Photovoltaic-greening synergistic interaction system and method for building roof

Abstract

The invention discloses a photovoltaic-greening synergistic interaction system and method for a building roof, and belongs to the field of green buildings and renewable energy sources, and the photovoltaic-greening synergistic interaction system comprises a solar cell panel array which is erected above a roof base layer, and a heat absorption pipeline is attached to a back plate of the solar cell panel array; the vegetation layer is located below the solar cell panel array, and a water supply pipeline is distributed in the vegetation layer; the execution module comprises a first circulation path communicated with the heat absorption pipeline and a second circulation path communicated with the water supply pipeline; the monitoring module comprises a temperature probe, a humidity probe and a weather station; and the control module is electrically connected with the monitoring module, the first power pump and the water volume regulator, cooperative control logic is solidified in the control module, and the working medium flow of the first circulation path and the water supply flow of the second circulation path are synchronously determined in a rolling optimization mode based on real-time sensing data driving. According to the system, collaborative optimization of photovoltaic cooling and greening irrigation and photovoltaic waste heat recycling can be achieved, and the comprehensive efficiency of the system is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of green building and renewable energy technology, and in particular relates to a photovoltaic-greening synergistic efficiency enhancement system and method for building roofs. Background Technology

[0002] Building-integrated photovoltaics (BIPV) integrates photovoltaic modules into the building envelope, effectively utilizing building surface space for renewable energy generation and serving as a thermal insulation layer to reduce indoor air conditioning energy consumption, resulting in significant energy and environmental benefits. Meanwhile, green roofs, as an effective means to improve the urban thermal environment, mitigate the urban heat island effect, and enhance building ecological performance, are gradually being promoted and applied in high-density cities. Given the increasing scarcity of space resources, combining photovoltaic power generation systems with green roofs to form a "photovoltaic-green" composite roof has become one of the important technological pathways that balance energy production and ecological functions. Existing research shows that there is a complex thermo-humidity interaction process between photovoltaic panels and green layers: the evaporation of the green layer can lower the surrounding air temperature, theoretically benefiting the heat dissipation of the photovoltaic panels; simultaneously, the shading of solar radiation by the photovoltaic panels can reduce moisture evaporation from the green layer, helping to maintain substrate humidity. The two have potential complementary and synergistic effects in terms of heat flux, and have already shown preliminary effects in improving photovoltaic power generation efficiency and reducing building heat gain in some demonstration projects.

[0003] However, current applications of photovoltaic (PV) and green roof combinations mostly employ simple stacking, with the two structures and functions independent of each other, lacking coordinated design and proactive control of heat and moisture transfer processes. In existing technologies, PV panels are typically mounted directly above the green layer, forming a closed or semi-closed airflow channel between the PV backsheet and the vegetation canopy. The heat exchange mechanism within this channel has not been fully understood or effectively utilized. Under high-temperature, high-irradiance conditions in summer, heat dissipation from the back of the PV panel is hindered, leading to a significant increase in module operating temperature and a marked decrease in photoelectric conversion efficiency. Meanwhile, the evaporative cooling potential of the green layer is difficult to fully realize due to untimely or excessive water supply, and improper irrigation may even result in water waste or stunted vegetation growth. Furthermore, there is a lack of quantitative monitoring and control methods for the heat flux (including sensible and latent heat exchange) between the PV panels and the green roof, and the dynamic thermodynamic coupling relationship between the two has not been effectively modeled and utilized, making it difficult to achieve coordinated optimization of the overall system energy efficiency. Existing control systems mostly adopt simple logic control based on thresholds, which cannot make forward-looking and global decisions based on weather changes, system status and vegetation needs. This leads to a trade-off between improving the efficiency of photovoltaic power generation and the ecological benefits of greening, and the overall performance of the system is not maximized. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a photovoltaic-greening synergistic efficiency enhancement system for building rooftops, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, in a first aspect, the present invention provides a photovoltaic-greening synergistic efficiency enhancement system for building rooftops, comprising: A solar panel array mounted on the roof base, with heat-absorbing pipes attached to its back panel; A vegetation layer located below the solar panel array, with water supply pipes distributed within the vegetation layer; The execution module includes a first circulation path and a second circulation path. The first circulation path is connected to the heat absorption pipeline and is equipped with a first power pump and a heat exporter. The second circulation path is connected to the water supply pipeline and is equipped with a second power pump and a water flow regulator. The monitoring module includes at least a temperature probe for detecting the temperature of the solar panel, a humidity probe for detecting the humidity of the vegetation layer, and a weather station for collecting meteorological parameters. The control module is electrically connected to the monitoring module, the first power pump, and the water flow regulator. The control module has a built-in collaborative control logic. The collaborative control logic is driven by real-time sensing data and synchronously determines the working fluid flow rate of the first circulation path and the water supply flow rate of the second circulation path in a rolling optimization manner, and outputs corresponding instructions.

