Dynamic Modeling and Optimal Scheduling Methods for Building Photovoltaic and Solar Thermal Systems

Abstract

This application provides a method for dynamic modeling and optimal scheduling of a building photovoltaic (PV) and solar thermal system. The method includes: acquiring multiple system structural parameters of the PV and solar thermal system, determining multiple environmental parameters, multiple user demand parameters, and multiple scenario parameters; acquiring a dynamic heat transfer model; inputting the multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain hourly PV panel power generation and hourly system heat generation power; acquiring an auxiliary electrothermal optimization model; inputting the multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly PV panel power generation, and hourly system heat generation power into the auxiliary electrothermal optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly charging power, and hourly heat dissipation power, thereby generating an auxiliary electrothermal optimization scheme. This method minimizes electricity purchase costs while improving the overall energy utilization efficiency of the system, achieving the optimal operating state of the PV and solar thermal system.

Description

Technical Field

[0001] This application relates to the field of intelligent energy management technology, and more specifically, to a method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems. Background Technology

[0002] Building photovoltaic (PV) and solar thermal (STP) systems, as integrated energy supply solutions, combine the functions of photovoltaic power generation and solar thermal utilization. They offer high energy conversion efficiency and low installation costs, providing buildings with clean electricity and hot water, reducing reliance on traditional energy sources, and becoming an important support for the clean transformation of urban energy systems. However, the system's operating efficiency is easily affected by the external environment (solar radiation, temperature, cloud cover, etc.), and there is high uncertainty in electricity and hot water output. At the same time, the building's electricity load and hot water load demand also fluctuate significantly, posing challenges to the system's stable operation and efficient scheduling.

[0003] Therefore, how to effectively predict and schedule building electricity and hot water load demands to achieve the optimal operating state of photovoltaic and solar thermal systems, reduce energy waste, and improve energy supply efficiency has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this application is to provide a dynamic modeling and optimal scheduling method for building photovoltaic and solar thermal systems, addressing the shortcomings of the existing technology, so as to solve the problem that the existing technology cannot effectively predict and schedule building electricity load demand and hot water load demand.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a dynamic modeling and optimal scheduling method for a building photovoltaic (PV) thermal system. The method is applied to a PV thermal system, which includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The method includes: Obtain multiple system structural parameters of the photovoltaic-thermal system, and determine multiple environmental parameters, multiple user requirement parameters, and multiple scenario parameters of the photovoltaic-thermal system; Obtain the dynamic heat transfer model of the dynamic heat transfer subsystem, and input the multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain the hourly photovoltaic power generation and the hourly system heat generation power. Obtain the auxiliary electrothermal optimization model of the auxiliary electrothermal optimization subsystem, and input the multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power into the auxiliary electrothermal optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power; Based on the hourly power purchased from the grid, the hourly operating power of the electric heater, the hourly charging power, and the hourly dissipating power, an auxiliary electric heating optimization scheme is generated.

[0006] Optionally, determining the multiple environmental parameters, multiple user demand parameters, and multiple scenario parameters of the photovoltaic-thermal system includes: Based on multiple historical environmental parameters obtained in advance, multiple environmental parameters are predicted; Based on multiple previously obtained user historical parameters, multiple user demand parameters are predicted. Based on multiple historical scene parameters obtained in advance, multiple scene parameters are predicted.

[0007] Optionally, the dynamic heat transfer model includes an evaporator model, a refrigerant dynamic model, and a condenser dynamic model; the step of inputting the multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain the hourly photovoltaic panel power generation and the hourly system heat output includes: By inputting the multiple system structural parameters and multiple environmental parameters into the evaporator model, the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe are obtained. The system structure parameters and the average temperature of the working fluid in the evaporator pipe are input into the dynamic model of the refrigerant to obtain the average temperature of the working fluid in the evaporator pipe, the inlet temperature of the working fluid in the evaporator pipe, the outlet temperature of the working fluid in the evaporator pipe, the average temperature of the working fluid in the condenser pipe, the inlet temperature of the working fluid in the condenser pipe, and the outlet temperature of the working fluid in the condenser pipe. The average temperature of the working fluid in the evaporation pipe, the inlet temperature of the working fluid in the evaporation pipe, the outlet temperature of the working fluid in the evaporation pipe, the average temperature of the working fluid in the condenser tube, the inlet temperature of the working fluid in the condenser tube, and the outlet temperature of the working fluid in the condenser tube are input into the dynamic model of the condenser to obtain the hourly system heat output power.

[0008] Optionally, the step of inputting the multiple system structural parameters and multiple environmental parameters into the evaporator model to obtain the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe includes: By inputting the multiple system structural parameters and multiple environmental parameters into the glass plate formula, photovoltaic panel formula, heat absorber formula, evaporation pipe formula and heat insulation plate formula in the evaporator model, the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe are obtained. The formula for the glass plate is as follows:

[0009] in, , as well as These are the mass, heat capacity, and temperature of the glass plate in the system structural parameters, respectively. It refers to the hourly ambient temperature among the environmental parameters. It is the hourly temperature of the photovoltaic panel. It is the hourly solar radiation intensity among the environmental parameters. It is the light transmittance coefficient of the glass plate in the system structural parameters. It is the area of ​​the glass plate in the system structural parameters. and These are the heat transfer coefficients between the glass plate and the environment, and between the glass plate and the photovoltaic panel, respectively, in the system structural parameters. and These are the radiative heat transfer between the glass plate and the photovoltaic panel, and the radiative heat transfer between the glass plate and the environment, respectively, in the environmental parameters mentioned above. The formula for the photovoltaic panel is:

[0010] in, , and These are the mass, heat capacity, and area of ​​the photovoltaic panel, which are the structural parameters of the system. It is the light absorption coefficient of the photovoltaic panel in the system structural parameters. This refers to the hourly power generation of the photovoltaic panels. It is the heat transfer coefficient between the photovoltaic panel and the heat absorber in the system structural parameters. It is the hourly temperature of the heat absorption plate; The environmental parameters refer to the radiative heat exchange between the photovoltaic panel and the heat absorber, and the hourly photovoltaic power generation is obtained through... It is confirmed that, among them, It is the transfer coefficient of the photovoltaic panel in the system structural parameters; This refers to the reference power generation efficiency of the photovoltaic panel in the system structure parameters. It is the photovoltaic panel efficiency degradation coefficient in the system structural parameters; It is the reference power generation temperature of the photovoltaic panel in the system structure parameters; The formula for the heat absorber plate is:

[0011] in, , and These are the mass, heat capacity, and area of ​​the heat absorber plate in the system structural parameters. and These are the heat transfer coefficients between the heat absorber and the evaporation pipe, and between the heat absorber and the insulation plate, which are the structural parameters of the system. and These are the hourly evaporator pipe temperature and the hourly insulation plate temperature, respectively. The formula for the evaporation pipe is:

[0012] in, , and These are the mass, heat capacity, and outer surface area of ​​the evaporation pipe in the system structural parameters. and These are the heat transfer coefficients between the evaporation pipe and the insulation plate, and the heat transfer coefficients between the evaporation pipe and the working fluid, which are the structural parameters of the system. and These are the radius and length of the evaporation pipe in the system structure parameters, respectively. It is the average temperature of the working fluid inside the evaporator pipe; The formula for the insulation board is:

[0013] in, , and These are the mass, heat capacity, and area of ​​the insulation board, respectively.

