A heat pump energy supply system based on subway tunnel air waste heat recovery and a method for operating the same

Abstract

The application provides a heat pump energy supply system based on subway tunnel air waste heat recovery and an operation method thereof, and belongs to the technical field of building energy saving and low-grade waste heat utilization. In view of the low utilization rate of subway tunnel waste heat and the low-temperature operation efficiency reduction of air source heat pump, a multi-heat source collaborative energy supply system is provided: the heat pump outdoor unit is arranged in the subway air shaft to directly extract low-grade waste heat of the tunnel; a tunnel thermal environment multi-source coupling characteristic system is constructed, a temperature prediction model is established based on data driving, and feedforward sensing is realized; a multi-heat source dynamic switching and collaborative control strategy is provided to realize heat source optimization, unit group control and system collaborative scheduling; and a full life cycle optimization configuration model is established to integrally optimize equipment selection, capacity matching and operation strategy, so as to minimize the life cycle cost as the target, and take into account the economy and energy efficiency.

Description

Technical Field

[0001] This invention belongs to the field of building energy conservation and low-grade waste heat utilization technology, and particularly relates to a heat pump energy supply system that uses subway tunnel air as a heat source and its operation control method. Background Technology

[0002] During operation, subway systems continuously release a large amount of heat through train traction, braking, and the operation of auxiliary equipment. This results in tunnel air temperatures that are consistently higher than the outdoor environment and exhibit a relatively stable characteristic, indicating a high potential for the recovery and utilization of low-grade waste heat. However, in current engineering practices, this heat is mainly discharged to the outdoor environment through ventilation systems, lacking effective recovery and utilization methods. This not only leads to energy waste but also fails to realize its potential value in the field of building energy supply.

[0003] Air source heat pumps, as one of the main technologies in current building cooling and heating systems, have advantages such as system maturity and wide application. However, their operating performance is highly sensitive to ambient temperature. In low-temperature winter conditions, the low outdoor air temperature leads to a reduced temperature difference on the evaporator side, decreasing the system's heating capacity. Simultaneously, frost easily forms on the evaporator surface, requiring frequent defrosting operations, further increasing energy consumption and lowering the system's seasonal energy efficiency coefficient, severely limiting its application in cold and frigid regions. Therefore, introducing relatively stable subway tunnel air as an auxiliary or alternative heat source would help improve the heat pump's operating conditions and enhance the overall system energy efficiency.

[0004] However, the thermal environment of subway tunnels is not constant. Its temperature is affected by the coupling effects of multiple factors, such as train operating density, ventilation operation mode, passenger flow, and external weather conditions, exhibiting significant time-varying and nonlinear characteristics, leading to uncertainty in the availability of heat sources. In addition, the complex spatial structure of tunnels and the unstable airflow path make it difficult to precisely control the heat extraction and heat exchange process.

[0005] In summary, existing technologies for utilizing waste heat in subway tunnels have the following main shortcomings: (1) Low utilization rate of waste heat resources: The existing subway system mainly discharges waste heat in the tunnel directly into the environment through ventilation and exhaust, lacking efficient recovery and utilization technology for low-grade waste heat, and failing to achieve energy cascade utilization.

[0006] (2) Limited performance of air source heat pumps at low temperatures: Traditional air source heat pumps have problems such as reduced heat exchange capacity, severe frost and reduced energy efficiency in low temperature environments, which leads to unstable system operation, increased energy consumption and difficulty in meeting the high-efficiency heating needs of cold regions.

[0007] (3) Lack of stable system construction and operation control methods: Due to the significant fluctuations in tunnel air temperature caused by the coupling of multiple factors, the existing technology lacks system construction methods and dynamic operation control strategies for the residual heat characteristics of tunnels, making it difficult to achieve stable utilization of heat sources and efficient system operation.

[0008] Therefore, it is necessary to propose a heat pump energy supply system and method that can stably utilize waste heat from subway tunnel air and achieve efficient operation control under multiple working conditions, in order to overcome the above-mentioned technical defects. Summary of the Invention

[0009] The purpose of this invention is to provide a waste heat recovery heat pump energy supply system based on subway tunnel air and its operation method. By constructing a stable tunnel air heat extraction structure, establishing a multi-source information-driven temperature prediction mechanism, and forming a multi-heat source collaborative operation control strategy, this invention solves the problems of low waste heat utilization rate in subway tunnels, decreased operating efficiency of air source heat pumps in low-temperature environments, and insufficient system operation stability in existing technologies. This achieves efficient utilization of low-grade waste heat and an overall improvement in system energy efficiency. The core technical solution is systematically designed in four stages: system structure construction, tunnel thermal environment perception and prediction, multi-heat source collaborative operation control, and full life-cycle optimization configuration.

[0010] To achieve the above-mentioned technical objectives, the present invention provides the following: A waste heat recovery heat pump energy supply system based on subway tunnel air includes four stages: system structure construction, tunnel thermal environment perception and prediction, multi-heat source collaborative operation control, and full life cycle optimization configuration. Step S10: Construct the system structure: The ventilation shaft structure guides the air from the subway tunnel to the heat exchange area, and the outdoor unit of the air source heat pump unit is placed in the ventilation shaft so that it can directly exchange heat with the tunnel air. Step S20, Tunnel Thermal Environment Sensing and Prediction: A tunnel air temperature prediction model is constructed based on multi-source input features to achieve dynamic prediction of tunnel temperature. Step S30, Multi-heat source coordinated operation control: Heat source selection and operation mode switching are performed based on the air conditions in the tunnel and the outside air, and the heat pump unit and water system are coordinated and scheduled. Step S40, Full Lifecycle Optimization Configuration: With the goal of minimizing system lifecycle costs, we will collaboratively optimize equipment selection and operation strategies.