[0006] Preferably, the heat extractor is connected to a liquid storage container, the outlet of which is connected to the inlet of the second circulation path to heat the irrigation water using heat energy recovered from the solar panel.

[0007] Preferably, the collaborative control logic includes: establishing the transient thermal balance equation of the solar panel and the water budget equation of the vegetation layer based on the data from the temperature probe, humidity probe and weather station; and determining the setpoint of the working fluid flow rate and the setpoint of the water supply flow rate at the current moment by solving the optimal control sequence in the future finite time domain, under the constraint of satisfying the safe temperature of the solar panel and the suitable humidity range of the vegetation layer, guided by the preset comprehensive efficiency index.

[0008] Preferably, the comprehensive performance index is the net value of power generation revenue after deducting pumping energy consumption and water resource consumption, and its expression includes adjustable weighting coefficients to adapt to the operating preferences of different seasons or regions.

[0009] Preferably, the transient heat balance equation considers the coupling effect of solar irradiation heating, convective heat dissipation, radiative heat transfer, and active cooling of the working fluid; the water budget equation considers the balance relationship of natural precipitation, artificial irrigation, vegetation transpiration, and deep seepage.

[0010] Preferably, the control module is further configured to compare the actual measured value at the next moment with the predicted value of the equation, and when the accumulated deviation exceeds the allowable range, automatically correct the heat transfer coefficient in the transient heat balance equation or the evapotranspiration coefficient in the moisture balance equation.

[0011] Secondly, the present invention also provides a synergistic enhancement method based on the said system, comprising the following steps: S1: Real-time data on solar panel temperature, vegetation layer humidity, irradiance, and air temperature are obtained through the monitoring module; S2: Calculate the power loss of the solar panel due to temperature rise based on the solar panel temperature, irradiance, and air temperature data, and estimate the latent heat flux of the vegetation layer based on the vegetation layer humidity, irradiance, air temperature data and vegetation parameters. S3: Construct a predictive model that includes the thermal dynamics of the solar panel and the wet dynamics of the vegetation layer; S4: With the goal of maximizing the comprehensive benefit function, solve for the optimal control quantity that satisfies the upper limit of temperature and the humidity range in the prediction time domain. The control quantity includes the cooling medium flow rate and the irrigation water volume. S5: Convert the solved cooling medium flow rate and irrigation water volume into control commands to drive the first power pump and water flow regulator to execute; S6: Based on the error between the measured state and the model prediction state, adjust the key parameters in the prediction model online.

[0012] Preferably, the formula for calculating the power loss in step S2 is: P loss (t) = A pv ·G(t)·η ref ·β·(T pv (t)-T a (t)); Among them, A pv For the area of ​​the photovoltaic panel, η ref β represents the standard test efficiency, and β is the power temperature coefficient.

