Ground source heat pump system optimization scheduling method based on probabilistic graph digital twinborn model

Abstract

The invention discloses a ground source heat pump system optimization scheduling method based on a probability graph digital twinborn model, and belongs to the field of ground source heat pump system operation optimization, and the method comprises the following steps: S1, constructing a digital twinborn model based on a dynamic probability graph decision network in a modeling stage; s2, calibrating the digital twin model in a calibration stage; and S3, the digital twinborn model is updated in the operation stage, and the optimal operation method of the ground source heat pump system is output in real time through the updated digital twinborn model. A physical entity-twin system is abstracted into a group of coupled dynamic systems, and the systems evolve along with time in a physical state space and a digital state space and interact with each other through observation data and control input; the problems that multi-physical-field coupling modeling and dynamic optimization control of the ground source heat pump system are complex in calculation and high in calculation resource requirement are solved, and real-time and automatic optimization control of the ground source heat pump system under the complex and changeable underground environment and load requirement is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of ground source heat pump system operation optimization, and specifically relates to a ground source heat pump system optimization scheduling method based on a probability graph digital twin model. Background Art

[0002] Although the ground source heat pump unit shows a high energy efficiency ratio in laboratory testing and production processes, it is often difficult to achieve the energy efficiency level calibrated at the factory in actual applications. The reasons for this difference are complex and varied, including the diversity of geological conditions, the complexity of underground heat sources, the difference between actual working conditions and experimental conditions, unreasonable initial design, and insufficient equipment operation and maintenance. The actual efficiency is significantly lower than the theoretical value, which will lead to low energy utilization and failure to achieve energy saving effects. In addition, due to the high initial investment in the ground source heat pump system, poor actual operating efficiency will extend the investment payback period, weaken its economy and market competitiveness, and thus affect the popularization and promotion of ground source heat pump technology. Therefore, improving the operating efficiency of the ground source heat pump system under actual conditions and optimizing the initial design plan are the core keys to the widespread application of the ground source heat pump system.

[0003] In order to solve the above problems, it is necessary to perceive the operating status of the ground source heat pump system in real time and accurately, and automatically optimize the operating parameters according to dynamic factors such as the load demand of the application scenario and the characteristics of the geological heat source, and quickly diagnose equipment failures and respond to abnormal operation in a timely manner. Digital twin technology provides an effective solution to achieve these goals. The digital twin of the ground source heat pump system, by building a virtual model corresponding to the ground source heat pump system, obtains the operating data, environmental parameters and application scenario load changes of the ground source heat pump in real time, and realizes the seamless connection between the physical system and the digital space, so as to accurately model, monitor in real time and dynamically adjust the system operation. At the same time, based on the real-time collected information, the physical system of the ground source heat pump and the characteristics of the geological heat source, the digital twin combines big data and artificial intelligence technology for in-depth analysis, identifies the inefficient state in the system operation, simulates and dynamically optimizes the control under different working conditions and load conditions, and implements predictive maintenance for the equipment, thereby minimizing the gap between the actual energy efficiency and the calibrated energy efficiency. In addition, the digital twin's simulation function is based on geological exploration data and building load forecasts. It evaluates the performance of the ground source heat pump system in different scenarios and working conditions during the design phase, helping designers evaluate the feasibility of the system and find the optimal heat exchanger layout, equipment selection, geothermal well network layout, and underground pipe size and quantity configuration to ensure that the system design is highly matched with the actual working conditions and achieve the best balance between initial investment and subsequent operating efficiency.

[0004] In recent years, digital twin technology has made significant progress, especially in data management, real-time monitoring and visualization, and there are also studies and applications related to digital twin HVAC systems. However, at present, there is still a lack of systematic practice and specialized research on digital twin systems for ground-source heat pumps at home and abroad, especially the modeling methods and mathematical forms for building digital twins for ground-source heat pump systems. Compared with building digital twins for HVAC systems, building digital twins for ground-source heat pump systems involves modeling the underground environment and its coupling with ground equipment, detailed calculations of heat conduction processes, and optimization of long-term operations, which requires processing more variables and more complex model calculations. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the geothermal heat pump system optimization scheduling method based on the probabilistic graph digital twin model provided by the present invention uses low-computational-cost digital twin technology to realize real-time and automatic optimization control of the geothermal heat pump system, solving the problems of complex calculations and high computing resource requirements for multi-physical field coupling modeling and dynamic optimization control of the geothermal heat pump system.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a ground source heat pump system optimization scheduling method based on a probability graph digital twin model, comprising the following steps: S1. Construct a digital twin model based on a dynamic probability graph decision network in the modeling stage; S2. Calibrate the digital twin model in the calibration phase; S3. Update the digital twin model during the operation phase, and output the optimal operation method of the ground source heat pump system in real time through the updated digital twin model.

