Cooperative heating control method and system for ground source heat, air energy and photovoltaic energy in cold region

Abstract

The invention relates to the technical field of heating data processing, and discloses a cold region ground source heat, air energy and photovoltaic cooperative heating control method and system.The cold region ground source heat, air energy and photovoltaic cooperative heating control method comprises the steps that the tolerable temperature drop threshold value of residents and the defrosting period of an air source heat pump serve as a multi-source state parameter set of a target region; and obtaining a thermal inertia time constant of a residential building in the target area, converting the residential building into a virtual energy storage body based on the thermal inertia time constant and the multi-source state parameter set, and calculating the thermal inertia time constant of the residential building according to the photovoltaic power supply fluctuation of the target area and the schedulable thermal capacity value of the virtual energy storage body. The method comprises the steps that a target area is obtained, a virtual heat price signal of the target area is generated, a load distribution instruction of heating equipment in the target area is regulated and controlled according to the virtual heat price signal, and an execution result of the load distribution instruction is synchronously obtained. And low-efficiency operation of equipment and even heating interruption are easily caused.

Description

Technical Field

[0001] This invention relates to the field of heating data processing technology, and in particular to a method and system for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions. Background Technology

[0002] In the heating sector of cold regions, existing geothermal, air source, and photovoltaic combined heating systems generally lack a dynamic integration mechanism for resident comfort and equipment operating characteristics. Traditional technologies rely heavily on fixed parameter control, failing to incorporate residents' tolerable temperature drop thresholds into the core control logic. This leads to a disconnect between heating load allocation and actual user needs, frequently resulting in overheating or temperature drops exceeding tolerable ranges, impacting both living experience and energy waste. Furthermore, the defrosting cycle of air source heat pumps, a critical operating parameter, is often statically set in existing control methods, failing to link with building thermal characteristics, easily causing inefficient equipment operation or even heating interruptions.

[0003] Furthermore, existing technologies do not adequately explore the thermal energy storage potential of buildings and lack virtual energy storage models, making it difficult to cope with the volatility of photovoltaic power supply. Photovoltaic power supply is significantly affected by weather, and traditional control methods lack dynamic adjustment mechanisms based on power supply fluctuations and the building's dispatchable heat capacity. They also cannot guide load optimization through heat price signals, resulting in low efficiency of multi-energy synergy and frequent mismatches between energy supply and demand. This reduces the utilization rate of renewable energy and increases the operating costs of the heating system. Summary of the Invention

[0004] This invention provides a method and system for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for coordinated heating control of ground source heat, air energy, and photovoltaic power in cold regions, the method comprising:

[0006] S1. The tolerable temperature drop threshold for residents and the defrosting cycle of the air source heat pump are used as the multi-source state parameter set for the target area;

[0007] S2. Obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set;

[0008] S3. Generate a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage body;

[0009] S4. Adjust the load distribution command of the heating equipment in the target area according to the virtual heat price signal, and simultaneously obtain the execution result of the load distribution command.

[0010] Preferably, the step of using the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump as the multi-source state parameter set for the target area includes:

[0011] Collect questionnaire survey information from residents in the target area, and separate the residents' tolerable temperature drop threshold from the questionnaire survey information;

[0012] Collect real-time operation records of air source heat pumps in the target area, and analyze the defrosting cycle of the air source heat pumps in the real-time operation records;

[0013] The defrosting cycle and the tolerable temperature drop threshold are packaged into a multi-source state parameter set for the target region.

[0014] Preferably, the step of packaging the defrosting cycle and the tolerable temperature drop threshold into a multi-source state parameter set for the target region includes:

[0015] Based on historical survey data of the target area, outliers in the defrosting cycle and the tolerable temperature drop threshold are removed;

[0016] The results after elimination are integrated into a multi-source state parameter set for the target region.

[0017] Preferably, obtaining the thermal inertia time constant of residential buildings in the target area includes:

[0018] The thermal performance parameters of the building envelope in the aforementioned residential buildings were investigated.

[0019] Based on the aforementioned thermal performance parameters, a degradation test environment for the building envelope under standard operating conditions was constructed.

[0020] Plot the time-domain curve of temperature decay in the decay test environment;

[0021] The characteristic decay rate of the time-domain curve is calibrated as the thermal inertia time constant of the residential building.

[0022] Preferably, the step of converting the residential building into a virtual energy storage body based on the thermal inertia time constant and the multi-source state parameter set includes:

[0023] The thermal decay phase transition characteristics in the thermal inertia time constant are analyzed, and the thermal capacity spectrum of the residential building is constructed using the thermal decay phase transition characteristics.

[0024] The tolerable temperature drop threshold is converted into a temperature drop constraint of the heat capacity spectrum;

[0025] The release control window of the heat capacity spectrum is divided using the time-domain characteristics of the defrosting cycle;

[0026] The virtual energy storage body of the residential building is constructed based on the temperature drop constraint and the release control window.

[0027] Preferably, generating a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage includes:

[0028] Extract the period of irradiance decrease during the photovoltaic power supply fluctuation;

[0029] The photovoltaic fluctuation trough value during the irradiance decline period is matched with the dispatchable heat capacity value of the virtual energy storage to generate the heat load supply gap index of the target area.

[0030] The heat load supply gap index and the gradient change during the irradiance decline period are coupled and analyzed to generate a real-time updated value of the heat price in the target area;

[0031] The real-time updated heat price value is converted into a virtual heat price signal for the target region.

[0032] Preferably, the formula for calculating the real-time updated value of the heat price is:

[0033]

[0034] in: For real-time updates of heat prices, As the benchmark value for heat prices, The system response gain coefficient. For the gap direction function, This refers to the gradient change during the irradiation decrease period. The heat load supply gap index is the index. For time period, is the damping coefficient.

[0035] Preferably, the step of adjusting the load distribution command of the heating equipment in the target area according to the virtual heat price signal includes:

[0036] The stage of demand-supply imbalance in the target region is determined based on the time variation characteristics of the virtual heat price signal;

[0037] Based on the imbalance stage, the equipment load of the heating equipment is prioritized to obtain the load priority of the heating equipment;

[0038] The load allocation instruction for the target area is generated based on the load priority and the tolerable temperature drop threshold.

[0039] Preferably, the step of synchronously acquiring the execution result of the load allocation instruction includes:

[0040] Extract real-time information of the target region during the execution of the load allocation instruction.

[0041] The areas with abnormal temperature differences in the real-time information are marked as temperature anomaly points;

[0042] The temperature anomaly point is superimposed with the heat conduction path of the virtual energy storage body to obtain the predicted heat loss trajectory of the target area.

[0043] Monitor the energy loss within the predicted heat loss trajectory range and generate the execution result of the load allocation command.

[0044] A coordinated heating control system for geothermal energy, air energy, and photovoltaic power in cold regions, the system comprising:

[0045] The data acquisition module is used to collect the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump as a set of multi-source state parameters for the target area.

[0046] The virtual construction module is used to obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set.

[0047] Virtual signal generation module. Used to generate a virtual heat price signal for the target area based on the photovoltaic power supply fluctuations in the target area and the schedulable heat capacity of the virtual energy storage body;

[0048] The execution module is used to adjust the load distribution instructions of the heating equipment in the target area according to the virtual heat price signal, and to simultaneously obtain the execution results of the load distribution instructions.