[0013] Preferably, the calculation process of latent heat flux in step S2 is as follows: Calculate the reference crop evapotranspiration ET0(t); According to the soil moisture stress coefficient K s (t) and crop coefficient Kc are used to calculate the actual evapotranspiration ET. c (t): ETc(t) = Ks(t)·Kc·ET0(t); Calculate the latent heat flux: LE(t) = (λ·ρ) w ·ETc(t)) / 3600, where λ is the latent heat of vaporization of water, ρ w This is the density of water.

[0014] Preferably, the formula for calculating the comprehensive benefit function in step S4 is: J = w1·P elec - w2·E pump - w3·V water ; Among them, P elec To predict power generation revenue, E pump For pumping energy consumption, V water For irrigation water consumption, w1, w2, and w3 are configurable weighting coefficients.

[0015] Compared with the prior art, the present invention has the following advantages and technical effects: This invention electrically connects the control module, monitoring module, first power pump, and water flow regulator. Internally, it incorporates a collaborative control logic that synchronously determines the working fluid flow rate of the first circulation path and the water supply flow rate of the second circulation path based on real-time sensing data and using a rolling optimization method. This enables the system to dynamically coordinate the operation of the cooling and irrigation subsystems according to real-time sensing data on the solar panel temperature, vegetation layer humidity, and meteorological parameters, rather than controlling them independently or simply superimposing them. When the solar panels need cooling, the cooling fluid flow rate can be increased accordingly; when the vegetation layer needs water replenishment, the irrigation water supply can be adjusted synchronously. This avoids resource waste or mutual constraints caused by single-target control, achieving synergistic effects between photovoltaic power generation and greening ecological functions.

[0016] This invention employs a technical solution where "the execution module includes a first circulation path connected to the heat absorption pipeline and a second circulation path connected to the water supply pipeline, with the first circulation path equipped with a heat exporter." This allows the heat absorbed by the photovoltaic panel to be transferred to the second circulation path via the execution module for preheating irrigation water. By recovering waste heat generated during photovoltaic power generation and using it to raise the irrigation water temperature, this invention reduces heat loss from the photovoltaic panel to the environment and utilizes waste heat to replace some of the heating energy consumption, lowering the energy demand of the irrigation circuit. This achieves cascaded utilization of energy within the system and improves overall energy efficiency.

[0017] This invention utilizes a monitoring module comprising a temperature probe, a humidity probe, and a weather station to comprehensively collect data on solar panel temperature, vegetation layer humidity, and meteorological parameters; and a control module that drives collaborative control logic based on this real-time data. This enables the system to accurately perceive environmental changes and system status, and adjust accordingly. Compared to traditional timed control or single threshold control, this system can predict system needs in advance and respond based on dynamic factors such as changes in solar irradiance, temperature fluctuations, and vegetation water consumption rates. This avoids problems such as temperature exceeding limits or insufficient / excessive irrigation caused by control lag, significantly improving control accuracy and system operational stability.

[0018] This invention achieves a high degree of physical integration between the photovoltaic power generation system and the greening system through its technical features of "layering the solar panel array and the vegetation layer vertically, attaching heat absorption pipes to the back of the solar panels, distributing water supply pipes within the vegetation layer, and forming an organic whole through execution and control modules." The resulting technical advantages are: full utilization of the three-dimensional space of the building roof, reducing the area occupied; and the integrated design of the pipe connections and control circuits between modules, reducing the complexity and construction cost of on-site installation, making the system easier to retrofit and promote on existing building roofs.

