An energy-saving control system for a combined heat pump

Abstract

The present invention relates to the technical field of heat pump energy-saving control, and specifically discloses an energy-saving control system for a combined heat pump, which realizes an adaptive control adjustment feedback to ensure the off-peak operation of the combined heat pump and completes the optimization process of energy-saving control. Specifically, it includes: a collection module for collecting heat pump heat energy data, phase change heat storage device heat energy data and environmental parameters within a target time period; an analysis module for performing time-environment dynamic analysis on the heat pump heat energy data, phase change heat storage device heat energy data and environmental parameters to obtain a distribution state coefficient; extracting a first heat energy characteristic parameter according to the heat pump heat energy data, and extracting a second heat energy characteristic parameter according to the phase change heat storage device heat energy data; a control module for mobilizing an adaptive distribution control model based on a preset PID control algorithm and a distribution state coefficient, optimizing the adaptive distribution control model and then outputting a control instruction; an output module for outputting an energy efficiency optimization result according to the control instruction.

Description

Technical Field

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

[0002] An air source heat pump combined system combines an air source heat pump with other energy sources or devices 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 an air source heat pump with other devices 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 electric energy through energy conservation can achieve the sustainable development of resources. Therefore, flexible heating and cooling are required for different regions, and the efficient use of electric energy at different times needs to be ensured; based on this, a system that combines an air source heat pump with a phase change heat storage device can achieve the control process of heat storage and heat release by selecting appropriate time periods according to different regions and different electricity prices, ensuring the effective utilization of electricity according to different price difference time intervals; however, in the existing technology, the method of combining an air source heat pump with a phase change heat storage device is relatively single, lacking a flexible system, resulting in problems with the adaptive control adjustment feedback of off-peak operation during development. Summary of the Invention

[0004] The purpose of the present invention is to provide an energy-saving control system for a combined heat pump, and solve the following technical problems:

[0005] How to ensure the accuracy of the adaptive control adjustment feedback of off-peak operation of the combined heat pump and achieve the optimization process of energy-saving control.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An energy-saving control system for a combined heat pump includes:

[0008] A collection module for collecting heat pump thermal energy data, phase change heat storage device thermal energy data, and environmental parameters within a target time period;

[0009] An analysis module for performing time-environment dynamic analysis on the heat pump thermal energy data, phase change heat storage device thermal energy data, and environmental parameters to obtain a distribution state coefficient; extracting a first thermal energy characteristic parameter according to the heat pump thermal energy data, and extracting a second thermal energy characteristic parameter according to the phase change heat storage device thermal energy data;

[0010] A control module, configured to activate an adaptive distribution control model based on a preset PID control algorithm and a distribution status coefficient, optimize the adaptive distribution control model, and then output a control instruction;

[0011] An output module, configured to output an energy efficiency optimization result according to the control instruction.

[0012] Preferably, the steps for the analysis module to extract the first thermal energy characteristic parameter according to the heat pump thermal energy data and the second thermal energy characteristic parameter according to the thermal energy data of the phase change heat storage device are as follows:

[0013] Extract the heat pump efficiency correlation index parameter according to the relationship change characteristic between the evaporator / condenser temperature difference and the COP of the historical heat pump, and record the heat pump efficiency correlation index parameter as the first thermal energy characteristic parameter; extract the phase change state index parameter according to the PCM temperature gradient and the latent heat release rate of the historical phase change heat storage device, and record the phase change state index parameter as the second thermal energy characteristic parameter.

[0014] Preferably, the process for the analysis module to perform time-environment dynamic analysis on the heat pump thermal energy data and the thermal energy data of the phase change heat storage device is as follows:

[0015] Input the historical data sets of the first thermal energy characteristic parameter and the second thermal energy characteristic parameter within a preset time period into a feedforward neural network model to construct a distribution model;

[0016] Input the first thermal energy characteristic parameter, the second thermal energy characteristic parameter and the real-time environmental parameters obtained in real time into the distribution model for training, and output the thermal energy distribution status parameter;

[0017] Calculate the thermal energy distribution status parameter to obtain the distribution status coefficient.