[0014] Optionally, the dynamic model of the refrigerant includes the formulas for the working fluid in the evaporator pipe and the working fluid in the condenser pipe. The working fluid formula for the evaporation pipe is: The working fluid formula for the condenser is: ,in, and These are the mass and heat capacity of the refrigerant in the system structural parameters, respectively. , and These are the average temperature of the working fluid in the evaporation pipe, the inlet temperature of the working fluid in the evaporation pipe, and the outlet temperature of the working fluid in the evaporation pipe, respectively. , , These are the average temperature of the working fluid inside the condenser, the inlet temperature of the working fluid inside the condenser, and the outlet temperature of the working fluid inside the condenser, respectively. and These are the diameter and length of the condenser tube in the system structure parameters, respectively; the inlet temperature of the working fluid in the evaporation pipe is equal to the outlet temperature of the working fluid in the condenser tube; and the outlet temperature of the working fluid in the evaporation pipe is equal to the inlet temperature of the working fluid in the condenser tube.

[0015] Optionally, the dynamic model of the condenser includes: condenser tube formula, condensate formula, and system heat generation formula; The formula for the condenser tube is:

[0016] The condensate formula is: The system heat generation formula is: ,in, , and These are the mass of the condenser tube, the heat capacity of the condenser tube, and the hourly condenser tube temperature in the system structural parameters. and These are the heat transfer coefficients between the condenser tube and the condensate, and the heat transfer coefficients between the condenser tube and the working fluid, which are the structural parameters of the system. and These are the radius and length of the condenser tube in the system structural parameters, respectively. and These are the average temperature of the condensate inside the condenser tube and the average temperature of the working fluid inside the condenser tube, respectively. It is the hourly system heat output.

[0017] Optionally, the auxiliary electrothermal optimization model includes formulas for electric heaters, hot water storage tanks, objective functions, and constraints; the step of inputting the multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power into the auxiliary electrothermal optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power includes: The multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power are input into the electric heater formula and the hot water storage tank formula in the auxiliary electric heating optimization model. Based on the objective function and the constraints, optimization is performed to obtain the hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power.

[0018] Optionally, the formula for the electric heater is: ,in, It is the hourly heat release power of the electric heater. It refers to efficiency in the system's structural parameters. It is the hourly operating power of the electric heater; The formula for the hot water storage tank is: ,in, This refers to the capacity status of the hot water storage tank. It represents the capacity status of the hot water storage tank at the next moment; It is the heat loss coefficient in the system structural parameters. and These are the heat charging efficiency and heat release efficiency of the hot water storage tank, which are part of the system structural parameters. and These are the hourly charging power and hourly dissipation power of the hot water storage tank, respectively. This represents the scheduling time step of the model.

[0019] Optionally, the objective function is ,in, It is the Pth preset scene within a preset time period. It is the total set of scenes in the scene parameters. The probability of the Pth preset scene appearing in the scene parameters is given, where t is time. It is the set of times in the scene parameters. It is the electricity purchase price in the user's demand parameters. The amount of electricity purchased from the power grid on an hourly basis; The constraints include at least the following: , ,in, It is the hourly power generation of photovoltaic panels. This is the hourly operating power of the electric heater. It is the building electrical load in the user requirement parameters. It is the hourly heat release power of the electric heater. It is the hourly system heat output power. It is the hourly heat release power of the hot water storage tank. It is the building heat load in the user requirement parameters. It is the hourly heat release power of the hot water storage tank.

[0020] Secondly, this application provides a photovoltaic-thermal system, which includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The dynamic heat transfer subsystem includes a glass plate, a photovoltaic panel, a heat absorption plate, an evaporation pipe, an insulation plate, an evaporator, and a condenser. The condenser includes condenser tubes. The auxiliary electrothermal optimization subsystem includes an electric heater and a hot water storage tank. The photovoltaic-thermal system is used to execute the building photovoltaic-thermal system dynamic modeling and optimal scheduling method as described in the first aspect.

[0021] The beneficial effects of this application are as follows: It constructs a dynamic heat transfer model for the dynamic heat transfer subsystem and an auxiliary electrothermal optimization model for the auxiliary electrothermal optimization subsystem. By incorporating environmental parameters, user demand parameters, and scenario parameters into the same optimization framework, it achieves deep coupling between the environment, load, and auxiliary electrothermal optimization scheme, thereby improving system operational stability. By combining scenario parameters for scheme optimization, it avoids the simplification of operating conditions through the quantitative uncertainty of scenarios, covering multiple potential operating scenarios. The final generated auxiliary electrothermal optimization scheme can be dynamically adapted to actual operating conditions, significantly improving its applicability under complex and variable conditions. This embodiment, through the dynamic heat transfer model and the auxiliary electrothermal optimization model, improves the overall energy utilization efficiency of the system while minimizing electricity purchase costs, achieving the optimal operating state of the photovoltaic thermal system. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the structure of a photovoltaic-thermal system proposed in an embodiment of this application; Figure 2 This is a flowchart illustrating a dynamic modeling and optimal scheduling method for a building photovoltaic and solar thermal system provided in an embodiment of this application; Figure 3 This is a flowchart illustrating a process for determining environmental parameters, user requirement parameters, and scenario parameters, provided in an embodiment of this application. Figure 4 This is a schematic diagram of a process for obtaining hourly photovoltaic panel power generation and hourly system heat generation power according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0025] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0027] In existing technologies, the operating efficiency of photovoltaic and solar thermal systems is easily affected by the external environment, and there is a high degree of uncertainty in electricity output and hot water output. At the same time, the demand for building electricity load and hot water load also fluctuates significantly. Currently, there is no effective method to predict and schedule the demand for building electricity load and hot water load to achieve the optimal operating state of photovoltaic and solar thermal systems, reduce energy waste, and improve energy supply efficiency.

[0028] Based on this, this application proposes a dynamic modeling and optimal scheduling method for building photovoltaic and solar thermal systems. This method outputs hourly photovoltaic panel power generation and hourly system heat generation power through multiple dynamic formulas in the dynamic heat transfer model. Then, it optimizes based on the electric heater formula, hot water storage tank formula, objective function, and constraints in the auxiliary electrothermal optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power, thereby improving the operating energy efficiency of the photovoltaic and solar thermal system and reducing building energy consumption.

[0029] Figure 1This is a schematic diagram of a photovoltaic (PV) solar thermal system proposed in an embodiment of this application. Before introducing the dynamic modeling and optimal scheduling method for building photovoltaic (PV) solar thermal systems, the PV solar thermal system in which it is applied will be introduced first.

