Intelligent energy efficiency management method and system for solar photo-thermal-photovoltaic-heat pump coupling system

Abstract

The invention provides an intelligent energy efficiency management method and system for a solar photo-thermal-photovoltaic-heat pump coupling system. The method comprises the steps that coupling energy state vectors including photovoltaic power generation real-time output power, the hot air temperature of an air duct outlet of a solar heat collector, the operation frequency of a heat pump compressor and the water temperature of the middle of a heat storage water tank are constructed; dynamically calculating a continuous discriminant quantity and a discrete risk sign based on the vector so as to evaluate the supply and demand trend and the energy efficiency risk of the system; generating a multi-path combined control strategy covering an electric side link, a photo-thermal link, a heat pump link and a heat storage link, and performing effective filtering on a control command in combination with an equipment state inspection result; and finally, executing instructions are issued in stages according to path priorities, so that closed-loop energy efficiency adjustment is realized. According to the invention, the cooperation efficiency and the operation safety of the multi-energy coupling system are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of heat pumps, and particularly relates to an intelligent energy efficiency management method and system for a solar-thermal photovoltaic-heat pump coupled system. BACKGROUND

[0002] The combined application of solar-thermal photovoltaic and heat pump has become an important technical route for improving the proportion of renewable energy utilization in building energy supply systems. Among them, the solar collector is used to provide low-grade heat energy, the photovoltaic module is used to provide power support, and the heat pump device undertakes the core function of upgrading low-grade energy to usable heat energy. However, in actual engineering applications, the above three types of devices usually operate in a relatively independent manner, and the system control more relies on single device state or fixed experience rules, lacking overall characterization and dynamic constraints of the coupling relationship between multiple energy paths, resulting in problems such as supply-demand imbalance and inconsistent energy path response when light fluctuates, environmental temperature changes or load rapidly adjusts. Especially in low temperature, low light or rapid working condition switching scenarios, the availability changes between photovoltaic power, collector heat source and heat storage buffer have obvious timing and uncertainty. If the control system cannot timely identify the effectiveness and potential abnormalities of the key path, it will continue to exert control actions on the link that does not have execution conditions, thereby causing problems such as unstable heat pump operation, frequent defrosting, energy efficiency decline and even protection action triggering. In the prior art, the control method for the multi-source coupled system mainly focuses on single strategy calculation or parameter adjustment, and less considers the structured checking and constraints of the key energy path before control execution, which is difficult to achieve energy efficiency regulation with pertinence and implementability in complex working conditions. SUMMARY

[0003] The purpose of the present application is to design an intelligent energy efficiency management method and system for a solar-thermal photovoltaic-heat pump coupled system, which can realize multi-energy path coordination, risk perception and execution constraint combined energy efficiency management without increasing the complexity of the system, and improve the operation stability and comprehensive energy efficiency level of the heat pump system in complex environments.

[0004] In order to achieve the above purpose, in the first aspect of the present application, an intelligent energy efficiency management method for a solar-thermal photovoltaic-heat pump coupled system is provided, which comprises: constructing a coupled energy state vector, the coupled energy state vector being based on photovoltaic power generation real-time output power, solar collector air duct outlet hot air temperature, heat pump compressor operating frequency and middle water temperature of the heat storage water tank, and being obtained after standardization processing; dynamically calculating system supply-demand trend and energy efficiency risk according to the coupled energy state vector, obtaining continuous discriminant and discrete risk flag, the discrete risk flag representing the risk level of the current system operation; A multi-path joint control strategy is generated based on the continuous discriminant and the discrete risk indicator, a command tree including an electrical side link, a light-heat link, a heat pump link and a heat storage link is constructed, and device state inspection is performed on each link according to the command tree to obtain an inspection result vector; The command tree is filtered for effectiveness according to the inspection result vector, and a control adjustment range is determined in combination with the discrete risk indicator to generate a final execution instruction vector which is issued to corresponding execution components in stages according to path priority order to complete the energy efficiency adjustment closed loop.

