Ground source heat pump system multi-energy-flow matching and scheduling system coupled with clean energy

Abstract

The invention relates to the technical field of control, and discloses a ground source heat pump system multi-energy flow matching and scheduling system coupled with clean energy, and the system comprises a data collection module which is used for obtaining data such as a soil temperature field, pipe network fluid and clean energy power generation power in real time; the thermal unbalance calculation module is used for calculating enthalpy deviation according to the soil data and outputting a thermal unbalance potential energy index; the fluctuation analysis module is used for calculating real-time surplus power and a fluctuation rate index; the matching scheduling module is used for calculating target hydraulic lagging time according to the volatility, deducing ideal flow and generating a control instruction; and the execution control module converts the instruction into a bottom layer signal and performs safety correction according to the water supply and return temperature. According to the method, dynamic matching of the ground source heat pump and the clean energy is achieved by quantifying the soil enthalpy deviation and stabilizing fluctuation through thermal inertia, the problems of underground thermal unbalance and frequent starting and stopping of equipment are solved, and the multi-energy-flow scheduling stability is improved.

Description

Technical Field

[0001] This invention relates to the field of control technology, specifically to a multi-energy flow matching and scheduling system for a ground source heat pump system coupled with clean energy. Background Technology

[0002] In building energy conservation and green energy applications, ground-source heat pump systems coupled with clean energy, as a highly efficient energy supply form that combines distributed clean energy power generation with shallow geothermal energy utilization, have become an important feature of low-carbon buildings. This system aims to reduce the building's dependence on the external power grid and improve energy self-sufficiency by physically connecting photovoltaic or wind power generation units to the ground-source heat pump unit and using locally generated clean electricity to drive the heat pump cycle.

[0003] In practical applications, existing ground-source heat pump systems coupled with clean energy typically employ control strategies based on real-time power matching or simple threshold judgment. These systems primarily focus on meeting the real-time heating and cooling loads at the building's terminals. Their scheduling logic often involves directly starting the heat pump unit when the clean energy generation capacity reaches its rated value, or simply controlling the compressor's start and stop based on feedback signals from indoor thermostats. This control mode only focuses on the energy supply and demand balance at a single moment, maintaining the system's basic operation.

[0004] However, existing ground-source heat pump systems coupled with clean energy have significant technical shortcomings in multi-energy flow matching and scheduling. On the one hand, due to the inherent intermittency and high-frequency fluctuation characteristics of distributed clean energy, existing technologies lack quantitative analysis of energy volatility and effective utilization of the thermal inertia of the circulating pipe network. This leads to frequent start-ups and shutdowns of heat pump units when dealing with transient power changes at the source, severely affecting equipment lifespan and operational stability. On the other hand, existing scheduling strategies often ignore the long-term thermal state evolution of the soil in the buried pipe area and lack accurate assessment of the degree of soil thermal or cold accumulation. Under long-term operation, this can easily lead to ground thermal balance imbalance, resulting in a continuous decline in heat exchange efficiency and failing to meet the system's safety and high-efficiency matching requirements under complex operating conditions.

[0005] Therefore, this invention proposes a multi-energy flow matching and scheduling system for ground source heat pump systems coupled with clean energy to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a multi-energy flow matching and scheduling system for ground source heat pump systems coupled with clean energy. This system solves the problems in existing technologies, such as frequent equipment start-ups and shutdowns due to a lack of effective responses to fluctuations in clean energy, and ground heat balance imbalances caused by neglecting the evolution of soil thermal state.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy, comprising: The data acquisition module is configured to acquire real-time operating status data, which includes soil temperature field data at different depths and in different areas of the buried pipe heat exchanger group, supply and return water temperature and fluid flow data of the main pipe of the circulating pipe network, real-time load data on the building side, and real-time power generation data of the distributed clean energy power generation unit. The thermal imbalance calculation module is configured to calculate the enthalpy deviation of the current thermal state of each sub-region relative to the initial geological baseline state based on the soil temperature field data, and output the thermal imbalance potential energy index. The volatility analysis module is configured to calculate real-time surplus power values ​​and clean energy volatility indicators. The matching and scheduling module is configured to calculate the target hydraulic lag time based on the clean energy volatility index and deduce the ideal flow setpoint of the circulating pipe network in reverse, and generate flow control instructions and compressor operating frequency instructions. The execution control module is configured to convert the flow control command and the compressor operating frequency command into a low-level drive signal and send them to the variable frequency circulating water pump, the ground source heat pump unit and the electric regulating valve array, and to truncate or correct the low-level drive signal based on the main pipe supply and return water temperature monitoring value of the circulating pipe network fed back by the data acquisition module.

[0008] Preferably, the data acquisition module further includes a step of cleaning and timing synchronization processing of the raw signal of the running status data to obtain the running status data that includes a unified timestamp.