[0011] Preferably, in step S10, the system structure includes a subway tunnel, a ventilation shaft structure, an air source heat pump unit, a water system, a building load-side system, and a control system; wherein: The subway tunnel is used to provide low-grade waste heat air; The ventilation shaft structure is used to organize the airflow in the tunnel and form a stable heat exchange channel; The air source heat pump unit achieves cooling or heating operation by exchanging heat with the tunnel air. The water system is used for the distribution of cold and heat; The control system is used to realize heat source switching and coordinated operation of equipment; Step S10 further includes: Step S11: System basic data collection: Collect tunnel thermal environment data, outdoor meteorological data, train operation data, pedestrian disturbance data, soil temperature data, time characteristic data, and building load data; Step S12, Data Preprocessing: Time alignment of multi-source data is performed using a unified timestamp, abnormal data is identified and removed, missing data is interpolated and completed, and normalization or standardization is performed.

[0012] Preferably, the ventilation shaft structure is coupled with the existing ventilation system of the subway. By optimizing the airflow organization path, the tunnel air forms a stable flow before entering the heat exchange area and maintains a uniform velocity distribution during the heat exchange process, thereby improving heat exchange efficiency and reducing airflow short-circuiting and local stagnation.

[0013] Preferably, the water system adopts a variable operating condition control strategy in coordination with the heat pump unit, dynamically adjusting the circulating water flow rate according to the system load to maintain a matching relationship with the load demand, and optimizing the energy consumption of the water pump through efficiency correction.

[0014] Preferably, in step S20, the multi-source input features include external meteorological features, soil temperature features, tunnel history features, train operation features, pedestrian disturbance features, and time features, which are used to comprehensively characterize the dynamic change process of tunnel air temperature. The specific steps of tunnel thermal environment perception and prediction in step S20 include: Step S21, Construction of tunnel temperature lag characteristics: Construct short-term lag characteristics and cross-day lag characteristics; Step S22, Construction of external disturbance characteristics: Construct meteorological lag characteristics and formation temperature characteristics; Step S23, Construction of internal disturbance characteristics: Construct characteristic variables of train operation and passenger flow activities; Step S24, Time Feature Encoding and Feature Assembly: Encode the hour and day of the week information and integrate them to form the input feature vector; Step S25, Model Training: Based on historical data, establish a mapping relationship between input features and tunnel temperature; Step S26, Temperature Prediction and Application: The trained model is used to predict the tunnel temperature at future moments, and the prediction results are used as feedforward inputs for system operation control.

[0015] Preferably, the external meteorological characteristics include the current outdoor temperature, the outdoor temperature several hours ago, and the outdoor temperature at the same time several days ago, used to characterize the immediate and delayed effects of meteorological disturbances; the soil temperature characteristics are used to characterize the long-term regulating effect of the thermal inertia of the strata on the tunnel temperature. The tunnel's own historical characteristics include the current tunnel temperature and its short-term and cross-day periodic lag characteristics, which are used to reflect the influence of the tunnel's thermal inertia and historical state on the current temperature.

[0016] Preferably, the train operation characteristics include departure interval, number of trains and their lag characteristics; the pedestrian disturbance characteristics include pedestrian intensity and its lag characteristics, used to characterize the nonlinear impact of operation disturbance on the tunnel thermal environment.

[0017] Preferably, in step S30, the multi-heat source coordinated operation control specifically includes: Step S31, Heat source identification and operation mode selection: Based on the comparison results of tunnel air and outdoor air temperature, the system judges the quality of heat source and adaptively switches between tunnel source mode, air source mode and dual heat source coupling mode. Step S32, Heat pump unit group control and scheduling: Based on the building load demand, control the start-up and shutdown of multiple heat pump units and sort their operation, giving priority to units with higher energy efficiency. Step S33, Load Distribution and System Coordination Control: Distribute the load of the operating units to ensure they operate within a reasonable load range, and adjust the operating status of the water pumps to match the circulating water flow with the system load, thereby achieving coordinated operation of the distribution system and the heat pump system. Introducing a hysteresis control mechanism during heat source switching, and setting different switching threshold ranges, can prevent the system from switching frequently under critical conditions, thereby improving operational stability.

[0018] Preferably, the tunnel temperature prediction model in the tunnel thermal environment perception and prediction adopts a nonlinear regression model based on random forest. By training on multi-source input features, the future state prediction of tunnel air temperature is realized and used as a feedforward input for heat source availability discrimination and operation control. The multi-heat source collaborative operation control stage optimizes heat sources based on the temperature difference between tunnel air and outdoor air, and constructs a comprehensive evaluation index in conjunction with the temperature difference change trend to achieve adaptive switching between tunnel source, air source and dual heat source coupling mode.

