Energy-saving control system of combined heat pump

Abstract

The invention relates to the technical field of heat pump energy-saving control, and particularly discloses an energy-saving control system of a combined heat pump, which realizes self-adaptive control regulation feedback of peak shifting operation of the combined heat pump and completes an optimization process of energy-saving control. The system specifically comprises an acquisition module used for acquiring heat energy data of a heat pump, heat energy data of a phase change heat storage device and environmental parameters in a target time period; the analysis module is used for performing time-environment dynamic analysis on the heat energy data of the heat pump, the heat energy data of the phase change heat storage device and the environment parameters to obtain a distribution state coefficient; first heat energy characteristic parameters are extracted according to the heat pump heat energy data, and second heat energy characteristic parameters are extracted according to the phase change heat storage device heat energy data; the control module is used for transferring a self-adaptive distribution control model based on a preset PID control algorithm and the distribution state coefficient, optimizing the self-adaptive distribution control model and then outputting a control instruction; and the output module is used for outputting an energy efficiency optimization result according to the control instruction.

Description

Technical Field

[0001] The invention relates to the technical field of heat pump energy-saving control, and in particular to an energy-saving control system combined with a heat pump. Background Art

[0002] The air source heat pump combined system combines air source heat pumps with other energy sources or equipment to form an efficient and environmentally friendly heating and cooling combined system; a stable heating and cooling process is achieved through the combined design of heating and cooling of the combined heat pump; the combined heat pump mainly combines air source heat pumps with other equipment to ensure the formation of an efficient and environmentally friendly heating and cooling system.

[0003] Due to the increasing cost of living, the existing clean and environmentally friendly use of electricity through energy conservation can achieve sustainable development of resources. Therefore, flexible heating and cooling are needed for different regions, and the efficiency of energy conservation and use in different periods of time is guaranteed; based on this, a system that combines air source heat pumps with phase change heat storage devices can realize the control process of heat storage and heat release by selecting appropriate time periods according to the use of different regions and different electricity prices, and ensure the effective use of electricity according to different price difference time intervals; however, the existing technology combines air source heat pumps with phase change heat storage devices in a relatively single way, lacks a flexible system, and leads to the problem of adaptive control and adjustment feedback of peak operation in development. Summary of the invention

[0004] The purpose of the present invention is to provide an energy-saving control system combined with a heat pump to solve the following technical problems: How to ensure the accuracy of adaptive control adjustment feedback of combined heat pump peak-shifting operation and achieve the optimization process of energy-saving control.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An energy-saving control system for a combined heat pump, comprising: A collection module is used to collect heat pump thermal energy data and phase change thermal storage device thermal energy data and environmental parameters within a target time period; An analysis module is used to perform time-environment dynamic analysis on the heat pump thermal energy data, the phase change thermal storage device thermal energy data and the environmental parameters to obtain a distribution state coefficient; extract a first thermal energy characteristic parameter based on the heat pump thermal energy data, and extract a second thermal energy characteristic parameter based on the phase change thermal storage device thermal energy data; A control module, used for mobilizing an adaptive distributed control model based on a preset PID control algorithm and a distributed state coefficient, and outputting a control instruction after optimizing the adaptive distributed control model; The output module is used to output energy efficiency optimization results according to control instructions.

[0006] Preferably, the analysis module extracts the first thermal energy characteristic parameter according to the thermal energy data of the heat pump, and extracts the second thermal energy characteristic parameter according to the thermal energy data of the phase change thermal storage device as follows: According to the historical change characteristics of the relationship between the evaporator / condenser temperature difference and COP of the heat pump, the heat pump efficiency-related index parameters are extracted, and the heat pump efficiency-related index parameters are recorded as the first thermal energy characteristic parameters; according to the historical PCM temperature gradient and latent heat release rate of the phase change heat storage device, the phase change state index parameters are extracted, and the phase change state index parameters are recorded as the second thermal energy characteristic parameters.

[0007] Preferably, the process of the analysis module for performing time-environment dynamic analysis on the heat pump thermal energy data and the phase change thermal storage device thermal energy data is as follows: Inputting a historical data set of a first thermal energy characteristic parameter and a historical data set of a second thermal energy characteristic parameter within a preset time period into a feedforward neural network model to construct a distribution model; Inputting the first thermal energy characteristic parameter and the second thermal energy characteristic parameter acquired in real time and the real-time environmental parameter into the distribution model for training, and outputting the thermal energy distribution state parameter; The heat energy distribution state parameters are calculated to obtain the distribution state coefficient.