[0049] Beneficial effects

[0050] 1. By integrating the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump into a multi-source state parameter set, precise matching of heating control with user needs and equipment characteristics is achieved. Based on the thermal inertia time constant, residential buildings are transformed into virtual energy storage bodies, fully tapping the building's thermal capacity potential. This allows for heat storage when photovoltaic power supply is sufficient, and for maintaining stable indoor temperatures by rationally releasing heat during power fluctuations. This effectively avoids the problem of sudden temperature rises and falls caused by supply and demand imbalances in traditional systems, thus improving living comfort. On the other hand, by eliminating abnormal data and optimizing the parameter set, the accuracy of the control logic is ensured, ineffective heating energy consumption is reduced, and system operating costs are lowered.

[0051] 2. The virtual heat price signal provides a dynamic control basis for multi-energy synergy, significantly improving energy utilization efficiency. By coupling photovoltaic power supply fluctuations with the dispatchable heat capacity of the virtual energy storage, the heat price signal can reflect the supply and demand relationship of heat load in real time, guiding heating equipment to prioritize the use of clean energy when photovoltaic power supply is sufficient, and to rationally allocate ground source heat and air source heat loads when power supply is insufficient, reducing dependence on traditional energy sources. At the same time, the instruction allocation mechanism based on load priority and temperature drop threshold ensures that heating equipment can still operate stably during special periods such as defrosting cycles, avoiding equipment inefficiency or interruption problems, further enhancing the reliability and economy of the system. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for controlling the synergistic heating of ground source heat, air energy, and photovoltaic power in cold regions, as provided in an embodiment of the present invention.

[0053] Figure 2 A functional block diagram of a synergistic heating control system for geothermal energy, air energy and photovoltaic power in cold regions, provided in an embodiment of the present invention;

[0054] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] This application provides a method for coordinated heating control of ground source heat, air source heat pumps, and photovoltaics in cold regions. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for coordinated heating control of ground source heat, air source heat pumps, and photovoltaics in cold regions can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0057] Reference Figure 1 The diagram shown is a flowchart illustrating a method for coordinated heating control of ground source heat, air energy, and photovoltaic power in cold regions, according to an embodiment of the present invention. In this embodiment, the method for coordinated heating control of ground source heat, air energy, and photovoltaic power in cold regions includes:

[0058] S1. The residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump are used as the multi-source state parameter set for the target area.

[0059] In this embodiment, using the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump as the multi-source state parameter set for the target area includes:

[0060] Collect questionnaire survey information from residents in the target area, and separate the residents' tolerable temperature drop threshold from the questionnaire survey information;

[0061] Collect real-time operation records of air source heat pumps in the target area, and analyze the defrosting cycle of the air source heat pumps in the real-time operation records;

[0062] The defrosting cycle and the tolerable temperature drop threshold are packaged into a multi-source state parameter set for the target region.

[0063] Specifically, the tolerable temperature drop threshold for residents is the maximum decrease in indoor temperature that residents in cold regions, such as northern cities where outdoor temperatures are consistently below 0°C in winter, can accept while maintaining basic comfort. For example, a drop from 22°C to 19°C would have a temperature drop threshold of 3°C.

[0064] In cold regions, winter heating requires maintaining a stable indoor temperature, but excessive heating wastes energy. This parameter directly reflects the user's tolerance limit for temperature fluctuations. For example, in extremely cold regions, residents' tolerance threshold for temperature drop is usually low, such as 2°C, while in cold regions, the threshold may be relaxed to 3-4°C.

[0065] The defrost cycle of an air source heat pump is the time interval at which the defrost mode needs to be activated periodically when the evaporator surface of the air source heat pump in cold regions is frosted due to low temperature during heating operation. For example, defrosting is performed once every 60 minutes, with a cycle of 60 minutes.

[0066] When the outdoor temperature is below 5℃ and the humidity is high, the heat pump evaporator is prone to frosting, which affects the heating efficiency. In northern winters, the defrosting cycle will dynamically change with the outdoor temperature and humidity. For example, when the temperature is -10℃ and the humidity is 80%, the defrosting cycle may be shortened to 40 minutes.

[0067] The multi-source state parameter set is a comprehensive set of parameters that integrates the residential tolerable temperature drop threshold and the defrosting cycle of the air source heat pump, serving as the basic input data for subsequent heating control.

[0068] In multi-energy coordinated heating scenarios in cold regions, this parameter set is simultaneously linked to user needs, temperature drop thresholds, and equipment characteristics, providing data support for balancing comfort and energy efficiency.

[0069] In detail, the survey was conducted through offline questionnaires or online platforms, targeting specific regions, such as a community in Northeast China. The questionnaires focused on how much the indoor temperature drops in winter would cause discomfort.

[0070] After collecting the questionnaires, data cleaning tools, such as Excel's filtering function, were used to remove invalid samples and extract valid temperature drop threshold data. For example, the average tolerable temperature drop threshold for this region was found to be 2.5℃.

[0071] Residents in cold regions are highly sensitive to temperature, so the survey needs to be designed with options that take into account local climate characteristics, such as setting up gradient options of 1-5℃, to ensure that the data matches actual needs.

[0072] Secondly, the system collects real-time operating parameters of the equipment, including heating duration, defrost start time, and outdoor temperature, through the sensor data interface of the heat pump controller, such as the RS485 protocol, and stores them in a local database. Data analysis software, such as Python, is then used to perform time-series analysis on the records, identifying the time interval between two consecutive defrost starts and calculating the average defrost cycle. For example, data from three consecutive days yields an average cycle of 55 minutes.

[0073] In snowy or windy weather, the defrosting cycle will be shortened, requiring an increased data collection frequency, such as collecting data every 5 minutes, to ensure that the analysis results reflect the equipment status in real-time conditions.

[0074] Finally, outliers were removed from the analyzed defrosting cycle and temperature drop threshold. For example, based on the historical data of the region over the past three years, extreme values ​​that deviated from the mean by three times the standard deviation were removed. Then, the two types of parameters were integrated into a structured dataset using a data formatting tool.

[0075] In cold regions, the winter climate fluctuates greatly. When a cold wave strikes, the parameter set needs to be updated regularly to ensure that it matches the real-time environment and provides accurate input for subsequent control logic.

[0076] In this embodiment, packaging the defrosting cycle and the tolerable temperature drop threshold into a multi-source state parameter set for the target region includes:

[0077] Based on historical survey data of the target area, outliers in the defrosting cycle and the tolerable temperature drop threshold are removed;

[0078] The results after elimination are integrated into a multi-source state parameter set for the target region.

[0079] Specifically, we first collect long-term operation records of air source heat pumps in the target cold region, including defrosting cycle data under different outdoor temperature and humidity conditions, as well as historical data on residents' heating comfort surveys.

[0080] For example, the defrosting cycle time series data under different weather conditions in winter for several consecutive years can be exported through the heat pump controller backend; data on residents' feelings about temperature drop can be extracted from the community heating feedback platform and past questionnaire archives.

[0081] Next, data analysis tools, such as Python's Pandas library and Excel's data analysis functions, are used in conjunction with statistical methods, such as the 3σ principle, which identifies data that deviates from the mean by three times the standard deviation as abnormal, to filter the defrosting cycle and tolerable temperature drop threshold data.

[0082] For example, for defrosting cycles, calculate the historical data mean and standard deviation, and identify and remove data that deviates significantly from the normal range, such as extremely short or long defrosting cycles temporarily caused by equipment failure, or extreme temperature drop thresholds caused by the special preferences of individual residents.

[0083] In detail, in the physical environment of cold regions, the impact of different seasons and weather on the data must be considered to ensure that the data removed are true anomalies and that data that reflects normal climate, equipment and residents' habits are retained.