[0005] Further, in the step of constructing the coupled energy state vector, the real-time output power of photovoltaic power generation is obtained through an inverter power sampling unit, the hot air temperature at the outlet of the solar collector air duct is obtained through a thermocouple or platinum resistance temperature sensor, the heat pump compressor operating frequency is read through a frequency conversion control module, and the water temperature in the middle of the heat storage water tank is obtained through a thermistor or digital temperature sensor, and all data are sent to the main controller through RS485 or CAN bus.

[0006] Further, in the step of dynamically calculating the supply and demand trend and the energy efficiency risk, the continuous discriminant comprehensively considers the coupling relationship among photovoltaic energy supply intensity, collector heat source intensity, heat pump load level and heat storage availability, and introduces an electrical side mismatch penalty term and a buffer loss penalty term to quantify the system operation risk.

[0007] Further, the discrete risk indicator is divided into three levels, corresponding to normal operation state, mild risk state and serious risk state, and each level corresponds to a different control response strategy.

[0008] Further, in the step of generating a multi-path joint control strategy, the path priority score of the command tree is dynamically calculated according to the discrete risk indicator, the continuous discriminant, and preset sensitivity coefficients and disturbance cost constants.

[0009] Further, the device state inspection includes reading the inverter state word, DC bus sampling value and power supply protection indicator of the electrical side link, actuating the collector fan and detecting the temperature rise response of the light-heat link, reading the compressor operating state word, high and low voltage protection indicators and fault code register of the heat pump link, and actuating the heat storage water pump and detecting the return water temperature change of the heat storage link.

[0010] Further, each component of the inspection result vector is synthesized according to the command return value of the corresponding link according to a preset rule to determine whether each execution component has an executable condition.

[0011] Further, in the step of generating a final execution instruction vector, only the path indicated by the inspection result as available generates an effective control instruction, and the control instruction corresponding to the unavailable path is suppressed.

[0012] Furthermore, in the step of issuing execution instructions in stages, the working status of the collector fan and the hot water storage pump is adjusted first, then the operating settings of the heat pump compressor are adjusted, and a preset stable waiting time is maintained after each instruction is issued.

[0013] A second aspect of the invention provides an intelligent energy efficiency management system for a solar thermal-photovoltaic-heat pump coupled system, the system comprising: The coupled state construction module is used to acquire real-time output power of photovoltaic power generation, hot air temperature at the outlet of solar collector duct, operating frequency of heat pump compressor, and water temperature in the middle of hot water storage tank. The acquired data is standardized to form a coupled energy state vector. The risk discrimination module is used to calculate the system supply and demand trend and energy efficiency risk based on the coupled energy state vector, and output continuous discrimination quantity and discrete risk indicator; The strategy generation module is used to generate a multi-path joint control strategy based on the continuous discrimination quantity and the discrete risk indicator, construct a command tree including the electric side link, the solar thermal link, the heat pump link and the thermal storage link, and perform equipment status inspection on each link according to the command tree to obtain the inspection result vector. The instruction execution module is used to filter the command tree for effectiveness based on the inspection result vector, determine the control adjustment range in combination with the discrete risk flag, generate the final execution instruction vector, and issue it to the corresponding execution components in stages according to the path priority order to complete the energy efficiency adjustment closed loop.

[0014] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned issues, this invention provides an intelligent energy efficiency management method and system for solar thermal-photovoltaic-heat pump coupled systems. By uniformly mapping photovoltaic power supply, collector heat source, heat pump load, and thermal storage buffer into a standardized state vector, it further constructs continuous risk quantities and hierarchical indicators reflecting the supply-demand relationship and the degree of coupling imbalance, enabling the system to clearly define the risk range of the current operating state before control decisions. Based on this, a risk-driven multi-path command tree generation mechanism is introduced, transforming key energy paths such as the electricity side, heat source side, heat pump side, and thermal storage side into executable inspection links. The inspection scope and sequence are dynamically determined through path priority and threshold constraints, thereby verifying the effectiveness of key execution links before control execution. Finally, under the constraints of the inspection results, control commands are generated and issued, ensuring that control actions only apply to paths with execution conditions, and limiting the adjustment range according to the risk level, achieving stable implementation of energy efficiency regulation in the physical system. Through the above overall design, this invention achieves an energy efficiency management method that combines multi-energy path coordination, risk perception and execution constraints without increasing system complexity, effectively improving the operational stability and overall energy efficiency of the heat pump system in complex environments. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a flowchart of the intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to the present invention.