[0008] Preferably, the process of obtaining the distribution state coefficient is: By formula Calculate the distribution state coefficient ; in, is the total number of combined heat pumps, and ∈ ; , They are the start time and end time of the combined heat pump in the heat release period respectively; , They are the start time and end time of the heat storage period of the combined heat pump respectively; For the Thermal energy distribution state parameters of a combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > >0.

[0009] Preferably, the method of mobilizing the adaptive distributed control model is: Calculate the distribution state coefficient based on the preset PID control algorithm to obtain the adjustment parameters: By formula Calculate the parameters to obtain the ;in, is the thermal storage system regulation coefficient, and >0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient; Adjust parameters based on judgment With target threshold To compare the sizes: like ≥ , then an optimization adjustment strategy is generated, and the adaptive distributed control model is optimized according to the optimization adjustment strategy.

[0010] Preferably, the method of outputting the control instruction after optimizing the adaptive distributed control model is: Execute the optimization adjustment strategy: input the load data under the current control state into the adaptive distributed control model, update the controller parameters; and optimize the adaptive distributed control model after the controller parameters are updated; The real-time updated distribution state coefficients are input into the adaptive distributed control model and the control instructions are output.

[0011] Preferably, the control instructions include: The heat pump load data is obtained to determine the load stability coefficient, and when the load stability coefficient does not fall within a preset threshold, a control instruction is obtained.

[0012] Preferably, the system further comprises: The model building module is used to establish a target mathematical model based on the distribution state coefficients, and to build an adaptive distributed control model according to the target value generated by the system and the preset PID control algorithm.

[0013] Beneficial effects of the present invention: (1) The present invention sets an analysis module to control and analyze the first thermal energy characteristic parameter, the second thermal energy characteristic parameter and the environmental parameter through a time-environment dynamic analysis method, obtains a distribution state coefficient, and ensures that the temperature distribution change of the heat pump heat storage and heat release is fed back according to the distribution state coefficient.

[0014] (2) The present invention sets a control module to control and adjust the distribution state coefficient based on the PID control algorithm and builds a machine model adaptive setting control method, thereby ensuring timely feedback on the analysis results of the analysis module and subsequent optimization of the machine model of the algorithm and outputting corresponding control instructions; and sets an output module to respond to the control instruction result, thereby ensuring output of thermal energy efficiency optimization results according to the control instruction.

[0015] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 This is a module diagram of an energy-saving control system combined with a heat pump according to the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention is an energy-saving control system combined with a heat pump, comprising: A collection module is used to collect heat pump thermal energy data and phase change thermal storage device thermal energy data and environmental parameters within a target time period; An analysis module is used to perform time-environment dynamic analysis on the heat pump thermal energy data, the phase change thermal storage device thermal energy data and the environmental parameters to obtain a distribution state coefficient; extract a first thermal energy characteristic parameter based on the heat pump thermal energy data, and extract a second thermal energy characteristic parameter based on the phase change thermal storage device thermal energy data; A control module, used for mobilizing an adaptive distributed control model based on a preset PID control algorithm and a distributed state coefficient, and outputting a control instruction after optimizing the adaptive distributed control model; The output module is used to output energy efficiency optimization results according to control instructions.

[0020] In the above technical scheme, the energy-saving control system of the combined heat pump based on the air source heat pump and the heat storage device realizes the energy-saving optimization control process through four modules; the heat storage device provided in this embodiment is a phase change heat storage device, and the connection mode of the air source heat pump and the phase change heat storage device is a direct series connection. The air source heat pump first transfers heat to the phase change heat storage device, and then transfers the heat to the user end. Moreover, since such a connection mode is obviously affected by the environment, it is also necessary to collect environmental data. The collection of environmental parameters includes ambient temperature (primary) and ambient humidity (secondary); therefore, a collection module is set to collect target time The data in the segment include: heat pump thermal energy data, phase change thermal storage device thermal energy data, and environmental parameters; an analysis module is also set to ensure control and analysis of the data parameters collected by the acquisition module, wherein the control analysis method is a time-environment dynamic analysis, and the main analysis parameter objects include environmental parameters, as well as feature extraction based on the heat pump thermal energy data and the phase change thermal storage device thermal energy data; the acquired feature parameters are: a first thermal energy feature parameter, a second thermal energy feature parameter; these parameters are subjected to time-environment dynamic analysis to obtain distribution state coefficients, which are used to feedback the temperature distribution changes of the heat pump thermal energy storage and heat release.