[0030] Optionally, the photovoltaic thermal system includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The dynamic heat transfer subsystem includes a glass plate, a photovoltaic plate, a heat absorption plate, an evaporation pipe, an insulation plate, an evaporator, and a condenser. The condenser includes condenser tubes. The auxiliary electrothermal optimization subsystem includes an electric heater and a hot water storage tank.

[0031] Specifically, in the dynamic heat transfer subsystem, glass plates, photovoltaic panels, heat absorption plates, evaporation pipes, and insulation plates are placed in sequence. The evaporation pipes are connected to the condenser tubes of the condenser via a pump. The condenser is then connected to a hot water storage tank, which is equipped with an electric heater. Finally, the heat energy that meets the hot water load is delivered to the building.

[0032] Optionally, the glass plate is transparent and used to protect the photovoltaic panel, which converts solar energy into electrical energy. The heat absorption plate absorbs solar heat and transfers it to the working fluid in the evaporation pipe. The refrigerant in the evaporation pipe absorbs heat and undergoes a phase change, circulating and transferring heat in the system. The insulation plate reduces heat loss from the evaporator. The evaporator realizes the conversion of solar energy from electricity to heat and the evaporation of the working fluid. The condenser releases heat from the working fluid through the condenser tube, completing heat exchange to provide heating energy. The pump drives the refrigerant to circulate between the evaporator and the condenser. The hot water storage tank stores the heat transferred by the condenser and regulates the time distribution of heat supply. The electric heater provides auxiliary heating when the system's heat production is insufficient, ensuring a stable supply of hot water.

[0033] Next, refer to Figure 2 This paper introduces the specific implementation methods for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems. Specifically, Figure 2 This is a flowchart illustrating a dynamic modeling and optimal scheduling method for a building photovoltaic (PV) and solar thermal system provided in an embodiment of this application. Optionally, this method can be applied to electronic devices, whereby the electronic devices output auxiliary electrothermal optimization schemes and control the PV and solar thermal systems to execute according to these schemes.

[0034] S201. Obtain multiple system structural parameters of the photovoltaic-thermal system, and determine multiple environmental parameters, multiple user requirement parameters, and multiple scenario parameters of the photovoltaic-thermal system.

[0035] Among them, system structural parameters refer to the inherent hardware performance and structural attribute parameters of the photovoltaic thermal system itself, which are static parameters determined during system design or factory delivery. Specifically, these include the electric heater efficiency, the heat loss coefficient of the hot water storage tank, the charging efficiency of the hot water storage tank, the heat release efficiency of the hot water storage tank, the upper and lower limits of the electric heater's operating power, the upper and lower limits of the charging power of the hot water storage tank, the upper and lower limits of the heat release power of the hot water storage tank, the upper and lower limits of the hot water storage tank's capacity, the scheduling time step, as well as the inherent structural and performance parameters of each component of the evaporator and condenser, such as the light transmittance coefficient of the glass plate, the area of ​​the photovoltaic panel, the heat transfer coefficient of the absorber plate, the diameter of the evaporator pipe, the thermal resistance of the insulation board, and the structural parameters of the condenser tubes.

[0036] Environmental parameters refer to external natural conditions that affect the operation of photovoltaic and solar thermal systems. These parameters are dynamically changing, and their predicted values ​​for future periods can be obtained based on historical environmental data through predictive models. Predictive models include time series models and machine learning models. Specific environmental parameters include hourly solar radiation intensity and hourly ambient temperature.

[0037] User demand parameters refer to the dynamic demand parameters of building users in terms of electricity and heat energy use, which can be predicted based on historical user energy consumption data and user behavior patterns. Specifically, they include hourly building electrical load, hourly building heat load, and electricity purchase price. Hourly building electrical load includes, for example, the power demand of an office building at different times, and hourly building heat load includes, for example, the heat demand for hot water at different times.

[0038] Scenario parameters refer to a set of possible scenarios and their corresponding probabilities constructed to address the uncertainties of environmental and user demand parameters. These parameters can be generated through analysis and prediction of environmental and user demand uncertainties. Specifically, they include a set of multiple random scenarios and the probability of each scenario occurring. Examples of random scenarios include a sunny day with high electrical load and a cloudy day with low heat load.

[0039] S202. Obtain the dynamic heat transfer model of the dynamic heat transfer subsystem, and input multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain the hourly photovoltaic power generation and the hourly system heat generation power.

[0040] Specifically, a dynamic heat transfer model of the dynamic heat transfer subsystem is first constructed. This model is used to simulate the heat transfer and energy conversion processes between various components in the photovoltaic-thermal system. The components of the dynamic heat transfer subsystem may include glass plates, photovoltaic panels, absorber plates, evaporation pipes, condensers, etc. Then, the system structural parameters and environmental parameters are input into this model. Through calculations using the formulas in the model, hourly photovoltaic panel power generation and system-generated heat power data are finally obtained. These data will serve as important inputs for subsequent auxiliary electrothermal optimization models to formulate optimal energy dispatch schemes.

[0041] It is worth noting that in this step, some parameters from the total system structural parameters are input into the dynamic heat transfer model.

[0042] S203. Obtain the auxiliary electric heating optimization model of the auxiliary electric heating optimization subsystem, and input multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power into the auxiliary electric heating optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly charging power, and hourly heat release power.

[0043] Specifically, an auxiliary electrothermal optimization model is first constructed to optimize the operation and scheduling of electric heaters and hot water storage tanks. Then, system structural parameters, environmental parameters, user demand parameters, scenario parameters, and hourly photovoltaic panel power generation and hourly system heat generation output from the dynamic heat transfer model are input into this model. Through an optimization process that selects the optimal solution from multiple feasible operating schemes while meeting electricity / heat load demands and equipment operating constraints, the model ultimately outputs hourly grid-purchased power, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power. The goal is to minimize electricity purchase costs while ensuring a stable supply of electricity and heat to meet the building's needs.

[0044] S204. Based on the hourly power purchased by the power grid, the hourly operating power of the electric heater, the hourly charging power, and the hourly dissipating power, generate an auxiliary electric heating optimization scheme.

[0045] Optionally, after obtaining the hourly grid power purchase, hourly electric heater operating power, hourly charging power, and hourly heat dissipation power, power balance verification, heat balance verification, and equipment boundary verification can be performed based on constraints. Specifically, power balance verification involves calculating and verifying whether the sum of hourly grid power purchase and hourly photovoltaic power generation equals the sum of hourly electric heater operating power and hourly building electrical load, ensuring no gap or waste in power supply and demand. Heat balance verification involves calculating whether the sum of hourly electric heater heat dissipation power, hourly system heat generation power, and hourly heat dissipation power equals the sum of hourly building heat load and hourly charging power, ensuring heat supply and demand matching. Equipment boundary verification verifies whether the hourly electric heater operating power exceeds its upper and lower power limits, whether the hourly charging and dissipation power exceeds the upper and lower limits of the hot water storage tank's charging and dissipation power, and whether the hot water storage tank's capacity is within its upper and lower limits. If any constraints are not met, the auxiliary electrothermal optimization model needs to be returned to iterate and adjust the power data until compliance is achieved throughout all time periods.