[0019] Preferably, the multi-heat source coordinated operation control stage further includes heat pump unit group control scheduling, which prioritizes scheduling units with higher energy efficiency to participate in operation by sorting the unit energy consumption of each unit, and dynamically allocates the load of each unit according to the building load. By setting a partial load rate constraint range for the operation of heat pump units and adjusting the number of units starting and stopping and the load distribution, the units can be operated in the high-efficiency range, thereby improving the overall energy efficiency of the system and reducing equipment wear. The step S40 full lifecycle optimization configuration specifically includes: Step S41, Hourly Operation Calculation: Using hourly load and environmental data as input, perform dynamic operation calculations throughout the year to obtain the equipment operating status and energy consumption data at each time point; Step S42: Dynamic adjustment of operation strategy: Based on real-time data and forecast results, dynamically optimize and adjust the heat source selection, unit scheduling and water system operation strategy; Step S43, System Performance Evaluation and Optimization: Based on the annual operation results, conduct a comprehensive evaluation of the system's energy efficiency, operating costs, and carbon emissions; The phase of the full life cycle optimization configuration takes the minimum life cycle cost as the optimization objective, and uses heat pump capacity configuration, water pump selection parameters, system structural parameters and operation strategy parameters as optimization variables to construct an integrated design and operation optimization model, and solves it under the condition of meeting building load requirements, equipment operating capacity and system operating constraints.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1) Utilize the air in the subway tunnel as a heat source to improve the utilization rate of low-grade waste heat; 2) Improve the operating performance of air source heat pumps in low-temperature environments and reduce frosting; 3) Improve system stability and adaptability by switching between multiple heat sources; 4) Reduce system operating energy consumption and lifecycle costs; 5) Reduce equipment installation capacity and improve system economy; 6) It has a more significant energy-saving effect in cold and frigid regions. Attached Figure Description

[0021] Figure 1 A schematic diagram of a waste heat recovery heat pump power supply system based on subway tunnel air is provided as an embodiment of the present invention; Figure 2 A schematic diagram of the overall layout of a waste heat recovery heat pump power supply system based on subway tunnel air, provided for an embodiment of the present invention; Figure 3An equipment operation logic diagram of a waste heat recovery heat pump power supply system based on subway tunnel air is provided for an embodiment of the present invention; Figure 4 An optimized design flowchart of a heat pump power supply system based on waste heat recovery from subway tunnel air is provided for embodiments of the present invention. Figure 5 This is a comparison chart showing the application results of a waste heat recovery heat pump energy supply system based on subway tunnel air in different cities, as provided by an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0023] like Figure 1 As shown, this invention provides a heat pump energy supply system based on waste heat recovery from subway tunnel air and its operation method, including four stages: system structure construction (S10), tunnel thermal environment perception and prediction (S20), multi-heat source collaborative operation control (S30), and full life cycle optimization configuration (S40). The system establishes a tunnel temperature prediction model through multi-source data acquisition and processing, and performs heat source selection, unit scheduling, and system collaborative control based on the prediction results. Furthermore, by combining annual dynamic operation calculations, the system's energy efficiency, operating costs, and carbon emissions are comprehensively evaluated, achieving collaborative optimization of equipment configuration and operation strategies.

[0024] Step S10: Construct the system structure: The first phase aims to construct a stable and efficient tunnel air heat exchange structure to achieve the continuous extraction and efficient utilization of low-grade waste heat from subway tunnels. Subway tunnels continuously release heat during train traction, braking, and the operation of electromechanical equipment, maintaining a relatively stable temperature field inside the tunnel that is higher than the outdoor environment over a long period, thus providing conditions for a usable low-grade heat source.

[0025] To achieve the engineering utilization of this waste heat, this invention guides tunnel air to the heat exchange area through a ventilation shaft structure, and arranges the outdoor unit of the air source heat pump unit inside the ventilation shaft, allowing it to directly exchange heat with the tunnel air, thereby constructing a waste heat recovery and utilization system based on the subway tunnel space. In this structure, tunnel air participates in the operation of the heat pump system as a continuously flowing heat source medium, realizing the direct extraction and utilization of heat.

[0026] The heat exchange process per unit time can be expressed as:

[0027] in, Air mass flow rate, The specific heat capacity of air at constant pressure. and These represent the air temperatures entering and leaving the heat exchange zone, respectively. This expression characterizes the energy transfer process on the air side of the air shaft heat exchange unit and reflects the relationship between heat exchange intensity, airflow conditions, and temperature difference.

[0028] Furthermore, considering the impact of airflow organization in tunnel space on heat exchange efficiency, this invention optimizes the airflow path in the tunnel by coupling the ventilation shaft system with the existing subway ventilation system. This allows the air to form a stable flow channel before entering the heat exchange area and maintain a relatively uniform velocity distribution during the heat exchange process. This reduces local stagnation, backflow, and airflow short-circuiting, thereby improving the stability of the heat exchange process and the overall heat exchange efficiency.

[0029] In terms of system operation, the heat pump unit uses tunnel air as the heat source on the evaporation side in heating mode, and improves the heating performance in low-temperature environments by increasing the evaporation temperature; in cooling mode, it uses tunnel air as the heat dissipation medium on the condensation side, and reduces the compressor's work requirements by lowering the condensation temperature, thereby improving the system's energy efficiency.

[0030] Based on the above heat exchange mechanism and operation mode, the structure of the subway tunnel waste heat recovery heat pump energy supply system constructed in this invention includes: 1) Subway tunnels, used to provide waste heat air with stable temperature characteristics; 2) A ventilation shaft structure, connected to the subway tunnel, is used to organize and guide the airflow in the tunnel; 3) Air source heat pump units, whose outdoor units are located inside the ventilation shaft and exchange heat directly with the tunnel air; 4) Water system, including chilled water or hot water pipes and circulating water pumps, used to realize the distribution of cold and heat; 5) A building load-side system, connected to the water system, for providing cooling or heating to the building terminals; 6) Control system, used to coordinate the operation of various equipment and realize the switching of operating modes and load distribution control.

[0031] The air source heat pump unit uses tunnel air in the ventilation shaft as the heat exchange medium on the evaporation or condensation side to achieve cooling or heating operation, thereby realizing the efficient utilization of low-grade waste heat resources in the subway tunnel.