[0021] Furthermore, in order to ensure timely feedback of the analysis results of the analysis module, the present embodiment also sets a control module to realize adaptive control and adjustment of the distribution state coefficient by setting a specific analysis method, and ensures that the machine model of the algorithm is subsequently optimized and the corresponding control instructions are output through the PID control algorithm and the control adjustment method of constructing the machine model; therefore, the energy-saving implementation method of the present embodiment is specifically to mobilize the adaptive distribution control model based on the preset PID control algorithm and the distribution state coefficient, optimize the adaptive distribution control model and output the control instruction; finally, the output module is set to realize the response result according to the control instruction, and ensure the output of the thermal energy efficiency optimization result according to the control instruction. The present embodiment ensures the adaptive control adjustment feedback of the staggered operation of the combined heat pump through the acquisition module, the analysis module, the control module and the output module, and realizes the optimization process of energy-saving control.

[0022] As an embodiment of the present invention, the analysis module extracts the first thermal energy characteristic parameter according to the thermal energy data of the heat pump, and extracts the second thermal energy characteristic parameter according to the thermal energy data of the phase change thermal storage device as follows: According to the historical change characteristics of the relationship between the evaporator / condenser temperature difference and COP of the heat pump, the heat pump efficiency-related index parameters are extracted, and the heat pump efficiency-related index parameters are recorded as the first thermal energy characteristic parameters; according to the historical PCM temperature gradient and latent heat release rate of the phase change heat storage device, the phase change state index parameters are extracted, and the phase change state index parameters are recorded as the second thermal energy characteristic parameters.

[0023] In the above technical scheme, feature screening of historical data is implemented according to the analysis module, thereby ensuring the extraction of thermal energy characteristic parameters; since it is necessary to obtain the dynamic heat changes of the heat pump and the phase change heat storage device during the dynamic analysis process, the heat pump efficiency and phase change state are reflected, and used as the target variable of the thermal energy characteristics; the heat pump efficiency is directly reflected by obtaining the relationship-related indicators between the evaporator / condenser temperature difference and COP; the state change of the phase change material in the phase change heat storage device is reflected by obtaining the PCM temperature gradient and the latent heat release rate. Therefore, in this embodiment, the chi-square test is used to obtain the correlation between the feature and the target variable, confirm the acquisition of the historical heat pump evaporator / condenser temperature difference and COP relationship change characteristics to extract the heat pump efficiency-related index parameters, and use the heat pump efficiency-related index as the first thermal energy characteristic parameter; at the same time, it is confirmed that the phase change state index parameters are extracted according to the PCM temperature gradient and latent heat release rate of the historical phase change heat storage device, and the phase change state index parameters are recorded as the second thermal energy characteristic parameters.

[0024] As an embodiment of the present invention, the process of the analysis module for performing time-environment dynamic analysis on the heat pump thermal energy data and the phase change thermal storage device thermal energy data is as follows: Inputting a historical data set of a first thermal energy characteristic parameter and a historical data set of a second thermal energy characteristic parameter within a preset time period into a feedforward neural network model to construct a distribution model; Inputting the first thermal energy characteristic parameter and the second thermal energy characteristic parameter acquired in real time and the real-time environmental parameter into the distribution model for training, and outputting the thermal energy distribution state parameter; The heat energy distribution state parameters are calculated to obtain the distribution state coefficient.

[0025] In the above technical solution, the analysis module uses the first thermal energy characteristic parameter and the second thermal energy characteristic parameter as the basis for time-environment dynamic analysis, and builds a feedforward neural network model through a machine learning algorithm. Specifically, the construction of the distribution model is to input the historical data set of the first thermal energy characteristic parameter and the second thermal energy characteristic parameter within a preset time period into the feedforward neural network model; output the corresponding standard parameters; further, the model is trained: the first thermal energy characteristic parameter and the second thermal energy characteristic parameter and the real-time environmental parameter obtained in real time are input into the distribution model for training, and the thermal energy distribution state parameters are output; finally, the thermal energy distribution state parameters are calculated, and the distribution state coefficient is output; the calculation of the distribution state coefficient is used to realize the confirmation feedback of the thermal energy mobilization of the heat pump and the heat storage state, so as to ensure that the output control result is more accurate.

[0026] As an implementation mode of the present invention, the process of obtaining the distribution state coefficient is as follows: By formula Calculate the distribution state coefficient ; in, is the total number of combined heat pumps, and ∈ ; , They are the start time and end time of the combined heat pump in the heat release period respectively; , They are the start time and end time of the heat storage period of the combined heat pump respectively; For the Thermal energy distribution state parameters of a combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > >0.