[0084] Winter climates in cold regions are highly variable. Historical data includes data on varying degrees of cold, such as extreme cold weather of -20°C, relatively mild weather of -5°C, and different humidity levels, such as high humidity on snowy days and low humidity on sunny days.

[0085] These environmental factors affect the defrosting cycle and residents' perception of temperature drop. By eliminating outliers based on these factors, the subsequent multi-source state parameter set can better match the equipment characteristics and user needs in actual cold environments.

[0086] Furthermore, after removing outliers, the effective data on defrosting cycles in this cold region were filtered to represent the defrosting patterns of air source heat pumps under normal cold conditions. This included effective data on reasonable defrosting cycles and tolerable temperature drop thresholds corresponding to different temperatures and humidity levels, reflecting the temperature drop range that local residents can accept under normal cold climate conditions. It covered the reasonable threshold situations for residents on different floors and in different apartment types, and was processed in a unified data format.

[0087] For example, a structured dataset can be constructed, with fields containing parameter names, values, and corresponding environmental condition descriptions, and then organized and combined. Database software can be used to create data tables, using defrosting cycles and tolerable temperature drop thresholds as different fields, and associating them with corresponding environmental descriptions, such as outdoor temperature and humidity ranges, to form a complete set of multi-source state parameters that can be called upon by subsequent heating control logic.

[0088] The integrated parameter set links the physical environment and user needs in cold regions, providing a basis for subsequent control of geothermal, air source and photovoltaic coordinated heating. This allows the control strategy to consider both equipment and residents' heating comfort in actual cold environments, achieving multi-energy coordinated adaptation to the physical environment and user needs.

[0089] S2. Obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set.

[0090] In this embodiment, obtaining the thermal inertia time constant of residential buildings in the target area includes:

[0091] The thermal performance parameters of the building envelope in the aforementioned residential buildings were investigated.

[0092] Based on the aforementioned thermal performance parameters, a degradation test environment for the building envelope under standard operating conditions was constructed.

[0093] Plot the time-domain curve of temperature decay in the decay test environment;

[0094] The characteristic decay rate of the time-domain curve is calibrated as the thermal inertia time constant of the residential building.

[0095] Specifically, the thermal inertia time constant is a characteristic parameter that reflects the lag of temperature changes in residential buildings behind external thermal disturbances in cold environments.

[0096] For example, brick-concrete structures with poor external wall insulation have low thermal inertia and their temperature fluctuates rapidly with the outside environment; while buildings with high-efficiency insulation materials and thick walls have high thermal inertia and their temperature changes slowly.

[0097] In cold regions, building thermal inertia directly affects the control strategy of heating systems. Buildings with high thermal inertia can utilize their heat storage capacity in conjunction with intermittent energy supply from geothermal and air sources. Buildings with low thermal inertia, on the other hand, need to adjust their heating power more frequently to avoid a sudden drop in room temperature.

[0098] In detail, for residential buildings in cold regions, such as a residential building in a community in Northeast China, key thermal parameters of the building envelope, including the exterior walls, roof, doors and windows, were tested one by one.

[0099] For example, a heat flow meter is used to measure the thermal conductivity of the exterior wall. During the test, a heat flow sensor is attached to the wall surface to record the heat transfer value per unit time and per unit area. An infrared thermal imager is used to scan doors and windows to test their airtightness and observe the heat leakage in the door and window gaps in the thermal image. The thickness of the insulation material, such as the exterior wall insulation layer, is tested by referring to the building design drawings or by sampling on-site. Holes are drilled to measure the actual thickness.

[0100] The following parameters are entered into the building thermal database: building envelope type (brick-concrete, reinforced concrete, prefabricated, etc.), thermal conductivity (e.g., K-value of 0.3 W / (m²・K) for exterior walls), heat capacity (the amount of heat required for a 1°C temperature change per unit volume of material), and parameters related to the density and specific heat capacity of insulation materials.

[0101] In detail, in cold regions, outdoor temperatures are extremely low in winter, and the thermal performance of the building envelope directly determines the rate of heat loss from the building. Exterior walls with high thermal conductivity and poor insulation make the building prone to heat loss, requiring a continuous heating system to replenish the heat; doors and windows with poor airtightness allow severe cold air infiltration, which will exacerbate indoor temperature fluctuations.

[0102] Investigating these parameters lays the foundation for subsequent calculations of building thermal inertia and adaptation to synergistic heating systems combining ground source heat pumps, air source heat pumps, and photovoltaics. For example, the slow energy supply characteristics of ground source heat pumps require pairing with buildings that have high thermal inertia and good insulation to stabilize room temperature.

[0103] Furthermore, based on the collected thermal parameters, a test environment for the building envelope is constructed in a laboratory or simulation software. If software simulation is used, such as EnergyPlus or DeST, parameters such as the building envelope's thermal conductivity, heat capacity, and dimensions are input, and a standard operating temperature is set to simulate a typical winter environment in a cold region.

[0104] For example, with an outdoor temperature of -15℃ and an indoor heating temperature of 20℃, the heat transfer process can be simulated for 24 hours. If physical testing is used, an environmental chamber can be built, and the enclosure structure specimen can be placed inside. The temperature, humidity, and wind speed inside the chamber can be precisely controlled to simulate a cold outdoor scenario with cold air infiltration, and the temperature changes on both sides of the specimen can be monitored.

[0105] More specifically, the standard operating conditions for cold regions need to be tailored to the actual local climate. For example, in the frigid Northeast, the average outdoor temperature is lower and the wind speed is higher. The test environment needs to simulate these extreme conditions so that the attenuation test results can truly reflect the building's thermal response in winter. Constructing this environment is to quantify the heat storage and release capacity of the building envelope in cold environments, so that subsequent thermal inertia calculations are more in line with actual heating needs.

[0106] Furthermore, during simulation or physical testing, temperature data on the inside of the building envelope is continuously collected and recorded in a time series, such as recording the temperature value every 5 minutes.

[0107] Plot a curve using time as the horizontal axis and temperature as the vertical axis, using graphing software such as Origin or Excel.

[0108] The curve shape can intuitively reflect the speed and trend of the temperature decay of the building envelope from the initial value to the outdoor low temperature under standard working conditions. If the curve drops slowly, it indicates that the thermal inertia is large; if it drops rapidly, it indicates that the thermal inertia is small.

[0109] It is obvious that the core requirement for heating in cold regions is to stabilize room temperature. The slope and inflection point of the temperature decay curve correspond to the risk of building temperature fluctuations.

[0110] For example, a rapid decline in the early stage of the curve indicates rapid heat loss in the building, suggesting the need for rapid heat replenishment via ground source heat pumps or air source heat pumps; a gradual decline in the later stage indicates that the building's thermal inertia is at play, allowing for flexible energy supply via photovoltaic + energy storage. Plotting the curve is intended to quantify the interaction between building thermal characteristics and the cold environment over time.

[0111] In this embodiment, converting the residential building into a virtual energy storage body based on the thermal inertia time constant and the multi-source state parameter set includes:

[0112] The thermal decay phase transition characteristics in the thermal inertia time constant are analyzed, and the thermal capacity spectrum of the residential building is constructed using the thermal decay phase transition characteristics.

[0113] The tolerable temperature drop threshold is converted into a temperature drop constraint of the heat capacity spectrum;

[0114] The release control window of the heat capacity spectrum is divided using the time-domain characteristics of the defrosting cycle;

[0115] The virtual energy storage body of the residential building is constructed based on the temperature drop constraint and the release control window.