[0019] In this invention, the control module has embedded collaborative control logic, which is driven by real-time sensing data. This technical feature allows the control strategy to be dynamically generated based on actual sensing data, rather than relying on fixed empirical parameters. The resulting technical benefits are: the system can automatically adapt to the climatic characteristics of different regions (such as differences between arid and rainy areas, and seasonal variations), eliminating the need to redesign control programs for different application scenarios, improving the versatility and environmental adaptability of the technical solution, and reducing debugging and maintenance costs when promoting its application in different regions. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a system schematic diagram according to an embodiment of the present invention; Figure 2 This is a flowchart of a method according to an embodiment of the present invention; Figure 3 This is a closed-loop control flowchart of the optimization method according to an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0023] Example 1 like Figure 1 As shown, this embodiment provides a photovoltaic-greening synergistic efficiency enhancement system for building rooftops, including: A solar panel array mounted on the roof base, with heat-absorbing pipes attached to its back panel; A vegetation layer located below the solar panel array, with water supply pipes distributed within the vegetation layer; The execution module includes a first circulation path and a second circulation path. The first circulation path is connected to the heat absorption pipeline and is equipped with a first power pump and a heat exporter. The second circulation path is connected to the water supply pipeline and is equipped with a second power pump and a water flow regulator. Furthermore, the heat extractor is connected to a liquid storage container, the outlet of which is connected to the water inlet of the second circulation path, so as to use the heat energy recovered from the solar panel to heat the irrigation water.

[0024] Specifically, as a physical adjustment device acting on the airflow channel, the execution module may include one or more combinations of the following: (1) Enhanced convection devices: such as controllable electric louvers / ventilation fans installed on the side wall of the flow channel for forced ventilation; or guide fins with a specific angle added to the photovoltaic back panel to disrupt laminar flow and enhance turbulent heat transfer.

[0025] (2) Evaporative cooling auxiliary device: such as micro-sprinklers buried in the green layer and linked to the irrigation system, which can be activated under specific conditions (such as high temperature and low humidity, and when the photovoltaic back panel is overheated) to enhance plant transpiration and air sensible heat cooling.

[0026] (3) Phase change heat storage device: a phase change material unit arranged in the flow channel or photovoltaic back panel, used to absorb heat during the day and release heat at night to smooth out temperature peaks.

[0027] The monitoring module includes at least a temperature probe for detecting the temperature of the solar panel, a humidity probe for detecting the humidity of the vegetation layer, and a weather station for collecting meteorological parameters. Specifically, the monitoring module includes at least a heat flux sensor (used to directly measure the heat exchange rate between the backsheet and the air) installed on the backsheet of the photovoltaic module, a temperature sensor (monitoring the backsheet temperature, channel air temperature, and greening substrate temperature), and an environmental weather station (monitoring solar irradiance, ambient temperature and humidity, and wind speed). These sensors together constitute a comprehensive sensing network for the thermal state of the system.

[0028] The control module is electrically connected to the monitoring module, the first power pump, and the water flow regulator. The control module has a built-in collaborative control logic. The collaborative control logic is driven by real-time sensing data and synchronously determines the working fluid flow rate of the first circulation path and the water supply flow rate of the second circulation path in a rolling optimization manner, and outputs corresponding instructions.

[0029] Furthermore, the collaborative control logic includes: establishing the transient thermal balance equation of the solar panel and the water budget equation of the vegetation layer based on the data from the temperature probe, humidity probe, and weather station; and determining the setpoints for the working fluid flow rate and water supply flow rate at the current moment by solving the optimal control sequence within a finite future time domain, under the constraint of maximizing the preset comprehensive efficiency index and satisfying the safe temperature range of the solar panel and the suitable humidity range of the vegetation layer.

[0030] Furthermore, the comprehensive performance index is the net value of power generation revenue after deducting pumping energy consumption and water resource consumption, and its expression includes adjustable weighting coefficients to adapt to the operating preferences of different seasons or regions.

[0031] Furthermore, the transient heat balance equation considers the coupling effect of solar irradiation heating, convective heat dissipation, radiative heat transfer, and active cooling of the working fluid; the water budget equation considers the balance relationship of natural precipitation, artificial irrigation, vegetation transpiration, and deep seepage.