[0027] In the above technical solution, the distribution state coefficient is obtained by the formula Calculate the distribution state coefficient ; Since the time period when the combined heat pump is in the heat release state and the time period when it is in the heat storage state affect the heat energy supply of the heat pump and the phase change heat storage device; when in the heat release state, the phase change material releases heat, and the evaporation process of the combined heat pump refrigerant realizes the state of heat transfer; when in the heat storage state, the phase change material absorbs heat, and the condensation process of the combined heat pump refrigerant realizes the state of heat storage; therefore, the dynamic change state difference of the heat energy distribution state parameters of the combined heat pump in the two state time periods of heat release (working) and heat storage (resting) is analyzed, the heat energy distribution state coefficient is obtained, the current heat energy state is determined, and further confirmation is guaranteed according to the control algorithm to confirm whether the model needs to be optimized and adjusted, so that the output control instructions are more in line with the optimization process of heat energy, reduce the identification and adjustment of local uneven heat distribution, and realize an adaptive energy-saving control process.

[0028] Among them, it needs to be explained that the first preset weight coefficient , the second preset weight coefficient They are all the weights of the influence of the product of the time occupied by heat release and heat storage respectively and the ambient temperature on the distribution of the total amount of thermal energy (the difference between the constant heat and the heat at ambient temperature is taken as the total amount of thermal energy); the larger the weight coefficient, the more obvious the influence of the environment and heating time on the overall heat distribution, and the larger the calculated result.

[0029] As an implementation mode of the present invention, the method of mobilizing the adaptive distributed control model is: Calculate the distribution state coefficient based on the preset PID control algorithm to obtain the adjustment parameters: By formula Calculate the parameters to obtain the ;in, is the thermal storage system regulation coefficient, and >0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient; Adjust parameters based on judgment With target threshold To compare the sizes: like ≥ , then an optimization adjustment strategy is generated, and the adaptive distributed control model is optimized according to the optimization adjustment strategy.

[0030] In the above technical solution, the adaptive distribution model is further adjusted according to the distribution state coefficient obtained by calculation. The adjustment process is mainly performed by calculating the distribution state coefficient based on a preset PID control algorithm. The calculation formula is: Calculate the parameters to obtain the ; The size of the adjustment coefficient reflects whether the optimization adjustment strategy needs to be started at present to ensure further optimization of the adaptive distribution model; among them, the adjustment coefficient of the heat storage system It is obtained based on a preset PID control algorithm. Specifically, in a heat pump system equipped with a heat storage device, the charging and discharging strategy of the corresponding heat storage device is determined according to factors such as the grid load, heat load demand, and heat storage status. The charging and discharging strategy includes the starting temperature and stop temperature of charging and discharging. Usually, these temperature values ​​are used as adjustment coefficients to optimize the automatic control operation of the phase change heat storage device, thereby improving the flexibility and economy of the system.

[0031] Standard value of distribution coefficient and The larger the absolute value of the difference, the more obvious the difference in heat distribution; and the standard value It is obtained by dynamic fitting based on the historical working data of the combined heat pump; the preset deviation value of the distribution state coefficient The deviation between the standard value and the historical value is set in advance and belongs to the ideal deviation range. When the adjustment parameter exceeds the specified target threshold range, the model needs to be optimized. The specific judgment process is: adjust the adjustment parameter With target threshold To compare the size, ≥ , then an optimization adjustment strategy is generated, and the adaptive distributed control model is optimized according to the optimization adjustment strategy to ensure that the optimization adjustment strategy is used to achieve accurate output of control instructions.

[0032] As an implementation mode of the present invention, the method of outputting control instructions after optimizing the adaptive distributed control model is: Execute the optimization adjustment strategy: input the load data under the current control state into the adaptive distributed control model, update the controller parameters; and optimize the adaptive distributed control model after the controller parameters are updated; The real-time updated distribution state coefficients are input into the adaptive distributed control model and the control instructions are output.

[0033] In the above technical scheme, the method of outputting control instructions after optimizing the adaptive distributed control model includes updating the controller parameters, and optimizing the adaptive distributed control model according to the updated controller parameters; finally, the model is optimized for the distributed state coefficients obtained by real-time update, and the output control instructions are the optimized results.

[0034] As an implementation mode of the present invention, the control instruction includes: The heat pump load data is obtained to determine the load stability coefficient, and when the load stability coefficient does not fall within a preset threshold, a control instruction is obtained.

[0035] In the above technical solution, the control instruction ensures that the parameter adjustment control is combined with the prediction and judgment of the load stability coefficient. When the optimized adaptive distributed control model determines the input distribution state coefficient and the heat pump load data distributed under the state coefficient, the load stability coefficient is obtained, and the load stability coefficient is judged. When it is judged that it does not belong to the preset threshold, the control instruction is obtained.

