Dynamic modeling and optimal scheduling method for building photovoltaic photo-thermal system

Abstract

The invention provides a dynamic modeling and optimal scheduling method for a building photovoltaic photo-thermal system, and the method comprises the steps: obtaining a plurality of system structure parameters of the photovoltaic photo-thermal system, determining a plurality of environment parameters, a plurality of user demand parameters and a plurality of scene parameters, obtaining a dynamic heat transfer model, inputting a plurality of system structure parameters and a plurality of environment parameters into the dynamic heat transfer model to obtain hourly photovoltaic panel generating capacity and hourly system heat production power, and obtaining an auxiliary electric heating optimization model; inputting a plurality of system structure parameters, a plurality of environment parameters, a plurality of user demand parameters, a plurality of scene parameters, hourly photovoltaic panel generating capacity and hourly system heat production power into the auxiliary electric heating optimization model to obtain hourly power grid electricity purchase power, hourly electric heater operation power, hourly heat charging power and hourly heat release power; therefore, an auxiliary electric heating optimization scheme is generated, the overall energy utilization efficiency of the system is improved while the power purchase cost is minimized, and the optimal operation state of the photovoltaic photo-thermal system is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent energy management, in particular to a building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method. BACKGROUND

[0002] As an integrated energy supply solution, the building photovoltaic and photo-thermal system has dual functions of photovoltaic power generation and photo-thermal utilization, high energy conversion efficiency, and low installation cost, which can provide clean electricity and hot water for buildings, reduce dependence on traditional energy, and become an important support for the clean transformation of urban energy systems. However, the system operation efficiency is easily affected by external environment (solar radiation, air temperature, cloud cover, etc.), and there is high uncertainty between the electrical output and the hot water output. At the same time, the building electrical load and hot water load demand also have obvious volatility, which brings challenges to the stable operation and efficient scheduling of the system.

[0003] Therefore, how to effectively predict and schedule the building electrical load demand and the hot water load demand to achieve the best operation state of the photovoltaic and photo-thermal system, reduce energy waste, and improve energy supply efficiency has become a problem to be solved. SUMMARY

[0004] The present application aims at the deficiencies in the prior art, and provides a building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method to solve the problem that the building electrical load demand and the hot water load demand cannot be effectively predicted and scheduled in the prior art.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows: In a first aspect, the present application provides a building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method, which is applied to a photovoltaic and photo-thermal system, the photovoltaic and photo-thermal system comprising a dynamic heat transfer subsystem and an auxiliary electric heating optimization subsystem, and the method comprising: obtaining a plurality of system structure parameters of the photovoltaic and photo-thermal system, and determining a plurality of environmental parameters, a plurality of user demand parameters, and a plurality of scene parameters of the photovoltaic and photo-thermal system; obtaining a dynamic heat transfer model of the dynamic heat transfer subsystem, and inputting the plurality of system structure parameters and the plurality of environmental parameters into the dynamic heat transfer model to obtain hourly photovoltaic panel power generation and hourly system heat generation power; obtaining an auxiliary electric heating optimization model of the auxiliary electric heating optimization subsystem, and inputting the plurality of system structure parameters, the plurality of environmental parameters, the plurality of user demand parameters, the plurality of scene parameters, the hourly photovoltaic panel power generation, and the hourly system heat generation power into the auxiliary electric heating optimization model to obtain hourly grid power purchase power, hourly electric heater operation power, hourly heat charging power, and hourly heat discharging power; generate an auxiliary electric heating optimization scheme based on the hourly grid purchase power, the hourly electric heater operation power, the hourly heat charging power, and the hourly heat discharging power.

[0006] Optionally, the determining the plurality of environment parameters, the plurality of user demand parameters, and the plurality of scene parameters of the photovoltaic and photo-thermal system comprises: predicting the plurality of environment parameters based on a plurality of historical environment parameters obtained in advance; predicting the plurality of user demand parameters based on a plurality of historical user parameters obtained in advance; predicting the plurality of scene parameters based on a plurality of historical scene parameters obtained in advance.

[0007] Optionally, the dynamic heat transfer model comprises an evaporator model, a refrigerant dynamic model, and a condenser dynamic model; and the inputting the plurality of system structure parameters and the plurality of environment parameters into the dynamic heat transfer model to obtain the hourly photovoltaic panel power generation and the hourly system heat production power comprises: inputting the plurality of system structure parameters and the plurality of environment parameters into the evaporator model to obtain the hourly photovoltaic panel power generation and the average temperature of the working medium in the evaporating pipeline; inputting the plurality of system structure parameters and the average temperature of the working medium in the evaporating pipeline into the refrigerant dynamic model to obtain the average temperature of the working medium in the evaporating pipeline, the inlet temperature of the working medium in the evaporating pipeline, the outlet temperature of the working medium in the evaporating pipeline, the average temperature of the working medium in the condensing pipeline, the inlet temperature of the working medium in the condensing pipeline, and the outlet temperature of the working medium in the condensing pipeline; inputting the average temperature of the working medium in the evaporating pipeline, the inlet temperature of the working medium in the evaporating pipeline, the outlet temperature of the working medium in the evaporating pipeline, the average temperature of the working medium in the condensing pipeline, the inlet temperature of the working medium in the condensing pipeline, and the outlet temperature of the working medium in the condensing pipeline into the condenser dynamic model to obtain the hourly system heat production power.