[0017] Figure 2 This is a framework diagram of the intelligent energy efficiency management system for solar thermal-photovoltaic-heat pump coupling systems of the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] In one or more embodiments, such as Figure 1 As shown, a smart energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems is disclosed, the method comprising the following: S1: Construct a coupled energy state vector, which is based on the real-time output power of photovoltaic power generation, the hot air temperature at the outlet of the solar collector duct, the operating frequency of the heat pump compressor, and the water temperature in the middle of the hot water storage tank, and is obtained through standardization. Specifically, this step is used to construct a structured state vector representing the current overall power supply state of the system. This serves as the sole input interface to the control chain. Therefore, it is necessary to extract four key types of information from the system: photovoltaic power generation, solar thermal collection, heat pump operation, and thermal storage capacity. These information must then be standardized and converted into a unified state representation.

[0020] In order to obtain the input vector representing the overall state of the system, this step selects the following four variables as basic components: variable This represents the real-time output power of the photovoltaic module, obtained by the power sampling unit built into the inverter, calculated by measuring and calculating the product of voltage and current. The sampling period of this device is typically less than 5 seconds, and it is commonly used in small to medium-sized grid-connected systems, with a rated power setting range between 3000 and 5000 kW. (Variable) This indicates the hot air temperature at the outlet of the solar collector's duct, measured by a type K thermocouple or a Pt100 sensor. The sensor is installed near the end of the collector, and its value typically fluctuates between 20 and 70 degrees Celsius. (Variable) This is the current operating frequency of the heat pump compressor, which can be directly read from the frequency converter control module; it is an integer control parameter. (Variable) The water temperature in the middle of the hot water storage tank is typically collected using an NTC or digital temperature sensor, representing the effective residual heat level of the heat buffer system. All data is sent to the main controller via RS485 or CAN interface, with a sampling period controlled between 5 and 10 seconds.

[0021] To unify the scale of physical quantities and their ability to characterize system energy efficiency, the input variables are transformed using difference and proportional normalization methods to construct a state vector. : ; in, It is the rated output power of the photovoltaic module, serving as a reference value on the power supply side; It is the ambient temperature, obtained through the collector inlet temperature sensor, used to calculate the temperature rise of the collector; It is a reference difference for the temperature rise of the heat source, and is generally set to 30. This is the rated operating frequency of the heat pump compressor, usually set to 50. This is the minimum effective working water temperature of the hot water storage tank, such as 35°C. This is the effective temperature zone width of the heat storage device, with a value such as 20.

[0022] Each dimension of the state vector represents the proportion of photovoltaic power to the design capacity, the temperature rise of the collector outlet relative to the normalized heat source intensity of the environment, the proportion of the heat pump operating frequency at its rated value, and the position of the current temperature of the thermal storage unit within the effective temperature range. For example, under a typical operating condition, if... , , , , Substituting this into the above formula, we get: ; The results indicate that the photovoltaic power supply is at a medium-to-high level, the heat source of the solar collector is sufficient, the compressor is operating at a high load, and the remaining heat energy of the heat storage device is close to the median value. The next step is to determine whether the system strategy needs to be switched to maintain the efficient operation of the heat pump.