[0032] Furthermore, in this embodiment, the ventilation shaft structure is coupled with the existing ventilation system of the subway. By optimizing the airflow organization path, the tunnel air forms a stable flow before entering the heat exchange area and maintains a uniform velocity distribution during the heat exchange process, thereby improving heat exchange efficiency and reducing airflow short-circuiting and local stagnation.

[0033] Furthermore, such as Figure 2As shown, the core units of the system include the subway tunnel (left), the ventilation shaft (central vertical passage), the air source heat pump unit (the "energy tower" inside the ventilation shaft), and the building heat users (right). The system has the ability to switch between tunnel air and outdoor air as dual heat sources, while avoiding the secondary accumulation of waste heat inside the tunnel. It has no significant impact on the original thermal environment of the tunnel, thus balancing energy utilization efficiency and the operational safety of transportation facilities.

[0034] Winter heating conditions ( Figure 2 a): After the heat pump system starts, the relatively warm and stable tunnel air (maintained by train heat dissipation and electromechanical equipment heat generation) rises through the ventilation shaft and enters the evaporator side of the heat pump unit. After extracting heat from the tunnel, the heat pump supplies heat to the building through the water system (hot water flows to heat users). At this time, the tunnel air acts as a heat source on the evaporator side, raising the heat pump's evaporation temperature and improving low-temperature heating performance. In addition, the relative stability of the tunnel air helps improve system operating efficiency and reduce defrosting frequency.

[0035] Summer cooling conditions ( Figure 2 (b) The heat pump transfers indoor heat to the condenser side via refrigerant circulation, where tunnel air serves as the cooling medium for heat exchange, effectively reducing the system's condensing temperature and improving energy efficiency. During high-temperature days or peak building load periods, the system can switch to outdoor air as an auxiliary heat source based on ambient temperature and load conditions, alleviating heat exchange pressure and ensuring stable system operation.

[0036] In this embodiment, the water system adopts a variable operating condition control strategy in coordination with the heat pump unit. The circulating water flow rate is dynamically adjusted according to the system load to maintain a matching relationship with the load demand, and the energy consumption of the water pump is optimized through efficiency correction.

[0037] Step S10 also includes: Step S11: System basic data collection: Collect tunnel thermal environment data, outdoor meteorological data, train operation data, pedestrian disturbance data, soil temperature data, time characteristic data, and building load data; Step S12, Data Preprocessing: Due to differences in sampling frequency, recording accuracy, and time scale among different data sources, the raw data needs to be processed uniformly. This invention employs the following steps: (1) Time alignment of multi-source data is performed by using a unified timestamp to ensure that all types of data have a consistent time resolution; (2) Identify and remove abnormal data to avoid extreme values ​​from interfering with model training and control decisions; (3) Interpolate and complete the missing data to ensure the continuity of the data sequence; (4) Normalize or standardize according to different data types to eliminate dimensional differences and improve model stability.

[0038] After processing, a dataset with a uniform structure is formed, which is used for subsequent feature construction and model training.

[0039] Step S20, Tunnel Thermal Environment Sensing and Prediction: The second phase aims to construct a multi-source coupled sensing and prediction model for the tunnel thermal environment, enabling early identification and dynamic assessment of tunnel heat source conditions. The air temperature in subway tunnels is influenced by multiple factors, including external meteorological disturbances, internal thermal inertia effects, train operation disturbances, pedestrian flow disturbances, and time-periodic variations. Its evolution process exhibits significant nonlinear and temporal coupling characteristics.

[0040] The multi-source coupling feature system includes the following: Step S21: Construction of Tunnel Temperature Lag Characteristics: Tunnel temperature exhibits significant thermal inertia, meaning the current temperature is significantly influenced by historical temperatures. Therefore, short-term lag characteristics and trans-day lag characteristics are constructed to describe the memory effect of the tunnel thermal environment.

[0041] Step S22: Construction of external disturbance characteristics: Considering the influence of outdoor meteorological conditions and soil temperature on tunnel temperature, meteorological lag characteristics and formation temperature characteristics are constructed to reflect the regulating effect of the external environment on the thermal state of the tunnel.

[0042] Step S23: Construction of internal disturbance characteristics: Combining train operation and pedestrian activity data, construct characteristic variables that reflect thermal disturbances inside the tunnel to describe the impact of human activities on temperature changes.

[0043] Step S24, Time Feature Encoding and Feature Assembly: By introducing time periodic features, information such as hours and days of the week is encoded to reflect the periodicity of temperature changes. These features are then integrated to form a complete input feature vector.

[0044] Tunnel temperature prediction modeling: In order to realize the early perception of the state of tunnel heat sources, this invention constructs a data-driven temperature prediction model.

[0045] Step S25, Model Training: Based on historical data, establish a mapping relationship between input features and tunnel temperature, and obtain the temperature change pattern through model training.

[0046] Step S26, Temperature Prediction and Application: Using the trained model, the tunnel temperature at future moments is predicted, and the prediction results are used as feedforward inputs for system operation control to determine the availability and changing trend of heat sources in advance.

[0047] To characterize this complex dynamic process, the tunnel air temperature is expressed as a multivariable function:

[0048] Each sub-variable represents an influencing factor from a different source: Indicates outdoor weather-related variables, Indicates variables related to soil temperature. This indicates the tunnel's own historical thermal state. Indicates the characteristics of train operation. Indicates characteristics of pedestrian flow disturbance. It indicates the characteristics of time period.

[0049] Based on this, a 28-dimensional input feature vector is constructed according to the actual characteristics, as shown below:

[0050] The specific components of each sub-module are as follows: Outdoor meteorological characteristics The system includes seven variables: the current outdoor air temperature B1, the outdoor air temperatures B2-B4 from the previous 1-3 hours, and the outdoor air temperatures B5-B7 from the previous 1-3 days. These variables are used to characterize the impact of short-term and trans-day meteorological disturbances on tunnel temperature.