[0008] Optionally, the inputting the plurality of system structure parameters and the plurality of environment parameters into the evaporator model to obtain the hourly photovoltaic panel power generation and the average temperature of the working medium in the evaporating pipeline comprises: inputting the plurality of system structure parameters and the plurality of environment parameters into a glass plate formula, a photovoltaic panel formula, a heat absorbing plate formula, an evaporating pipeline formula, and an adiabatic plate formula in the evaporator model to obtain the hourly photovoltaic panel power generation and the average temperature of the working medium in the evaporating pipeline; wherein the glass plate formula is:

[0009] wherein, , and respectively the mass, heat capacity and temperature of the glass sheet in the system structure parameters, is the hourly ambient temperature in the environmental parameters, is the hourly photovoltaic panel temperature, is the hourly solar radiation intensity in the environmental parameters, is the light transmission coefficient of the glass sheet in the system structure parameters, is the area of the glass sheet in the system structure parameters, and respectively the heat transfer coefficient between the glass sheet and the environment and the heat transfer coefficient between the glass sheet and the photovoltaic panel in the system structure parameters, and respectively the radiative heat exchange between the glass sheet and the photovoltaic panel and the radiative heat exchange between the glass sheet and the environment in the environmental parameters; the photovoltaic panel formula is:

[0010] wherein, , and respectively the mass, heat capacity and area of the photovoltaic panel in the system structure parameters, is the light absorption coefficient of the photovoltaic panel in the system structure parameters, is the hourly photovoltaic panel power generation, is the heat transfer coefficient between the photovoltaic panel and the heat absorption panel in the system structure parameters, is the hourly heat absorption panel temperature; is the radiative heat exchange between the photovoltaic panel and the heat absorption panel in the environmental parameters, the hourly photovoltaic panel power generation being determined by wherein, is the transfer coefficient of the photovoltaic panel in the system structure parameters; is the reference efficiency of power generation of the photovoltaic panel in the system structure parameters; is the efficiency decay coefficient of the photovoltaic panel in the system structure parameters; is the reference power generation temperature of the photovoltaic panel in the system structure parameters; the heat absorption panel formula is:

[0011] wherein, , and respectively the mass, heat capacity and area of the heat absorption panel in the system structure parameters, and respectively the heat transfer coefficient between the heat absorption panel and the evaporation duct and the heat transfer coefficient between the heat absorption panel and the heat insulation panel in the system structure parameters; and respectively are the hourly evaporator pipe temperature and the hourly insulation plate temperature; The evaporator pipe formula is:

[0012] wherein, , and respectively are the mass, the heat capacity and the outer surface area of the evaporator pipe in the system structure parameters, and respectively are the heat transfer coefficient between the evaporator pipe and the insulation plate and the heat transfer coefficient between the evaporator pipe and the working medium in the system structure parameters; and respectively are the radius and the length of the evaporator pipe in the system structure parameters; is the average temperature of the working medium in the evaporator pipe; The insulation plate formula is:

[0013] wherein, , and respectively are the mass, the heat capacity and the area of the insulation plate.

[0014] Optionally, the refrigerant dynamic model comprises an evaporator pipe working medium formula and a condenser pipe working medium formula; The evaporator pipe working medium formula is The condenser pipe working medium formula is wherein, and respectively are the mass and the heat capacity of the refrigerant in the system structure parameters; , and respectively are the average temperature of the working medium in the evaporator pipe, the inlet temperature of the working medium in the evaporator pipe and the outlet temperature of the working medium in the evaporator pipe; , , respectively are the average temperature of the working medium in the condenser pipe, the inlet temperature of the working medium in the condenser pipe and the outlet temperature of the working medium in the condenser pipe; and respectively are the diameter and the length of the condenser pipe in the system structure parameters, the inlet temperature of the working medium in the evaporator pipe is equal to the outlet temperature of the working medium in the condenser pipe, and the outlet temperature of the working medium in the evaporator pipe is equal to the inlet temperature of the working medium in the condenser pipe.

[0015] Optionally, the condenser dynamic model comprises a condenser pipe formula, a condenser water formula and a system heat production formula; The condenser pipe formula is

[0016] the condensing water formula is , and the system heat production formula is , wherein, , and are the mass, the heat capacity and the hourly condensing tube temperature of the condensing tube in the system structure parameters respectively; and are the heat transfer coefficient between the condensing tube and the condensing water and the heat transfer coefficient between the condensing tube and the working medium in the system structure parameters respectively; and are the radius and the length of the condensing tube in the system structure parameters respectively; and are the average temperature of the condensing water in the condensing tube and the average temperature of the working medium in the condensing tube respectively; is the hourly system heat production power.

[0017] Optionally, the auxiliary electric heating optimization model comprises an electric heater formula, a heat storage tank formula, an objective function and a constraint condition; the inputting of the plurality of system structure parameters, the plurality of environment parameters, the plurality of user demand parameters, the plurality of scene parameters, the hourly photovoltaic panel power generation and the hourly system heat production power into the auxiliary electric heating optimization model to obtain the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power comprises: inputting the plurality of system structure parameters, the plurality of environment parameters, the plurality of user demand parameters, the plurality of scene parameters, the hourly photovoltaic panel power generation and the hourly system heat production power into the electric heater formula and the heat storage tank formula in the auxiliary electric heating optimization model, and performing optimization based on the objective function and the constraint condition to obtain the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power.

[0018] Optionally, the electric heater formula is , wherein, is the hourly discharging power of the electric heater, is the efficiency in the system structure parameters, is the hourly electric heater operation power; the heat storage tank formula is , wherein, is the capacity state of the heat storage tank, is the capacity state of the heat storage tank at the next time; is the heat loss coefficient in the system structure parameters, and are the charging efficiency of the heat storage tank and the discharging efficiency of the heat storage tank in the system structure parameters respectively, and is the hourly charging power of the thermal storage tank, and is the scheduling time step of the model.

[0019] Optionally, the objective function is wherein, is the Pth preset scenario in the preset time, is the total scenario set in the scenario parameters, is the occurrence probability of the Pth preset scenario in the scenario parameters, and t is time, is the time set in the scenario parameters, is the electricity purchase price in the user demand parameters, is the hourly grid electricity purchase power; The constraint conditions at least include: , wherein, is the hourly photovoltaic panel power generation, is the hourly electric heater operating power, is the building electrical load in the user demand parameters, is the hourly heat release power of the electric heater, is the hourly system heat generation power, is the hourly charging power of the thermal storage tank, is the building heat load in the user demand parameters, is the hourly charging power of the thermal storage tank.