[0009] Preferably, the step of the thermal imbalance calculation module calculating the enthalpy deviation of the current thermal state of each sub-region relative to the initial geological baseline state based on the soil temperature field data, and outputting the thermal imbalance potential energy index includes: Based on the preset regional division logic of the buried pipe heat exchanger group, the soil physical parameters corresponding to each sub-region are retrieved. For each sub-region, a volume integral operation is performed, and the enthalpy deviation of the real-time soil temperature of each sub-region relative to the initial geological reference state temperature is calculated by a discretized weighted summation method. The enthalpy deviation is output as a thermal imbalance potential energy index characterizing the degree of thermal or cold accumulation in each sub-region.

[0010] Preferably, the step of the fluctuation analysis module calculating the real-time surplus power value includes: The real-time load data on the building side is defined as the basic load data that must be prioritized. The real-time surplus power value is obtained by performing an algebraic difference operation between the real-time power generation data of the distributed clean energy power generation unit and the base load data.

[0011] Preferably, the step of the volatility analysis module in calculating the clean energy volatility index includes: A sliding time window is constructed to process the real-time surplus power value sequence, and a first-in-first-out data queue with a preset length is maintained. The standard deviation of the data within the sliding time window is calculated to quantify the dispersion of power values, and the clean energy volatility index, which characterizes the continuity and stability of energy supply, is output.

[0012] Preferably, the matching and scheduling module has a built-in operation mode determination logic, which determines that the active repair conditions are met and switches the operation state to active repair mode only when the real-time surplus power value is greater than the minimum start-up power threshold of the ground source heat pump unit and the absolute value of the thermal imbalance potential energy index is greater than the preset thermal accumulation or cold accumulation tolerance limit.

[0013] Preferably, the step of the matching and scheduling module calculating the target hydraulic lag time based on the clean energy volatility index and deriving the ideal flow setpoint of the circulating pipeline network includes: The thermal inertia of the fluid in the circulating pipe network is used to smooth out power fluctuations. The preset benchmark cycle is corrected according to the clean energy volatility index and the preset fluctuation response coefficient to obtain the target hydraulic lag time. Based on the principle of fluid continuity, the ideal flow rate setting value of the circulating pipe network is derived by dividing the total volume of circulating fluid in the circulating pipe network by the target hydraulic lag time.

[0014] Preferably, the matching and scheduling module combines the built-in device safety constraint parameters to perform logical clamping and correction on the ideal flow setting value to generate flow control instructions; The equipment safety constraint parameters include the minimum flow threshold that ensures the fluid in the buried pipe is in a turbulent heat exchange state and the maximum flow threshold that the variable frequency circulating water pump is allowed to operate. The step of logically clamping and correcting the ideal flow rate setting value by combining the built-in device safety constraint parameters to generate flow control commands includes: When the ideal flow rate setting value is lower than the minimum flow rate threshold, the flow rate value corresponding to the flow control command is corrected to the minimum flow rate threshold. When the ideal flow rate setting value is between the minimum flow rate threshold and the maximum flow rate threshold, the flow rate value corresponding to the flow control command is set to the ideal flow rate setting value; When the ideal flow rate setting value is higher than the maximum flow rate threshold, the flow rate value corresponding to the flow control command is corrected to the maximum flow rate threshold.

[0015] Preferably, the step of the matching and scheduling module generating the compressor operating frequency command includes: Based on the real-time surplus power value and the performance coefficient of the ground source heat pump unit at the current inlet and outlet water temperatures, the theoretically convertible cooling or heating power is calculated and mapped to the target operating frequency of the compressor in the ground source heat pump unit. A boundary constraint based on temperature difference is introduced. When the predicted supply and return water temperature difference exceeds the preset safe temperature difference limit, the target operating frequency is proportionally reduced, and the compressor operating frequency command is generated.

[0016] Preferably, the step of the execution control module truncating or correcting the underlying drive signal based on the monitored values ​​of the main supply and return water temperatures of the circulating pipe network fed back by the data acquisition module includes: The temperature monitoring values ​​of the main supply and return water of the circulating pipe network are continuously compared with the preset safety thresholds, which include the underground pipe antifreeze protection temperature and the underground pipe overheat protection temperature. When the monitored value of the main supply and return water temperature of the circulating pipe network reaches or exceeds the preset safety threshold, the highest priority protection logic is triggered. The derating control algorithm is applied to force a proportional reduction in the underlying drive signal sent to the ground source heat pump unit, or the enable signal sent to the ground source heat pump unit is directly cut off and the underlying drive signal sent to the variable frequency circulating water pump is locked to maintain it at the rated flow state.

[0017] This invention provides a multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy. It has the following beneficial effects: 1. This invention quantifies the enthalpy deviation of each sub-region relative to the initial geological baseline state by performing volume integral calculations based on soil temperature field data from the buried pipe heat exchanger group using a thermal imbalance calculation module. This technical solution can objectively characterize the degree of thermal or cold accumulation in the stratum soil, providing a quantitative thermodynamic basis for determining the active repair mode of the ground source heat pump system, improving the underground thermal balance imbalance caused by long-term operation of traditional systems, and realizing the sustainable utilization of geothermal energy resources.