[0116] Specifically, phase transition features are extracted from the temperature decay curve corresponding to the thermal inertia time constant. Mathematical tools, such as Python's Matplotlib, are used to fit the curve and identify the decay stages: for example, the initial rapid decay stage, where heat is rapidly lost from the building surface; the middle gradual decay stage, where the heat storage of the building envelope plays a role and the temperature decreases more slowly; and the final stable stage, where the building and the outside temperature approach equilibrium.

[0117] Calculate the heat capacity parameters at each stage, and combine them with the heat capacity of the building envelope and the heat storage capacity of the material per unit volume to quantify the heat that the building can store / release at different decay stages.

[0118] The thermal capacity spectrum is constructed by plotting a curve with time as the horizontal axis, corresponding to the decay period of the thermal inertia time constant, and the adjustable heat per unit area / volume of the building as the vertical axis, which intuitively presents the heat storage and release capacity of the building at different time stages.

[0119] In cold regions, buildings need to withstand low outdoor temperatures continuously during winter, and the characteristics of thermal decay phase change determine the potential of passive heat storage in buildings.

[0120] For example, buildings with a long and gradual heat decay period, such as those with good external wall insulation, high thermal inertia, and strong heat capacity, can maintain room temperature by relying on their own heat storage when the air source heat pump stops defrosting; conversely, buildings with rapid heat decay require continuous heat replenishment from the ground source heat pump.

[0121] Constructing a thermal capacity spectrum is to facilitate the subsequent matching of active energy supply from ground source heat + air source heat + photovoltaic with passive building thermal storage.

[0122] In detail, based on the tolerable temperature drop threshold, such as if residents can accept the room temperature dropping from 22°C to 19°C, the threshold is 3°C, and the temperature constraint boundary is marked on the heat capacity spectrum.

[0123] For example, if the building's thermal capacity spectrum shows that the building temperature can drop by a maximum of 5°C within 2 hours, but residents can tolerate a temperature drop of 3°C, then the temperature drop will be limited to ≤3°C in the capacity spectrum, and the upper limit of the heat that the building can release will be calculated in reverse.

[0124] In the data model, it is set that when the building temperature drops by nearly 3°C, heating replenishment is triggered, which transforms user demand into a hard constraint for the release of the building's stored heat.

[0125] Residents in cold regions are sensitive to fluctuations in room temperature. For example, in an outdoor environment of -30°C, a 3°C drop in room temperature will make them feel noticeably cold. Temperature drop constraints are directly related to heating comfort. If this constraint is not considered, the building temperature may drop beyond the threshold during defrosting by an air source heat pump, leading to resident complaints.

[0126] The purpose of converting temperature drop constraints is to ensure that the heat release of the virtual energy storage body is always within an acceptable range for users, thus balancing energy saving and comfort.

[0127] Furthermore, the temporal characteristics of the defrosting cycle are extracted. For example, an air source heat pump defrosts every 60 minutes, with the defrosting lasting for 10 minutes. In the temporal domain, this is represented by a cycle of 50 minutes of power supply + 10 minutes of shutdown for defrosting.

[0128] During the defrosting period of the defrosting cycle, the building relies on its own heat storage to maintain room temperature, and needs to call on the heat storage that can be released quickly in the heat capacity spectrum, such as the heat storage in the medium-term gradual decline phase.

[0129] During periods of energy supply, the building's heat storage can be supplemented by using ground source heat pumps or photovoltaic power to drive air source heat pumps to provide additional heat to the building.

[0130] The division of release control windows involves defining the downtime during the defrosting cycle as the mandatory release window, which requires the use of building thermal storage; and the energy supply period as the flexible control window, which allows for either supplemental heating or continued thermal storage. The time interval and thermal storage usage amount for each window are marked on the thermal capacity spectrum.

[0131] In cold regions, air source heat pumps require frequent defrosting. In low-temperature and high-humidity environments, the defrosting cycle can be as short as 40 minutes. When defrosting stops, there is no energy supply, and the building is prone to heat loss. Dividing the control window is to ensure that the release rhythm of the building's stored heat is precisely matched with the energy supply and defrosting cycle of the air source heat pump. When defrosting stops, the building's stored heat is used to maintain the room temperature; when energy is supplied, multi-energy synergy is used to supplement heat and restore the building's stored heat, forming a closed loop of energy supply-heat storage-heat release.

[0132] More specifically, a mathematical model of a virtual energy storage body is constructed by integrating the thermal capacity spectrum, temperature drop constraint boundary, and release control window.

[0133] Use algorithms, such as reinforcement learning and rule engines, to define the charging and discharging rules of the energy storage body: when in the forced release window, prioritize the use of thermal storage that does not trigger temperature drop constraints in the thermal capacity spectrum;

[0134] When in a flexible control window, combining the stable energy supply of ground source heat pumps and the intermittent energy supply of photovoltaics, select the timing for supplemental heating. For example, when there is a surplus of photovoltaic power, drive air source heat pumps to supplement the building's heat and fill the virtual energy storage body.

[0135] Outputting a virtual energy storage model involves connecting the model to the heating control system, receiving real-time energy supply status from ground source heat, air source heat, and photovoltaics, and dynamically regulating the charging and discharging of building heat storage.

[0136] Furthermore, the core challenge of multi-energy coordinated heating in cold regions is balancing intermittent energy supply, with strong photovoltaic power during the day and weak power at night; and the need for stable indoor temperatures in buildings to balance defrosting shutdowns and continuous energy consumption.

[0137] Virtual energy storage transforms buildings into flexible energy storage units through dynamic regulation of heat storage and release, enabling stable energy supply from ground source heat pumps, dynamic energy supply from air source heat pumps, and intermittent energy supply from photovoltaics to adapt to the building's thermal needs.

[0138] For example, when solar power is weak at night and air source heat pumps are used for defrosting, ground source heat pumps are used to supplement heat and store building heat to maintain room temperature; when solar power is strong during the day, solar power drives air source heat pumps to charge the building and store excess energy.

[0139] S3. Generate a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage.

[0140] In this embodiment, generating a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage includes:

[0141] Extract the period of irradiance decrease during the photovoltaic power supply fluctuation;

[0142] The photovoltaic fluctuation trough value during the irradiance decline period is matched with the dispatchable heat capacity value of the virtual energy storage to generate the heat load supply gap index of the target area.

[0143] The heat load supply gap index and the gradient change during the irradiance decline period are coupled and analyzed to generate a real-time updated value of the heat price in the target area;

[0144] The real-time updated heat price value is converted into a virtual heat price signal for the target region.

[0145] Specifically, photovoltaic power supply fluctuations are a characteristic of photovoltaic power plants in cold regions, where output power varies with sunlight and weather. For example, on sunny winter days, sunlight intensity increases from morning to evening and then decreases, resulting in a bell-shaped curve for photovoltaic power output; on cloudy or snowy days, sunlight intensity is weak and fluctuates greatly, leading to low and unstable photovoltaic power output.

[0146] In cold regions, fluctuations in photovoltaic power supply directly affect the power input for multi-energy coordinated heating. When photovoltaic power is strong, more air source heat pumps can be driven; when photovoltaic power is weak, ground source heat pumps are needed to supplement the power supply, or the heat storage of the building's virtual energy storage system can be utilized.

[0147] In detail, the dispatchable heat capacity of the virtual energy storage body refers to the heat regulation capability that the virtual energy storage body of the residential building constructed above can store / release during a specific period. For example, each square meter of the building envelope can release 2000 J / ℃ of heat during a defrosting cycle. It reflects the potential of the building's passive heat storage to regulate the heating load and is strongly correlated with the building's thermal inertia in cold regions and the residents' tolerable temperature drop threshold.