[0007] Further: In S1, the nodes in the digital twin model based on the dynamic probability graph decision network include random variables of physical state, digital state, observation data, research quantity, control input and reward equation at each time point.

[0008] Further: the set of physical states ,in, For virtual moments, represents the modeling phase, represents the calibration phase of the digital twin model, For prediction and decision-making moments, To reflect the values ​​of relevant parameter variables of the ground source heat pump system operating status at different times; A collection of digital states ,in, To construct the digital state values ​​of relevant parameter variables of the digital twin calculation model of the ground source heat pump system at different times; A collection of observation data ,in, The observation data reflecting the changes in physical states collected by sensors or detection equipment in the ground source heat pump system at different times; A collection of research ,in, is the coefficient of performance of the ground source heat pump system at different times;

[0009] In the formula, For Time has come The average value of the heat at the load end at all times, For Time has come The average value of the input power of the heat pump unit at the moment, For Time has come The average value of the pump's input power at the moment, For Time has come The average power consumption of the fan coil at the load end at all times. For Time has come Control the average value of system energy consumption at all times; The set of control inputs is ,in, It is the operation control scheme of the ground source heat pump system at different times; The set of reward equations is ,in, Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0010] In the formula, is the first duration of the model calibration phase. and are the observed values ​​of the load at each moment and the predicted values ​​of the digital twin model, s Collect moments for observations; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0011] In the formula, The second duration of the model calibration phase, and are the observed value of the outlet temperature on the ground source side at each moment and the predicted value of the digital twin model; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0012] In the formula, The third duration of the model calibration phase. and They are the observed value of the return water temperature at the load end at each moment and the predicted value of the digital twin model; For digital twin models t The reward equation for the moment model operation phase;

[0013] In the formula, is the coefficient of performance of the ground source heat pump system at each observation collection moment.

[0014] Further: In S1, the expression of the digital twin model based on the dynamic probability graph decision network is specifically:

[0015] In the formula, Deadline , given historical observation data and control input, the system exhibits a digital state , calculate the research volume and get rewards The conditional probability distribution function of The digital state of the ground source heat pump system at the last moment is , control input executed , and collect observation data at this moment Update the digital twin to get the digital state The conditional probability distribution function of Given the updated digital state of the digital twin at the current moment Get research volume The conditional probability distribution function of For a given digital state at the current moment , research volume , control input and observational data , get the reward value The conditional probability distribution function of .

[0016] Further: S2 is specifically: The geothermal heat pump system mainly consists of three parts: the load end, the ground source side and the geothermal heat pump unit. The load end refers to users such as buildings that are heated, cooled or heated by the geothermal heat pump system; the ground source side is the underground heat source, and the system usually exchanges heat with the soil, groundwater or surface water through buried pipes; the geothermal heat pump unit is composed of a compressor, a heat exchanger, etc., and is the core equipment of the system, transferring the heat pump on the ground source side to the load end, or vice versa. Correspondingly, the digital twin model of the geothermal heat pump system consists of three parts: the load end model, the ground source side model and the geothermal heat pump unit model. During the model calibration stage, these three models need to be calibrated separately. Always calibrate the load end model and related parameters to minimize As the target, adjust the load end model; Calibrate the ground source model and related parameters at all times to minimize As the target, adjust the ground source side model; Calibrate the ground source heat pump unit model and related parameters at all times to minimize As the target, adjust the unit model.