[0018] 2. This invention, through the collaboration of a fluctuation analysis module and a matching scheduling module, introduces a clean energy volatility index to calculate the target hydraulic lag time and derive the ideal flow setpoint in reverse. This mechanism utilizes the thermal inertia characteristics of fluids in the circulating pipe network to smooth out power fluctuations in distributed clean energy, achieving dynamic matching between power generation changes and the operating load of the ground source heat pump unit. This effectively reduces the risk of frequent unit start-ups and shutdowns caused by intermittent energy supply and improves the operational stability of the multi-energy flow coupling system.

[0019] 3. This invention constructs a closed-loop safety feedback mechanism based on the monitored supply and return water temperatures of the main pipe within the execution control module, and logically clamps and corrects the underlying drive signals in conjunction with equipment safety constraint parameters. This scheme ensures that the fluid inside the buried pipe is in a turbulent heat exchange state while utilizing trigger-based protection logic to prevent the buried pipe medium from freezing or soil thermal overload. This multi-constraint design guarantees the physical safety of the system during multi-energy flow scheduling, which is beneficial for extending the service life of the ground source heat pump unit and the buried pipe heat exchange system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the multi-energy flow matching and scheduling system architecture of the ground source heat pump system coupled with clean energy according to the present invention. Figure 2 This is a flowchart of the volatility-based flow control logic in the matching and scheduling module of the present invention. Figure 3 This is a dynamic response curve of the clean energy volatility and the set value of the circulating pipeline flow rate of the present invention.

[0021] Among them, 100 is the data acquisition module; 200 is the thermal imbalance calculation module; 300 is the fluctuation analysis module; 400 is the matching and scheduling module; and 500 is the execution control module. Detailed Implementation

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see the appendix Figure 1 This invention provides a multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy. It is applied in a hardware environment including distributed clean energy power generation units, ground-source heat pump units, variable frequency circulating water pumps, electric regulating valve arrays, and buried pipe heat exchanger groups. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy includes: The data acquisition module 100 is configured to connect to sensors and metering instruments distributed in the hardware environment to acquire real-time operating status data. The operating status data includes soil temperature field data at different depths and in different areas of the buried pipe heat exchanger group, supply and return water temperature and fluid flow data of the main pipe of the circulating pipe network, real-time load data on the building side, and real-time power generation data of the distributed clean energy power generation unit.

[0024] The thermal imbalance calculation module 200 is connected to the data acquisition module 100 to receive soil temperature field data. Based on the preset regional division logic of the buried pipe heat exchanger group, the thermal imbalance calculation module 200 retrieves the corresponding soil physical parameters for each sub-region, calculates the enthalpy deviation of the current thermal state of each sub-region relative to the initial geological baseline state through volume integration, and outputs a thermal imbalance potential energy index characterizing the degree of thermal or cold accumulation in each sub-region.

[0025] The fluctuation analysis module 300 is connected to the data acquisition module 100 and receives real-time power generation data and real-time load data. The fluctuation analysis module 300 calculates the difference between the real-time power generation data and the base load data to obtain the real-time surplus power value. Simultaneously, the fluctuation analysis module 300 performs dispersion statistical analysis on the surplus power value within the sliding time window and outputs a clean energy volatility index characterizing the continuity and stability of energy supply.

[0026] The matching and scheduling module 400 is connected to the thermal imbalance calculation module 200 and the fluctuation analysis module 300, respectively, and receives thermal imbalance potential energy indicators, real-time surplus power values, and clean energy volatility indicators. The matching and scheduling module 400 has built-in operating mode determination logic and equipment safety constraint parameters. When the active repair conditions are met, the matching and scheduling module 400 calculates the target hydraulic lag time based on the clean energy volatility indicator, reverse-derives the ideal flow setpoint of the circulating pipe network, and performs logical clamping and correction on the ideal flow setpoint in conjunction with the equipment safety constraint parameters, generating the final flow control command and compressor operating frequency command.

[0027] The execution control module 500, connected to the matching and scheduling module 400, receives flow control commands and compressor operating frequency commands. The execution control module 500 converts these commands into underlying drive signals, which are then sent to the variable frequency circulating water pump, the ground source heat pump unit, and the electric regulating valve array. Simultaneously, the execution control module 500 reads the main supply and return water temperature monitoring values ​​of the circulating pipe network fed back by the data acquisition module 100. When the temperature data approaches a preset safety threshold, the output drive signal is truncated or corrected.

[0028] To further clarify the technical details of the system in this embodiment, the specific implementation methods, principles and technical contents of each of the above modules will be described in detail below.

[0029] See attached document Figure 1 The data acquisition module 100 is configured as the sensing interface of the multi-energy flow matching and scheduling system for the ground source heat pump system. It establishes physical connections with sensors and metering instruments distributed in the hardware environment through wired or wireless industrial communication protocols. The data acquisition module 100 periodically polls and acquires the underlying signals according to a preset sampling frequency strategy, and converts the acquired analog signals or raw digital signals into standardized operating status data.