[0148] Virtual heat price signals are control instructions that transform the heat supply and demand relationship of a heating system into a price-like signal. When the heat price is high, it indicates a large heat load gap, requiring ground source heat pumps and air source heat pumps to provide full energy supply, or calling upon virtual energy storage to release heat; when the heat price is low, energy supply can be reduced, prioritizing the use of photovoltaic-driven air source heat pumps, or charging virtual energy storage.

[0149] Specifically, real-time irradiance data of photovoltaic power plants in cold regions is collected, such as using sensors on photovoltaic inverters to record irradiance intensity every 5 minutes. Data analysis software, such as Python's Pandas library, is used to identify periods of decreasing irradiance data. For example, if irradiance drops from 1000 W / m² to 200 W / m² between 12 noon and 3 pm, this is considered a period of decreasing irradiance. Similarly, if irradiance drops sharply due to cloud cover or snowfall, such as from 800 W / m² to 100 W / m² within one hour, this is also marked as a period of decreasing irradiance.

[0150] Extract and label the time intervals of all periods of irradiance decline, such as [14:00-17:00], as the basic input for subsequent analysis.

[0151] In detail, cold regions experience short hours of sunshine in winter. For example, in Northeast China, sunshine hours are only 6-8 hours in winter, and the weather is changeable, with frequent and prolonged periods of reduced irradiance. Identifying these periods is crucial for accurately capturing critical windows of weakened photovoltaic power supply. During these times, the heating system needs to switch to a synergistic mode of ground source heat + virtual energy storage heat release to address the heat load shortfall.

[0152] Furthermore, the photovoltaic fluctuation trough during the period of declining irradiance is extracted. Within the period of declining irradiance, the lowest value of photovoltaic power is found. For example, when the irradiance drops from 1000W / m² to 200W / m², the corresponding photovoltaic power drops from 500kW to 50kW, with a trough of 50kW.

[0153] Calculate the heat load gap and, based on the photovoltaic power off-peak, calculate the energy supply gap for the air source heat pump. For example, if the photovoltaic off-peak power is 50kW, the corresponding heating power that the air source heat pump can drive is 40kW. Assuming the heat pump COP=3, 40kW heating requires 13.3kW of electricity. 50kW of photovoltaic power can drive multiple heat pumps, but there is insufficient power during off-peak hours.

[0154] If the current heat load demand is 100kW, and buildings in cold regions require 0.1kW of heating per square meter, then a 1000㎡ building would require 100kW, resulting in a heat load gap of 100kW - 40kW = 60kW.

[0155] Match the dispatchable heat capacity of the virtual energy storage unit, query the building's virtual energy storage model, and calculate whether the released heat can fill the gap.

[0156] A heat load supply gap index is generated to quantify the severity of the gap. For example, the index = (heat load gap power × required maintenance time) ÷ total heat that the virtual energy storage can release. The higher the index, the larger the gap, requiring emergency supplementary heating from ground source heat pumps or air source heat pumps.

[0157] In detail, cold regions have high winter heat load demand, and room temperature needs to be maintained above 20°C. When photovoltaic irradiance decreases, the energy supply of air source heat pumps weakens, and the defrosting cycle may also have an additional impact, resulting in a high risk of heat load shortfall.

[0158] Matching the dispatchable heat capacity of the virtual energy storage body to determine whether the building's own thermal energy storage can independently provide heating is the key to multi-energy coordinated heating in cold regions. If the virtual energy storage body cannot fill the gap, the ground source heat pump must be dispatched immediately to supplement the heat and avoid a sudden drop in room temperature.

[0159] Furthermore, the gradient change during the irradiance decline period is calculated, i.e. the rate of irradiance decline. The coupling analysis involves constructing a mathematical model that multiplies / weights the heat load supply gap index with the irradiance decline gradient.

[0160] For example, the model formula is: Heat price update value = Gap index × (1 + |gradient change| / baseline gradient). Assuming the baseline gradient is 100W / (m²·h), if the gap index is 0.8 and the gradient change is -200W / (m²·h), then the heat price update value = 0.8 × (1 + 200 / 100) = 2.4.

[0161] The higher the real-time updated value of the output heat price, the more acute the contradiction between heat supply and demand, and the more powerful the energy supply regulation is required.

[0162] More specifically, cold regions experience large gradients in irradiance reduction. For example, during a blizzard, irradiance can drop from 800 W / m² to 0 within one hour, a gradient of -800 W / (m²・h), causing the heat load deficit to amplify instantaneously. Coupled analysis of gradient changes can help predict the worsening trend of the deficit in advance.

[0163] The steeper the gradient, the higher the heat price update value, requiring the ground source heat pump to be started at full load immediately, or the air source heat pump to be forced to supply energy first. Even during the defrosting cycle, the defrosting strategy needs to be adjusted to prioritize energy supply.

[0164] Furthermore, establishing a heat price signal mapping rule involves mapping real-time heat price updates to specific control commands. For example:

[0165] Heat price update value ≤ 1: Photovoltaic power supply is sufficient, the virtual energy storage body can be charged with heat, instruction = prioritize using photovoltaic to drive air source heat pump to charge the virtual energy storage body with heat;

[0166] 1 < Heat price update value ≤ 2: Heat load gap is small, instruction = start ground source heat pump auxiliary energy supply, maintain the heat release rhythm of virtual energy storage;

[0167] Heat price update value > 2: Large heat load gap, instruction = ground source heat pump and air source heat pump to operate at full load, forced virtual energy storage to release maximum heat.

[0168] The conversion to a virtual heat price signal uses communication protocols such as MQTT to package control commands into signals that can be recognized by the heating control system and send them in real time to the ground source heat pump, air source heat pump controller, and virtual energy storage management module.

[0169] Specifically, in cold regions, heating system equipment is dispersed, including ground source heat pump rooms, air source heat pump units, and building virtual energy storage. Virtual heat price signals need to be accurately delivered to each device to coordinate their operation.

[0170] For example, on a snowy night when the irradiance is 0, the heat price signal triggers the ground source heat pump to operate at full load. At the same time, it calls on the building's virtual energy storage to release heat, compensating for the energy supply gap caused by the air source heat pump shutting down for defrosting, and ensuring that the room temperature remains stable within the tolerable temperature drop threshold.

[0171] In this embodiment, the formula for calculating the real-time updated value of the heat price is:

[0172]

[0173] in: For real-time updates of heat prices, As the benchmark value for heat prices, The system response gain coefficient. For the gap direction function, This refers to the gradient change during the irradiation decrease period. The heat load supply gap index is the index. For time period, is the damping coefficient.

[0174] Specifically, It is the real-time updated value of the heat price, at any given moment. This is a dynamic control signal value that reflects the contradiction between heat supply and demand in heating systems in cold regions.

[0175] The higher the value, the more severe the heat load shortage, requiring ground source heat pumps and air source heat pumps to increase energy supply, or the use of building virtual energy storage to release heat. In cold regions, the demand for heating in winter is rigid, and fluctuations in photovoltaic power supply and shutdowns of air source heat pumps for defrosting can lead to an imbalance between heat supply and demand.

[0176] Real-time quantification of the severity of this imbalance guides regulatory decisions for multi-energy coordinated heating.

[0177] The heat price benchmark value is defined as the initial benchmark value for the heat price under ideal conditions where photovoltaic power supply is stable and there is no heat load shortage. For example, setting... (Dimensionless processing facilitates subsequent calculations) This represents a balance between heat supply and demand, requiring no additional control.