[0017] Further: In S3, the method for updating the digital twin model is specifically: At the moment of operation t , collect observation data , based on the observed data The digital state , research volume and the reward equation The predicted value of is updated to The calculated true value is used to update the marginal / conditional probability distribution function in the model using the expectation maximization algorithm to obtain the joint probability distribution function of the random variables of all nodes in the digital twin model, and then the predicted value is output through the joint probability distribution function, including the time t +1 to time Digital Status , research volume and the reward equation ;

[0018] In the formula, is the joint probability distribution function, The digital state of the ground source heat pump system at the last moment is , control input for ground source heat pump system Then get the digital state The conditional probability distribution function of For a given digital state , get the observed data The conditional probability distribution function of For at the moment Given the digital status of the ground source heat pump system and research volume , taking control input The conditional probability distribution function of , which is the optimal control solution solved at the previous moment.

[0019] Further: In S3, the optimal operation method of the ground source heat pump system is obtained as follows: Based on time t The predicted value of the output is used to construct a Markov decision process model to solve and select the optimal control scheme as the optimal operation method of the ground source heat pump system; Among them, the Markov decision process model The specific expression is:

[0020] In the formula, The reward equation for the energy efficiency of the ground source heat pump system is: , is the time at which the observations are collected The previous observation collection time The coefficient of performance of the ground source heat pump system, is the discount factor for future rewards, , To find the maximum Operation strategy of ground source heat pump system .

[0021] The beneficial effects of the present invention are as follows: the present invention provides a method for optimizing the scheduling of a ground source heat pump system, and uses a dynamic probability graph decision network to construct a digital twin model, which has the following effects compared with the prior art: (1) The physical entity-twin system is abstracted into a set of coupled dynamic systems. These systems evolve over time in the physical state space and the digital state space, and interact with each other through observation data and control inputs. This solves the problem of complex multi-physics field coupling modeling and dynamic optimization control of the ground-source heat pump system, and the high computational resource requirements. This enables real-time, automatic optimization control of the ground-source heat pump system under complex and changeable underground environments and load requirements.

[0022] (2) The physical-data optimization decision-making process is abstracted into multi-dimensional time series variables and the correlations between them. The construction form of the digital twin model can be adjusted according to the application scenario, decision-making requirements and data collection capabilities of the ground source heat pump system. It can also make use of mature theories and methods such as Bayesian statistics, control theory, and stochastic processes. It has universality, flexibility and economy, and can transform one-off digital twins based on customized implementation into robust digital twins that can be deployed on a large scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the method for optimizing the scheduling of a ground source heat pump system based on a probabilistic graph digital twin model of the present invention.

[0024] Figure 2 Schematic diagram of the digital twin model based on the dynamic probability graph decision network. DETAILED DESCRIPTION

[0025] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0026] like Figure 1 As shown, in one embodiment of the present invention, a method for optimizing the scheduling of a ground source heat pump system based on a probability graph digital twin model comprises the following steps: S1. Construct a digital twin model based on a dynamic probability graph decision network in the modeling stage; S2. Calibrate the digital twin model in the calibration phase; S3. Update the digital twin model during the operation phase, and output the optimal operation method of the ground source heat pump system in real time through the updated digital twin model.

[0027] In this embodiment, a digital twin model based on a dynamic probability graph decision network is constructed for the ground source heat pump system. Figure 2 As shown in Figure 1. Time is discretized into hours or days (depending on decision requirements or data storage capacity). The nodes in the digital twin model represent random variables of physical state, digital state, observation data, study quantity, control input and reward equation at each time point. In S1, the nodes in the digital twin model based on the dynamic probability graph decision network include physical states, digital states, observation data, research quantities, control inputs, and random variables of reward equations at each time point.