[0030] This section focuses on soil temperature field data at different depths and in different areas within a buried pipe heat exchanger group. The buried pipe heat exchanger group is a heat exchange network composed of multiple sets of vertical or horizontal pipes buried underground, typically using high-density polyethylene pipes. An internal heat transfer medium circulates within the network to exchange heat with the soil. The data acquisition module 100 first logically divides the buried pipe heat exchanger group into logically defined areas based on its geographical distribution. The system comprises several sub-regions. Within each sub-region, a temperature sensor array is arranged along the vertical drilling direction. The temperature sensor array contains multiple temperature sensors distributed at preset vertical intervals (e.g., one measuring point every 10 meters). For the specific selection and installation process of the temperature sensors, those skilled in the art can choose conventionally applicable industrial-grade products (such as series-connected PT1000 platinum resistance thermometers or distributed fiber optic temperature measurement systems) based on geological exploration results and engineering accuracy requirements. These are well-known technologies in the field and will not be elaborated upon here. The data acquisition module 100 reads the real-time temperature values ​​of each measuring point and maps them using the three-dimensional spatial coordinate index of each measuring point, thereby obtaining soil temperature field data for different depths and regions within the buried pipe heat exchanger group.

[0031] The circulating pipe network, a distribution system connecting the ground source heat pump unit and the underground pipe heat exchanger group, is used to collect data on the supply and return water temperatures and fluid flow rates of the main pipe of the circulating pipe network. This network includes the main supply pipe, the main return pipe, and branch pipes connecting different zones. A data acquisition module 100 connects to temperature transmitters and electromagnetic flow meters installed on the main supply and return pipes. The module reads in real-time the fluid temperature entering and exiting the underground pipe, as well as the instantaneous flow rate of the circulating working fluid. To ensure the data supports subsequent precise control, the flow meter's range must cover the system's designed minimum turbulent flow rate to its maximum rated flow rate. The data acquisition module 100 integrates the collected temperature and flow rate values ​​to obtain the main supply and return water temperatures and fluid flow rates of the circulating pipe network.

[0032] For real-time load data on the building side, which refers to the terminal load objects of the ground source heat pump system, including indoor air conditioning terminal equipment (such as fan coil units and radiant floor units) and the corresponding indoor temperature control system, the data acquisition module 100 obtains the data directly through the communication interface with the Building Energy Management System (BEMS) or by reading the register values ​​of the building's terminal heat meters. In implementation scenarios without a total heat meter, the data acquisition module 100 calculates the current heat demand value using fluid thermodynamics formulas based on the feedback flow rate of the terminal circulation pump and the temperature difference between the terminal supply and return water. The data acquisition module 100 obtains real-time load data on the building side through the aforementioned direct reading or indirect calculation methods.

[0033] For real-time power generation data of distributed clean energy generation units, which are on-site energy supply facilities consisting of photovoltaic panel arrays or small wind turbine generators and matching grid-connected inverters, the data acquisition module 100 connects to the communication interface of the photovoltaic inverter or wind power controller to directly read its output power register data; or it connects to a bidirectional smart meter connected to the grid connection point to obtain real-time active power values. Considering the high-frequency fluctuation characteristics of clean energy power generation, the sampling frequency of this data is set to be higher than that of temperature data (e.g., the power data sampling period is 1 second, and the temperature data sampling period is 5 minutes) to capture instantaneous power fluctuations caused by environmental factors. The data acquisition module 100 obtains the real-time power generation data of the distributed clean energy generation unit through the instantaneous value sequence captured by high-frequency sampling.

[0034] After acquiring the raw signals (raw operating status data), the data acquisition module 100 performs data cleaning and time-series synchronization processing. To address the data asynchrony issue caused by different sampling frequencies, the data acquisition module 100 uses a zero-order hold or linear interpolation algorithm to map the low-frequency sampled thermodynamic data onto a high-frequency time axis, achieving time-series alignment of multi-source data. Simultaneously, the data acquisition module 100 applies a moving average filtering algorithm to remove noise and outliers from the raw operating status data. After these processes, the data acquisition module 100 finally outputs standardized operating status data containing a unified timestamp. This standardized operating status data integrates the soil temperature field data at different depths and regions within the previously acquired buried pipe heat exchanger group, the main supply and return water temperature and fluid flow data of the circulating pipe network, the real-time load data on the building side, and the real-time power generation data of the distributed clean energy power generation units.

[0035] See attached document Figure 1 The thermal imbalance calculation module 200 is connected to the data acquisition module 100 via a communication interface to receive soil temperature field data output by the data acquisition module 100. The core function of the thermal imbalance calculation module 200 is to convert discrete temperature monitoring point data into physical quantities characterizing the energy state of the formation based on thermodynamic principles, thereby quantifying the charge and discharge state shift of the soil as an energy storage carrier.

[0036] The thermal imbalance calculation module 200 initializes the calculation objects according to the preset region division logic of the buried pipe heat exchanger group. The preset region division logic is determined based on the correspondence between the physical piping topology of the buried pipe heat exchanger group and the ground control valves, dividing the entire buried pipe heat exchanger group into... Each independent controllable sub-region (denoted as) Each sub-region corresponds to an independent set of heat exchange tube bundles and solenoid valve control units, thus establishing the spatial boundary for thermal imbalance calculation.