[0018] Preferably, the process for obtaining the distribution status coefficient is as follows:

[0019] Obtain the distribution status coefficient through the formula ;

[0020] Wherein, is the total number of combined heat pumps, and ∈ ; , are respectively the start time and the end time of the heat release period of the combined heat pump; , are respectively the start time and the end time of the heat storage period of the combined heat pump; is the thermal energy distribution status parameter of the th combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > >0。

[0021] Preferably, the method for activating the adaptive distribution control model is as follows:

[0022] Calculate the distribution state coefficient based on the preset PID control algorithm to obtain the activation parameter:

[0023] Through the formula Calculate to obtain the activation parameter ; where is the regulation coefficient of the heat storage system, and >0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient;

[0024] According to the judgment of the activation parameter and the target threshold Compare their magnitudes:

[0025] If ≥ , then generate an optimization adjustment strategy, and optimize the adaptive distribution control model according to the optimization adjustment strategy.

[0026] Preferably, the method for outputting the control instruction after optimizing the adaptive distribution control model is as follows:

[0027] Execute the optimization adjustment strategy: Input the load data in the current control state into the adaptive distribution control model to update the controller parameters; and optimize the adaptive distribution control model after the controller parameters are updated;

[0028] Input the real-time updated distribution state coefficient into the adaptive distribution control model and output the control instruction.

[0029] Preferably, the control instruction includes:

[0030] Obtain the heat pump load data to determine the load stability coefficient. When the load stability coefficient does not belong to the preset threshold, obtain the control instruction.

[0031] Preferably, the system further includes:

[0032] A model construction module, which is used to establish a target mathematical model based on the distribution state coefficient, and build an adaptive distribution control model according to the target value generated by the system and the preset PID control algorithm.

[0033] The beneficial effects of the present invention:

[0034] (1) The present invention controls and analyzes the first heat energy characteristic parameter, the second heat energy characteristic parameter and the environmental parameter through the analysis module in a time-environment dynamic analysis manner to obtain the distribution state coefficient, and ensures that the temperature distribution change of the heat storage and heat release of the heat pump heat energy is fed back according to the distribution state coefficient.

[0035] (2) The present invention ensures timely feedback on the results analyzed by the analysis module by setting a control module with a control adjustment method adaptively set for the distribution state coefficient based on the PID control algorithm and constructing a machine model, and also ensures that after the machine model of the algorithm is optimized subsequently, corresponding control instructions are output; and an output module is set to implement the response result according to the control instruction, and ensure the output of the heat energy efficiency optimization result according to the control instruction.

[0036] Of course, it is not necessary for any product implementing the present invention to achieve all the above-described advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0038] Figure 1 It is a module diagram of an energy-saving control system for a combined heat pump of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0040] Please refer to Figure 1 As shown, the present invention is an energy-saving control system for a combined heat pump, including:

[0041] A collection module for collecting heat pump heat energy data, phase change heat storage device heat energy data and environmental parameters within a target time period;

[0042] An analysis module for performing time-environment dynamic analysis on the heat pump heat energy data, phase change heat storage device heat energy data and environmental parameters to obtain a distribution state coefficient; extracting a first heat energy characteristic parameter according to the heat pump heat energy data, and extracting a second heat energy characteristic parameter according to the phase change heat storage device heat energy data;

[0043] A control module, configured to activate an adaptive distribution control model based on a preset PID control algorithm and a distribution state coefficient, and output a control instruction after optimizing the adaptive distribution control model;

[0044] An output module, configured to output an energy efficiency optimization result according to the control instruction.

[0045] In the above technical solution, the energy-saving control system of the combined heat pump based on the combination of the air source heat pump and the heat storage device realizes the energy-saving optimization control process through four modules; in this embodiment, the heat storage device provided is a phase change heat storage device, and the connection mode between the air source heat pump and the phase change heat storage device is a series connection of direct connection. The air source heat pump first transfers heat to the phase change heat storage device, and then transfers heat to the user end. And because such a connection mode is significantly affected by the environment, it is also necessary to collect environmental data. The collection of environmental parameters includes environmental temperature (main) and environmental humidity (secondary); therefore, a collection module is set to collect data within a target time period, including: heat pump thermal energy data, phase change heat storage device thermal energy data, and environmental parameters; an analysis module is also set to ensure the control analysis of the data parameters collected by the collection module. The control analysis method is to perform time-environment dynamic analysis. The main parameter objects to be analyzed include environmental parameters, and those obtained by respectively performing feature extraction based on the heat pump thermal energy data and the phase change heat storage device thermal energy data; the obtained feature parameters are: the first thermal energy feature parameter and the second thermal energy feature parameter; these parameters obtain a distribution state coefficient after time-environment dynamic analysis, which is used to feedback the temperature distribution change of heat storage and heat release of the heat pump thermal energy.