[0028] A collection of physical states ,in, For virtual moments, represents the modeling phase, represents the calibration phase of the digital twin model, For prediction and decision-making moments, To reflect the values ​​of relevant parameter variables of the ground source heat pump system operating status at different times; In this embodiment, Represents three different types of parameter calibration phases, each phase can contain multiple moments in hours or days, It reflects the values ​​of relevant parameter variables at different times of the operating status of the ground source heat pump system, including the ground source side inlet temperature, ground source side inlet pressure, ground source side inlet flow, ground source side outlet temperature, ground source side outlet pressure, ground source side outlet flow, load end water supply temperature, load end water supply pressure, load end water supply flow, load end return water temperature, load end return water pressure, load end return water flow, heat pump unit inlet temperature, heat pump unit outlet water temperature, heat pump unit inlet pressure, heat pump unit outlet water pressure, heat pump unit inlet flow, heat pump unit outlet flow, ground source side water pump flow, ground source side water pump inlet pressure, ground source side water pump outlet pressure, load end water pump flow, load end water pump inlet Pressure, outlet pressure of the water pump at the load end, pressure difference of the filter, pressure of the main and sub-collector of the buried pipe, inlet temperature of the heat exchanger on the ground source side, inlet pressure of the heat exchanger on the ground source side, inlet flow of the heat exchanger on the ground source side, outlet temperature of the heat exchanger on the ground source side, outlet pressure of the heat exchanger on the ground source side, outlet flow of the heat exchanger on the ground source side, inlet temperature of the heat exchanger on the load end, inlet pressure of the heat exchanger on the load end, inlet flow of the heat exchanger on the load end, outlet temperature of the heat exchanger on the load end, outlet pressure of the heat exchanger on the load end, outlet flow of the heat exchanger on the load end, energy consumption of the compressor, energy consumption of the water pump, energy consumption of the fan coil unit on the load end, energy consumption of the control system, working status (fault) of the unit, ancillary equipment, valves and other equipment, load, temperature of each measuring point of the soil, etc.

[0029] A collection of digital states ,in, To construct the digital state values ​​of relevant parameter variables of the digital twin calculation model of the ground source heat pump system at different times; In this embodiment, It is the digital state value of the relevant parameter variables at different times for constructing the digital twin calculation model of the ground source heat pump system. The parameters are consistent with the parameters of the physical state.

[0030] A collection of observation data ,in, The observation data reflecting the changes in physical states collected by sensors or detection equipment in the ground source heat pump system at different times; In this embodiment, It is the observation data reflecting the changes of physical state collected by sensors or detection equipment in the ground source heat pump system at different times, including the inlet temperature of the ground source side, the inlet pressure of the ground source side, the inlet flow of the ground source side, the outlet temperature of the ground source side, the outlet pressure of the ground source side, the outlet flow of the ground source side, the water supply temperature of the load end, the water supply pressure of the load end, the water supply flow of the load end, the return water temperature of the load end, the return water pressure of the load end, the return water flow of the load end, the energy consumption of the compressor, the energy consumption of the water pump, the energy consumption of the fan coil unit at the load end, the energy consumption of the control system, the inlet water temperature of the heat pump unit, the outlet water temperature of the heat pump unit, the inlet water pressure of the heat pump unit, the outlet water pressure of the heat pump unit, the inlet flow of the heat pump unit, the outlet water flow of the heat pump unit, the ground source side water pump flow, the ground source side water pump inlet pressure, the ground source side water pump outlet pressure, the load end water pump flow, the load end water pump inlet pressure, the load end water Pump outlet pressure, filter inlet pressure, filter outlet pressure, ground source side heat exchanger inlet temperature, ground source side heat exchanger inlet pressure, ground source side heat exchanger inlet flow, ground source side heat exchanger outlet temperature, ground source side heat exchanger outlet pressure, ground source side heat exchanger outlet flow, load end heat exchanger inlet temperature, load end heat exchanger inlet pressure, load end heat exchanger inlet flow, load end heat exchanger outlet temperature, load end heat exchanger outlet pressure, load end heat exchanger outlet flow, working status (fault) of units, auxiliary equipment, valves and other equipment, load end outdoor temperature, load end outdoor humidity, load end outdoor radiation sunlight, load end operating rules, load end personnel number and indoor rate, load end lighting number and utilization rate, load end energy-consuming equipment number and utilization rate, total buried pipe score, water collector pressure, soil temperature at each measuring point.

[0031] A collection of research ,in, is the coefficient of performance of the ground source heat pump system at different times;

[0032] In the formula, For Time has come The average value of the heat at the load end at all times, For Time has come The average value of the input power of the heat pump unit at the moment, For Time has come The average value of the pump's input power at the moment, For Time has come The average power consumption of the fan coil at the load end at all times. For Time has come Control the average value of system energy consumption at all times; The set of control inputs is ,in, It is the operation control scheme of the ground source heat pump system at different times; In this embodiment, It is the operation control scheme of the ground source heat pump system at different times, including the operation mode (cooling, heating, off), the ground source side water pump flow, the ground source side water pump head, the ground load end water pump flow, the load end water pump head, the load end water supply temperature, and the compressor flow.