[0051] Soil temperature characteristics This includes the current month's soil temperature C1, which reflects the slow regulating effect of the formation's thermal inertia on tunnel temperature.

[0052] Tunnel's own historical characteristics The system includes seven variables: the current tunnel temperature A1, the tunnel temperatures A2-A4 from the previous 1-3 hours, and the tunnel temperatures A5-A7 from the previous 1-3 days. These variables are used to characterize the tunnel's thermal inertia and long-term memory effect.

[0053] Train operation characteristics Five variables, including train departure interval D1, current hourly train count D2, and train counts from the previous 1 to 3 hours D3 to D5, are used to describe the instantaneous and delayed effects of train operation on tunnel thermal disturbance.

[0054] Characteristics of pedestrian flow disturbance The system includes four variables: the current pedestrian flow intensity E1 and the pedestrian flow intensity E2-E4 from the previous 1-3 hours. These variables are used to reflect the additional disturbance of human activities to the tunnel's thermal environment.

[0055] Time characteristics This includes hour F1, day of the week F2, weekday / weekend F3, and time period coding features. (F4) and (F5), a total of 5 variables, are used to characterize the periodic variation of the tunnel thermal environment.

[0056] Construct a complete input vector based on the above features:

[0057] AF corresponds to the tunnel itself, outdoor weather, soil temperature, train operation, pedestrian disturbance and time feature modules, with an overall input dimension of 28 dimensions.

[0058] During model construction, the input data is normalized to eliminate dimensional differences, and key influencing factors are extracted using feature dimensionality reduction analysis to reduce feature redundancy and improve model generalization ability. Based on this, a nonlinear regression prediction model based on random forest is established to achieve dynamic prediction of tunnel air temperature, expressed as follows:

[0059] in, This represents a tunnel temperature prediction function constructed based on the random forest algorithm.

[0060] The prediction results are used to characterize the thermal environment of the tunnel at future moments and serve as an important input for heat source availability assessment and system operation control. This enables the advance scheduling and optimized utilization of tunnel waste heat resources, avoiding the lag and energy efficiency loss problems caused by relying solely on real-time feedback control.

[0061] Step S30, Multi-heat source coordinated operation control: The third phase aims to construct a dynamic operation control strategy based on multi-heat source collaboration, to achieve adaptive optimization and efficient switching under dual heat source conditions of subway tunnel air and outdoor air, thereby improving the system's operational stability and energy efficiency under complex weather and load fluctuation conditions.

[0062] Figure 3 This is a rule-based operation flowchart of the system of the present invention. The system selects heat sources based on heat source conditions and determines the operating mode in conjunction with the hourly heating and cooling load of the building. Within each calculation period, the system determines the operating mode based on the building's heating and cooling load and performs group control scheduling and load allocation for the heat pump units. By comparing the operating performance of each unit, the number of units put into operation and the partial load rate are determined, so that the system can meet the load demand while maintaining high operating efficiency. At the same time, the circulating water pumps are coordinated and adjusted according to the system flow demand. By dynamically determining the number of operating water pumps and their operating status, the coordinated operation of the heat pump system and the distribution system is achieved. After completing the equipment operating status calculation, the system summarizes the operating parameters of the heat pump units, the water pump system, and the overall system, and outputs results such as energy consumption, operating costs, and carbon emissions as the basis for system performance evaluation and optimization analysis.

[0063] Step S31, Heat Source Identification and Operation Mode Selection Stage: Based on the comparison results of tunnel air and outdoor air temperatures, the system determines the quality of the heat source and selects between tunnel source mode, air source mode, and dual heat source coupling mode.

[0064] To achieve optimal heat source selection and switching control, this invention first defines a temperature difference discrimination function between tunnel air and outdoor air:

[0065] in, The temperature of the air in the tunnel. The outdoor air temperature is used to characterize the instantaneous thermodynamic advantage / disadvantage relationship between two types of heat sources, and serves as a core criterion for heat source selection.

[0066] Based on this, the present invention further introduces a heat source availability evaluation function to comprehensively consider the temperature difference range and its changing trend, and its expression form is as follows:

[0067] in, As a comprehensive evaluation index for heat sources, and This is a weighting coefficient. This indicator helps avoid frequent switching issues caused by relying solely on instantaneous temperature differences, thus improving the stability of operational decisions.

[0068] Based on the above discriminant function and evaluation index, the system selects its operating mode according to hierarchical decision-making rules: when and When the system prioritizes tunnel source operation mode, it will use tunnel source operation mode first. At that time, a coordinated operation mode of tunnel air and outdoor air is adopted; when and At that time, the system switches to air source operation mode, in which and The threshold parameter is used for operation.

[0069] To avoid frequent switching of operating modes in the critical range, this invention introduces a hysteresis control mechanism, which sets an upper and lower switching threshold difference during the heat source switching process. This ensures that the system must meet stricter reverse conditions after entering a certain operating mode before it is allowed to exit, thereby improving the continuity and stability of system operation.

[0070] Step S32, Heat Pump Unit Group Control and Scheduling Stage: Based on the building load demand, start-up and shutdown control and operation sequence of multiple heat pump units are performed, and units with higher energy efficiency are given priority for operation.

[0071] After determining the operating mode, the system enters the heat pump unit group control and scheduling phase. First, based on the performance curves of each heat pump unit under the current operating conditions, its unit load energy consumption index is calculated:

[0072] in, Indicates the first The unit energy consumption level of the generator set at the current moment. For input power, This refers to the cooling / heating output. Based on this indicator, the system ranks all operable units and prioritizes the use of units with lower unit energy consumption to reduce the overall energy consumption of the system.