[0023] S2: Dynamically calculate the system's supply and demand trends and energy efficiency risks based on the coupled energy state vector, and obtain continuous discrimination quantities and discrete risk indicators. The discrete risk indicators represent the risk level of the current system operation. Specifically, this step follows the system state vector obtained in the previous step. This transforms the "structured input describing the current state" into "supply and demand trends and energy efficiency risks that can directly drive subsequent strategy choices." The previous step... We have already compressed four key information categories—solar thermal, photovoltaic, heat pump load, and thermal storage buffer—into the same scale. Now, we are establishing a judgment logic based on the actual operational contradictions of the coupled system: when the load component is too high and the power and heat source components are insufficient, the heat pump is prone to entering the inefficient zone; when the power component is sufficient but the heat source component is weak, simply maintaining the frequency may also cause the evaporator side conditions to deteriorate; when the thermal storage component is close to the lower limit, the system loses its buffering capacity, and short-term fluctuations will be amplified.

[0024] Input is To avoid ambiguity, let The four components correspond to the four positions in the vector from the previous step, and their meanings remain unchanged: Reflecting the intensity of photovoltaic energy supply, Reflects the heat source intensity of the solar collector. Reflects the current load level of the heat pump. This reflects the availability of thermal storage. Because... Having completed the standardization process, this step directly involves combining and constructing constraint terms on these components.

[0025] First, construct a continuous discriminant measure between supply and demand trends and energy efficiency risks. This quantity uses "supply output minus load" as its core, while incorporating two constraints: one is a "power-side mismatch penalty," used to suppress strong mismatches between heat pump load and photovoltaic power supply; the other is a "buffer deficiency penalty," used to reflect the system's vulnerability to short-term fluctuations when thermal storage is insufficient. Specifically: ; in, It is a continuous discriminant; All come from the input vector The corresponding components; This is the electrical mismatch penalty coefficient, used to directly incorporate the "gap between load level and electrical support capacity" into the risk. As a buffer for missing penalty coefficients, it is used to further reduce the discrimination value when "the load exceeds the combined force of the electric side and the thermal storage". This is for absolute value operations; The positive part operator is defined as follows: The first term in the formula The most intuitive correspondence is the supply and demand margin; the second item. Regarding the structural characteristics of "photovoltaic direct-drive heat pumps": when the heat pump load component approaches or exceeds the electrical support component, even if the heat source component is still adequate, unstable power supply and decreased energy efficiency are likely to occur on the electrical side; the third point. Given the reality that "thermal storage is a buffer rather than an unlimited supply": when the load exceeds the combined capacity of the electrical side and thermal storage, the system may be forced into an inefficient operating range or trigger a more aggressive strategy switch. Therefore, positive terms are used to penalize only when this contradiction occurs, avoiding the introduction of unnecessary suppression under normal operating conditions.

[0026] After obtaining the continuous discriminant This step then compresses it into discrete risk indicators. This step is used to quickly select the control path in subsequent steps. To accommodate the intermediate state of "adjustable but not necessarily requiring immediate drastic action" in real-world systems, this step employs a three-level risk assessment instead of simple binarization, allowing the next strategy generation to distinguish between "minor adjustments" and "forced switching." The definitions are as follows: ; in, As a marker of discrete risk; This is the normal threshold. As a severity threshold, and by convention .when When, it indicates that the current supply and demand margins and coupling constraints are both within acceptable ranges; when When this indicates a trend risk exists but can still be corrected through minor adjustments (such as changing the operating state of a certain actuator or slightly altering the operating point); when At this point, it indicates that the supply-demand imbalance is already evident, and continuing to maintain the current operating point will rapidly amplify energy efficiency losses, necessitating a stronger path combination strategy in the next step. Threshold The settings are given by system engineering parameters, and a conservative range can be determined based on the device's operating records under typical conditions; because It is already composed of normalized components, and the threshold does not depend on the introduction of additional quantities. When implementing control, it only needs to be fixed in the form of a constant within the controller.