[0037] The thermal imbalance calculation module 200 internally stores or retrieves soil physical parameters corresponding to each sub-region from an external database. These soil physical parameters specifically include the average soil density for each sub-region. (Unit: kg / m³) 3 ), soil specific heat capacity (Unit: J / (kg·K)) and initial geological baseline temperature (Unit: °C). The initial geological reference temperature is selected from the original average temperature of the soil before the construction of the ground source heat pump system, which is the original temperature of the soil before artificial heat exchange disturbance as recorded in the geological survey report. It is used as the zero potential energy reference point for calculating the enthalpy deviation of the current thermal state relative to the initial geological reference state.

[0038] Based on the received soil temperature field data and the aforementioned soil physical parameters, the thermal imbalance calculation module 200 performs volume integration calculations. Since the actual monitoring data is based on discrete values ​​from a finite number of measuring points, this volume integration calculation is implemented in practice using a discretized weighted summation method. The thermal imbalance calculation module 200 calculates the volume integration for each sub-region. Calculate its in The deviation of the current thermal state from the initial geological reference temperature at any given time, i.e., the thermal imbalance potential energy index. The calculation formula is as follows: ; in, Indicates the first Each sub-region The thermal imbalance potential energy at a given moment, i.e. the enthalpy deviation, is expressed in joules (J). Indicates the first The total number of temperature measuring points or interpolation nodes vertically distributed within each sub-region; For the node index in the vertical direction; Indicates the first Sub-region Each node The real-time soil temperature at time 1 is obtained from soil temperature field data (i.e., by performing spatial coordinate analysis on the soil temperature field data, extracting the value corresponding to the 1st moment). Sub-regions and the Temperature monitoring values ​​at each depth node); The initial geological baseline temperature; Indicates the first Sub-region The effective soil control volume represented by each node is determined by the node spacing and the heat transfer influence radius of a single pore.

[0039] The thermal imbalance calculation module 200 derives the thermal imbalance potential energy index for each sub-region through the above calculations. The sign and magnitude of this index directly reflect the direction and degree of shift in the thermal state of the formation in that region, that is, characterize the degree of thermal or cold accumulation in each sub-region. Specifically, when the calculated... A positive value indicates that the sub-region is in a state of thermal accumulation, meaning that the current heat stored in the soil is higher than the initial level; the larger the value, the more severe the thermal accumulation. When the calculated value is... A negative value indicates that the sub-region is in a state of cold accumulation, meaning that the current heat stored in the soil is lower than the initial level; the larger the absolute value, the more severe the cold accumulation. The thermal imbalance calculation module 200 will include... The sequence of calculation results for each sub-region is output as a thermal imbalance potential energy index, providing a quantitative thermodynamic basis for subsequent modules to identify target regions that need to be prioritized for repair and to determine the direction of energy dispatch.

[0040] See attached document Figure 1 The fluctuation analysis module 300 is connected to the data acquisition module 100 via a communication bus to receive real-time power generation data from the distributed clean energy power generation unit after synchronous processing, as well as real-time load data from the building side. The fluctuation analysis module 300 uses the above data to perform energy supply and demand difference calculation and time-domain statistical analysis to quantify the net value of available energy and its stability characteristics of the multi-energy flow matching and scheduling system of the ground source heat pump system at the current moment.

[0041] The fluctuation analysis module 300 first calculates the real-time surplus power value. In this calculation logic, the system defines the real-time load data on the building side as the base load data that must be prioritized (i.e., the real-time power consumption required to maintain normal building operation; this value serves as the deduction benchmark when calculating surplus power). The fluctuation analysis module 300 performs an algebraic difference operation between the real-time power generation data of the distributed clean energy generation units and the base load data to obtain the real-time surplus power value. This calculation process follows the following mathematical expression: ; in, Indicates in Real-time surplus power value at any given time, in kilowatts (kW). express Real-time power generation data of distributed clean energy power generation units at any given moment; express Real-time load data for the building at any given moment. When calculated... A value greater than zero indicates that there is still surplus clean energy available for dispatch after meeting the building's basic energy needs; when When the value is less than or equal to zero, it indicates that there is currently no surplus energy and the energy conditions for active repair are not available.

[0042] To assess the quality of energy supply, the fluctuation analysis module 300 constructs a sliding time window to process the real-time surplus power value sequence. The fluctuation analysis module 300 maintains a data set with a length of [length missing] in memory. A first-in-first-out (FIFO) data queue is used to cache data from the most recent period. Numerical value. Length of the sliding time window. It is a preset integer representing the number of sample points participating in the statistical evaluation (for example, when the sampling interval is 1 minute, it is set to...). (This represents using data from the past 10 minutes as the evaluation window). As the system runs, the latest real-time surplus power value is pushed to the head of the queue, and the oldest data is moved to the tail of the queue, achieving dynamic sliding of the window.

[0043] Based on the data cached within the sliding time window, the volatility analysis module 300 performs dispersion statistical analysis. This module 300 quantifies the dispersion of power values ​​by calculating the standard deviation of the data within the window, thereby generating a clean energy volatility index. This index is specifically calculated using the following formula: ; ; in, express The clean energy volatility index at any given time has a unit of measurement consistent with the power unit. The length of the sliding time window; The data sampling interval; Indicates the number of digits before the current time. Real-time surplus power value of each sampling point; This represents the arithmetic mean of the real-time surplus power values ​​within the current sliding time window.