[0073] Step S33, Load Distribution and System Coordination Control Stage: Load is distributed to the operating units to ensure they operate within a reasonable load range. Simultaneously, the operating status of the water pumps is adjusted to match the flow rate with the system load, achieving coordinated operation between the distribution system and the heat pump system.

[0074] During load allocation, the system dynamically allocates power to each unit proportionally while meeting the total building load demand. The expression for this allocation is:

[0075] in, The total heating and cooling load of the building, For the first The unit bears the load. This is the load allocation factor. This factor is dynamically adjusted based on the real-time performance of the generating units and the characteristics of the partial load to ensure that the entire system operates within its efficient range.

[0076] Meanwhile, this invention constrains and controls the partial load factor of the unit, which is defined as:

[0077] in, For the first Taiwan unit partial load factor, This is the rated cooling (heating) capacity. The system's operating range is constrained as follows:

[0078] when When the constraints are exceeded, dynamic correction is achieved by adjusting the number of units starting and stopping and the load redistribution, thereby avoiding the problems of decreased efficiency at low loads and overload operation at high loads.

[0079] In terms of coordinated control of water systems, this invention adopts a load-flow coupling control strategy to maintain a linear matching relationship between circulating water flow and system load:

[0080] in, This represents the system flow matching coefficient. Further considering the variable operating conditions, a pump efficiency correction function is introduced:

[0081] It is used to reflect the efficiency variation of water pumps under partial load conditions, thereby enabling precise calculation and optimized operation of water pump energy consumption.

[0082] Ultimately, this invention achieves closed-loop optimized operation of the system on an hourly scale by constructing a three-level control system of "heat source selection - unit scheduling - water system coordination". This enables a dynamic coupling relationship between heat source selection, load allocation and transmission and distribution system, thereby significantly improving the overall energy efficiency and operational stability of the system.

[0083] Step S40, Full Lifecycle Optimization Configuration: The fourth phase aims to establish a full life-cycle optimization configuration model for waste heat recovery heat pump energy supply systems in subway tunnels, and to achieve synergistic optimization among system equipment selection, capacity matching and operation strategies, thereby achieving overall optimal economy and energy efficiency while ensuring energy supply reliability.

[0084] like Figure 4 As shown, this invention provides an optimization configuration method for the proposed system. First, equipment configuration schemes are generated based on a heat pump equipment library, a water pump equipment library, building load data, and heat source data, and the constraints on the number of configurations and system capacity are checked. For configuration schemes that meet the constraints, the system's hourly operation model is invoked to perform dynamic annual operation calculations, obtaining results such as equipment operating status, energy consumption, and operating costs. Subsequently, the system's lifecycle cost is calculated by combining the initial investment cost of the equipment, maintenance costs, and discount factors. Finally, configuration schemes that meet the equipment operation constraints and energy balance constraints are comprehensively evaluated, and the optimal configuration scheme with the lowest lifecycle cost is selected, outputting the system optimization design results.

[0085] During the system design phase, this invention uses the heat pump unit capacity configuration, water pump selection parameters, system structural parameters, and operation strategy-related parameters as optimization variables to construct a multi-dimensional decision vector:

[0086] in, This indicates the capacity configuration of the heat pump unit. This indicates the configuration parameters of the water pump system. Represents the set of system structure parameters. This represents the set of parameters for the operational strategy. This optimization problem is essentially a typical "design-operation integrated" hybrid optimization problem, characterized by strong coupling, multiple constraints, and nonlinearity.

[0087] The optimization objective function is to minimize the system's life cycle cost (LCC), which can be expressed as follows:

[0088] in, This indicates the initial investment cost, including equipment purchase costs, installation costs, and system construction costs; Indicates the first The annual system operating energy consumption cost mainly includes the power consumption cost of the heat pump unit and the energy consumption cost of water pump transmission and distribution. This indicates the system maintenance and repair costs; The discount rate; Design the system's lifespan.

[0089] Regarding constraints, this invention takes into account the following constraints: (1) Building heating and cooling load balance constraints:

[0090] (2) Equipment operating capacity constraints:

[0091] (3) Heat pump partial load factor constraint:

[0092] (4) Flow constraints of the water system:

[0093] The above multiple constraints ensure that the optimization results are feasible and stable in engineering practice.

[0094] In terms of optimization methods, this invention adopts an iterative solution strategy based on intelligent optimization algorithms, such as genetic algorithms or particle swarm optimization algorithms, to perform global search and local convergence optimization on the design variables. The optimization process is based on the simulation results of the system running hourly throughout the year. Through 8760 hours of dynamic simulation calculations, the energy consumption and cost performance under different design schemes are calculated, thereby achieving the global optimal selection of the design scheme.

[0095] The final optimization result can be expressed as:

[0096] in, This is the optimal system configuration scheme.

[0097] Through the above optimization process, this invention achieves integrated collaborative optimization design from "equipment selection - capacity configuration - operation strategy", which enables the system to significantly reduce the total life cycle cost while meeting the building's heating and cooling load requirements, and improve the system's economy, operational stability and engineering applicability, especially showing better comprehensive performance advantages under long-term operation conditions.

[0098] The specific steps for dynamic system operation and optimization are as follows: Step S41, Hourly Operation Calculation Stage: The system takes hourly load and environmental data as input and performs dynamic operation calculations throughout the year to obtain the equipment operating status and energy consumption data at each time point.