[0020] In a second aspect, the application provides a photovoltaic-thermal system, which comprises a dynamic heat transfer subsystem and an auxiliary electric heating optimization subsystem, the dynamic heat transfer subsystem comprises a glass plate, a photovoltaic panel, a heat absorption plate, an evaporation pipeline, an insulation plate, an evaporator, and a condenser, the condenser comprises a condensing pipe, the auxiliary electric heating optimization subsystem comprises an electric heater and a thermal storage tank, and 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 the present application are: a dynamic heat transfer model of a dynamic heat transfer subsystem and an auxiliary electric heating optimization model of an auxiliary electric heating optimization subsystem are constructed, environment parameters, user demand parameters and scene parameters are included in the same optimization framework, so that the environment and load and the auxiliary electric heating optimization scheme are deeply coupled, the system operation stability is improved, the scheme optimization is carried out by combining the scene parameters, so as to avoid the singleization of working conditions through the scene quantization uncertainty, cover multiple potential operation scenes, and finally the generated auxiliary electric heating optimization scheme can be dynamically adapted according to the actual working condition, and the applicability under complex and variable working conditions is significantly improved. The dynamic heat transfer model and the auxiliary electric heating optimization model in the embodiment can minimize the purchase cost while improving the overall energy utilization efficiency of the system, so as to realize the best operation state of the photovoltaic and light heat system. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 is a structural schematic diagram of a photovoltaic and light heat system provided by an embodiment of the present application; Figure 2 is a flowchart of a building photovoltaic and light heat system dynamic modeling and optimal scheduling method provided by an embodiment of the present application; Figure 3 is a flowchart of determining environment parameters, user demand parameters and scene parameters provided by an embodiment of the present application; Figure 4 is a flowchart of obtaining hourly photovoltaic panel power generation and hourly system heat power provided by an embodiment of the present application; Figure 5 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the purposes, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the drawings in the present application serve only the purpose of description and illustration, and do not serve to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in the present application show the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowcharts or one or more operations can be removed from the flowcharts under the guidance of the content of the present application.

[0025] In addition, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

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

[0027] In the prior art, the operation efficiency of the photovoltaic photo-thermal system is easily affected by external environment, the electric output and hot water output have high uncertainty, and the building electric load and hot water load demand also have obvious fluctuation. At present, there is no effective method to predict and schedule the building electric load demand and hot water load demand to realize the best operation state of the photovoltaic photo-thermal system, reduce energy waste, and improve energy supply efficiency.

[0028] Based on this, the present application proposes a building photovoltaic photo-thermal system dynamic modeling and optimal scheduling method. The method outputs the hourly photovoltaic panel power generation and the hourly system heat production power through a plurality of dynamic formulas in the dynamic heat transfer model, and then optimizes the electric heater formula, the heat storage tank formula, the objective function, and the constraint condition in the auxiliary electric heating optimization model to obtain the hourly grid power purchase power, the hourly electric heater running power, the hourly charging power, and the hourly discharging power, so as to improve the operation energy efficiency of the photovoltaic photo-thermal system and reduce the building energy consumption.

[0029] Figure 1is a structural schematic diagram of a photovoltaic and photo-thermal system provided by an embodiment of the present application. Before introducing a dynamic modeling and optimal scheduling method for a building photovoltaic and photo-thermal system, a photovoltaic and photo-thermal system to which the method is applied is introduced.

[0030] Optionally, the photovoltaic and photo-thermal system comprises a dynamic heat transfer subsystem and an auxiliary electric heating optimization subsystem, the dynamic heat transfer subsystem comprises a glass plate, a photovoltaic panel, a heat absorption plate, an evaporation pipeline, an insulation plate, an evaporator, and a condenser, the condenser comprises a condensing pipeline, and the auxiliary electric heating optimization subsystem comprises an electric heater and a heat storage water tank.

[0031] Specifically, in the dynamic heat transfer subsystem, the glass plate, the photovoltaic panel, the heat absorption plate, the evaporation pipeline, and the insulation plate are placed in sequence, the evaporation pipeline is connected to the condensing pipeline of the condenser through a pump, the condenser is in communication with the heat storage water tank, the heat storage water tank is provided with the electric heater, and finally the heat energy satisfying the hot water load is delivered to the building.

[0032] Optionally, the glass plate is transparent and used for protecting the photovoltaic panel, the photovoltaic panel converts solar energy into electric energy, the heat absorption plate absorbs solar heat and transfers the solar heat to the working medium in the evaporation pipeline, the refrigerant in the evaporation pipeline absorbs heat and changes phase, circulates and transfers heat in the system, the insulation plate reduces heat loss of the evaporator, the evaporator realizes conversion of solar energy from electricity to heat and evaporation of the working medium, the condenser releases heat of the working medium through the condensing pipeline to complete heat exchange to provide heat supply energy, the pump drives the refrigerant to circulate between the evaporator and the condenser, the heat storage water tank stores heat delivered by the condenser to adjust time distribution of heat supply, and the electric heater assists heating when heat produced by the system is insufficient to ensure stable supply of the hot water load.

[0033] Next, reference is made to Figure 2 A specific embodiment of a dynamic modeling and optimal scheduling method for a building photovoltaic and photo-thermal system is introduced. Among them, Figure 2 is a flowchart of a dynamic modeling and optimal scheduling method for a building photovoltaic and photo-thermal system provided by an embodiment of the present application. Optionally, the method can be applied on an electronic device, the electronic device outputs an auxiliary electric heating optimization scheme and controls the photovoltaic and photo-thermal system to execute the scheme based on the auxiliary electric heating optimization scheme.

[0034] S201, a plurality of system structure parameters of a photovoltaic and photo-thermal system are acquired, and a plurality of environment parameters, a plurality of user demand parameters, and a plurality of scene parameters of the photovoltaic and photo-thermal system are determined.