[0046] Moreover, in order to ensure the timely feedback of the analysis results of the analysis module, this embodiment also realizes the adaptive control adjustment of the distribution state coefficient by setting a control module to set a specific analysis method. Through the PID control algorithm and the control adjustment method of constructing a machine model, it is ensured that the machine model of this algorithm is optimized later and corresponding control instructions are output; therefore, the energy-saving implementation method of this embodiment is specifically to activate an adaptive distribution control model based on a preset PID control algorithm and a distribution state coefficient, and output a control instruction after optimizing the adaptive distribution control model; finally, an 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. This embodiment ensures the adaptive control adjustment feedback of the combined heat pump for off-peak operation through the collection module, the analysis module, the control module and the output module, and realizes the optimization process of energy-saving control.

[0047] As an implementation manner of the present invention, the steps for the analysis module to extract the first thermal energy feature parameter according to the heat pump thermal energy data and extract the second thermal energy feature parameter according to the phase change heat storage device thermal energy data are:

[0048] Extract the heat pump efficiency correlation index parameters according to the variation characteristics of the relationship between the evaporator / condenser temperature difference and the COP of the historical heat pump, and record the heat pump efficiency correlation index parameters as the first thermal energy characteristic parameters; extract the phase change state index parameters according to the PCM temperature gradient and the latent heat release rate of the historical phase change heat storage device, and record the phase change state index parameters as the second thermal energy characteristic parameters.

[0049] In the above technical solution, the analysis module is used to perform feature screening on historical data, so as to ensure the extraction of thermal energy characteristic parameters; since the dynamic heat changes of the heat pump and the phase change heat storage device need to be obtained during the dynamic analysis process, the heat pump efficiency and the phase change state are reflected and used as the target variables of the thermal energy characteristics; the heat pump efficiency is directly reflected by obtaining the relevant indexes of the relationship between the evaporator / condenser temperature difference and the 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 this feature and the target variable, confirm that the variation characteristics of the relationship between the evaporator / condenser temperature difference and the COP of the historical heat pump are used to extract the heat pump efficiency correlation index parameters, and use the heat pump efficiency correlation index as the first thermal energy characteristic parameter; at the same time, confirm that the phase change state index parameters are extracted according to the PCM temperature gradient and the latent heat release rate of the historical phase change heat storage device, and record the phase change state index parameters as the second thermal energy characteristic parameters.

[0050] As an implementation manner 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 heat storage device thermal energy data is as follows:

[0051] Input the historical data sets of the first thermal energy characteristic parameters and the historical data sets of the second thermal energy characteristic parameters within a preset time period into the feedforward neural network model to construct a distribution model;

[0052] Input the first thermal energy characteristic parameters, the second thermal energy characteristic parameters and the real-time environmental parameters obtained in real time into the distribution model for training, and output the thermal energy distribution state parameters;

[0053] Calculate the thermal energy distribution state parameters to obtain the distribution state coefficient.

[0054] 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 constructs a feedforward neural network model through a machine learning algorithm. Specifically, to construct the distribution model, the historical data sets of the first thermal energy characteristic parameter and the second thermal energy characteristic parameter within a preset time period are input into the feedforward neural network model; corresponding standard parameters are output; further, the model is trained: by inputting the first thermal energy characteristic parameter, the second thermal energy characteristic parameter and the real-time environment parameter obtained in real time into the distribution model for training, the thermal energy distribution state parameter is output; finally, the thermal energy distribution state parameter is calculated to output the distribution state coefficient; through the calculation of the distribution state coefficient, the confirmation feedback of the thermal energy transfer of the heat pump and the heat storage state is realized, ensuring that the output control result is more accurate.

[0055] As an implementation manner of the present invention, the process of obtaining the distribution state coefficient is as follows:

[0056] The distribution state coefficient is calculated by the formula to obtain the distribution state coefficient ;

[0057] wherein, is the total number of combined heat pumps, and ∈ ; , are respectively the start time and the end time of the heat release period of the combined heat pump; , are respectively the start time and the end time of the heat storage period of the combined heat pump; is the thermal energy distribution state parameter of the th combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > >0.