[0032] Furthermore, the control module is also used to compare the actual measured value at the next moment with the predicted value of the equation, and when the accumulated deviation exceeds the allowable range, automatically correct the heat transfer coefficient in the transient heat balance equation or the evapotranspiration coefficient in the moisture balance equation.

[0033] Specifically, this involves an embedded controller or microprocessor that pre-stores or runs a heat flux optimization algorithm online. The algorithm takes data from the monitoring module as input and outputs control commands to the execution module. The core logic of the algorithm is: based on the real-time monitored heat flux value and the photovoltaic backsheet temperature, it determines whether the current heat exchange state is within the "high-efficiency heat dissipation range"; if it deviates, it dynamically adjusts the execution module (e.g., opening the louver angle, adjusting the fan speed, or initiating short-term misting) to redirect the heat flux in a direction conducive to reducing the photovoltaic panel temperature, until it returns to the optimization range.

[0034] This embodiment provides a green roof-photovoltaic synergistic efficiency enhancement system, including a waterproof layer, a green base layer, and a photovoltaic module array supported above the green base layer from bottom to top on the building roof substrate. An air flow channel is formed between the back panel of the photovoltaic module and the plant canopy of the green base layer.

[0035] Typical roof types in Southwest China (traditional tile roofs, concrete flat roofs, high reflectivity roofs, green roofs, etc.) were selected to analyze their thermophysical properties (albedo, thermal inertia, emissivity) and their impact mechanism on the urban heat island effect.

[0036] The thermophysical properties of the roof type are shown in Table 1.

[0037] Table 1 Mechanisms of the urban heat island effect: 1. Albedo (α): High-reflectivity roofs (such as white coatings) reflect more shortwave radiation, reducing surface heat absorption and lowering roof temperature.

[0038] The calculation formula is: (1) in, S down For downlink shortwave radiation, H For sensible heat flux, LE Latent heat flux 2. Thermal inertia (P): Materials with high thermal inertia (such as concrete) store more heat during the day and release it slowly at night, thus prolonging the duration of the urban heat island effect.

[0039] The calculation formula is: (2) in, ρ For density, c Specific heat capacity.

[0040] 3. Emissivity (ε): High emissivity materials (such as green roofs) can efficiently dissipate heat through long-wave radiation, reducing surface temperature.

[0041] Formula for calculating urban heat island intensity (UHI): (3) Based on the energy balance equation, the roof temperature T roof It can be represented as: (4) Among them, h c L is the convective heat transfer coefficient. down It is a downward long-wave radiation.

[0042] Based on the rainy and humid climate characteristics of Southwest China, photovoltaic panel installation parameters (tilt angle, spacing) were designed to simulate the regulating effect of photovoltaic panels on roof energy balance under different seasons.

[0043] Dynamic simulation and evaluation of heat flux: Based on the EnergyPlus software platform, the impact of photovoltaic panels on roof convective heat flux was simulated, with a focus on analyzing the differences in heat transfer under high temperature and humidity conditions in summer and rainy conditions in winter.

[0044] The energy balance equation for the photovoltaic panel heat transfer model is: (5) In the formula, Qsolar is the incident solar radiation, Qelectrical is the power generation, and Qconvection and Qradiation are the convective and radiative heat losses.

[0045] Convection heat transfer coefficient (hc): The differences between summer (weaker natural convection) and winter (stronger natural convection) are significant and can be correlated using the Grashof number (Gr) and Prandtl number (Pr): (6) in, Nu For Nusselt numbers, the coefficients are... C and n It is determined by the orientation of the wall (horizontal or inclined).

[0046] The diurnal heat release characteristics of photovoltaic panel and rooftop composite systems were quantified to explore the influence of temperature on photovoltaic heat dissipation efficiency and convective heat flux.

[0047] The efficiency (η) of a photovoltaic panel decreases as temperature increases: (7) Where β is the temperature coefficient (approximately -0.4%℃ to -0.5%℃). Research on multi-objective optimization strategies: This study assesses the evaporative cooling potential of green roofs in the rainy climate of Southwest China and proposes a low-water-consumption green roof and photovoltaic synergy scheme, taking into account irrigation needs and water resource constraints.