[0044] The volatility analysis module 300 outputs a calculated clean energy volatility index to characterize the continuity and stability of energy supply. Numerically, the volatility analysis module 300 will calculate... Compare with a preset stability threshold (e.g., set to 10% to 2096 of the rated power of the ground source heat pump unit). When the value is less than the preset stability threshold, it indicates that the surplus power change is gradual within the current time window, the energy supply has high continuity, and it is suitable for instructing the ground source heat pump unit to perform continuous heat repair operations; when When the value exceeds the preset stability threshold, it indicates that the surplus power exhibits drastic fluctuations or intermittent characteristics.

[0045] See attached document Figure 1 and Figure 2The matching and scheduling module 400, serving as the computational unit for executing logic judgments and generating parameters, is connected to the thermal imbalance calculation module 200 and the fluctuation analysis module 300 via a data bus. This matching and scheduling module 400 receives the thermal imbalance potential energy index output by the thermal imbalance calculation module 200, and the real-time surplus power value and clean energy volatility index output by the fluctuation analysis module 300. The matching and scheduling module 400 has built-in operating mode determination logic and equipment safety constraint parameters, used to calculate and output control commands for the ground source heat pump system while meeting the physical limitations of the equipment.

[0046] The matching and scheduling module 400 assesses the current system status based on its built-in operating mode determination logic. This logic includes two parallel criteria: an energy condition, which determines whether the received real-time surplus power value exceeds the minimum starting power threshold of the ground source heat pump unit (e.g., set to 20% of the unit's rated power); and a thermodynamic condition, which determines whether the absolute value of the received thermal imbalance potential energy index exceeds the preset thermal accumulation or cold accumulation tolerance limit (e.g., set to the enthalpy corresponding to a soil average temperature deviating from the reference temperature by 0.5 degrees Celsius). The matching and scheduling module 400 determines that the active repair condition is met and switches the system to active repair mode only if both conditions are true. If either condition is not met, the system maintains its normal operating mode or standby state.

[0047] When the conditions for active repair are met, the matching and scheduling module 400 performs a correlation calculation between fluid dynamics and energy matching. Considering the instability of clean energy supply, the matching and scheduling module 400 calculates the target hydraulic lag time based on the clean energy volatility index. This calculation logic aims to utilize the thermal inertia of the fluid in the circulating pipe network to mitigate the impact of power fluctuations on the buried pipe heat exchanger. The calculation formula is as follows: ; in, Indicates the target hydraulic delay time, in seconds (s); The preset baseline circulation period for the system is obtained by dividing the total volume of the circulation network by the rated flow rate. The preset fluctuation response coefficient (unit: kW) -1 (i.e., the reciprocal of the power unit), used to balance physical dimensions and adjust the system's sensitivity to energy fluctuations; This is a volatility indicator for clean energy.

[0048] After obtaining the target hydraulic lag time, the matching and scheduling module 400 uses the principle of fluid continuity to deduce the ideal flow rate setpoint for the circulating pipe network. The calculation formula is as follows: ; in, This represents the ideal flow rate setpoint, expressed in cubic meters per second. This represents the total volume of circulating fluid within the circulating pipe network. This parameter is pre-stored as a fixed physical attribute in the matching and scheduling module 400 database.

[0049] To ensure that the calculated flow rate value is within the physical safe operating range of the equipment, the matching and scheduling module 400 uses built-in equipment safety constraint parameters to logically clamp and correct the ideal flow rate setpoint. These equipment safety constraint parameters include at least the minimum flow rate threshold required to ensure the fluid in the buried pipe is in a turbulent heat transfer state. And the maximum flow rate threshold that the variable frequency circulating water pump is allowed to operate on. The matching and scheduling module 400 executes the following segmentation correction logic to generate the final flow control instruction: ; in, This refers to the flow rate value corresponding to the final output flow control command.

[0050] Please see the appendix Figure 3 As shown in the figure, the horizontal axis represents the system sampling time series. The solid curve corresponding to the left vertical axis represents the clean energy volatility index output by the volatility analysis module 300; the dashed curve corresponding to the right vertical axis represents the flow rate value corresponding to the flow control command generated by the matching scheduling module 400. (See attached...) Figure 3 The real-time adjustment characteristics of the system of this invention can be observed: when the clean energy volatility index is in a low and stable state, the flow rate value corresponding to the flow control command is maintained at a high level to ensure efficient heat exchange; while when the clean energy volatility index increases significantly (i.e., the solid line peak section in the figure, representing an increase in the output instability of the distributed clean energy power generation unit), the system automatically reduces the flow rate setpoint according to the aforementioned calculation logic of the target hydraulic lag time, so that the flow rate value corresponding to the flow control command shows a reverse downward trend (i.e., the dashed line trough section in the figure). This reverse response mechanism increases the hydraulic lag time by actively reducing the flow velocity, thereby using the thermal inertia of the fluid in the pipe network to smooth the impact of power fluctuations on the ground source heat pump unit and the buried pipe heat exchanger group, proving that the invention has adaptive real-time activity capability for unstable energy input.