[0099] Step S42, Dynamic Adjustment Stage of Operation Strategy: During operation, the heat source selection, unit scheduling and water system operation strategies are dynamically optimized and adjusted based on real-time data and forecast results.

[0100] Step S43, System Performance Evaluation and Optimization Stage: Based on the annual operation results, a comprehensive evaluation of the system's energy efficiency, operating costs, and carbon emissions is conducted to provide a basis for system optimization design and operational strategy improvement.

[0101] To quantify and verify the "cross-regional applicability" of the system, Figure 5 The performance of the dual heat source system and the benchmark air source system in typical cities in different climate zones is compared.

[0102] like Figure 5 As shown, the subplot unfolds from three dimensions: LCC, energy consumption, and seasonal energy efficiency ratio (HSPF / SEER). Figure 5 a (LCC Distribution): In severely cold / cold regions (Harbin, Beijing), the LCC of dual-heat-source systems is significantly lower than the baseline system (the proportion of green "operating costs" decreases), due to longer winter heating loads and significant efficiency improvements from tunnel heat sources; in temperate regions (Wuhan, Chengdu), LCC optimization mainly comes from reduced initial investment (the proportion of blue "initial investment" decreases). Figure 5 As shown, the reduction in initial investment cost is mainly attributed to the optimization of system capacity configuration. Since tunnel air provides an effective temperature buffer during the cooling season, it reduces the design load under extreme high-temperature conditions, thereby directly reducing the system's installed capacity. This makes the initial investment level of the dual-heat-source scheme in most cities roughly equivalent to or even slightly lower than the benchmark scheme.

[0103] Figure 5 b (Annual Energy Consumption Distribution): During the heating season (northern cities), the energy consumption of the dual heat source system decreased by 20% to 40%, while during the cooling season (southern cities), the decrease was less than 10%, verifying the conclusion that "tunnel heat sources are more valuable in winter".

[0104] Figure 5 c (HSPF distribution during the heating season): HSPF increases by 15%~30% in northern cities (tunnel air increases evaporation temperature and COP increases), while it increases by less than 5% in southern cities (outdoor temperature is sufficient to meet evaporation requirements).

[0105] Figure 5 d (SEER distribution during the cooling season): The increase in SEER in both the north and south is less than 10%, because the outdoor air temperature fluctuates little in summer, and the improvement of heat dissipation on the condensing side by tunnel air is limited.

[0106] Figure 5 Through quantitative comparisons based on "climate zones and multiple dimensions," the invention's dual advantages of "energy saving and economy" in different regions have been verified, making it particularly suitable for northern cities with high heating demands.

[0107] The above results are mainly related to the optimization of system capacity configuration and the temperature buffering effect provided by tunnel air during the cooling season. By reducing the design load under extreme operating conditions, the system installed capacity can be reduced. In terms of life cycle cost, the dual heat source scheme shows significant advantages in severe cold and cold regions. Its LCC reduction mainly comes from the significant reduction in energy consumption during operation, especially in areas with a high proportion of heating load and long operating time, where the system's energy-saving benefits are more prominent. As the climate changes from north to south, heating demand gradually weakens, the energy-saving contribution during operation decreases, and the improvement in LCC decreases accordingly. In some temperate climate areas, the improvement in system economy mainly comes from the optimization of initial investment, and the overall benefits are relatively limited.

[0108] From an operational energy consumption perspective, the dual-heat-source system exhibits significant energy-saving effects during the heating season. This is because tunnel air, acting as an auxiliary heat source, increases the temperature level on the evaporator side of the heat pump, improving heat exchange conditions in low-temperature environments, thereby significantly enhancing heating efficiency and reducing energy consumption. However, during the cooling season, due to relatively gradual changes in outdoor ambient temperature, the improvement effect of tunnel air on the condenser side temperature is limited, resulting in a smaller overall energy saving. Corresponding performance indicators also show the same pattern: significant performance improvement during the heating season, but limited improvement during the cooling season. In summary, the dual-heat-source energy supply system proposed in this invention demonstrates good adaptability to different climatic conditions. In areas with high heating demand, it can fully utilize the advantages of tunnel waste heat resources, achieving a significant improvement in system energy efficiency and economy. In areas with weaker heating demand, its optimization effect is relatively smaller, but it still has certain application value.