[0178] In cold regions, baseline values ​​need to be set based on local climate and building characteristics. For example, in extremely cold regions, the baseline for heat load is high. It can be adjusted appropriately, such as Relatively mild and cold regions It can be set to 1 to match a lower baseline heat requirement.

[0179] The system response gain coefficient is a sensitivity parameter for adjusting the heat price update magnitude. The larger the value, the more drastic the response of heat price to changes in heat load gap and irradiance gradient; The smaller the value, the smoother the response.

[0180] The characteristics of heating system equipment in cold regions, such as the start-up time of ground source heat pumps and the defrosting cycle of air source heat pumps, determine the heating system's performance. Values ​​can be selected. For example, a ground source heat pump may start slowly, requiring 5 minutes to reach full load. It needs to be set small, such as =0.5, to avoid frequent start-ups and shutdowns of equipment due to sudden changes in heat price signal; if the air source heat pump has a fast response, the defrosting cycle can be dynamically adjusted. It can be set to large, such as =1.2, rapid response to heat load gap.

[0181] The damping coefficient is a pre-set value, and a relatively large one is... It can enhance the damping effect, and the system suppresses the response earlier; smaller It can reduce the damping effect, allowing the system to maintain a longer high sensitivity range.

[0182] The optimal threshold point is At this time, the entire Reaching the peak corresponds to the optimal response range for the intensity of price fluctuations.

[0183] This is the gap direction function, which determines the direction of the heat load supply gap, outputting 1 (positive gap, heat needs to be added) or -1 (negative gap, heat is excessive). In heating scenarios in cold regions, the frequent fluctuations in photovoltaic power supply often lead to "insufficient energy supply." In practical applications, it is often simplified to only focus on the positive gap (output 1), but the formula retains its general applicability.

[0184] In cold regions, heat demand persists throughout winter, with a near-complete heat load deficit, necessitating additional heat supply. The output is usually 1, which means that multiple energy sources need to work together to supplement heat and match the energy supply logic of ground source heat and air source heat.

[0185] The gradient change during the irradiance decrease period is the time period. At that time, the rate of decrease in photovoltaic irradiance, the change in irradiance intensity per unit time, and the capture of sudden changes in irradiance, such as the instantaneous rate of cloud cover.

[0186] In cold regions, the weather is highly variable, with snowy and cloudy days resulting in significant variations in irradiance gradients. The steeper the gradient, the faster the photovoltaic power supply drops, leading to a greater explosive increase in the heat load gap. This necessitates a rapid response to heat price signals and the dispatch of ground-source heat pumps for emergency supplementary heating.

[0187] The heat load supply gap index is a parameter that quantifies the severity of the heat load gap, such as... This indicates that the energy gap requires 1.5 times the baseline energy supply to fill. This is calculated based on the heat load demand during off-peak solar power supply, the energy supply that can be driven by air source heat pumps, and the heat release capacity of virtual energy storage.

[0188] The thermal inertia of buildings and the threshold of temperature drop that residents can tolerate in cold regions directly affect Buildings with low thermal inertia (such as those with poor insulation) experience rapid temperature drops. Residents with a high tolerance for temperature drop are more sensitive to gaps and require [further action]. Precise calculations prevent room temperature from exceeding limits.

[0189] The time period is a time variable, representing a time slice where heat prices are updated in real time, such as calculated every minute or every 5 minutes. ;

[0190] Heating in cold regions requires real-time response. For example, if the defrosting cycle of an air source heat pump is 60 minutes, the heat price signal needs to be updated every minute to adjust the defrosting strategy.

[0191] The finer the granularity, the more accurate the heat price signal, but the greater the computational load. It is necessary to balance the dynamic changes of the physical environment with the system's computing power. It is usually set to 5 minutes to adapt to the control cycle of ground source heat pumps and air source heat pumps.

[0192] Furthermore, in cold regions during winter, when Negative increase (rapid decrease in irradiance), and When the heat load gap is large and severe, The price will first increase and then decrease, simulating the rapid response of heat prices in the initial stage of a sudden drop in irradiance.

[0193] After a continuous decline in irradiance, the growth rate of heat prices has slowed down, but they still remain high, which matches the demand for slow start-up of ground source heat pumps and dynamic adjustment of defrosting cycles of air source heat pumps.

[0194] For example, during a blizzard, the radiation level drops sharply, and the steeper the change in the radiation gradient... The heat price signal initially increases rapidly, then decays due to the exponential term, preventing the heat price from increasing indefinitely. This simulates the limited response capability of the heating system and the upper limit of the energy supply of ground source heat pumps and air source heat pumps, thus limiting the excessive amplification of the heat price signal.

[0195] Specifically, when heat prices are high, the ground source heat pump is triggered to operate at full load, the air source heat pump shortens the defrosting cycle, and the virtual energy storage body is called upon to release heat;

[0196] When heat prices are low, photovoltaic-driven air source heat pumps are preferred to heat the virtual energy storage body and extend the intermittent time of ground source heat pumps.

[0197] Furthermore, the real-time update value of the heat price The gradient change of photovoltaic power supply fluctuations was taken into account. and the heat load supply gap index Factors such as weather conditions also play a role. In cold regions, photovoltaic power generation is greatly affected by weather; for example, photovoltaic irradiance decreases rapidly on snowy or cloudy days. It can accurately capture the rate of irradiance decline, combined with It can accurately assess the severity of the heat load gap, thereby generating a heat price signal that can reflect the current heat supply and demand imbalance in the heating system in real time and with precision.

[0198] This enables the heating system to promptly and rationally allocate energy sources such as geothermal heat, air source heat pumps, and photovoltaics based on actual heat supply and demand, ensuring the stability and comfort of heating.

[0199] System response gain coefficient Adjustments can be made based on the characteristics of heating system equipment in cold regions, such as the start-up speed of ground source heat pumps and the defrosting cycle of air source heat pumps.

[0200] For example, for ground source heat pumps that start up slowly, a smaller setting can be used. To avoid excessively drastic changes in heat price signals that could lead to frequent equipment start-ups and shutdowns, thus extending equipment lifespan and reducing energy consumption; for fast-responding air source heat pumps, a larger setting can be used. This enables it to respond quickly to changes in heat load gaps.

[0201] In addition, the benchmark value of heat price It can be set according to local climate and building characteristics to adapt to the basic heat supply and demand pressure in regions with different degrees of cold.

[0202] This term enables the heat price signal to strengthen rapidly in the early stages of changes in the irradiance gradient in order to cope with sudden increases in the heat load gap. However, as irradiance continues to decrease, this term will prevent the heat price from increasing indefinitely due to the exponential decay effect.

[0203] This matches the actual energy supply limit of equipment such as ground source heat pumps and air source heat pumps in the heating system, preventing unreasonable energy dispatch due to excessive amplification of heat price signals, and ensuring the rationality of the operation of the heating system and the high efficiency of energy utilization.

[0204] Based on the above conditions, the formula can more comprehensively and holistically reflect the actual operating status of the heating system and the relationship between heat supply and demand, providing a more accurate and scientific basis for intelligent energy dispatching, which is different from the traditional simple and one-sided heat price calculation or energy dispatching signal generation methods.

[0205] S4. Adjust the load distribution command of the heating equipment in the target area according to the virtual heat price signal, and simultaneously obtain the execution result of the load distribution command.