[0033] The set of reward equations is ,in, Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0034] In the formula, is the first duration of the model calibration phase. and are the observed values ​​of the load at each moment and the predicted values ​​of the digital twin model, s Collect moments for observations; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0035] In the formula, The second duration of the model calibration phase, and are the observed value of the outlet temperature on the ground source side at each moment and the predicted value of the digital twin model; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase;

[0036] In the formula, The third duration of the model calibration phase. and They are the observed value of the return water temperature at the load end at each moment and the predicted value of the digital twin model; For digital twin models t The reward equation for the moment model operation phase;

[0037] In the formula, is the coefficient of performance of the ground source heat pump system at each observation collection moment.

[0038] In this embodiment, the reward equation is used to evaluate the operating efficiency of the system. The higher the value, the more efficient and energy-saving the system operation is.

[0039] In S1, the expression of the digital twin model based on the dynamic probability graph decision network is specifically:

[0040] In the formula, Deadline , given historical observation data and control input, the system exhibits a digital state , calculate the research volume and get rewards The conditional probability distribution function of The digital state of the ground source heat pump system at the last moment is , control input executed , and collect observation data at this moment Update the digital twin to get the digital state The conditional probability distribution function of Given the updated digital state of the digital twin at the current moment Get research volume The conditional probability distribution function of For a given digital state at the current moment , research volume , control input and observational data , get the reward value The conditional probability distribution function of .

[0041] like Figure 2 As shown, the arrows connecting the nodes represent the correlation between the variables, which are expressed by the conditional probability density function To define it, the digital twin model has the ability to simulate uncertainty and optimize decision-making considering uncertainty.

[0042] The physical state of the ground source heat pump system and the digital state of its digital twin form two dynamic systems that evolve over time. The digital twin of the ground source heat pump system uses the observation data collected by sensors to predict the physical state of the system operation, thereby determining the optimal control input for the ground source heat pump system, while minimizing the control cost and ensuring the observability of the system state, and improving the performance coefficient of the ground source heat pump system. The optimized ground source heat pump system generates new observation data, updates the model and digital state in its digital twin, and makes it simulate the physical state of the real system more accurately. The updated digital twin further analyzes the operation process of the ground source heat pump system, predicts the future state evolution of the ground source heat pump system through a more accurate algorithm, and thus better optimizes the control input for the next step of the decision. This process evolves cyclically, forming a coordinated multi-directional coupling between the ground source heat pump system, the digital twin, data collection, the computational model, and the optimization control decision, and finally forms a ground source heat pump digital twin model that accurately simulates and supports predictive decision-making.

[0043] like Figure 2 As shown in the modeling stage At this moment, according to the mathematical theory of geothermal heat pump system, such as building load calculation model, compressor model, condenser model, evaporator model, geothermal heat pump system model, and buried pipe heat exchange model, a twin of the geothermal heat pump system is constructed, including the heat absorption of the heat exchanger on the ground source side, the input work of the compressor, the heat dissipation of the heat exchanger on the load end, the heat exchange of the equipment on the load end, the energy consumption of the water pump on the ground source side, the energy consumption of the water pump on the load end, the heat exchange of the buried pipe, the system performance coefficient, the load, the indoor temperature at the load end and the load balance, the heat exchange between the load end and the fan coil, the compressor power, the soil temperature, the inlet and outlet temperature of the ground source side and other calculation models.

[0044] S2 is specifically: The geothermal heat pump system mainly consists of three parts: the load end, the ground source side and the geothermal heat pump unit. The load end refers to users such as buildings that are heated, cooled or heated by the geothermal heat pump system; the ground source side is the underground heat source, and the system usually exchanges heat with the soil, groundwater or surface water through buried pipes; the geothermal heat pump unit is composed of a compressor, a heat exchanger, etc., and is the core equipment of the system, transferring the heat pump on the ground source side to the load end, or vice versa. Correspondingly, the digital twin model of the geothermal heat pump system consists of three parts: the load end model, the ground source side model and the geothermal heat pump unit model. During the model calibration stage, these three models need to be calibrated separately. Calibrate the load end model and related parameters at all times. Calibrate the ground source model and related parameters at all times. Calibrate the ground source heat pump unit model and related parameters at all times.