[0109] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An operation method for a waste heat recovery heat pump energy supply system based on subway tunnel air, characterized in that, It includes four stages: system structure construction, tunnel thermal environment perception and prediction, multi-heat source collaborative operation control, and full life cycle optimization configuration; The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air is as follows: Step S10: Construct the system structure: The ventilation shaft structure guides the air from the subway tunnel to the heat exchange area, and the outdoor unit of the air source heat pump unit is placed in the ventilation shaft so that it can directly exchange heat with the tunnel air. Step S20, Tunnel Thermal Environment Sensing and Prediction: A tunnel air temperature prediction model is constructed based on multi-source input features to achieve dynamic prediction of tunnel temperature. In step S20, the multi-source input features include external meteorological disturbances, thermal inertia effects inside the tunnel, train operation disturbances, pedestrian flow disturbances, and time-periodic changes, which are used to comprehensively characterize the dynamic change process of tunnel air temperature. The specific steps of tunnel thermal environment perception and prediction in step S20 include: Step S21, Construction of tunnel temperature lag characteristics: Construct short-term lag characteristics and cross-day lag characteristics; Step S22, Construction of external disturbance characteristics: Construct meteorological lag characteristics and formation temperature characteristics; Step S23, Construction of internal disturbance characteristics: Construct characteristic variables of train operation and passenger flow activities; Step S24, Time Feature Encoding and Feature Assembly: Encode the hour and day of the week information and integrate them to form the input feature vector; Step S25, Model Training: Based on historical data, establish a mapping relationship between input features and tunnel temperature; Step S26, Temperature Prediction and Application: Use the trained model to predict the tunnel temperature at future moments, and use the prediction results as the feedforward input for system operation control; The tunnel temperature prediction model in the tunnel thermal environment perception and prediction adopts a nonlinear regression model based on random forest. By training on multi-source input features, it realizes the prediction of the future state of tunnel air temperature and is used for heat source availability discrimination and operation control feedforward input. The multi-heat source collaborative operation control stage optimizes heat sources based on the difference between tunnel air temperature and outdoor air temperature, and constructs a comprehensive evaluation index in conjunction with the temperature difference change trend to achieve adaptive switching between tunnel source, air source and dual heat source coupling mode. Step S30, Multi-heat source coordinated operation control: Heat source selection and operation mode switching are performed based on the air conditions in the tunnel and the outside air, and the heat pump unit and water system are coordinated and scheduled. In step S30, the multi-heat source coordinated operation control specifically includes: Step S31, Heat source identification and operation mode selection: Based on the comparison results of tunnel air and outdoor air temperature, the system judges the quality of heat source and adaptively switches between tunnel source mode, air source mode and dual heat source coupling mode. Step S32, Heat pump unit group control and scheduling: Based on the building load demand, control the start-up and shutdown of multiple heat pump units and sort their operation, giving priority to units with higher energy efficiency. Step S33, Load Distribution and System Coordination Control: Distribute the load of the operating units to ensure they operate within a reasonable load range, and adjust the operating status of the water pumps to match the circulating water flow with the system load, thereby achieving coordinated operation of the distribution system and the heat pump system. Introducing a hysteresis control mechanism during heat source switching, and setting different switching threshold ranges, avoids frequent switching of the system under critical conditions, thereby improving operational stability; The multi-heat source coordinated operation control stage further includes heat pump unit group control scheduling, which prioritizes scheduling units with higher energy efficiency to participate in operation by sorting the unit energy consumption of each unit, and dynamically allocates the load of each unit according to the building load. By setting a partial load rate constraint range for the operation of heat pump units and adjusting the number of units starting and stopping and the load distribution, the units can be operated in the high-efficiency range, thereby improving the overall energy efficiency of the system and reducing equipment wear. Step S40, Full Lifecycle Optimization Configuration: With the goal of minimizing system lifecycle costs, equipment selection and operation strategies are collaboratively optimized, specifically including: Step S41, Hourly Operation Calculation: Using hourly load and environmental data as input, perform dynamic operation calculations throughout the year to obtain the equipment operating status and energy consumption data at each time point; Step S42: Dynamic adjustment of operation strategy: Based on real-time data and forecast results, dynamically optimize and adjust the heat source selection, unit scheduling and water system operation strategy; Step S43, System Performance Evaluation and Optimization: Based on the annual operation results, conduct a comprehensive evaluation of the system's energy efficiency, operating costs, and carbon emissions; The phase of the full life cycle optimization configuration takes the minimum life cycle cost as the optimization objective, and uses heat pump capacity configuration, water pump selection parameters, system structural parameters and operation strategy parameters as optimization variables to construct an integrated design and operation optimization model, and solves it under the condition of meeting building load requirements, equipment operating capacity and system operating constraints.

2. The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air according to claim 1, characterized in that, In step S10, the system structure includes a subway tunnel, a ventilation shaft structure, an air source heat pump unit, a water system, a building load-side system, and a control system; wherein: The subway tunnel is used to provide low-grade waste heat air; The ventilation shaft structure is used to organize the airflow in the tunnel and form a stable heat exchange channel; The air source heat pump unit achieves cooling or heating operation by exchanging heat with the tunnel air. The water system is used for the distribution of cold and heat; The control system is used to realize heat source switching and coordinated operation of equipment; Step S10 further includes: Step S11: System basic data collection: Collect tunnel thermal environment data, outdoor meteorological data, train operation data, pedestrian disturbance data, soil temperature data, time characteristic data, and building load data; Step S12, Data Preprocessing: Time alignment of multi-source data is performed using a unified timestamp, abnormal data is identified and removed, missing data is interpolated and completed, and normalization or standardization is performed.

3. The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air according to claim 2, characterized in that, The ventilation shaft structure is coupled with the existing ventilation system of the subway. By optimizing the airflow organization path, the tunnel air forms a stable flow before entering the heat exchange area and maintains a uniform velocity distribution during the heat exchange process, thereby improving heat exchange efficiency and reducing airflow short-circuiting and local stagnation.

4. The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air according to claim 2, characterized in that, The water system adopts a variable operating condition control strategy in coordination with the heat pump unit. It dynamically adjusts the circulating water flow rate according to the system load to maintain a matching relationship with the load demand, and optimizes the energy consumption of the water pump through efficiency correction.

5. The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air according to claim 1, characterized in that, The external weather disturbances include the current outdoor temperature, the outdoor temperature several hours ago, and the outdoor temperature at the same time several days ago, which are used to characterize the immediate and delayed effects of the weather disturbances. The internal thermal inertia effect of the tunnel includes the current tunnel temperature and its short-term lag and cross-day periodic lag characteristics, which are used to reflect the influence of tunnel thermal inertia and historical state on the current temperature.

6. The operation method of the waste heat recovery heat pump energy supply system based on subway tunnel air according to claim 1, characterized in that, The train operation disturbances include departure intervals, the number of trains in operation, and their lag characteristics; The pedestrian disturbance includes pedestrian intensity and its hysteresis characteristics, which are used to characterize the nonlinear impact of operational disturbance on the tunnel thermal environment.

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