[0046] Optionally, after all data verification and compliance, the hourly grid-purchased power, hourly electric heater operating power, hourly charging power, and hourly heat dissipation power are converted into specific operating instructions for the auxiliary electric heating subsystem to generate an auxiliary electric heating optimization scheme. This auxiliary electric heating optimization scheme can be used to guide the operation of various components in the photovoltaic thermal system. The scheme may also include precautions, such as indicating equipment maintenance periods and modes under extreme weather conditions. For example, if the electric heater is under maintenance every Wednesday, the charging power of the hot water storage tank needs to be increased in advance for the corresponding period. During rainstorms when there is no photovoltaic output, the system automatically switches to a mode where the electric heater operates at full load when the grid is purchasing power.

[0047] In this embodiment, a dynamic heat transfer model for the dynamic heat transfer subsystem and an auxiliary electrothermal optimization model for the auxiliary electrothermal optimization subsystem are constructed. By incorporating environmental parameters, user demand parameters, and scenario parameters into the same optimization framework, the environment, load, and auxiliary electrothermal optimization scheme are deeply coupled, improving system operational stability. By combining scenario parameters for scheme optimization, the quantitative uncertainty of scenarios avoids the simplification of operating conditions, covering multiple potential operating scenarios. The final generated auxiliary electrothermal optimization scheme can be dynamically adapted to actual operating conditions, significantly improving its applicability under complex and variable conditions. This embodiment, through the dynamic heat transfer model and the auxiliary electrothermal optimization model, improves the overall energy utilization efficiency of the system while minimizing electricity purchase costs, achieving the optimal operating state of the photovoltaic thermal system.

[0048] Next, refer to Figure 3 The steps in S201 above, which involve determining multiple environmental parameters, multiple user requirement parameters, and multiple scenario parameters for the photovoltaic-thermal system, are described below. Figure 3 This is a flowchart illustrating the process of determining environmental parameters, user requirement parameters, and scenario parameters, as provided in an embodiment of this application.

[0049] S301. Based on multiple historical environmental parameters obtained in advance, multiple environmental parameters are predicted.

[0050] Optionally, multiple environmental parameters can be determined based on the periodicity and correlation of historical environmental parameters over time.

[0051] As an optional implementation method, multiple environmental parameters are predicted based on time series models. Specifically, by utilizing the variation patterns of historical environmental parameters with preset periods, such as the diurnal cycle of solar radiation and the seasonal trend of temperature, multiple environmental parameters are obtained by extrapolating the predicted values ​​for future periods through fitting the temporal characteristics of historical data.

[0052] As an alternative implementation, multiple environmental parameters are predicted based on machine learning models. Specifically, historical hourly environmental data is used as input to learn the dynamic relationships between parameters and output future hourly predictions. This method is particularly suitable for capturing nonlinear change patterns.

[0053] As another alternative implementation method, based on the similar day method, dates with similar meteorological conditions to the predicted date are selected from historical data. Using these dates as a benchmark and combined with short-term trend fine-tuning, the predicted value can be obtained quickly.

[0054] S302. Based on multiple previously obtained historical user parameters, multiple user demand parameters are predicted.

[0055] Alternatively, multiple user demand parameters can be predicted based on similarity date methods, machine learning models, or user behavior modeling, according to multiple previously obtained user historical parameters.

[0056] S303. Based on multiple historical scene parameters obtained in advance, multiple scene parameters are predicted.

[0057] Alternatively, multiple user demand parameters can be predicted based on similarity date methods, machine learning models, or user behavior modeling, according to multiple previously obtained user historical parameters.

[0058] Optionally, multiple user demand parameters can be predicted based on clustering and probability estimation, Monte Carlo simulation, or Bayesian update, using multiple previously obtained historical user parameters.

[0059] In this embodiment, multiple environmental parameters, user requirement parameters, and scenario parameters are determined based on historical data to improve the timeliness and accuracy of the parameters and to better fit actual operating conditions.

[0060] Next, refer to Figure 4 The method for obtaining hourly photovoltaic panel power generation and hourly system heat output in step S202 above is described in detail. Figure 4 This is a schematic diagram of a process for obtaining hourly photovoltaic panel power generation and hourly system heat generation power, provided in an embodiment of this application.

[0061] S401. Input multiple system structural parameters and multiple environmental parameters into the evaporator model to obtain the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe.

[0062] Specifically, the evaporator model is used to convert solar energy into electrical and thermal energy, which is then transferred to the refrigerant.

[0063] Optionally, the system structure parameters input to the evaporator model include the inherent properties of each component of the evaporator, such as the light transmittance of the glass plate, the area of ​​the photovoltaic panel, and the photoelectric conversion efficiency. These parameters determine the energy conversion and transfer capabilities of the evaporator, and multiple environmental parameters affect the external conditions for solar energy absorption.

[0064] Optionally, when the evaporator model is running, solar radiation is first transmitted to the photovoltaic panel through the glass plate. The photovoltaic panel converts part of the solar energy into electrical energy based on its own conversion efficiency, forming hourly photovoltaic power generation. At the same time, the solar energy that is not converted by the photovoltaic panel is transferred to the heat absorption plate through heat conduction. The heat absorption plate then transfers the heat to the internal evaporation pipe, causing the refrigerant in the pipe to absorb heat and rise in temperature. Finally, the average temperature of the refrigerant in the evaporation pipe is calculated.

[0065] S402. Input multiple system structural parameters and the average temperature of the working fluid in the evaporator pipe into the dynamic model of the refrigerant to obtain the average temperature of the working fluid in the evaporator pipe, the inlet temperature of the working fluid in the evaporator pipe, the outlet temperature of the working fluid in the evaporator pipe, the average temperature of the working fluid in the condenser, the inlet temperature of the working fluid in the condenser, and the outlet temperature of the working fluid in the condenser.

[0066] Optionally, a dynamic model of the refrigerant is used to simulate the circulation and temperature changes of the refrigerant between the evaporator and condenser pipes.

[0067] Optionally, during model operation, the temperature distribution of the working fluid within the evaporation pipe is calculated based on the flow characteristics of the working fluid and the heat transfer characteristics of the pipe: When the working fluid flows in from the inlet of the evaporation pipe, it absorbs heat and its temperature gradually increases, forming the working fluid inlet temperature and the working fluid outlet temperature within the evaporation pipe. The outlet temperature is higher than the inlet temperature, and the difference reflects the heating effect of the evaporator. Subsequently, the working fluid flows into the condenser tube of the condenser through the pipe. During the flow process, due to heat dissipation from the pipe and heat exchange with the condenser, the temperature gradually changes. Finally, the average working fluid temperature, the working fluid inlet temperature, and the working fluid outlet temperature within the condenser tube are output. The outlet temperature is lower than the inlet temperature, and the difference reflects the heat released by the working fluid to the condenser.