[0048] Climate characteristics and evaporation efficiency: The southwestern rainy region has high annual precipitation (e.g., Chengdu receives approximately 1000 mm annually), but there is a difference between a concentrated rainy season (May-September) and a dry season (winter). Evaporative cooling potential (ECP) can be calculated using latent heat flux: (8) Where L is the latent heat of vaporization of water (approximately 2.45 MJ / kg) and ET is the vegetation evapotranspiration (mm / day).

[0049] The Penman-Monteith modified formula can be used to estimate actual evapotranspiration: (9) In the formula, R n Where γ is net radiation, G is soil heat flux, γ is the wet-dry constant, u2 is wind speed, and e is the soil heat flux. s With e a The values ​​represent the saturation and actual water vapor pressures.

[0050] Explore the compatibility of high-reflectivity materials with photovoltaic panels to reduce the blocking effect of photovoltaic panels on long-wave radiation from rooftops and balance power generation efficiency with heat island mitigation.

[0051] Example 2 like Figure 2 As shown, this embodiment provides a synergistic enhancement method based on the system described in Embodiment 1, including the following steps: S1: Real-time data on solar panel temperature, vegetation layer humidity, irradiance, and air temperature are obtained through the monitoring module; S2: Calculate the power loss of the solar panel due to temperature rise based on the solar panel temperature, irradiance, and air temperature data, and estimate the latent heat flux of the vegetation layer based on the vegetation layer humidity, irradiance, air temperature data and vegetation parameters. S3: Construct a predictive model that includes the thermal dynamics of the solar panel and the wet dynamics of the vegetation layer; S4: With the goal of maximizing the comprehensive benefit function, solve for the optimal control quantity that satisfies the upper limit of temperature and the humidity range in the prediction time domain. The control quantity includes the cooling medium flow rate and the irrigation water volume. S5: Convert the solved cooling medium flow rate and irrigation water volume into control commands to drive the first power pump and water flow regulator to execute; S6: Based on the error between the measured state and the model prediction state, adjust the key parameters in the prediction model online.

[0052] Specifically, configure the basic parameters, including: Photovoltaic parameters: Photovoltaic panel area A pv Standard efficiency η ref Power temperature coefficient β, absorption rate α, etc.

[0053] Green roof parameters: planting substrate type, area A g Field water holding capacity θ FC Withering point θ WP Vegetation coefficient K c .

[0054] Control parameter: Photovoltaic panel temperature safety threshold T max Target range of matrix humidity [θmin, θmax], and optimization target weight coefficients (power generation side weight w) p Ecological side weight w e , satisfy w p +w e =1.

[0055] Set the sampling period Δt s With control period Δt c (usually Δt) c ≥Δt s ).

[0056] Data is collected synchronously in each sampling period using sensors deployed in various locations: Meteorological data: Total solar irradiance G(t), ambient temperature T a (t), ambient relative humidity RH(t), wind speed v(t).

[0057] Photovoltaic system data: Photovoltaic panel backsheet temperature T pv (t), DC side output power P dc (t).

[0058] Green roof data: substrate volumetric moisture content θ(t), substrate temperature T s (t).

[0059] Based on the collected data, the control unit calculates the key status indicators of the current system: Photovoltaic power generation efficiency and temperature rise loss: The current actual power generation efficiency η of photovoltaic panels pv (t) Temperature-dependent correction: η pv (t)=η ref· [1−β·(T pv (t)−T ref (10) Among them, T ref =25℃ is the standard test temperature. Instantaneous power generation loss P is defined. loss (t) represents the difference between the output power at the current temperature and the theoretical power at the reference temperature (e.g., ambient temperature), calculated using the following formula: P loss (t) = A pv ·G(t)·η ref ·β·(T pv (t)-T a (t)) (11) Among them, A pv For the area of ​​the photovoltaic panel, η ref β represents the standard test efficiency, and β is the power temperature coefficient.