[0058] In the above technical solution, the distribution state coefficient is obtained by calculating the formula to obtain the distribution state coefficient Since the periods when the combined heat pump is in the heat release state and 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 refrigerant of the combined heat pump realizes the state of heat transfer; when in the heat storage state, the phase change material absorbs heat, and the condensation process of the refrigerant of the combined heat pump realizes the state of heat storage; therefore, analyze the dynamic change state difference of the thermal energy distribution state parameters during the two state time periods when the combined heat pump is in the heat release (working) and heat storage (resting) states, obtain the thermal energy distribution state coefficient, determine the current state of the thermal energy, ensure further confirmation according to the control algorithm, confirm whether the optimization adjustment of the model is needed, make the output control instruction more in line with the optimization process of the thermal energy, reduce the identification and adjustment of uneven local heat distribution, and realize the adaptive energy-saving control process.

[0059] Among them, it should be explained that the first preset weight coefficient and the second preset weight coefficient are both the proportions of the influence of the duration occupied by heat release and heat storage respectively and the product of the ambient temperature on the distribution of the total heat energy (the difference between the constant heat and the heat under the ambient temperature is used as the total heat energy); the larger the weight coefficient, the more obvious the influence of the environment and heating duration on the overall heat distribution, and the larger the calculated result.

[0060] As an implementation manner of the present invention, the method for mobilizing the adaptive distribution control model is:

[0061] Calculate the distribution state coefficient based on the preset PID control algorithm to obtain the mobilization parameter:

[0062] Obtain the mobilization parameter through the formula ; where ; is the regulation coefficient of the heat storage system, and >0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient;

[0063] According to the judgment of the mobilization parameter and the target threshold compare their magnitudes:

[0064] If ≥ , then generate an optimization adjustment strategy, and optimize the adaptive distribution control model according to the optimization adjustment strategy.

[0065] In the above technical solution, further mobilization processing of the adaptive distribution model is performed according to the calculated distribution state coefficient. The mobilization processing mainly calculates the distribution state coefficient based on the preset PID control algorithm, and the calculation formula is through the formula Calculate and obtain the transfer parameter ; The magnitude of the transfer coefficient reflects whether the optimization adjustment strategy needs to be activated currently, ensuring further optimization of the adaptive distribution model; among them, the heat storage system adjustment coefficient is obtained based on the pre-set PID control algorithm. Specifically, in a heat pump system equipped with a heat storage device, according to factors such as grid load, heat load demand, and heat storage status, the charge and discharge strategies of the corresponding heat storage device are determined. The set charge and discharge strategies include the starting temperature, stopping temperature, etc. of the charge and discharge. 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.

[0066] The standard value of the distribution state coefficient and The larger the absolute value of the difference, the more obvious the heat distribution difference; and the standard value 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 is set in advance according to the deviation between the standard value and the historical value, belonging to the ideal deviation range; when is on the large side, the value of the transfer parameter will also increase accordingly. When the transfer parameter exceeds the specified target threshold range, the model needs to be optimized. The specific judgment process is as follows: Compare the transfer parameter with the target threshold . When ≥ , an optimization adjustment strategy is generated, and the adaptive distribution control model is optimized according to the optimization adjustment strategy to ensure the precise output of control instructions using the optimization adjustment strategy.

[0067] As an implementation manner of the present invention, the method for outputting control instructions after optimizing the adaptive distribution control model is as follows:

[0068] Execute the optimization adjustment strategy: Input the load data in the current control state into the adaptive distribution control model to update the controller parameters; and optimize the adaptive distribution control model after the controller parameters are updated;

[0069] Input the real-time updated distribution state coefficient into the adaptive distribution control model and output control instructions.

[0070] In the above technical solution, the method for outputting control instructions after optimizing the adaptive distribution control model includes updating the controller parameters, and optimizing the adaptive distribution control model according to the updated controller parameters; finally, for the distribution state coefficient obtained by real-time update, model optimization is performed, and the output control instructions are the optimized results.

[0071] As an implementation manner of the present invention, the control instruction includes:

[0072] Obtain the heat pump load data to determine the load stability coefficient. When the load stability coefficient does not belong to the preset threshold, obtain the control instruction.