[0051] While determining the flow parameters, the matching and scheduling module 400 generates a compressor operating frequency command. Specifically, the matching and scheduling module 400 multiplies the real-time surplus power value by the coefficient of performance (COP) of the ground source heat pump unit at the current inlet and outlet water temperatures to obtain the theoretically convertible cooling or heating power. Based on a preset unit power-frequency characteristic curve, it linearly maps the theoretically convertible cooling or heating power to the compressor's target operating frequency. To prevent the risk of heat exchanger freezing or overheating due to flow fluctuations, the matching and scheduling module 400 introduces a temperature difference-based boundary limit: when the predicted supply and return water temperature difference exceeds a preset safe temperature difference limit (e.g., 5 to 7 degrees Celsius), the matching and scheduling module 400 proportionally lowers the target operating frequency. After the above calculations and boundary limit processing, the matching and scheduling module 400 finally outputs the flow control command and the compressor operating frequency command to the execution control module 500.

[0052] See attached document Figure 1 The execution control module 500 is connected to the matching and scheduling module 400 via an electrical interface or industrial communication bus, and receives the flow control command and compressor operating frequency command generated by it.

[0053] The execution control module 500 has a built-in protocol conversion unit. Upon receiving a flow control command, the execution control module 500 converts it into a speed control signal (such as a 0-10V analog voltage signal or a Modbus frequency setting message) corresponding to the variable frequency circulating water pump, based on a pre-calibrated pump characteristic curve. Upon receiving a compressor operating frequency command, the execution control module 500 encapsulates it into a communication protocol data packet that the ground source heat pump unit controller can parse, for adjusting the compressor rotor speed. For the electric regulating valve array, the execution control module 500 generates on / off signals or analog regulation signals for the corresponding branch valves based on the target area index locked in the current active repair mode. The converted underlying drive signals are sent to the variable frequency circulating water pump, the ground source heat pump unit, and the electric regulating valve array, respectively, driving the aforementioned hardware devices to perform the physical actions of fluid distribution and heat transfer.

[0054] To ensure the physical safety of the system during dynamic repair, the execution control module 500 establishes a closed-loop feedback mechanism. This execution control module 500 reads the monitored values ​​of the main supply and return water temperatures of the circulating pipe network from the data acquisition module 100 in real time via shared memory or an independent communication link. The execution control module 500's internal registers store preset safety thresholds. These preset safety thresholds specifically include: the underground pipe antifreeze protection temperature to prevent the medium inside the underground pipe from freezing. (For example, set to 4 degrees Celsius), and the overheat protection temperature of the buried pipe to prevent excessive heat accumulation in the soil around the buried pipe. (For example, set to 35 degrees Celsius).

[0055] During system operation, the execution control module 500 continuously compares the monitored values ​​of the main supply and return water temperatures of the circulating pipe network with preset safety thresholds. When the monitored values ​​of the main supply and return water temperatures of the circulating pipe network reach or exceed the preset safety thresholds, the execution control module 500 triggers the highest priority protection logic (or safety strong control logic) to truncate or correct the output underlying drive signals.

[0056] The correction of the output drive signal specifically refers to: the execution control module 500 applying a derating control algorithm (such as a PID limiting algorithm) to forcibly and proportionally reduce the drive signal sent to the compressor operating frequency of the ground source heat pump unit, thereby reducing the heat exchange intensity. The truncation of the output drive signal specifically refers to: when the monitored temperature continues to deteriorate and exceeds a preset safety threshold, the execution control module 500 immediately cuts off the enable signal sent to the ground source heat pump unit, causing it to stop operating. Simultaneously, it locks the drive signal sent to the variable frequency circulating water pump at its rated flow rate to maintain heat dissipation or absorption in the pipe network circulation until the supply and return water temperatures recover to the safe lag range.

[0057] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy, characterized in that, include: The data acquisition module is configured to acquire real-time operating status data, which includes soil temperature field data at different depths and in different areas of the buried pipe heat exchanger group, supply and return water temperature and fluid flow data of the main pipe of the circulating pipe network, real-time load data on the building side, and real-time power generation data of the distributed clean energy power generation unit. The thermal imbalance calculation module is configured to calculate the enthalpy deviation of the current thermal state of each sub-region relative to the initial geological baseline state based on the soil temperature field data, and output the thermal imbalance potential energy index. The volatility analysis module is configured to calculate real-time surplus power values ​​and clean energy volatility indicators. The matching and scheduling module is configured to calculate the target hydraulic lag time based on the clean energy volatility index and deduce the ideal flow setpoint of the circulating pipe network in reverse, and generate flow control instructions and compressor operating frequency instructions. The execution control module is configured to convert the flow control command and the compressor operating frequency command into a low-level drive signal and send them to the variable frequency circulating water pump, the ground source heat pump unit and the electric regulating valve array, and to truncate or correct the low-level drive signal based on the main pipe supply and return water temperature monitoring value of the circulating pipe network fed back by the data acquisition module.

2. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy as described in claim 1, characterized in that, The data acquisition module further includes steps for cleaning and timing synchronization of the raw signals of the running status data to obtain the running status data that is standardized with a unified timestamp.