[0060] Assessment of the evapotranspiration cooling potential of green roofs: The reference crop evapotranspiration ET0(t)(mm / h) for green roofs was calculated using the simplified Penman formula: (12) Where Δ is the saturated vapor pressure slope, Rn is the net radiation, G is the soil heat flux (usually neglected), γ is the wet / dry surface constant, and e s and e a These are the saturated and actual water vapor pressures, respectively.

[0061] Actual evapotranspiration of green roofs (ET) c (t) is: ET c (t)=K s (t)⋅K c ⋅ET0(t) (13) Among them, K c K is the crop coefficient. s (t) is the soil moisture stress coefficient, when θ(t) < θ FC At that time, the latent heat flux LE(t) (W / m²) corresponding to evapotranspiration is: (14) Where λ is the latent heat of vaporization of water (approximately 2.45 MJ / kg). This is the density of water.

[0062] This step is the core of this invention. The control unit, aiming to maximize the overall system benefit, performs rolling optimization calculations to solve for the optimal control command. The overall benefit function J is defined as follows: J = w1·P elec - w2·E pump - w3·V water (15) Among them, P elec To predict power generation revenue, E pump For pumping energy consumption, V water For irrigation water consumption, w1, w2, and w3 are configurable weighting coefficients.

[0063] (16) in, The predicted power generation for the next control cycle. The system's rated power is used for normalization; To predict the required irrigation water, The maximum allowable irrigation volume is used for normalization; the minus sign indicates that water consumption needs to be minimized.

[0064] Optimization solution refers to solving under constraints (such as T) pv ≤T max θ min ≤θ≤θ max Find the control vector u(t) = [F that maximizes J] cool (t),V irr (t)] T , where F cool V is the flow rate of the cooling medium. irr This refers to the amount of irrigation water.

[0065] The optimal control command u∗(t) obtained in the previous step is sent to the actuator: Adjust the speed or start / stop of the cooling circulation pump to control the flow rate F of the cooling medium flowing through the photovoltaic panel backsheet pipes. cool .

[0066] Controlling the opening duration of the irrigation solenoid valve enables precise irrigation water volume V. irr The application of.

[0067] After the system has been running for a period of time, compare it with the predicted state (such as the predicted T). pv The model parameters (such as heat transfer coefficient and evapotranspiration coefficient) are compared with the actual measured values. If the deviation continues to exceed the threshold, the adaptive update of the model parameters (such as heat transfer coefficient and evapotranspiration coefficient) is triggered, so that the prediction model inside the system is closer to the actual physical process, thereby maintaining long-term control accuracy.

[0068] Figure 3 The closed-loop optimization process centered on model predictive control is demonstrated, and the detailed calculations for each control cycle are as follows: Establish a simplified discrete state-space model of the system: x(k+1)=f(x(k),u(k),d(k)) (17) Wherein, the state vector x=[T pv ,θ] T Control vector u=[F cool V irr ] T The perturbation vector d=[G,T] a ,RH,v] T .

[0069] Photovoltaic panel temperature prediction model: T pv (k+1)=T pv (k)+(Δt / C pv )·[α·G-η·G -h·(T pv -T a )-Q cool (18) Among them, C pv Let Q be the heat capacity, α be the absorptivity, h be the convective heat transfer coefficient, and Q be the heat capacity. cool The heat removed by the cooling system; Q solar =αG is the absorbed solar radiation heat flux; Q conv =h(T pv -T a ) is for convection cooling; Q rad For radiative heat dissipation; Q cool =c p ρF cool (T out -Tin ) / A pv The heat removed by the cooling system.

[0070] Matrix moisture prediction model: (19) Among them, D z D represents the root layer depth. r This refers to the volume of water discharged.