[0035] The system structure parameters are inherent hardware performance and structure attribute parameters of the photovoltaic and photo-thermal system, and are static parameters determined when the system is designed or delivered from the factory. Specifically, the system structure parameters include an electric heater efficiency, a heat loss coefficient of a heat storage tank, a heat charging efficiency of the heat storage tank, a heat discharging efficiency of the heat storage tank, an upper and lower limit of an electric heater operating power, an upper and lower limit of a heat charging power of the heat storage tank, an upper and lower limit of a heat discharging power of the heat storage tank, an upper and lower limit of a capacity of the heat storage tank, a dispatch time step, and inherent structure and performance parameters of components of an evaporator and a condenser, such as a glass plate light transmission coefficient, a photovoltaic panel area, a heat absorption plate heat transfer coefficient, an evaporation pipe diameter, an insulation plate thermal resistance, and a condenser condensing pipe structure parameter.

[0036] The environment parameters are external natural condition parameters that affect the operation of the photovoltaic and photo-thermal system, and have dynamic variability. The future period prediction values can be obtained based on historical environment data through a prediction model, such as a time series model and a machine learning prediction model. The environment parameters specifically include hourly solar radiation intensity and hourly environmental temperature.

[0037] The user demand parameters are dynamic demand parameters of the building user in terms of electric and thermal energy use, and can be obtained based on historical user energy consumption data combined with user behavior patterns. Specifically, the user demand parameters include hourly building electric load, hourly building thermal load, and electricity purchase price. The hourly building electric load is, for example, the power demand of an office building at different times, and the hourly building thermal load is, for example, the hot water heat power demand at different times.

[0038] The environment parameters are a set of multiple possible situations and corresponding probabilities constructed to cope with the uncertainty of the environment parameters and the user demand parameters, and can be generated through analysis and prediction of the uncertainty of the environment and the user demand. Specifically, the environment parameters include a set of multiple random scenes and the probability of occurrence of each scene. The random scenes are, for example, a sunny day high electric load scene and an overcast day low thermal load scene.

[0039] In S202, a dynamic heat transfer model of the dynamic heat transfer subsystem is obtained, and the multiple system structure parameters and the multiple environment parameters are input into the dynamic heat transfer model to obtain hourly photovoltaic panel power generation and hourly system heat generation power.

[0040] Specifically, a dynamic heat transfer model of the dynamic heat transfer subsystem is first constructed. The model is a tool for simulating the heat transfer and energy conversion process between components in the photovoltaic and photo-thermal system. The components of the dynamic heat transfer subsystem can include a glass plate, a photovoltaic panel, a heat absorption plate, an evaporation pipe, a condenser, etc. Then, the system structure parameters and the environment parameters are input into the model. Through calculation of the formulas in the model, the hourly photovoltaic panel power generation and the system heat generation power data are finally obtained. These data will be important inputs for the subsequent auxiliary electric heating optimization model, and will be used to develop an optimal energy dispatching scheme.

[0041] It is worth noting that in this step, part of the system structure parameters is input into the dynamic heat transfer model.

[0042] S203, an auxiliary electric heating optimization model of the auxiliary electric heating optimization subsystem is obtained, and the plurality of system structure parameters, the plurality of environment parameters, the plurality of user demand parameters, the plurality of scene parameters, the hourly photovoltaic panel power generation and the hourly system heat generation power are input into the auxiliary electric heating optimization model to obtain the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power.

[0043] Specifically, the auxiliary electric heating optimization model is first constructed, which is used to optimize and schedule the operation of the electric heater and the heat storage tank. Then, the system structure parameters, the environment parameters, the user demand parameters, the scene parameters, and the hourly photovoltaic panel power generation and the hourly system heat generation power output by the dynamic heat transfer model are input into the model. Through the optimization process of selecting the optimal solution from a plurality of feasible operation schemes under the premise of meeting the electric / thermal load demand and the equipment operation constraint, the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power are finally output. The goal is to minimize the purchase power cost while ensuring stable supply of building electricity and heat demand.

[0044] S204, based on the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power, an auxiliary electric heating optimization scheme is generated.

[0045] Optionally, after obtaining the hourly power grid purchase power, the hourly electric heater operation power, the hourly charging power and the hourly discharging power, the electrical energy balance check, the thermal energy balance check and the device boundary check can be performed based on the constraint conditions. Specifically, the electrical energy balance check is to calculate whether the sum of the hourly verification hourly power grid purchase power and the hourly photovoltaic panel power generation is equal to the sum of the hourly electric heater operation power and the hourly building electricity load, to ensure that there is no gap and no redundant waste in electricity supply and demand. The thermal energy balance check is to calculate whether the sum of the hourly electric heater discharging power, the hourly system heat generation power and the hourly discharging power is equal to the sum of the hourly building heat load and the hourly charging power, to ensure that the heat supply and demand are matched. The device boundary check is used to verify whether the hourly electric heater operation power exceeds the upper and lower limits of its power, whether the hourly charging and discharging power exceeds the upper and lower limits of the charging and discharging power of the heat storage tank, whether the capacity state of the heat storage tank is within the upper and lower limits of the capacity, etc. If there is a condition that does not meet the constraint, the power data needs to be re-iterated and adjusted in the auxiliary electric heating optimization model until the whole period is in compliance.

[0046] Optionally, after all data checking compliance, the hourly grid purchase power, hourly electric heater running power, hourly charging power and hourly discharging power are converted into specific operation instructions of the auxiliary electric heating subsystem to generate an auxiliary electric heating optimization scheme. The auxiliary electric heating optimization scheme can be used to guide the work of each component in the photovoltaic-thermal system. The auxiliary electric heating optimization scheme can also include matters needing attention, such as marking equipment maintenance period, mode under extreme weather. For example, if the electric heater is overhauled every week, the charging power of the thermal storage tank needs to be increased in advance during the corresponding period, and if there is no output of photovoltaic under heavy rain, the system automatically switches to the mode of grid purchase power, i.e. full load of electric heater.