[0027] S3: Generate a multi-path joint control strategy based on the continuous discrimination quantity and the discrete risk indicator, construct a command tree including the electric side link, the solar thermal link, the heat pump link and the thermal storage link, and perform equipment status inspection on each link according to the command tree to obtain the inspection result vector; Specifically, this step uses the continuous quantity output from step two. With graded quantity As input, a "command tree" is dynamically generated based on the actual failure mechanisms of the four coupled links of solar thermal, photovoltaic, heat pump, and thermal storage, and equipment status inspections are performed according to the tree structure. Step two The relative relationship between "supply-side combined force and load-side pressure" has been compressed into a continuous quantity, and the contradiction between electrical mismatch and buffer deficiency has been explicitly converted into the same dimension space through the penalty term. This maps risks into discrete risk levels. This step uses these two quantities to accomplish two things: first, to determine the path sequence and coverage of the inspection; and second, to limit the "disturbance of system operation by inspection actions" to a range that matches the risk level. This makes inspection an executable verification step before the generation of subsequent multi-path joint control strategies, rather than an independent additional process.

[0028] The inspection objects are divided into four types of path sets according to their coupling paths. Each path is a sequence of commands that can be executed directly in the controller. For the corresponding electrical link, the command sequence consists of "reading inverter status word, reading DC bus sample value, reading power supply protection flag", etc. For the corresponding photothermal link, the command sequence consists of "reading the fan drive input, jogging the fan and holding it for a preset short time, and reading the temperature rise sampling difference before and after jogging". For the corresponding heat pump link, the command sequence consists of "reading the compressor operating status word, reading the high and low pressure protection and exhaust over-temperature flags, and reading the inverter fault code register". For the corresponding thermal storage link, the command sequence consists of "reading the water pump control bit, jogging the water pump, and reading the return water temperature change and circulation status flags". The "reading" of the above commands is completed by reading the register or reading the sampling channel through the bus. The "jogging" is completed by writing the driver input to the controller output port and maintaining it for a short time before restoring the original value. The command parameters (register address, channel number, jogging duration, critical threshold) are written into the parameter area of ​​the controller during the factory or commissioning stage and are fixed in the form of table entries. They are called sequentially during runtime without the need for external network participation.

[0029] In order to and Convert to "path priority of command tree" for each inspection path. Calculate priority score The amplification effect of electrical mismatch and buffer deficiency under high-risk conditions is explicitly written into the score: ; in, Representing a path Priority score; This is the risk classification quantity output from step two; This is the continuous risk quantity output from step two; Defined as , used only Incremental input is introduced when entering the risk side; This is a graded sensitivity coefficient, used to increase the priority of the path under different risk levels; It is a continuous risk sensitivity coefficient, used to increase priority as the risk intensity increases; This is the coupling amplification factor, used to represent "the same amplitude at high-risk levels". This will lead to a situation where "stronger coupling instability" occurs, causing path ordering to change. Change, not fix; This is a disturbance cost constant used to suppress the frequent triggering of inspection paths that cause significant disturbances to system operation under low-risk conditions. (Coefficient) Instead of learning and updating during operation, it is stored as four sets of constant items in the controller parameter table; for example, in a "photovoltaic direct-drive heat pump" system, and Higher configurability and This allows for priority checks of the electrical side and compressor protection link during high-risk situations; however, in systems where the "thermal collector fan participates in evaporator side lifting," Higher configurability ,make Check the wind turbine link for effective response earlier when the situation deteriorates.

[0030] Command Tree The generation process uses a "sort-truncate-expand" approach: first, based on... Sort the path numbers in descending order to obtain the sequence. Then, based on the risk level, determine the number of paths to be expanded, and use this to generate the first-level path nodes of the tree. This ensures that the number of expanded paths changes continuously with the risk level and is consistent with... Consistency, define path selection flags Used to determine the path Enter command tree: ; in, Select a flag for the path; The path priority score is calculated using the above formula; This is the risk classification quantity output from step two; These are two-level threshold constants, written into the controller parameter area, and are defined as follows: This threshold structure allows when Time threshold is Only a very small number of high-priority paths will be expanded to achieve low-disturbance inspection; when Time threshold reduced to More paths are incorporated into the command tree for more comprehensive link verification. This will satisfy... The paths are sorted sequentially and used as the first-level child nodes of the tree. Then, each path node is expanded to form the second-level command node by expanding its fixed command sequence. During execution, a preorder traversal is used: first, all commands of the highest priority path are executed, then the commands of the second highest priority path are executed, until all selected paths are completed or the "critical exception early termination" condition is triggered (e.g., the protection flag is read as set), so as to avoid continuing to execute jog commands in a clear abnormal state and causing additional disturbances.