[0068] S403. Input the average temperature of the working fluid in the evaporator pipe, the inlet temperature of the working fluid in the evaporator pipe, the outlet temperature of the working fluid in the evaporator pipe, the average temperature of the working fluid in the condenser pipe, the inlet temperature of the working fluid in the condenser pipe, and the outlet temperature of the working fluid in the condenser pipe into the dynamic model of the condenser to obtain the hourly system heat output power.

[0069] Optionally, the condenser dynamic model is used to transfer the heat carried by the refrigerant to the heating system, converting it into usable thermal energy.

[0070] Optionally, during model operation, the heat released by the working fluid is calculated based on the structural parameters of the condenser and the thermophysical properties of the working fluid through the temperature change of the working fluid in the condenser. At the same time, the heat released by the working fluid is converted into the heat energy that the system can output, based on the heat exchange efficiency between the condenser and the external heating system, and finally the hourly system heat production power is obtained.

[0071] In this embodiment, by determining the hourly photovoltaic panel power generation and the hourly system heat generation power, the energy changes of each link can be accurately quantified by combining system structural parameters and environmental parameters.

[0072] Next, we will introduce the implementation steps of step S401 above.

[0073] Optionally, multiple system structural parameters and multiple environmental parameters are input into the glass plate formula, photovoltaic panel formula, heat absorber formula, evaporation pipe formula, and adiabatic panel formula in the evaporator model to obtain the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe.

[0074] The formula for the glass plate is shown in formula (1) below: (1) in, , as well as These are the mass, heat capacity, and temperature of the glass plate, which are parameters in the system structure. It refers to the hourly ambient temperature among environmental parameters. It is the hourly temperature of the photovoltaic panel. It is the hourly solar radiation intensity among environmental parameters. It is the light transmittance coefficient of the glass plate in the system structural parameters. It refers to the area of ​​the glass plate in the system structural parameters. and These are the heat transfer coefficients between the glass plate and the environment, and between the glass plate and the photovoltaic panel, which are part of the system structural parameters. and These are the radiative heat transfer between the glass panel and the photovoltaic panel, and the radiative heat transfer between the glass panel and the environment, respectively, in the environmental parameters.

[0075] Optionally, the glass plate formula is used to express the following relationship: the rate of change of heat storage of the glass plate is equal to the sum of the heat absorbed by solar radiation through the glass plate, the convective heat transfer between the glass plate and the air, the convective heat transfer between the glass plate and the photovoltaic panel, and the radiative heat transfer from the glass plate to the photovoltaic panel, and the difference between the radiative heat transfer from the glass plate to the air.

[0076] As an optional implementation method, radiative heat transfer between the glass panel and the photovoltaic panel And radiative heat transfer between the glass plate and the environment This can be a preset value. As another optional implementation, radiative heat transfer between the glass panel and the photovoltaic panel... And radiative heat transfer between the glass plate and the environment The results can be obtained based on the following formulas (2) and (3): (2) (3) Where σ is the Stefan-Boltzmann constant; and These are the emissivity of the glass panel and the photovoltaic panel, respectively, in the system structural parameters.

[0077] Alternatively, the photovoltaic panel formula is as shown in formula (4) below: (4) in, , and These are the mass, heat capacity, and area of ​​the photovoltaic panel, which are parameters in the system structure. It is the light absorption coefficient of the photovoltaic panel in the system structural parameters. This refers to the hourly power generation of the photovoltaic panels. It is the heat transfer coefficient between the photovoltaic panel and the absorber panel in the system structural parameters. It is the hourly temperature of the heat absorption plate. It refers to the radiative heat exchange between the photovoltaic panel and the heat absorber in the environmental parameters.

[0078] Optionally, the photovoltaic panel formula is used to express the following relationship: the rate of change of heat storage of the photovoltaic panel is equal to the sum of the negative values ​​of the solar radiation heat absorbed by the photovoltaic panel, the negative values ​​of the electrical energy output by the photovoltaic panel, the convective heat transfer between the glass panel and the photovoltaic panel, the negative values ​​of the radiative heat transfer from the glass panel to the photovoltaic panel, the negative values ​​of the convective heat transfer between the photovoltaic panel and the absorber panel, and the negative values ​​of the radiative heat transfer from the photovoltaic panel to the absorber panel. In other words, it quantifies the dynamic temperature change of the photovoltaic panel under the influence of solar radiation, convection, radiation, and its own power generation.

[0079] As an optional implementation method, radiative heat transfer between the photovoltaic panel and the absorber panel... This can be a preset value. As another optional implementation, radiative heat exchange between the photovoltaic panel and the absorber panel... It can be obtained based on the following formula (5): (5) in, The emissivity of the heat-absorbing version.

[0080] The hourly photovoltaic power generation is calculated using the following formula (6): (6) in, It is the transfer coefficient of the photovoltaic panel in the system structural parameters. It is the reference efficiency of photovoltaic panels in the system structure parameters. It is the photovoltaic panel efficiency degradation coefficient in the system structural parameters. It is the reference power generation temperature of the photovoltaic panel in the system structure parameters.

[0081] Optionally, in the process of calculating the hourly photovoltaic power generation, the power generation under ideal conditions is first obtained by multiplying the solar radiation intensity, photovoltaic panel area, light transmittance and reference efficiency, and then the power generation value is corrected by the effect of temperature on efficiency to obtain the actual hourly photovoltaic power generation.

[0082] The formula for the heat absorber plate is shown in the following formula (7): (7) in, , and These are the mass, heat capacity, and area of ​​the heat absorber plate in the system structural parameters. and These are the heat transfer coefficients between the heat absorber plate and the evaporator pipe, and between the heat absorber plate and the insulation plate, respectively, in the system structural parameters. and These are the hourly evaporator pipe temperature and the hourly insulation plate temperature, respectively.

[0083] Optionally, the formula for the heat absorber plate is used to express the following relationship: the rate of change of heat storage of the heat absorber plate is equal to the sum of the convective heat transfer between the photovoltaic panel and the heat absorber plate, the radiative heat transfer from the photovoltaic panel to the heat absorber plate, the convective heat transfer between the heat absorber plate and the evaporation pipe, and the convective heat transfer between the heat absorber plate and the insulation plate. That is, the rate of change of heat storage is the sum of the total heat transferred to the heat absorber plate from all components, quantifying the dynamic temperature change of the heat absorber plate under the heat transfer effects of components such as the photovoltaic panel, evaporation pipe, and insulation plate.

[0084] The formula for the evaporation pipe is shown in the following formula (8): (8) in, , and These are the mass, heat capacity, and outer surface area of ​​the evaporator pipe, which are key parameters in the system's structural parameters. and These are the heat transfer coefficients between the evaporator pipe and the insulation plate, and between the evaporator pipe and the working fluid, respectively, in the system structural parameters. and These are the radius and length of the evaporation pipe in the system structure parameters. It is the average temperature of the working fluid inside the evaporation pipe.