[0047] In this embodiment, a dynamic heat transfer model of the dynamic heat transfer subsystem and an auxiliary electric heating optimization model of the auxiliary electric heating optimization subsystem are constructed, and by incorporating environmental parameters, user demand parameters and scene parameters into the same optimization framework, the environment and load and the auxiliary electric heating optimization scheme are deeply coupled, the system operation stability is improved, the scheme optimization is carried out by combining scene parameters, so as to avoid single working condition by quantifying scene uncertainty, cover multiple potential operating scenarios, and finally the generated auxiliary electric heating optimization scheme can be dynamically adapted according to the actual working condition, which significantly improves the applicability under complex and variable working conditions. In this embodiment, by means of the dynamic heat transfer model and the auxiliary electric heating optimization model, the overall energy utilization efficiency of the system is improved while ensuring the minimization of the purchase power cost, and the best operating state of the photovoltaic-thermal system is realized.

[0048] Next, referring to Figure 3 The step of determining the plurality of environmental parameters, the plurality of user demand parameters and the plurality of scene parameters in step S201 is introduced. Among them, Figure 3 is a flowchart provided by the embodiment of the application for determining environmental parameters, user demand parameters and scene parameters.

[0049] S301, based on the plurality of historical environmental parameters obtained in advance, a plurality of environmental parameters are predicted.

[0050] Optionally, the plurality of environmental parameters can be determined according to the periodicity and correlation of historical environmental parameters over time.

[0051] As an optional implementation, the plurality of environmental parameters are predicted based on a time series model. Specifically, the change rule of the historical environmental parameters in a preset period, such as the diurnal cycle of solar radiation, the seasonal trend of air temperature, etc., is used to fit the time sequence characteristics of the historical data, and the predicted value of the future period is extrapolated to obtain the plurality of environmental parameters.

[0052] As another optional implementation, the plurality of environment parameters are predicted based on a machine learning model. Specifically, the historical hourly environment data is taken as input, the dynamic correlation between parameters is learned, and the future hourly prediction result is output. This method is particularly suitable for capturing nonlinear change rules.

[0053] As yet another optional implementation, the similar day method is used to quickly obtain the prediction value, that is, the dates with similar weather conditions to the prediction day are screened from the historical data, and the environment parameters thereof are used as the basis combined with short-term trend fine-tuning.

[0054] S302, based on the plurality of user historical parameters obtained in advance, a plurality of user demand parameters are predicted.

[0055] Optionally, the plurality of user demand parameters can be predicted based on the similar day method, the machine learning model, or the user behavior modeling according to the plurality of user historical parameters obtained in advance.

[0056] S303, based on the plurality of historical scene parameters obtained in advance, a plurality of scene parameters are predicted.

[0057] Optionally, the plurality of user demand parameters can be predicted based on the similar day method, the machine learning model, or the user behavior modeling according to the plurality of user historical parameters obtained in advance.

[0058] Optionally, the plurality of user demand parameters can be predicted based on clustering and probability estimation, Monte Carlo simulation, or Bayesian update according to the plurality of user historical parameters obtained in advance.

[0059] In this embodiment, the plurality of environment parameters, user demand parameters, and scene parameters are determined based on historical data to improve the timeliness and accuracy of the parameters and to fit the actual operating conditions.

[0060] Next, refer to Figure 4 The method for obtaining the hourly photovoltaic panel power generation and the hourly system heat generation power in step S202 is described in detail. Among them, Figure 4 is a flowchart of obtaining the hourly photovoltaic panel power generation and the hourly system heat generation power provided by the embodiment of the present application.

[0061] S401, input the plurality of system structure parameters and the plurality of environment parameters into the evaporator model to obtain the hourly photovoltaic panel power generation and the average temperature of the working medium in the evaporator pipeline.

[0062] Specifically, the evaporator model is used to convert solar energy into electrical energy and heat energy, and transfer to the refrigerant.

[0063] Optionally, the system structure parameters of the evaporator model input include inherent properties of each component of the evaporator, such as the light transmission coefficient of the glass plate, the area of the photovoltaic panel, and the photoelectric conversion efficiency, etc., which determine the energy conversion and transmission capacity of the evaporator, and multiple environmental parameters affect the external conditions of solar energy absorption.

[0064] Optionally, when the evaporator model is running, the solar radiation is first transmitted through the glass plate to the photovoltaic panel, and the photovoltaic panel converts part of the solar energy into electrical energy based on its conversion efficiency to form the hourly photovoltaic panel power generation; at the same time, the solar energy that is not converted by the photovoltaic panel is transmitted to the heat absorption plate through heat conduction, and the heat absorption plate further transmits the heat to the internal evaporating pipeline, so that the refrigerant in the pipeline absorbs heat and warms up, and finally the average temperature of the refrigerant in the evaporating pipeline is calculated.

[0065] S402, input multiple system structure parameters and the average temperature of the refrigerant in the evaporating pipeline into the refrigerant dynamic model to obtain the average temperature of the refrigerant in the evaporating pipeline, the inlet temperature of the refrigerant in the evaporating pipeline, the outlet temperature of the refrigerant in the evaporating pipeline, the average temperature of the refrigerant in the condensing pipeline, the inlet temperature of the refrigerant in the condensing pipeline, and the outlet temperature of the refrigerant in the condensing pipeline.

[0066] Optionally, the refrigerant dynamic model is used to simulate the circulation flow and temperature change of the refrigerant between the evaporating pipeline and the condensing pipeline.

[0067] Optionally, when the model is running, based on the flow characteristics of the refrigerant and the heat transfer characteristics of the pipeline, the temperature distribution of the refrigerant in the evaporating pipeline is calculated: the refrigerant absorbs heat and the temperature gradually rises when flowing into the evaporating pipeline from the inlet, forming the inlet temperature of the refrigerant in the evaporating pipeline and the outlet temperature of the refrigerant in the evaporating pipeline, wherein the outlet temperature is higher than the inlet temperature, and the difference reflects the heating effect of the evaporator; then, the refrigerant flows into the condensing pipeline of the condenser, and the temperature gradually changes during the flow process due to the heat dissipation of the pipeline and the heat exchange with the condenser, and finally the average temperature of the refrigerant in the condensing pipeline, the inlet temperature of the refrigerant in the condensing pipeline, and the outlet temperature of the refrigerant in the condensing pipeline are output, wherein the outlet temperature is lower than the inlet temperature, and the difference reflects the heat released by the refrigerant to the condenser.