[0031] Inspection results in vector form Output, each component The return values ​​of commands under this path are synthesized according to fixed rules: read commands are determined by whether a key flag is set; jog commands are determined by whether the sampling difference before and after jogging reaches the minimum response threshold. Thresholds and synthesis rules are also fixed in the form of parameter tables. For example, for the solar thermal link, "the sampling difference of the outlet temperature after jogging the fan is not lower than the threshold" can be used as an effective response criterion; for the thermal storage link, "the direction of the return water temperature difference after jogging the water pump is consistent with the expectation" can be used as an effective response criterion; and for the electric side and heat pump link, protection flags and fault code registers are the core criteria. Command Tree It is stored in the form of a node table (each node records the path number and command number), which makes it easy to directly issue execution according to the node sequence or reuse the execution results in the next step.

[0032] S4: Filter the command tree for effectiveness based on the inspection result vector, determine the control adjustment range in conjunction with the discrete risk flag, generate the final execution instruction vector, and issue it to the corresponding execution components in stages according to the path priority order to complete the energy efficiency adjustment closed loop; Specifically, the input for this step includes two quantities: the command tree. With inspection result vector The command tree clearly defines the order of paths to be executed in this round and the command sequence under each path; the inspection result vector provides information on whether each path currently has the conditions for execution, such as whether the electrical side is in a protected state, whether the heat collector fan has the ability to respond, and whether the compressor is in a state where adjustment is prohibited.

[0033] Before execution begins, the controller first performs a validity filter on the command tree based on the inspection result vector. For paths deemed unusable, their corresponding command nodes are marked as skipped and do not participate in this round of execution; for paths deemed usable, their command nodes maintain their original order. This filtering process is completed directly within the controller through logical judgment. For example, when the inspection flag for a certain path indicates that the execution component has an anomaly, the controller will not write any new driver input to that component, but will maintain its original operating state.

[0034] The next stage involves generating and issuing control commands. The control intent vector has already been formed in the previous stage. Each component corresponds to an execution object such as the solar collector fan, heat pump compressor, and hot water storage pump. This step, combined with the inspection results, transforms them into a final, deployable execution command vector. The relationship is expressed as follows: ; in, It is a vector of inspection results The mapped path is an executable flag vector. When the inspection conclusion of a certain path indicates that the execution unit is available, the corresponding flag component takes an allowed value; when the inspection conclusion indicates that the execution unit is not suitable for adjustment, the corresponding component takes an inhibited value. This indicates component-wise computation, ensuring that the control instructions corresponding to the suppressed path have no actual effect in this round of execution. This process ensures that the control strategy is not executed on abnormal or unstable hardware paths, thereby avoiding amplifying system fluctuations.

[0035] In obtaining Subsequently, the controller does not immediately write the data to each actuator all at once, but instead adopts a phased, limited execution method. Specifically, the controller first reads the reference control vector recorded in the previous stable operating cycle from the local parameter storage area. This vector stores the drive settings of each actuator in the previous stable state, such as the drive level of the fan, the operating frequency setting of the compressor, and the start / stop status of the water pump. Then, based on the risk classification... Calculate the allowable control changes for this round: ; in, This is a scaling factor related to the risk level, used to limit the magnitude of a single adjustment. When the system is at a lower risk level, this factor takes a smaller value, allowing the actuators to make only minor adjustments; when the system is at a higher risk level, this factor takes a larger value, allowing for more significant adjustments to improve the supply and demand situation as quickly as possible. This scaling factor is implemented in the controller through table lookup or conditional judgment, without involving dynamic learning or online updates.