[0206] In this embodiment, the step of adjusting the load distribution command of the heating equipment in the target area according to the virtual heat price signal includes:

[0207] The stage of demand-supply imbalance in the target region is determined based on the time variation characteristics of the virtual heat price signal;

[0208] Based on the imbalance stage, the equipment load of the heating equipment is prioritized to obtain the load priority of the heating equipment;

[0209] The load allocation instruction for the target area is generated based on the load priority and the tolerable temperature drop threshold.

[0210] Specifically, equipment load priority assigns a priority level to heating equipment such as ground source heat pumps and air source heat pumps. For example, when there is a surplus of photovoltaic power, air source heat pumps have a higher priority and are driven primarily by photovoltaic power; when defrosting and shutting down, ground source heat pumps have a higher priority and are used to supplement the power supply. The priority changes dynamically to adapt to the needs of multi-energy synergy in cold regions.

[0211] Identify imbalance features in the curve. For example, if the heat price signal is 1.5 times higher than the benchmark value for 5 consecutive minutes, it is determined that the energy supply is in a short-term imbalance stage; if the heat price signal is 0.5 times lower than the benchmark value for 5 consecutive minutes, it is determined that the energy supply is in a surplus imbalance stage. Output the time interval of the imbalance stage and mark the imbalance type.

[0212] In cold regions during winter, photovoltaic (PV) irradiance fluctuates greatly, and air source heat pumps have short defrosting cycles, easily triggering frequent imbalance phases. By analyzing the temporal changes in heat price signals, we can accurately capture the chain imbalance of sudden PV drops – reduced air source heat pump supply – heat demand > supply, or PV surplus – full air source heat pump operation – supply > demand imbalance, providing a basis for subsequent load allocation.

[0213] In cold regions, ground-source heat pumps provide stable energy but have a slow response, while air-source heat pumps have a fast response but are limited by photovoltaic / defrosting systems. Prioritizing energy allocation during imbalance phases allows ground-source heat pumps to fill the gap when defrosting stops, and air-source heat pumps to operate at full capacity when photovoltaic power is abundant, thus matching equipment characteristics with the energy demands of cold environments.

[0214] Furthermore, the heat load to be borne by each device is calculated based on the equipment load priority. For example, during periods of energy shortage, the ground source heat pump needs to bear 60% of the heat load (priority 1), the air source heat pump (non-defrosting) bears 30% (priority 2), the virtual energy storage body releases heat to bear 10% (priority 3).

[0215] The generation command is to convert the load allocation into instructions that the equipment can execute, such as turning on the ground source heat pump at 70% power and the air source heat pump at 30% power, and sending them to the equipment controller via the industrial bus.

[0216] Residents in cold regions are sensitive to temperature drops, and load distribution must strictly adhere to tolerable thresholds. If these thresholds are not validated during periods of imbalance, it could lead to a sudden drop in room temperature, triggering discomfort among residents.

[0217] Through thermal model simulation and threshold verification, the load distribution is ensured to both supplement energy and maintain comfort, thus meeting the rigid heating needs of cold regions.

[0218] In this embodiment, the step of synchronously obtaining the execution result of the load allocation instruction includes:

[0219] Extract real-time information of the target region during the execution of the load allocation instruction.

[0220] The areas with abnormal temperature differences in the real-time information are marked as temperature anomaly points;

[0221] The temperature anomaly point is superimposed with the heat conduction path of the virtual energy storage body to obtain the predicted heat loss trajectory of the target area.

[0222] Monitor the energy loss within the predicted heat loss trajectory range and generate the execution result of the load allocation command.

[0223] Specifically, load allocation commands are instructions to regulate the energy load supplied by heating equipment such as ground source heat pumps and air source heat pumps. For example, if a ground source heat pump operates at 60% power, the command is generated based on the virtual heat price signal, the imbalance stage, and equipment priority, and serves as the execution command for multi-energy coordinated heating. In cold regions, the commands need to be adapted to photovoltaic fluctuations and building thermal storage characteristics to ensure stable room temperature.

[0224] Real-time information refers to dynamic data of the target area during the execution of load distribution commands. This includes: environmental data, such as outdoor temperature, irradiance, and defrosting status of air source heat pumps; building data, such as indoor temperature distribution and the heat storage / release status of virtual energy storage systems; and equipment data, such as ground source heat pump power and air source heat pump heating capacity. This reflects the real-time operating status of heating systems in cold regions and is fundamental to evaluating the effectiveness of command execution.

[0225] Temperature anomalies are areas where indoor temperatures deviate from the tolerable temperature drop threshold. For example, if an instruction requires the room temperature to be maintained at 22°C, and a room drops to 18°C, it is marked as a temperature anomaly. In cold regions, poor insulation of the building envelope, such as air leakage through doors and windows, can easily cause localized temperature anomalies, requiring precise identification.

[0226] The heat conduction path of a virtual energy storage body refers to the channels through which heat is transferred, stored, and released within the building's virtual energy storage body, such as heat conduction from the exterior walls to the interior and from the roof to the interior. The heat conduction path determines the distribution and flow of heat stored in the building and is strongly correlated with the building's thermal inertia and the thermal performance of the building envelope in cold regions.

[0227] The heat loss prediction trajectory combines temperature anomalies with heat conduction paths to predict the direction and rate of heat loss from a building. For example, if a temperature anomaly is near a north-facing window and the heat conduction path shows heat loss from the window to the interior, it can be predicted that heat loss in that area will continue to increase, requiring adjustments to the heating command.

[0228] Energy loss is the amount of heat lost from a building per unit time due to abnormal temperatures or defects in the heat conduction path. In cold regions, energy loss directly affects heating energy consumption. When the loss is large, ground source heat pumps or air source heat pumps are needed to supplement the energy; otherwise, the room temperature will drop below the tolerable threshold.

[0229] In detail, temperature sensors, outdoor weather stations, and equipment controllers are installed in buildings in the target area. Sensor data is acquired through an Internet of Things (IoT) platform, stored in a real-time database, outliers are cleaned, and environmental, building, and equipment data are merged to generate a real-time information dataset.

[0230] In cold regions, the harsh winter environment makes sensors susceptible to interference, requiring high-frequency data acquisition and preprocessing to ensure that real-time information reflects the true heating status.

[0231] Furthermore, based on the tolerable temperature drop threshold, a temperature anomaly threshold is set. Spatial analysis algorithms, such as Python's GeoPandas, are used to identify areas where the room temperature deviates from the threshold, analyze the causes of temperature anomalies, such as querying the room's building envelope data, whether it has north-facing windows, insulation layer thickness, equipment operating status, whether the air source heat pump is defrosting, and marking the associated environment of the anomaly, such as air leakage from north-facing windows and air source heat pump defrosting.

[0232] In detail, buildings in cold regions are prone to temperature anomalies due to defects in the building envelope, such as the detachment of the external wall insulation layer. When marking temperature anomaly points, it is necessary to consider these physical environmental factors and distinguish between normal fluctuations caused by equipment control and anomalies caused by environmental defects.

[0233] Furthermore, superimposing temperature anomalies involves mapping the marked temperature anomalies onto the heat conduction path model to locate the heat conduction starting point of the anomaly.

[0234] Predicting heat loss trajectories involves combining the rate and direction of heat conduction paths to predict the heat loss trend in anomaly areas. For example, if a temperature anomaly is located at a north-facing window, and the heat conduction path shows heat loss from the window into the room while the outdoor temperature continues to decrease, then the predicted heat loss rate in that area will increase, generating a trajectory curve.

[0235] Specifically, based on the predicted heat loss trajectory, the energy loss per unit time is calculated by integration.