[0068] S403, input the average temperature of the refrigerant in the evaporating pipeline, the inlet temperature of the refrigerant in the evaporating pipeline, the outlet temperature of the refrigerant in the evaporating pipeline, the average temperature of the refrigerant in the condensing pipeline, the inlet temperature of the refrigerant in the condensing pipeline, and the outlet temperature of the refrigerant in the condensing pipeline into the condenser dynamic model to obtain the hourly system heat production power.

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

[0070] Optionally, the model runtime, based on the structure parameters of the condenser tube and the thermophysical properties of the working medium, calculates the heat released by the working medium through the temperature change of the working medium in the condenser tube; at the same time, combined with the heat exchange efficiency of the condenser and the external heating system, the heat released by the working medium is converted into the heat energy that can be output by the system, and the hourly system heat output power is finally obtained.

[0071] In this embodiment, by determining the hourly photovoltaic panel power generation and the hourly system heat output power, the energy changes of each link are accurately quantified in combination with the system structure parameters and the environmental parameters.

[0072] Further, the implementation steps of the above step S401 are introduced next.

[0073] Optionally, the multiple system structure parameters and the multiple environmental parameters are input into the glass plate formula, the photovoltaic panel formula, the heat absorption plate formula, the evaporator tube formula and the heat insulation plate formula in the evaporator model to obtain the hourly photovoltaic panel power generation and the average temperature of the working medium in the evaporator tube.

[0074] The glass plate formula is shown in the following formula (1): (1) wherein, , and are the mass, the heat capacity and the temperature of the glass plate in the system structure parameters, is the hourly environmental temperature in the environmental parameters, is the hourly photovoltaic panel temperature, is the hourly solar radiation intensity in the environmental parameters, is the light transmission coefficient of the glass plate in the system structure parameters, is the area of the glass plate in the system structure parameters, and are the heat transfer coefficient between the glass plate and the environment and the heat transfer coefficient between the glass plate and the photovoltaic panel in the system structure parameters, and are the radiation heat exchange between the glass plate and the photovoltaic panel and the radiation heat exchange between the glass plate and the environment in the environmental parameters.

[0075] Optionally, the glass plate formula is used to represent the following relationship: the heat storage change rate of the glass plate is equal to the sum of the absorbed heat of the solar radiation through the glass plate, the convective heat exchange amount of the glass plate and the air, the convective heat exchange amount of the glass plate and the photovoltaic panel, and the radiation heat exchange amount of the glass plate to the photovoltaic panel, and is different from the radiation heat exchange amount of the glass plate to the air.

[0076] As an optional implementation, the radiation heat exchange between the glass plate and the photovoltaic panel and the radiation heat exchange between the glass plate and the environment may be a preset value. As another alternative implementation, the radiative heat exchange between the glass plate and the photovoltaic panel and the radiative heat exchange between the glass plate and the environment may be obtained based on the following equations (2) and (3), respectively: (2) (3) where σ is the Stefan-Boltzmann constant; and are the emissivities of the glass plate and the photovoltaic panel, respectively, in the system structure parameters.

[0077] Optionally, the photovoltaic panel is formulated as shown in the following equation (4): (4) where , and are the mass, the heat capacity and the area of the photovoltaic panel, respectively, in the system structure parameters, is the light absorption coefficient of the photovoltaic panel in the system structure parameters, is the hourly photovoltaic panel power generation, is the heat transfer coefficient between the photovoltaic panel and the heat absorption plate in the system structure parameters, is the hourly heat absorption plate temperature. is the radiative heat exchange between the photovoltaic panel and the heat absorption plate in the environmental parameters.

[0078] Optionally, the photovoltaic panel formula is used to represent the following relationship: the thermal storage change rate of the photovoltaic panel is equal to the sum of the solar radiation heat absorbed by the photovoltaic panel, the negative value of the electrical energy output by the photovoltaic panel power generation, the convective heat exchange between the glass plate and the photovoltaic panel, the negative value of the radiative heat exchange from the glass plate to the photovoltaic panel, the convective heat exchange between the photovoltaic panel and the heat absorption plate, and the negative value of the radiative heat exchange from the photovoltaic panel to the heat absorption plate. That is, the temperature dynamic change of the photovoltaic panel under the effects of solar radiation, convection, radiation and its own power generation is quantified.

[0079] As an alternative implementation, the radiative heat exchange between the photovoltaic panel and the heat absorption plate may be a preset value. As another alternative implementation, the radiative heat exchange between the photovoltaic panel and the heat absorption plate may be obtained based on the following equation (5): (5) where is the emissivity of the heat absorption plate.

[0080] where the hourly photovoltaic panel power generation is calculated by the following equation (6): (6) wherein, is the transfer coefficient of the photovoltaic panel among the system structure parameters. is the power generation reference efficiency of the photovoltaic panel among the system structure parameters. is the photovoltaic panel efficiency decay coefficient among the system structure parameters. is the reference power generation temperature of the photovoltaic panel among the system structure parameters.

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

[0082] The heat absorption plate formula is shown in the following formula (7): (7) wherein, , and are the mass, the heat capacity, and the area of the heat absorption plate among the system structure parameters, and are the heat transfer coefficient between the heat absorption plate and the evaporation pipeline and the heat transfer coefficient between the heat absorption plate and the heat insulation plate among the system structure parameters. and are the hourly evaporation pipeline temperature and the hourly heat insulation plate temperature.

[0083] Optionally, the heat absorption plate formula is used to represent the following relationship: the heat storage change rate of the heat absorption plate is equal to the sum of the convective heat transfer amount between the photovoltaic panel and the heat absorption plate, the radiative heat transfer amount from the photovoltaic panel to the heat absorption plate, the convective heat transfer amount between the heat absorption plate and the evaporation pipeline, and the convective heat transfer amount between the heat absorption plate and the heat insulation plate. That is, the heat storage change rate is the sum of the total heat transferred to the heat absorption plate by each component, quantifying the temperature dynamic change of the heat absorption plate under the heat transfer action of the photovoltaic panel, the evaporation pipeline, the heat insulation plate, and the like.