[0045] During the calibration phase, Calibrate the load end model and related parameters at all times. According to the outdoor weather, personnel flow, equipment and lighting usage at the user end, build a simulation model of the load end operation, and simulate experiments to adjust the heat transfer coefficient and parameters such as personnel, equipment, and lighting heat dissipation. The control input at this moment Decision making for designing simulation experiments.

[0046] exist Calibrate the ground source side model and related parameters at all times. According to the "Technical Specifications for Ground Source Heat Pump System Engineering" (GB-50366-2009) and "Engineering Investigation Standards for Ground Source Heat Pump System" (CJJ / T-291-2019), geotechnical engineering investigation and thermal response testing are carried out to adjust the convective heat transfer coefficient, soil thermal conductivity, volumetric specific heat coefficient, heat transfer per linear meter of heat exchange holes, and comprehensive heat transfer coefficient of heat exchange wells. The control input at this moment is To make decisions on on-site testing plans such as the number of drilling holes, drilling depth, drilling location, buried pipe form, and buried pipe outer diameter.

[0047] exist Calibrate the ground source heat pump unit model and related parameters at all times. Construct a simulation model of the unit side loop and the load end loop in the ground source heat pump system, and simulate experiments to adjust the actual power consumption of the compressor, refrigerant specific volume, polytropic index, enthalpy value, valve adjustment constant and other parameters. The control input at this moment Decision making for designing simulation experiments.

[0048] In S3, the method for updating the digital twin model is as follows: At the moment of operation t , collect observation data , based on the observed data The digital state , research volume and the reward equation The predicted value of is updated to The calculated true value is used to update the marginal / conditional probability distribution function in the model using the expectation maximization algorithm to obtain the joint probability distribution function of the random variables of all nodes in the digital twin model, and then the predicted value is output through the joint probability distribution function, including the time t +1 to time Digital Status , research volume and the reward equation ;

[0049] In the formula, is the joint probability distribution function, The digital state of the ground source heat pump system at the last moment is , control input for ground source heat pump system Then get the digital state The conditional probability distribution function of For a given digital state , get the observed data The conditional probability distribution function of For at the moment Given the digital status of the ground source heat pump system and research volume , taking control input The conditional probability distribution function of , which is the optimal control solution solved at the previous moment.

[0050] In this embodiment, at each moment of the operation phase Update the digital twin model through Bayes' theorem and conditional independence principle.

[0051]

[0052] In S3, the optimal operation method of the ground source heat pump system is obtained as follows: Based on time t The predicted value of the output is used to construct a Markov decision process model to solve and select the optimal control scheme as the optimal operation method of the ground source heat pump system; Among them, the Markov decision process model The specific expression is:

[0053] In the formula, The reward equation for the energy efficiency of the ground source heat pump system is: , is the time at which the observations are collected The previous observation collection time The coefficient of performance of the ground source heat pump system, is the discount factor for future rewards, , To find the maximum Operation strategy of ground source heat pump system .

[0054] In this embodiment, the state of the Markov decision process is a set of digital states and research quantities. By solving the optimization problem with the goal of maximizing the expected cumulative reward within the selected prediction time range, a control solution is found using a deep learning algorithm. , thus obtaining the optimal scheduling method.

[0055]

[0056] In the formula, For the moment The actual value of the digital status of the ground source heat pump system before, For the moment The actual values ​​of the research quantities of the previous ground source heat pump system.

[0057] In the description of the present invention, it is necessary to understand that the orientation or positional relationship indicated by the terms "center", "thickness", "upper", "lower", "horizontal", "top", "bottom", "inner", "outer", "radial", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", and "third" are used only for descriptive purposes, and cannot be understood as indicating or implying the relative importance or the number of implicitly specified technical features. Therefore, the features defined by "first", "second", and "third" may explicitly or implicitly include one or more of the features.

Claims

1. An optimization scheduling method for a ground source heat pump system based on a probabilistic graph digital twin model, characterized in that: The following steps are involved: S1. Construct a digital twin model based on a dynamic probability graph decision network in the modeling stage; S2. Calibrate the digital twin model in the calibration phase; S3. Update the digital twin model during the operation phase, and output the optimal operation method of the ground source heat pump system in real time through the updated digital twin model.

2. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 1, characterized in that: In S1, the nodes in the digital twin model based on the dynamic probability graph decision network include physical states, digital states, observation data, research quantities, control inputs, and random variables of reward equations at each time point.

3. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 2, characterized in that: A collection of physical states ,in, For virtual moments, represents the modeling phase, represents the calibration phase of the digital twin model, For prediction and decision-making moments, To reflect the values ​​of relevant parameter variables of the ground source heat pump system operating status at different times; A collection of digital states ,in, To construct the digital state values ​​of relevant parameter variables of the digital twin calculation model of the ground source heat pump system at different times; A collection of observation data ,in, The observation data reflecting the changes in physical states collected by sensors or detection equipment in the ground source heat pump system at different times; A collection of research ,in, is the coefficient of performance of the ground source heat pump system at different times; In the formula, For Time has come The average value of the heat at the load end at all times, For Time has come The average value of the input power of the heat pump unit at the moment, For Time has come The average value of the pump's input power at the moment, For Time has come The average power consumption of the fan coil at the load end at all times. For Time has come Control the average value of system energy consumption at all times; The set of control inputs is ,in, It is the operation control scheme of the ground source heat pump system at different times; The set of reward equations is ,in, Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase; In the formula, is the first duration of the model calibration phase. and are the observed values ​​of the load at each moment and the predicted values ​​of the digital twin model, s Collect moments for observations; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase; In the formula, The second duration of the model calibration phase, and are the observed value of the outlet temperature on the ground source side at each moment and the predicted value of the digital twin model; Digital twin system for ground source heat pump The reward equation during the moment-to-moment model calibration phase; In the formula, The third duration of the model calibration phase, and They are the observed value of the return water temperature at the load end at each moment and the predicted value of the digital twin model; For digital twin models t The reward equation for the moment model operation phase; In the formula, is the coefficient of performance of the ground source heat pump system at each observation collection moment.

4. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 3, characterized in that: In S1, the expression of the digital twin model based on the dynamic probability graph decision network is specifically: In the formula, Deadline , given historical observation data and control input, the system exhibits a digital state , calculate the research volume and get rewards The conditional probability distribution function of The digital state of the ground source heat pump system at the last moment is , control input executed , and collect observation data at this moment Update the digital twin to get the digital state The conditional probability distribution function of Given the updated digital state of the digital twin at the current moment Get research volume The conditional probability distribution function of For a given digital state at the current moment , research volume , control input and observational data , get the reward value The conditional probability distribution function of .

5. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 3, characterized in that: In S2, the digital twin model includes the load-side model, the ground-source side model, and the ground-source heat pump unit model; exist Always calibrate the load end model and related parameters to minimize As the target, adjust the load end model; Calibrate the ground source model and related parameters at all times to minimize As the target, adjust the ground source side model; Calibrate the ground source heat pump unit model and related parameters at all times to minimize As the goal, adjust the ground source heat pump unit model.

6. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 4, characterized in that: In S3, the method for updating the digital twin model is as follows: At the moment of operation t , collect observation data , based on the observed data The digital state , research volume and the reward equation The predicted value of is updated to The calculated true value is used to update the marginal / conditional probability distribution function in the model using the expectation maximization algorithm to obtain the joint probability distribution function of the random variables of all nodes in the digital twin model, and then the predicted value is output through the joint probability distribution function, including the time t +1 to time Digital Status , research volume and the reward equation ; In the formula, is the joint probability distribution function, The digital state of the ground source heat pump system at the last moment is , control input for ground source heat pump system Then get the digital state The conditional probability distribution function of For a given digital state , get the observed data The conditional probability distribution function of For at the moment Given the digital status of the ground source heat pump system and research volume , taking control input The conditional probability distribution function of , which is the optimal control solution solved at the previous moment.

7. The method for optimizing and scheduling a ground source heat pump system based on a probabilistic graph digital twin model according to claim 6, characterized in that: In S3, the optimal operation method of the ground source heat pump system is obtained as follows: Based on time t The predicted value of the output is used to construct a Markov decision process model to solve and select the optimal control scheme as the optimal operation method of the ground source heat pump system; Among them, the Markov decision process model The specific expression is: In the formula, The reward equation for the energy efficiency of the ground source heat pump system is: , is the time at which the observations are collected The previous observation collection time The coefficient of performance of the ground source heat pump system, is the discount factor for future rewards, , To find the maximum Operation strategy of ground source heat pump system .

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