[0036] In the actual command delivery process, the controller follows the command tree. The control actions are executed according to the given path sequence. Typically, instructions are first issued to the actuators related to the heat source and buffer, such as adjusting the drive status of the collector fan or the operating status of the storage water pump before adjusting the operating settings of the heat pump compressor. This sequence conforms to the physical response characteristics of the system: improving the conditions on the heat source side or buffer side can often increase the overall operating margin without significantly changing the load side conditions, thereby reducing the direct impact on the heat pump body.

[0037] During the execution of each path, the controller writes new setpoints to the corresponding execution module via bus registers, analog outputs, or digital control ports. For example, for the fan path, the controller writes new drive setpoints; for the compressor path, the controller updates the inverter's operating settings; and for the water pump path, the controller switches the state of relays or solid-state switches. After each write, the controller maintains a preset stable waiting time to allow the execution components to complete their physical response before proceeding to the next path. This phased execution method avoids coupling oscillations caused by simultaneous large changes in multiple execution components. Once all selected paths in the command tree have been executed, this step outputs the final execution instruction vector. And an execution completion flag. The execution completion flag indicates that all control actions in this round have been issued and taken effect, and the system can enter the next control cycle. In the next cycle, the system will start again from state construction and risk assessment to form a new closed-loop adjustment process.

[0038] In one or more embodiments, such as Figure 2 As shown, an intelligent energy efficiency management system for a solar thermal-photovoltaic-heat pump coupled system is disclosed, the system comprising: The coupled state construction module is used to acquire real-time output power of photovoltaic power generation, hot air temperature at the outlet of solar collector duct, operating frequency of heat pump compressor, and water temperature in the middle of hot water storage tank. The acquired data is standardized to form a coupled energy state vector. The risk discrimination module is used to calculate the system supply and demand trend and energy efficiency risk based on the coupled energy state vector, and output continuous discrimination quantity and discrete risk indicator; The strategy generation module is used to generate a multi-path joint control strategy based on the continuous discrimination quantity and the discrete risk indicator, construct a command tree including the electric side link, the solar thermal link, the heat pump link and the thermal storage link, and perform equipment status inspection on each link according to the command tree to obtain the inspection result vector. The instruction execution module is used to filter the command tree for effectiveness based on the inspection result vector, determine the control adjustment range in combination with the discrete risk flag, generate the final execution instruction vector, and issue it to the corresponding execution components in stages according to the path priority order to complete the energy efficiency adjustment closed loop.

[0039] It is worth noting that the specific workflow of the intelligent energy efficiency management system for solar thermal-photovoltaic-heat pump coupling system provided in this embodiment of the invention is the same as that of the intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupling system described in the above embodiment, and will not be repeated here.

[0040] This invention also provides an intelligent energy efficiency management device for a solar thermal-photovoltaic-heat pump coupled system, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiments of the intelligent energy efficiency management method for a solar thermal-photovoltaic-heat pump coupled system, for example... Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0041] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the intelligent energy efficiency management device for a solar thermal-photovoltaic-heat pump coupling system.

[0042] The intelligent energy efficiency management device for the solar thermal-photovoltaic-heat pump coupling system can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the device may also include input / output devices, network access devices, buses, etc.

[0043] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the intelligent energy efficiency management equipment for the solar thermal-photovoltaic-heat pump coupling system, connecting all parts of the equipment via various interfaces and lines.

[0044] The memory can be used to store the computer programs and / or modules. The processor, by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the intelligent energy efficiency management device for the solar thermal-photovoltaic-heat pump coupling system. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the operation of the air conditioner controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0045] The modules integrated into the intelligent energy efficiency management equipment for the solar thermal-photovoltaic-heat pump coupling system, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0046] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0047] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A smart energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems, characterized in that, The method includes: A coupled energy state vector is constructed, which is based on the real-time output power of photovoltaic power generation, the hot air temperature at the outlet of the solar collector duct, the operating frequency of the heat pump compressor, and the water temperature in the middle of the hot water storage tank, and is obtained through standardization. Based on the coupled energy state vector, the system supply and demand trend and energy efficiency risk are dynamically calculated to obtain continuous discrimination quantity and discrete risk indicator. The discrete risk indicator represents the risk level of the current system operation. Based on the continuous discriminant and the discrete risk indicator, a multi-path joint control strategy is generated, a command tree is constructed including the electric side link, the solar thermal link, the heat pump link and the thermal storage link, and the equipment status inspection is performed on each link according to the command tree to obtain the inspection result vector. The command tree is filtered for validity based on the inspection result vector, and the control adjustment range is determined in combination with the discrete risk flag. The final execution instruction vector is generated and distributed to the corresponding execution components in stages according to the path priority order to complete the energy efficiency adjustment closed loop.

2. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, In the step of constructing the coupled energy state vector, the real-time output power of photovoltaic power generation is obtained through the inverter power sampling unit, the hot air temperature at the outlet of the solar collector duct is obtained through a thermocouple or platinum resistance temperature sensor, the operating frequency of the heat pump compressor is read through the frequency conversion control module, and the water temperature in the middle of the hot water storage tank is obtained through a thermistor or digital temperature sensor. All data are sent to the main controller through RS485 or CAN bus.

3. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, In the step of dynamically calculating the supply and demand trend and energy efficiency risk of the system, the continuous discrimination quantity comprehensively considers the coupling relationship between photovoltaic energy supply intensity, collector heat source intensity, heat pump load level and thermal storage availability, and introduces an electrical mismatch penalty term and a buffer deficiency penalty term to quantify the system operation risk.

4. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, The discrete risk indicators are divided into three levels, corresponding to normal operation, mild risk, and severe risk, respectively, with different control response strategies for each level.

5. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, In the step of generating a multi-path joint control strategy, the path priority score of the command tree is dynamically calculated and determined based on the discrete risk indicator, the continuous discrimination quantity, and the preset sensitivity coefficient and disturbance cost constant.

6. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, The equipment status inspection includes reading the inverter status word, DC bus sampling value, and power supply protection flag for the power supply link; jogging the collector fan and detecting the temperature rise response for the solar thermal link; reading the compressor operating status word, high and low pressure protection flags, and fault code register for the heat pump link; and jogging the storage water pump and detecting the return water temperature change for the storage link.

7. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, The components in the inspection result vector are synthesized according to the command return value of the corresponding link according to preset rules to determine whether each execution component has the conditions for execution.

8. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, In the step of generating the final execution instruction vector, only valid control instructions are generated for paths that are available according to the inspection results, while control instructions for unavailable paths are suppressed.

9. The intelligent energy efficiency management method for solar thermal-photovoltaic-heat pump coupled systems according to claim 1, characterized in that, In the step of issuing execution instructions in stages, the working status of the collector fan and the hot water storage pump is adjusted first, and then the operating settings of the heat pump compressor are adjusted, and a preset stable waiting time is maintained after each instruction is issued.

10. An intelligent energy efficiency management system for solar thermal-photovoltaic-heat pump coupled systems, characterized in that, The system includes: The coupled state construction module is used to acquire real-time output power of photovoltaic power generation, hot air temperature at the outlet of solar collector duct, operating frequency of heat pump compressor, and water temperature in the middle of hot water storage tank. The acquired data is standardized to form a coupled energy state vector. The risk discrimination module is used to calculate the system supply and demand trend and energy efficiency risk based on the coupled energy state vector, and output continuous discrimination quantity and discrete risk indicator; The strategy generation module is used to generate a multi-path joint control strategy based on the continuous discrimination quantity and the discrete risk indicator, construct a command tree including the electric side link, the solar thermal link, the heat pump link and the thermal storage link, and perform equipment status inspection on each link according to the command tree to obtain the inspection result vector. The instruction execution module is used to filter the command tree for effectiveness based on the inspection result vector, determine the control adjustment range in combination with the discrete risk flag, generate the final execution instruction vector, and issue it to the corresponding execution components in stages according to the path priority order to complete the energy efficiency adjustment closed loop.

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