[0084] The evaporation pipeline formula is shown in the following formula (8): (8) wherein, , and are the mass, the heat capacity, and the outer surface area of the evaporation pipeline among the system structure parameters, and are the heat transfer coefficient between the evaporation pipeline and the heat insulation plate and the heat transfer coefficient between the evaporation pipeline and the working medium among the system structure parameters. and respectively are the radius and length of the evaporating pipe. is the average temperature of the working medium in the evaporating pipe.

[0085] Optionally, the evaporating pipe formula is used to express the following relationship: the heat storage rate of change of the evaporating pipe is equal to the sum of the convective heat exchange between the heat absorbing plate and the evaporating pipe, the convective heat exchange between the heat insulation plate and the evaporating pipe, and the difference of the convective heat exchange between the evaporating pipe and the internal working medium. That is, the heat storage rate of change of the pipe is the difference between the sum of the heat transferred from the heat absorbing plate and the heat exchanged by the heat insulation plate and the heat transferred to the working medium, which quantifies the temperature dynamic change of the evaporating pipe under the heat transfer action of the heat absorbing plate, the heat insulation plate and the internal working medium.

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

[0087] The heat insulation plate formula is used to express the following relationship: the heat storage rate of change of the heat insulation plate is equal to the sum of the convective heat exchange between the evaporating pipe and the heat insulation plate, the convective heat exchange between the heat insulation plate and the air, and the convective heat exchange between the heat absorbing plate and the heat insulation plate. That is, the temperature dynamic change of the heat insulation plate under the heat transfer action of each component is quantified, and the core function is to quantify the heat exchange of the heat insulation plate to ensure that the heat of the evaporator is used for heating the working medium as much as possible.

[0088] As an optional implementation, the refrigerant dynamic model includes an evaporating pipe working medium formula and a condensing pipe working medium formula.

[0089] The evaporating pipe working medium formula is shown in the following formula (10): (10) The condensing pipe working medium formula is shown in the following formula (11): (11) wherein, and are the mass and heat capacity of the refrigerant in the system structure parameters, respectively. , and are the average temperature of the working medium in the evaporating pipe, the inlet temperature of the working medium in the evaporating pipe and the outlet temperature of the working medium in the evaporating pipe, respectively. , , are the average temperature of the working medium in the condensing pipe, the inlet temperature of the working medium in the condensing pipe and the outlet temperature of the working medium in the condensing pipe, respectively. and respectively, are the diameter and length of the condenser tube in the system structure parameters. Optionally, the model ignores the pipe friction and heat loss, so the working fluid inlet temperature in the evaporator pipe is equal to the working fluid outlet temperature in the condenser tube, and the working fluid outlet temperature in the evaporator pipe is equal to the working fluid inlet temperature in the condenser tube.

[0090] Optionally, the refrigerant dynamic model is used to simulate the circulation and temperature change of the working fluid between the evaporator pipe and the condenser tube. The heat transferred to the working fluid by each component of the evaporator directly affects the temperature of the working fluid in the evaporator pipe, for example, the temperature of the working fluid increases after absorbing heat in the evaporator pipe formula, and these working fluid temperature parameters are input into the refrigerant dynamic model, which calculates the temperature distribution of the working fluid in the evaporator pipe and the temperature change after entering the condenser tube based on the flow characteristics of the working fluid and the heat transfer characteristics of the pipe.

[0091] Specifically, in the refrigerant dynamic model, based on the principle of energy conservation of the working fluid, the temperature change from absorbing the heat of the evaporator to transferring the heat to the condenser in the working fluid cycle is quantified by setting the evaporator pipe outlet temperature equal to the condenser tube inlet temperature and the evaporator pipe inlet temperature equal to the condenser tube outlet temperature.

[0092] Optionally, the condenser dynamic model includes a condenser tube formula, a condenser water formula, and a system heat production formula.

[0093] The condenser tube formula is shown in the following formula (12): (12) The condenser water formula is shown in the following formula (13): (13) The system heat production formula is shown in the following formula (14): (14) wherein, , and are the mass of the condenser tube, the heat capacity of the condenser tube, and the hourly condenser tube temperature in the system structure parameters, respectively. and are the heat transfer coefficient between the condenser tube and the condenser water and the heat transfer coefficient between the condenser tube and the working fluid in the system structure parameters, respectively. and are the radius and length of the condenser tube in the system structure parameters, respectively. and are the average temperature of the condenser water in the condenser tube and the average temperature of the working fluid in the condenser tube, respectively. is the hourly system heat production power.

[0094] For the condenser tube formula, the heat storage rate of the condenser tube is equal to the sum of the convective heat transfer between the condenser tube and the condensing water and the convective heat transfer between the condenser tube and the working medium. For the condensing water formula, the heat storage rate of the condensing water is the sum of the convective heat transfer between the condenser tube and the condensing water and the heat change of the condensing water itself flowing. For the system heat production formula, the formula quantifies the heat absorbed by the condensing water, that is, the effective heat energy output by the system to the outside.

[0095] Next, the specific process of step S203 is introduced. The auxiliary electric heating optimization model includes an electric heater formula, a heat storage tank formula, an objective function, and a constraint condition.

[0096] Optionally, the plurality of system structure parameters, the plurality of environment parameters, the plurality of user demand parameters, the plurality of scene parameters, the hourly photovoltaic panel power generation, and the hourly system heat production power are input into the electric heater formula and the heat storage tank formula in the auxiliary electric heating optimization model, and optimization is performed based on the objective function and the constraint condition to obtain the hourly grid purchase power, the hourly electric heater operation power, the hourly charging power, and the hourly discharging power.

[0097] Specifically, under the premise of meeting the constraint condition, a large number of feasible scheduling schemes covering different time periods of grid purchase power, electric heater operation, and heat storage tank charging and discharging mode combinations are generated by the algorithm, and then the objective function value of each feasible scheme is quantitatively calculated based on the objective function of minimizing the total purchase cost. Finally, through comparison of the target values among multiple schemes, the optimal scheme that optimizes the objective function is selected, and the corresponding hourly grid purchase power, hourly electric heater operation power, and hourly charging and discharging power are the final scheduling output of the auxiliary electric heating optimization subsystem, thereby realizing the economic optimal operation of the system under the constraints of energy supply and demand and equipment operation.