3. The multi-energy flow matching and scheduling system for a ground source heat pump system coupled with clean energy as described in claim 1, characterized in that, The steps of the thermal imbalance calculation module to calculate the enthalpy deviation of the current thermal state of each sub-region relative to the initial geological baseline state based on the soil temperature field data and to output the thermal imbalance potential energy index include: Based on the preset regional division logic of the buried pipe heat exchanger group, the soil physical parameters corresponding to each sub-region are retrieved. For each sub-region, a volume integral operation is performed, and the enthalpy deviation of the real-time soil temperature of each sub-region relative to the initial geological reference state temperature is calculated by a discretized weighted summation method. The enthalpy deviation is output as a thermal imbalance potential energy index characterizing the degree of thermal or cold accumulation in each sub-region.

4. The multi-energy flow matching and scheduling system for a ground source heat pump system coupled with clean energy as described in claim 1, characterized in that, The steps for the fluctuation analysis module to calculate the real-time surplus power value include: The real-time load data on the building side is defined as the basic load data that must be prioritized. The real-time surplus power value is obtained by performing an algebraic difference operation between the real-time power generation data of the distributed clean energy power generation unit and the base load data.

5. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy as described in claim 1, characterized in that, The steps for calculating the clean energy volatility index by the volatility analysis module include: A sliding time window is constructed to process the real-time surplus power value sequence, and a first-in-first-out data queue with a preset length is maintained. The standard deviation of the data within the sliding time window is calculated to quantify the dispersion of power values, and the clean energy volatility index, which characterizes the continuity and stability of energy supply, is output.

6. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy as described in claim 1, characterized in that, The matching and scheduling module has a built-in operating mode determination logic. It determines that the active repair conditions are met and switches the operating state to active repair mode only when the real-time surplus power value is greater than the minimum start-up power threshold of the ground source heat pump unit and the absolute value of the thermal imbalance potential energy index is greater than the preset thermal accumulation or cold accumulation tolerance limit.

7. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy as described in claim 1, characterized in that, The steps of the matching and scheduling module to calculate the target hydraulic lag time based on the clean energy volatility index and to derive the ideal flow setpoint for the circulating pipeline network include: The thermal inertia of the fluid in the circulating pipe network is used to smooth out power fluctuations. The preset benchmark cycle is corrected according to the clean energy volatility index and the preset fluctuation response coefficient to obtain the target hydraulic lag time. Based on the principle of fluid continuity, the ideal flow rate setting value of the circulating pipe network is derived by dividing the total volume of circulating fluid in the circulating pipe network by the target hydraulic lag time.

8. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy according to claim 7, characterized in that, The matching and scheduling module combines the built-in device safety constraint parameters to perform logical clamping and correction on the ideal flow setting value to generate flow control instructions. The equipment safety constraint parameters include the minimum flow threshold that ensures the fluid in the buried pipe is in a turbulent heat exchange state and the maximum flow threshold that the variable frequency circulating water pump is allowed to operate. The step of logically clamping and correcting the ideal flow rate setting value by combining the built-in device safety constraint parameters to generate flow control commands includes: When the ideal flow rate setting value is lower than the minimum flow rate threshold, the flow rate value corresponding to the flow control command is corrected to the minimum flow rate threshold. When the ideal flow rate setting value is between the minimum flow rate threshold and the maximum flow rate threshold, the flow rate value corresponding to the flow control command is set to the ideal flow rate setting value; When the ideal flow rate setting value is higher than the maximum flow rate threshold, the flow rate value corresponding to the flow control command is corrected to the maximum flow rate threshold.

9. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy according to claim 1, characterized in that, The steps of the matching and scheduling module in generating the compressor operating frequency command include: Based on the real-time surplus power value and the performance coefficient of the ground source heat pump unit at the current inlet and outlet water temperatures, the theoretically convertible cooling or heating power is calculated and mapped to the target operating frequency of the compressor in the ground source heat pump unit. A boundary constraint based on temperature difference is introduced. When the predicted supply and return water temperature difference exceeds the preset safe temperature difference limit, the target operating frequency is proportionally reduced, and the compressor operating frequency command is generated.

10. The multi-energy flow matching and scheduling system for a ground-source heat pump system coupled with clean energy according to claim 1, characterized in that, The step of the execution control module truncating or correcting the underlying drive signal based on the monitored values ​​of the main supply and return water temperatures of the circulating pipe network fed back by the data acquisition module includes: The temperature monitoring values ​​of the main supply and return water of the circulating pipe network are continuously compared with the preset safety thresholds, which include the underground pipe antifreeze protection temperature and the underground pipe overheat protection temperature. When the monitored value of the main supply and return water temperature of the circulating pipe network reaches or exceeds the preset safety threshold, the highest priority protection logic is triggered. The derating control algorithm is applied to force a proportional reduction in the underlying drive signal sent to the ground source heat pump unit, or the enable signal sent to the ground source heat pump unit is directly cut off and the underlying drive signal sent to the variable frequency circulating water pump is locked to maintain it at the rated flow state.

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