[0073] In the above technical solution, the control instruction ensures that the parameter adjustment control is combined with the prediction and determination of the load stability coefficient. After the optimized adaptive distribution control model determines the input distribution state coefficient and the heat pump load data under this distribution state coefficient, the load stability coefficient is obtained, and the load stability coefficient is judged. When it does not belong to the preset threshold, the control instruction is obtained.

[0074] As an implementation manner of the present invention, the system further includes:

[0075] A model construction module, configured to establish a target mathematical model based on the distribution state coefficient, and build an adaptive distribution control model according to the target value generated by the system and the preset PID control algorithm.

[0076] In the above technical solution, the construction of the adaptive distribution control model includes establishing a target mathematical model: specifically, the target mathematical model is learned through the distribution state coefficient, the target value is output, and the target mathematical model is further built according to the target value and the preset PID control algorithm to generate the adaptive distribution control model.

[0077] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0078] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by this application, they should all belong to the protection scope of the present invention.

Claims

1. An energy-saving control system for a combined heat pump, characterized in that Including: A collection module for collecting heat pump thermal energy data, phase change thermal energy storage device thermal energy data, and environmental parameters during a target time period; An analysis module for performing time-environment dynamic analysis on the heat pump thermal energy data, phase change thermal energy storage device thermal energy data, and environmental parameters to obtain a distribution state coefficient; extracting a first thermal energy characteristic parameter according to the heat pump thermal energy data, and extracting a second thermal energy characteristic parameter according to the phase change thermal energy storage device thermal energy data; A control module for mobilizing an adaptive distribution control model based on a preset PID control algorithm and the distribution state coefficient, and outputting a control instruction after optimizing the adaptive distribution control model; An output module for outputting an energy efficiency optimization result according to the control instruction; The process of obtaining the distribution state coefficient is as follows: The distribution state coefficient is obtained by the formula and is ; Among them, is the total number of combined heat pumps, and ∈ ; and are the start time and end time of the heat release period of the combined heat pump respectively; and are the start time and end time of the heat storage period of the combined heat pump respectively; is the thermal energy distribution state parameter of the th combined heat pump; is the first preset weight coefficient, is the second preset weight coefficient, and > > 0; The method of mobilizing the adaptive distribution control model is as follows: Calculating the distribution state coefficient based on a preset PID control algorithm to obtain a mobilization parameter: The transfer parameter is calculated through the formula ; among which, where is the regulation coefficient of the heat storage system, and > 0; is the standard value of the distribution state coefficient; is the preset deviation value of the distribution state coefficient; Transfer parameters according to the judgment with the target threshold to compare the magnitudes: If ≥ , an optimization and adjustment strategy is generated, and the adaptive distribution control model is optimized according to the optimization and adjustment strategy.

2. The energy-saving control system of a combined heat pump according to claim 1, wherein, The steps for the analysis module to extract the first thermal energy characteristic parameter according to the heat pump thermal energy data and extract the second thermal energy characteristic parameter according to the phase change thermal energy storage device thermal energy data are as follows: Extracting a heat pump efficiency correlation index parameter according to the relationship change characteristic between the evaporator / condenser temperature difference and COP of the historical heat pump, and recording the heat pump efficiency correlation index parameter as the first thermal energy characteristic parameter; extracting a phase change state index parameter according to the PCM temperature gradient and latent heat release rate of the historical phase change thermal energy storage device, and recording the phase change state index parameter as the second thermal energy characteristic parameter.

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

4. The energy-saving control system of a combined heat pump according to claim 1, characterized in that The manner of outputting a control instruction after optimizing the adaptive distribution control model is as follows: Executing an optimization adjustment strategy: inputting the load data in the current control state into the adaptive distribution control model to update the controller parameters; and optimizing the adaptive distribution control model after the controller parameters are updated; Inputting the real-time updated distribution state coefficient into the adaptive distribution control model to output a control instruction.

5. The energy-saving control system of a combined heat pump according to claim 4, characterized in that The control instruction includes: Obtaining the heat pump load data to determine the load stability coefficient, and obtaining a control instruction when the load stability coefficient does not belong to a preset threshold.

6. The energy-saving control system of a combined heat pump according to claim 1, characterized in that, The system further includes: A model construction module for establishing a target mathematical model based on the distribution state coefficient, and building an adaptive distribution control model according to the target value generated by the system and a preset PID control algorithm.

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