[0036] As an embodiment of the present invention, the system further includes: The model building module is used to establish a target mathematical model based on the distribution state coefficients, and to build an adaptive distributed control model according to the target value generated by the system and the preset PID control algorithm.

[0037] In the above technical solution, the construction of the adaptive distributed control model includes establishing a target mathematical model: specifically, learning the target mathematical model through the distribution state coefficient, outputting the target value, and continuing to build the target mathematical model according to the target value and the preset PID control algorithm to generate an adaptive distributed control model.

[0038] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0039] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. An energy-saving control system combined with a heat pump, characterized in that: include: A collection module is used to collect heat pump thermal energy data and phase change thermal storage device thermal energy data and environmental parameters within a target time period; An analysis module is used to perform time-environment dynamic analysis on the heat pump thermal energy data, the phase change thermal storage device thermal energy data and the environmental parameters to obtain a distribution state coefficient; extract a first thermal energy characteristic parameter based on the heat pump thermal energy data, and extract a second thermal energy characteristic parameter based on the phase change thermal storage device thermal energy data; A control module, used for mobilizing an adaptive distributed control model based on a preset PID control algorithm and a distributed state coefficient, and outputting a control instruction after optimizing the adaptive distributed control model; The output module is used to output energy efficiency optimization results according to control instructions.

2. The energy-saving control system of a combined heat pump according to claim 1, characterized in that: The steps of extracting the first thermal energy characteristic parameter according to the thermal energy data of the heat pump by the analysis module and extracting the second thermal energy characteristic parameter according to the thermal energy data of the phase change thermal storage device are as follows: According to the historical change characteristics of the relationship between the evaporator / condenser temperature difference and COP of the heat pump, the heat pump efficiency-related index parameters are extracted, and the heat pump efficiency-related index parameters are recorded as the first thermal energy characteristic parameters; according to the historical PCM temperature gradient and latent heat release rate of the phase change heat storage device, the phase change state index parameters are extracted, and the phase change state index parameters are recorded as the second thermal energy characteristic parameters.

3. The energy-saving control system of a combined heat pump according to claim 2, characterized in that: The process of the analysis module for performing time-environment dynamic analysis on the heat pump thermal energy data and the phase change thermal storage device thermal energy data includes: Inputting a historical data set of a first thermal energy characteristic parameter and a historical data set of a second thermal energy characteristic parameter within a preset time period into a feedforward neural network model to construct a distribution model; Inputting the first thermal energy characteristic parameter and the second thermal energy characteristic parameter acquired in real time and the real-time environmental parameter into the distribution model for training, and outputting the thermal energy distribution state parameter; The heat energy distribution state parameter is calculated to obtain a distribution state coefficient.

4. The energy-saving control system of a combined heat pump according to claim 3, characterized in that: The process of obtaining the distribution state coefficient is as follows: By formula Calculate the distribution state coefficient ; in, is the total number of combined heat pumps, and ∈ ; , They are the start time and end time of the combined heat pump in the heat release period respectively; , They are the start time and end time of the heat storage period of the combined heat pump respectively; For the Thermal energy distribution state parameters of a combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > >

0.

5. The energy-saving control system of a combined heat pump according to claim 4, characterized in that: The method of mobilizing the adaptive distributed control model is: Calculate the distribution state coefficient based on the preset PID control algorithm to obtain the adjustment parameters: By formula Calculate the parameters to obtain the in, is the thermal storage system regulation coefficient, and >0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient; adjust the parameters according to the judgment With target threshold To compare the sizes: like , then an optimization adjustment strategy is generated, and the adaptive distributed control model is optimized according to the optimization adjustment strategy.

6. The energy-saving control system of a combined heat pump according to claim 5, characterized in that: The method of outputting control instructions after optimizing the adaptive distributed control model is: Execute the optimization adjustment strategy: input the load data under the current control state into the adaptive distributed control model, update the controller parameters; and optimize the adaptive distributed control model after the controller parameters are updated; The real-time updated distribution state coefficients are input into the adaptive distributed control model and the control instructions are output.

7. The energy-saving control system of a combined heat pump according to claim 6, characterized in that: The control instructions include: The heat pump load data is obtained to determine the load stability coefficient, and when the load stability coefficient does not fall within a preset threshold, a control instruction is obtained.

8. The energy-saving control system of a combined heat pump according to claim 1, characterized in that: The system further comprises: The model building module is used to establish a target mathematical model based on the distribution state coefficients, and to build an adaptive distributed control model according to the target value generated by the system and the preset PID control algorithm.

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