate control parameters and the external input parameters, so as to obtain the simulation results.

3. The method as described in claim 2, characterized in that, The candidate control parameters include M groups, where M is an integer greater than 1, and the simulation results include simulation data corresponding to each group of candidate control parameters in the M groups. The step of inputting the candidate control parameters and the external input parameters into the simulation model, so that the simulation model can simulate the future operation of the heat pump combined heat and power system based on the candidate control parameters and the external input parameters, includes: For each group of candidate control parameters in the M groups, the group of candidate control parameters and the external input parameters are input into the simulation model so that the simulation model can simulate the future operation of the heat pump dual-supply system based on the group of candidate control parameters and the external input parameters, so as to obtain the simulation data corresponding to the group of candidate control parameters.

4. The method as described in claim 3, characterized in that, The determination of target control parameters for heat storage during the off-peak electricity period based on the simulation results includes: The simulation data corresponding to each of the M groups of candidate control parameters is input into the parameter generation model, so that the parameter generation model can optimize the M groups of candidate control parameters to obtain the target control parameter.

5. The method as described in claim 4, characterized in that, The simulation data corresponding to each group of candidate control parameters includes energy consumption prediction values. The step of inputting the simulation data corresponding to each group of candidate control parameters from the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model optimizes among the M groups of candidate control parameters to obtain the target control parameters, includes: The parameter generation model is used to sort the M groups of candidate control parameters based on energy consumption prediction values ​​to obtain the sorting results. Based on the parameter generation model and the sorting results, the group of candidate control parameters with the lowest predicted energy consumption value among the M groups of candidate control parameters is obtained and used as the target control parameter.

6. The method as described in claim 4, characterized in that, Each set of candidate control parameters is a combination of multidimensional control parameters, and the simulation data of each set of candidate control parameters includes predicted energy consumption values ​​and predicted values ​​of other dimensions. The step of inputting the simulation data corresponding to each of the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model can optimize among the M groups of candidate control parameters to obtain the target control parameters, includes the following steps performed by the parameter generation model: Among the M groups of candidate control parameters, those whose predicted values ​​for other dimensions do not reach a preset threshold are removed to obtain the remaining groups of candidate control parameters. The remaining candidate control parameters are sorted based on the predicted energy consumption values ​​to obtain a sorting result. Based on the ranking results, the set of candidate control parameters with the lowest predicted energy consumption values ​​is obtained as the target control parameters.

7. The method as described in claim 4, characterized in that, Each set of candidate control parameters is a combination of multidimensional control parameters, and the simulation data of each set of candidate control parameters includes predicted values ​​in multiple dimensions; The step of inputting the simulation data corresponding to each of the M groups of candidate control parameters into the parameter generation model, so that the parameter generation model can optimize among the M groups of candidate control parameters to obtain the target control parameters, includes: For each dimension, the M predicted values ​​of that dimension in the M groups of candidate control parameters are normalized to obtain the normalized predicted value of that dimension. For each group of candidate control parameters, the normalized predicted values ​​of the candidate control parameters in each dimension are weighted and calculated to obtain the weighted calculation result corresponding to the candidate control parameters. The parameter generation model obtains the set of candidate control parameters with the smallest weighted sum from the M sets of candidate control parameters as the target control parameters.

8. The method as described in claim 1, characterized in that, The acquisition of historical operating data of the heat pump dual-supply system includes: According to a preset cycle and a preset time window, the historical operating data of the heat pump dual-supply system are obtained; The process of generating candidate control parameters for off-peak electricity periods based on the historical operating data and a pre-established parameter generation model includes: After acquiring the historical operating data of the heat pump dual-supply system each time, the currently acquired historical operating data is input into the parameter generation model; The parameter generation model processes the historical operating data acquired in the current iteration to generate M sets of candidate control parameters for the off-peak electricity period, where M is an integer greater than 1.

9. The method as described in claim 8, characterized in that, The step of processing the currently acquired historical operating data through the parameter generation model to generate M sets of candidate control parameters for the off-peak electricity period includes: The historical operational data is processed through the parameter generation model to obtain a given range of parameters; The parameter generation model generates M sets of candidate control parameters within the given parameter range.

10. The method as described in claim 9, characterized in that, The parameter generation model generates M sets of candidate control parameters within the given parameter range, including: After generating the candidate control parameters for the current group, determine whether the preset conditions are met; If not satisfied, the simulation data corresponding to the current group of candidate control parameters is fed back to the parameter generation model; so that the parameter generation model generates the next set of candidate control parameters for the off-peak electricity period based on the historical operating data and the simulation data corresponding to the current group of candidate control parameters and inputs them into the simulation model, so that the simulation model re-simulates the future operation process of the heat pump combined heat and power system using the next set of candidate control parameters; If the conditions are met, the target control parameter is obtained from the generated candidate control parameters.

11. An operation control device for a heat pump dual-supply system, characterized in that, include: The data acquisition unit is used to acquire historical operating data of the heat pump dual-supply system; The parameter generation unit is used to generate candidate control parameters for off-peak electricity periods based on the historical operating data and the pre-established parameter generation model. The simulation execution unit is used to input at least the candidate control parameters into the simulation model so that the simulation model can simulate the future operation of the heat pump combined heat and power system to obtain simulation results. The simulation model is a physical model of the heat pump combined heat and power system and its operating environment. The control execution unit is used to determine the target control parameters for heat storage during the off-peak electricity period based on the simulation results, and to control the operation of the combined heat pump system during the off-peak electricity period according to the target control parameters, so that the combined heat pump system can store heat during the off-peak electricity period.

12. A heat pump dual-supply system, characterized in that, include: Outdoor unit; The hydraulic module and multiple air conditioning terminals are respectively connected to the outdoor unit; Multiple water terminals are connected to the outdoor unit via the hydraulic module; processor; A memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the operation control method for a heat pump dual-supply system as described in any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the operation control method of the heat pump dual-supply system as described in any one of claims 1 to 10.

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