[0098] In this embodiment, by optimizing based on the objective function and the constraint condition, the purchase cost is accurately reduced, multiple working conditions and uncertainties are covered, and the energy utilization efficiency is improved.

[0099] The electric heater formula is shown in the following formula (15): (15) Wherein, is the hourly discharging power of the electric heater, is the efficiency in the system structure parameters, is the hourly electric heater operation power.

[0100] The heat storage tank formula is shown in the following formula (16): (16) Wherein, is the capacity state of the thermal storage tank, is the capacity state of the thermal storage tank at the next time. is the heat loss coefficient in the system structure parameters, and are the charging efficiency of the thermal storage tank and the discharging efficiency of the thermal storage tank in the system structure parameters, respectively, and are the hourly charging power of the thermal storage tank and the hourly discharging power of the thermal storage tank, respectively, is the scheduling time step of the model.

[0101] Optionally, the electric heater formula embodies the process of converting electric energy into heat energy through the electric heater, and the thermal storage tank formula embodies the dynamic heat balance of storage, loss, charging and discharging of the storage tank.

[0102] Optionally, the objective function is as follows formula (17): (17) wherein, is the Pth preset scene in a preset time, is a total scene set in the scene parameters, is the occurrence probability of the Pth preset scene in the scene parameters, and t is time, is a time set in the scene parameters, is the electricity purchase price in the user demand parameters, is the hourly grid electricity purchase power.

[0103] Specifically, for each preset scene P, first, according to its occurrence probability is given a weight, then the electricity purchase cost under each time t in this scene is calculated, and finally the weighted sum of the electricity purchase costs of all scenes and all times is calculated, and the optimal electricity purchase power combination that minimizes the sum is found through an optimization algorithm.

[0104] In this embodiment, both a variety of possible working conditions are covered and the priority of high-probability scenes is highlighted through scene probability, and the final optimization scheme can achieve lower electricity purchase cost in most actual working conditions, thereby improving the robustness and practicality of the scheme.

[0105] Optionally, the constraint conditions at least include conditions as shown in formulas (18) and (19): (18) (19) wherein, is the hourly photovoltaic panel power generation, is the hourly electric heater operating power, is the building electrical load in the user demand parameters, is the hourly discharging power of the electric heater, is the hourly system heat production power, is the hourly charging power of the thermal storage tank, is the building heat load in the user demand parameters, is the hourly discharging power of the thermal storage tank.

[0106] As an optional implementation, the constraint conditions can also include conditions as shown in formula (20), formula (21), formula (22) and formula (23): (20) (21) (22) (23) wherein, and are the lower and upper limits of the operation of the electric heater in the system structure parameters, and are the lower and upper limits of the charging of the thermal storage tank in the system structure parameters; and are the lower and upper limits of the discharging of the thermal storage tank; and are the lower and upper limits of the capacity of the thermal storage tank.

[0107] Specifically, through the above constraint conditions, formula (20) limits that the operation power of the electric heater must be within the power range designed by itself. Formula (21) limits that when the thermal storage tank is charging, the power must be within the allowed range, so as to avoid that the low charging power leads to low heat storage efficiency, or the high charging power causes damage to the structure of the storage tank and heat exchange components. Formula (22) limits that when the thermal storage tank is discharging, the power must be within the allowed range, so as to avoid that the low discharging power cannot meet the heat load demand, or the high discharging power leads to sudden drop of heat in the storage tank and damage to the equipment due to thermal shock. Formula (23) limits that the heat storage amount of the thermal storage tank must be within the safe range, and cannot be lower than the minimum capacity.

[0108] In this embodiment, the constraint conditions guarantee the safety and reliability of the equipment, reduce the operation and maintenance cost, enhance the robustness of the scheme, and adapt to multiple scene complex working conditions.

[0109] The embodiment of the present application further provides a photovoltaic and photo-thermal system, which comprises a dynamic heat transfer subsystem and an auxiliary electric heating optimization subsystem, the dynamic heat transfer subsystem comprises a glass plate, a photovoltaic plate, a heat absorbing plate, an evaporation pipeline, an insulation plate, an evaporator and a condenser, the condenser comprises a condensing pipeline, the auxiliary electric heating optimization subsystem comprises an electric heater and a heat storage water tank, and the photovoltaic and photo-thermal system is used for executing the building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method.

[0110] The embodiment of the present application further provides an electronic device. Figure 5 As shown in Fig. 1, an electronic device provided by the embodiment of the present application comprises a processor 501, a memory 502 and a bus. The memory 502 stores machine readable instructions executable by the processor 501, and the processor 501 and the memory 502 communicate through the bus when the computer device is running. The machine readable instructions are executed by the processor 501 to perform the processing of the building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method.

[0111] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to perform the steps of the building photovoltaic and photo-thermal system dynamic modeling and optimal scheduling method.

[0112] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system and the device described above can refer to the corresponding process in the method embodiment, and the present application will not be described again. In the several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. The device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and the actual implementation can be another division manner, for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be indirect coupling or communication connection through some communication interface, device or module, which can be electrical, mechanical or other forms.

[0113] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. When the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that are essential or contribute to the prior art can be embodied in the form of software products, which are stored in a storage medium and include a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0114] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present 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: 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.

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 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.

4. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 3, 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 plate and the evaporation pipe, and the heat transfer coefficients between the heat absorber plate 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.

5. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 3, 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 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 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.

6. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 3, 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.

7. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 1, characterized in that, 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. 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.

8. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 7, characterized in that, 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.

9. The method for dynamic modeling and optimal scheduling of building photovoltaic and solar thermal systems according to claim 7, characterized in that, 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 charging power of the hot water storage tank. It is the building heat load in the user requirement parameters. It is the hourly charging power and hourly releasing power of the hot water storage tank.

10. 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 1-9.

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