[0085] Optionally, the evaporator pipe formula is used to express the following relationship: the rate of change of heat storage in the evaporator pipe is equal to the difference between the sum of the convective heat transfer between the absorber plate and the evaporator pipe, the sum of the convective heat transfer between the adiabatic plate and the evaporator pipe, and the difference between the convective heat transfer between the evaporator pipe and the internal working fluid. That is, the rate of change of heat storage in the pipe is the difference between the sum of the heat transferred from the absorber plate and the heat exchanged with the adiabatic plate and the heat transferred to the working fluid, quantifying the dynamic temperature change of the evaporator pipe under the heat transfer effects of the absorber plate, the adiabatic plate, and the internal working fluid.

[0086] The formula for the insulation board is shown in the following formula (9): (9) in, , and These are the mass, heat capacity, and area of ​​the insulation board, respectively.

[0087] The formula for the insulation plate represents the following relationship: the rate of change of heat storage of the insulation plate is equal to the sum of the convective heat transfer between the evaporator pipe and the insulation plate, the convective heat transfer between the insulation plate and the air, and the convective heat transfer between the absorber plate and the insulation plate. In other words, it quantifies the dynamic temperature change of the insulation plate under the heat transfer effects of various components. Its core function is to quantify the heat exchange of the insulation plate, ensuring that the heat from the evaporator is used as much as possible for heating the working fluid.

[0088] As an optional implementation method, the dynamic model of the refrigerant includes formulas for the working fluid in the evaporator pipe and the working fluid in the condenser pipe.

[0089] The working fluid formula for the evaporation pipeline is shown in formula (10) below: (10) The working fluid formula for the condenser is shown in formula (11) below: (11) in, and These are the mass and heat capacity of the refrigerant in the system's structural parameters. , and These are the average temperature of the working fluid inside the evaporation pipe, the inlet temperature of the working fluid inside the evaporation pipe, and the outlet temperature of the working fluid inside the evaporation pipe, respectively. , , These are the average temperature of the working fluid inside the condenser, the inlet temperature of the working fluid inside the condenser, and the outlet temperature of the working fluid inside the condenser, respectively. and These are the diameter and length of the condenser tube in the system structural parameters, respectively. Optionally, this model ignores pipe friction and heat loss; therefore, the inlet temperature of the working fluid in the evaporator tube is equal to the outlet temperature of the working fluid in the condenser tube, and the outlet temperature of the working fluid in the evaporator tube is equal to the inlet temperature of the working fluid in the condenser tube.

[0090] Optionally, a dynamic model of the refrigerant is used to simulate the circulation and temperature changes of the refrigerant between the evaporator pipe and the condenser. The heat transferred to the refrigerant by each component of the evaporator directly affects the temperature of the refrigerant in the evaporator pipe. For example, the temperature of the refrigerant rises after absorbing heat in the evaporator pipe formula. These refrigerant temperature parameters are input into the dynamic model of the refrigerant. The model then calculates the temperature distribution of the refrigerant in the evaporator pipe and the temperature change after entering the condenser, based on the flow characteristics of the refrigerant and the heat transfer characteristics of the pipe.

[0091] Specifically, in the dynamic model of the refrigerant, based on the principle of conservation of refrigerant energy, the temperature change from the absorption of heat from the evaporator to the transfer of heat to the condenser is quantified by setting the outlet temperature of the refrigerant in the evaporator pipe to be equal to the inlet temperature of the refrigerant in the condenser pipe, and the inlet temperature of the refrigerant in the evaporator pipe to the outlet temperature of the refrigerant in the condenser pipe.

[0092] Optionally, the dynamic model of the condenser includes: condenser tube formula, condensate formula, and system heat generation formula.

[0093] The formula for the condenser is shown in formula (12) below: (12) The formula for condensate is shown in the following formula (13): (13) The system heat generation formula is shown in the following formula (14): (14) in, , and These are the condenser tube's mass, heat capacity, and hourly condenser tube temperature, which are part of the system's structural parameters. and These are the heat transfer coefficients between the condenser and the condensate, and between the condenser and the working fluid, respectively, in the system structural parameters. and These are the radius and length of the condenser tube, which are the system structural parameters. and These are the average temperature of the condensate inside the condenser tube and the average temperature of the working fluid inside the condenser tube, respectively. It is the hourly system heat output.

[0094] For the condenser formula, the rate of change of heat storage in the condenser is equal to the sum of the convective heat transfer between the condenser and the condensate and the convective heat transfer between the condenser and the working fluid. For the condensate formula, the rate of change of heat storage in the condensate is the sum of the convective heat transfer between the condenser and the condensate and the change in heat due to the condensate's own flow. For the system heat production formula, this formula quantifies the heat absorbed by the condensate, i.e., the effective heat energy output by the system.

[0095] Next, the specific process of step S203 above will be described. The auxiliary electrothermal optimization model includes the electric heater formula, the hot water storage tank formula, the objective function, and the constraints.

[0096] Optionally, multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power are input into the electric heater formula and hot water storage tank formula in the auxiliary electric heating optimization model, and optimized based on the objective function and constraints to obtain hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power.

[0097] Specifically, under the premise of satisfying the constraints, the algorithm generates a large number of feasible scheduling schemes covering the power purchased by the grid, the operation of the electric heater, and the charging and discharging modes of the hot water storage tank at different time periods. Then, based on the objective function with minimizing the total power purchase cost as the core, the objective function value of each feasible scheme is quantitatively calculated. Finally, by comparing the objective values ​​among multiple schemes, the optimal scheme that optimizes the objective function is selected. The corresponding hourly power purchased by the grid, hourly operating power of the electric heater, and hourly charging and discharging power are the final scheduling outputs of the auxiliary electric heating optimization subsystem, thereby achieving the economically optimal operation of the system under the constraints of energy supply and demand and equipment operation.

[0098] In this embodiment, optimization based on the objective function and constraints is performed to accurately reduce the cost of electricity purchase, cover multiple operating conditions and uncertainties, and improve energy utilization efficiency.

[0099] The formula for the electric heater is shown in formula (15) below: (15) in, It is the hourly heat release power of the electric heater. It refers to efficiency in the system's structural parameters. It is the hourly operating power of the electric heater.

[0100] The formula for the hot water storage tank is shown in the following formula (16): (16) in, This refers to the capacity status of the hot water storage tank. It represents the capacity status of the hot water storage tank at the next moment. It is the heat loss coefficient in the system structural parameters. and These are the heat charging efficiency and heat release efficiency of the hot water storage tank, which are part of the system structural parameters. and These are the hourly charging power and hourly dissipation power of the hot water storage tank, respectively. This represents the scheduling time step of the model.

[0101] Optionally, the electric heater formula reflects the process of converting electrical energy into heat energy through an electric heater, while the hot water storage tank formula reflects the dynamic heat balance of storage, loss, charging, and heat release in the tank.