[0236] The effectiveness of the instruction is determined by comparing the required room temperature with the actual room temperature plus energy loss. For example, if the load distribution instruction requires a room temperature of ≥20℃, but the actual room temperature drops to 19℃ due to a large energy loss (1000J / s), then the instruction is deemed not to have been executed correctly and adjustments are needed, such as increasing the power of the ground source heat pump.

[0237] It uses a visual interface to display energy loss, room temperature changes, and command compliance status, and outputs an execution result report.

[0238] By accurately capturing environmental, building, and equipment data of the heating system, the system provides a basis for the effectiveness of command execution; it also distinguishes between equipment control fluctuations and environmental defects to avoid misjudgments; and by combining the building's heat conduction characteristics, it predicts and quantifies energy loss, allowing for advance adjustments to heating commands.

[0239] Furthermore, from generating load commands to monitoring execution results, a complete closed-loop control is formed to adapt to the dynamic needs of multi-energy coordinated heating in cold regions; through the superposition analysis of heat conduction paths and temperature anomalies, heating problems caused by defects in building envelopes and equipment cycles in cold regions can be accurately addressed.

[0240] By monitoring energy loss in real time, we can ensure that the room temperature does not fall below the tolerable threshold and improve the stability and efficiency of multi-energy coordinated heating.

[0241] like Figure 2 The diagram shown is a functional block diagram of a reference information generation system based on artificial intelligence and smart home provided in an embodiment of the present invention.

[0242] The synergistic heating control system 100 for cold regions, integrating geothermal, air, and photovoltaic power, as described in this invention, can be installed in an electronic device. Depending on the functions implemented, the synergistic heating control system 100 may include a data acquisition module 101, a virtual construction module 102, a virtual signal generation module 103, and an execution module 104. The module described in this invention can also be referred to as a unit, which is a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0243] In this embodiment, the functions of each module / unit are as follows:

[0244] Data acquisition module 101 is used to take the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump as a multi-source state parameter set for the target area;

[0245] Virtual construction module 102 is used to obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set;

[0246] Virtual signal generation module 103 is used to generate a virtual heat price signal for the target area based on the photovoltaic power supply fluctuations in the target area and the schedulable heat capacity of the virtual energy storage body;

[0247] The execution module 104 is used to adjust the load distribution instructions of the heating equipment in the target area according to the virtual heat price signal, and simultaneously obtain the execution results of the load distribution instructions.

[0248] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0249] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0250] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0251] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0252] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0253] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for coordinated heating control of ground source heat, air energy, and photovoltaic power in cold regions, characterized in that, The method includes: S1. The tolerable temperature drop threshold for residents and the defrosting cycle of the air source heat pump are used as the multi-source state parameter set for the target area; S2. Obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set; S3. Generate a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage body; S4. Adjust the load distribution command of the heating equipment in the target area according to the virtual heat price signal, and simultaneously obtain the execution result of the load distribution command.

2. The method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 1, characterized in that, The set of multi-source state parameters for the target area, which includes the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump, includes: Collect questionnaire survey information from residents in the target area, and separate the residents' tolerable temperature drop threshold from the questionnaire survey information; Collect real-time operation records of air source heat pumps in the target area, and analyze the defrosting cycle of the air source heat pumps in the real-time operation records; The defrosting cycle and the tolerable temperature drop threshold are packaged into a multi-source state parameter set for the target region.

3. The method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 2, characterized in that, The step of packaging the defrosting cycle and the tolerable temperature drop threshold into a multi-source state parameter set for the target region includes: Based on historical survey data of the target area, outliers in the defrosting cycle and the tolerable temperature drop threshold are removed; The results after elimination are integrated into a multi-source state parameter set for the target region.

4. The method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 1, characterized in that, The process of obtaining the thermal inertia time constant of residential buildings in the target area includes: The thermal performance parameters of the building envelope in the aforementioned residential buildings were investigated. Based on the aforementioned thermal performance parameters, a degradation test environment for the building envelope under standard operating conditions was constructed. Plot the time-domain curve of temperature decay in the decay test environment; The characteristic decay rate of the time-domain curve is calibrated as the thermal inertia time constant of the residential building.

5. The method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 3, characterized in that, The process of converting the residential building into a virtual energy storage body based on the thermal inertia time constant and the multi-source state parameter set includes: The thermal decay phase transition characteristics in the thermal inertia time constant are analyzed, and the thermal capacity spectrum of the residential building is constructed using the thermal decay phase transition characteristics. The tolerable temperature drop threshold is converted into a temperature drop constraint of the heat capacity spectrum; The release control window of the heat capacity spectrum is divided using the time-domain characteristics of the defrosting cycle; The virtual energy storage body of the residential building is constructed based on the temperature drop constraint and the release control window.

6. The method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 1, characterized in that, The step of generating a virtual heat price signal for the target region based on the photovoltaic power supply fluctuations in the target region and the schedulable heat capacity of the virtual energy storage includes: Extract the period of irradiance decrease during the photovoltaic power supply fluctuation; The photovoltaic fluctuation trough value during the irradiance decline period is matched with the dispatchable heat capacity value of the virtual energy storage to generate the heat load supply gap index of the target area. The heat load supply gap index and the gradient change during the irradiance decline period are coupled and analyzed to generate a real-time updated value of the heat price in the target area; The real-time updated heat price value is converted into a virtual heat price signal for the target region.

7. A method for coordinated heating control of ground source heat, air energy, and photovoltaic power in cold regions as described in claim 6, characterized in that, The formula for calculating the real-time updated value of the heat price is as follows: ； in: For real-time updates of heat prices, This is the benchmark value for heat prices. The system response gain coefficient, For the gap direction function, This refers to the gradient change during the irradiation decrease period. The heat load supply gap index is the index. For time period, is the damping coefficient.

8. A method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 1, characterized in that, The instruction to adjust the load distribution of heating equipment in the target area based on the virtual heat price signal includes: The stage of demand-supply imbalance in the target region is determined based on the time variation characteristics of the virtual heat price signal; Based on the imbalance stage, the equipment load of the heating equipment is prioritized to obtain the load priority of the heating equipment; The load allocation instruction for the target area is generated based on the load priority and the tolerable temperature drop threshold.

9. A method for coordinated heating control of ground source heat, air energy and photovoltaic power in cold regions as described in claim 7, characterized in that, The process of synchronously acquiring the execution result of the load allocation instruction includes: Extract real-time information of the target region during the execution of the load allocation instruction. The areas with abnormal temperature differences in the real-time information are marked as temperature anomaly points; The temperature anomaly point is superimposed with the heat conduction path of the virtual energy storage body to obtain the predicted heat loss trajectory of the target area. Monitor the energy loss within the predicted heat loss trajectory range and generate the execution result of the load allocation command.

10. A coordinated heating control system for geothermal energy, air energy, and photovoltaic power in cold regions, characterized in that, The system includes: The data acquisition module is used to collect the residents' tolerable temperature drop threshold and the defrosting cycle of the air source heat pump as a set of multi-source state parameters for the target area. The virtual construction module is used to obtain the thermal inertia time constant of residential buildings in the target area, and convert the residential buildings into virtual energy storage bodies based on the thermal inertia time constant and the multi-source state parameter set. The virtual signal generation module is used to generate a virtual heat price signal for the target area based on the photovoltaic power supply fluctuations in the target area and the schedulable heat capacity of the virtual energy storage. The execution module is used to adjust the load distribution instructions of the heating equipment in the target area according to the virtual heat price signal, and to simultaneously obtain the execution results of the load distribution instructions.

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