[0102] Alternatively, the objective function can be expressed as formula (17): (17) in, It is the Pth preset scene within a preset time period. It is the total set of scenes in the scene parameters. is the probability of the Pth preset scene appearing in the scene parameters, and t is time. It is the set of times in the scene parameters. It is the electricity purchase price in the user's demand parameters. This refers to the hourly power purchased from the power grid.

[0103] Specifically, for each preset scenario P, first determine its probability of occurrence. Assign weights, then calculate the electricity purchase cost for each time t in the scenario, and finally sum the weighted electricity purchase costs for all scenarios and all times. Use an optimization algorithm to find the combination of electricity purchase power that minimizes the total sum.

[0104] In this embodiment, multiple possible operating conditions are covered, and the priority of high-probability scenarios is highlighted by scenario probability. The resulting optimized solution can achieve lower electricity purchase costs in most actual operating conditions, thus improving the robustness and practicality of the solution.

[0105] Optionally, the constraints may include at least the conditions shown in equations (18) and (19): (18) (19) in, It is the hourly power generation of photovoltaic panels. This is the hourly operating power of the electric heater. It refers to the building electrical load in the user's requirements parameters. It is the hourly heat release power of the electric heater. It is the hourly system heat output power. It is the hourly heat release power of the hot water storage tank. It is the building heat load in the user requirement parameters. It is the hourly charging power of the hot water storage tank.

[0106] As an optional implementation, the constraints may also include conditions as shown in formulas (20), (21), (22), and (23): (20) (twenty one) (twenty two) (twenty three) in, and These are the lower and upper limits of the electric heater's operation in the system structure parameters, respectively. and These are the lower and upper limits of the hot water storage tank's heat charging parameters in the system structure parameters, respectively. and These are the lower and upper limits of heat release from the water storage tank, respectively. and These represent the lower and upper limits of the heat storage tank's capacity, respectively.

[0107] Specifically, through the above constraints, Formula (20) limits the operating power of the electric heater to within its designed power range. Formula (21) limits the power of the hot water storage tank to within the allowable range during charging, to avoid low charging power leading to low heat storage efficiency, or excessive power causing damage to the tank structure and heat exchange components. Formula (22) limits the power of the hot water storage tank to within the allowable range during heat release, to avoid low heat release power failing to meet heat load requirements, or excessive power causing a sudden drop in tank heat and thermal shock damaging the equipment. Formula (23) limits the heat storage capacity of the hot water storage tank to within a safe range, i.e., it cannot be lower than the minimum capacity.

[0108] In this embodiment, constraints are used to ensure the safety and reliability of the equipment, reduce maintenance costs, enhance the robustness of the solution, and adapt to complex working conditions in multiple scenarios.

[0109] This application also provides a photovoltaic-thermal system, which includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The dynamic heat transfer subsystem includes a glass plate, a photovoltaic plate, a heat absorption plate, an evaporation pipe, an insulation plate, an evaporator, and a condenser. The condenser includes a condenser tube. The auxiliary electrothermal optimization subsystem includes an electric heater and a hot water storage tank. The photovoltaic-thermal system is used to execute a dynamic modeling and optimal scheduling method for building photovoltaic-thermal systems.

[0110] This application also provides an electronic device, such as... Figure 5 The diagram shown is a structural schematic of an electronic device provided in an embodiment of this application, including a processor 501, a memory 502, and a bus. The memory 502 stores machine-readable instructions executable by the processor 501. When the computer device is running, the processor 501 and the memory 502 communicate via the bus. When the machine-readable instructions are executed by the processor 501, they perform the processing of the aforementioned dynamic modeling and optimal scheduling method for building photovoltaic and solar thermal systems.

[0111] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems.

[0112] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0113] Furthermore, the functional units in the various embodiments of this application 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. If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0114] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems, characterized in that, The method is applied to a photovoltaic-thermal system, which includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The method includes: Multiple system structural parameters of the photovoltaic-thermal system are obtained, and multiple environmental parameters, multiple user demand parameters, and multiple scenario parameters of the photovoltaic-thermal system are determined. The environmental parameters refer to the external natural condition parameters that affect the operation of the photovoltaic-thermal system. The user demand parameters refer to the dynamic demand parameters of building users in terms of electricity and heat energy use. The scenario parameters refer to the set of multiple possible situations and their corresponding probabilities constructed to cope with the uncertainty of environmental parameters and user demand parameters. They are generated through analysis and prediction of the uncertainty of environment and user demand. Obtain the dynamic heat transfer model of the dynamic heat transfer subsystem, and input the multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain the hourly photovoltaic power generation and the hourly system heat generation power. Obtain the auxiliary electrothermal optimization model of the auxiliary electrothermal optimization subsystem, and input the multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power into the auxiliary electrothermal optimization model to obtain hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power; Based on the hourly power purchased from the grid, the hourly operating power of the electric heater, the hourly charging power, and the hourly dissipating power, an auxiliary electric heating optimization scheme is generated. The dynamic heat transfer model includes an evaporator model, a refrigerant dynamic model, and a condenser dynamic model; the step of inputting the multiple system structural parameters and multiple environmental parameters into the dynamic heat transfer model to obtain the hourly photovoltaic panel power generation and the hourly system heat output includes: By inputting the multiple system structural parameters and multiple environmental parameters into the evaporator model, the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe are obtained. The system structure parameters and the average temperature of the working fluid in the evaporator pipe are input into the dynamic model of the refrigerant to obtain the average temperature of the working fluid in the evaporator pipe, the inlet temperature of the working fluid in the evaporator pipe, the outlet temperature of the working fluid in the evaporator pipe, the average temperature of the working fluid in the condenser pipe, the inlet temperature of the working fluid in the condenser pipe, and the outlet temperature of the working fluid in the condenser pipe. The average temperature of the working fluid in the evaporation pipe, the inlet temperature of the working fluid in the evaporation pipe, the outlet temperature of the working fluid in the evaporation pipe, the average temperature of the working fluid in the condenser, the inlet temperature of the working fluid in the condenser, and the outlet temperature of the working fluid in the condenser are input into the dynamic model of the condenser to obtain the hourly system heat output power. The auxiliary electrothermal optimization model includes formulas for electric heaters, hot water storage tanks, objective functions, and constraints. The process involves inputting multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic panel power generation, and hourly system heat generation power into the auxiliary electrothermal optimization model to obtain hourly grid power purchases, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power, including: The multiple system structural parameters, multiple environmental parameters, multiple user demand parameters, multiple scenario parameters, hourly photovoltaic power generation, and hourly system heat generation power are input into the electric heater formula and the hot water storage tank formula in the auxiliary electric heating optimization model, and optimization is performed based on the objective function and the constraints to obtain the hourly grid power purchase, hourly electric heater operating power, hourly heat charging power, and hourly heat dissipation power. The formula for the electric heater is: ,in, It is the hourly heat release power of the electric heater. It refers to efficiency in the system's structural parameters. It is the hourly operating power of the electric heater; The formula for the hot water storage tank is: ,in, This refers to the capacity status of the hot water storage tank. It represents the capacity status of the hot water storage tank at the next moment; It is the heat loss coefficient in the system structural parameters. and These are the heat charging efficiency and heat release efficiency of the hot water storage tank, which are part of the system structural parameters. and These are the hourly charging power and hourly dissipation power of the hot water storage tank, respectively. This represents the scheduling time step of the model; The objective function is: ,in, It is the Pth preset scene within a preset time period. It is the total set of scenes in the scene parameters. The probability of the Pth preset scene appearing in the scene parameters is given, where t is time. It is the set of times in the scene parameters. It is the electricity purchase price in the user's demand parameters. The amount of electricity purchased from the power grid on an hourly basis; The constraints include at least the following: , ,in, It is the hourly power generation of photovoltaic panels. This is the hourly operating power of the electric heater. It is the building electrical load in the user requirement parameters. It is the hourly heat release power of the electric heater. It is the hourly system heat output power. It is the hourly heat release power of the hot water storage tank. It is the building heat load in the user requirement parameters. It is the hourly charging power of the hot water storage tank.

2. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 1, characterized in that, The determination of multiple environmental parameters, multiple user demand parameters, and multiple scenario parameters of the photovoltaic-thermal system includes: Based on multiple historical environmental parameters obtained in advance, multiple environmental parameters are predicted; Based on multiple previously obtained user historical parameters, multiple user demand parameters are predicted. Based on multiple historical scene parameters obtained in advance, multiple scene parameters are predicted.

3. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 1, characterized in that, The step of inputting the multiple system structural parameters and multiple environmental parameters into the evaporator model to obtain the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe includes: By inputting the multiple system structural parameters and multiple environmental parameters into the glass plate formula, photovoltaic panel formula, heat absorber formula, evaporation pipe formula and heat insulation plate formula in the evaporator model, the hourly photovoltaic power generation and the average temperature of the working fluid in the evaporation pipe are obtained. The formula for the glass plate is as follows: in, , as well as These are the mass, heat capacity, and temperature of the glass plate in the system structural parameters. It refers to the hourly ambient temperature among the environmental parameters. It is the hourly temperature of the photovoltaic panel. It is the hourly solar radiation intensity among the environmental parameters. It is the light transmittance coefficient of the glass plate in the system structural parameters. It is the area of ​​the glass plate in the system structural parameters. and These are the heat transfer coefficients between the glass plate and the environment, and between the glass plate and the photovoltaic panel, respectively, in the system structural parameters. and These are the radiative heat transfer between the glass plate and the photovoltaic panel, and the radiative heat transfer between the glass plate and the environment, respectively, in the environmental parameters mentioned above. The formula for the photovoltaic panel is: in, , and These are the mass, heat capacity, and area of ​​the photovoltaic panel in the system structural parameters, respectively. It is the light absorption coefficient of the photovoltaic panel in the system structural parameters. This refers to the hourly power generation of the photovoltaic panels. It is the heat transfer coefficient between the photovoltaic panel and the heat absorber in the system structural parameters. It is the hourly temperature of the heat absorption plate; The environmental parameters refer to the radiative heat exchange between the photovoltaic panel and the heat absorber, and the hourly photovoltaic power generation is obtained through... It is confirmed that, among them, It is the transfer coefficient of the photovoltaic panel in the system structural parameters; This refers to the reference power generation efficiency of the photovoltaic panel in the system structure parameters. It is the photovoltaic panel efficiency degradation coefficient in the system structural parameters; It is the reference power generation temperature of the photovoltaic panel in the system structural parameters; The formula for the heat absorber plate is: in, , and These refer to the mass, heat capacity, and area of ​​the heat absorber plate in the system structural parameters. and These are the heat transfer coefficients between the heat absorber and the evaporation pipe, and between the heat absorber and the insulation plate, which are the structural parameters of the system. and These are the hourly evaporator pipe temperature and the hourly insulation plate temperature, respectively. The formula for the evaporation pipe is: in, , and These are the mass, heat capacity, and outer surface area of ​​the evaporation pipe in the system structural parameters. and These are the heat transfer coefficients between the evaporation pipe and the insulation plate, and the heat transfer coefficients between the evaporation pipe and the working fluid, which are the structural parameters of the system. and These are the radius and length of the evaporation pipe in the system structure parameters, respectively. It is the average temperature of the working fluid inside the evaporator pipe; The formula for the insulation board is: in, , and These are the mass, heat capacity, and area of ​​the insulation board, respectively.

4. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 1, characterized in that, The dynamic model of the refrigerant includes the formulas for the working fluid in the evaporator pipe and the working fluid in the condenser pipe. The working fluid formula for the evaporation pipe is: The working fluid formula for the condenser is: ,in, and These are the mass and heat capacity of the refrigerant in the system structural parameters, respectively. , and These are the average temperature of the working fluid in the evaporation pipe, the inlet temperature of the working fluid in the evaporation pipe, and the outlet temperature of the working fluid in the evaporation pipe, respectively. , , These are the average temperature of the working fluid inside the condenser, the inlet temperature of the working fluid inside the condenser, and the outlet temperature of the working fluid inside the condenser, respectively. and These are the diameter and length of the condenser tube in the system structure parameters, respectively; the inlet temperature of the working fluid in the evaporation pipe is equal to the outlet temperature of the working fluid in the condenser tube; and the outlet temperature of the working fluid in the evaporation pipe is equal to the inlet temperature of the working fluid in the condenser tube.

5. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 1, characterized in that, The dynamic model of the condenser includes: the condenser tube formula, the condensate formula, and the system heat generation formula; The formula for the condenser tube is: The condensate formula is: The system heat generation formula is: ,in, , and These are the mass of the condenser tube, the heat capacity of the condenser tube, and the hourly condenser tube temperature in the system structural parameters. and These are the heat transfer coefficients between the condenser tube and the condensate, and the heat transfer coefficients between the condenser tube and the working fluid, which are the structural parameters of the system. and These are the radius and length of the condenser tube in the system structural parameters, respectively. and These are the average temperature of the condensate inside the condenser tube and the average temperature of the working fluid inside the condenser tube, respectively. It is the hourly system heat output.

6. A photovoltaic-thermal system, characterized in that, The photovoltaic-thermal system includes a dynamic heat transfer subsystem and an auxiliary electrothermal optimization subsystem. The dynamic heat transfer subsystem includes a glass plate, a photovoltaic panel, a heat absorption plate, an evaporation pipe, an insulation plate, an evaporator, and a condenser. The condenser includes condenser tubes. The auxiliary electrothermal optimization subsystem includes an electric heater and a hot water storage tank. The photovoltaic-thermal system is used to execute the dynamic modeling and optimal scheduling method for building photovoltaic-thermal systems as described in any one of claims 1-5.

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