[0071] At each control time k, based on the current measured state x(k) and the future disturbance prediction sequence {d^(k), d^(k+1), ...} (weather is generally assumed to remain unchanged in the short term), in the prediction time domain N p Internally solve the following optimization problem: (20) Where U(k) = [u(k), u(k+1), ..., u(k+Nc−1)] is the control sequence to be optimized, and Nc is the control time domain (Nc≤Np). This optimization problem can be solved online using numerical algorithms such as sequential quadratic programming.

[0072] The following constraints must be satisfied when solving for the optimal control command: Photovoltaic panel temperature constraint: T pv ≤ T max ; Matrix moisture constraint: θ min ≤ θ ≤ θ max ; Actuator flow constraint: F coolmin ≤ F cool ≤ F coolmax , 0 ≤ V irr ≤ V irrmax When the prediction error continues to exceed the set threshold, an adaptive update is triggered for the heat transfer coefficient h in the photovoltaic panel temperature prediction model or the evapotranspiration coefficient Kc in the matrix humidity prediction model.

[0073] After obtaining the optimal control sequence U∗(k), only the first step control quantity u∗(k) in the sequence is applied to the actual system. At the next control cycle k+1, the new actual state measurement value x(k+1) is read and compared with the predicted value x^(k+1). The error is used for state correction, and then rolling optimization is re-executed to form a closed loop of "prediction-optimization-execution-feedback".

[0074] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A photovoltaic-greening synergistic efficiency enhancement system for building rooftops, characterized in that, Includes the following steps: A solar panel array mounted on the roof base, with heat-absorbing pipes attached to its back panel; A vegetation layer located below the solar panel array, with water supply pipes distributed within the vegetation layer; The execution module includes a first circulation path and a second circulation path. The first circulation path is connected to the heat absorption pipeline and is equipped with a first power pump and a heat exporter. The second circulation path is connected to the water supply pipeline and is equipped with a second power pump and a water flow regulator. The monitoring module includes at least a temperature probe for detecting the temperature of the solar panel, a humidity probe for detecting the humidity of the vegetation layer, and a weather station for collecting meteorological parameters. The control module is electrically connected to the monitoring module, the first power pump, and the water flow regulator. The control module has a built-in collaborative control logic. The collaborative control logic is driven by real-time sensing data and synchronously determines the working fluid flow rate of the first circulation path and the water supply flow rate of the second circulation path in a rolling optimization manner, and outputs corresponding instructions.

2. The system according to claim 1, characterized in that, The heat extractor is connected to a liquid storage container, the outlet of which is connected to the inlet of the second circulation path to heat the irrigation water using heat energy recovered from the solar panel.

3. The system according to claim 1, characterized in that, The collaborative control logic includes: establishing the transient thermal balance equation of the solar panel and the water budget equation of the vegetation layer based on the data from the temperature probe, humidity probe and weather station; and determining the setpoint of the working fluid flow rate and the setpoint of the water supply flow rate at the current moment by solving the optimal control sequence in the future finite time domain, under the constraint of maximizing the preset comprehensive efficiency index and satisfying the safe temperature of the solar panel and the suitable humidity range of the vegetation layer.

4. The system according to claim 3, characterized in that, The comprehensive performance index is the net value of power generation revenue after deducting pumping energy consumption and water resource consumption. Its expression includes adjustable weighting coefficients to adapt to the operating preferences of different seasons or regions.

5. The system according to claim 3, characterized in that, The transient heat balance equation takes into account the coupling effect of solar irradiation heating, convective heat dissipation, radiative heat transfer, and active cooling of the working fluid; the water budget equation takes into account the balance relationship of natural precipitation, artificial irrigation, vegetation transpiration, and deep seepage.

6. The system according to claim 3, characterized in that, The control module is also used to compare the actual measured value at the next moment with the predicted value of the equation, and when the accumulated deviation exceeds the allowable range, automatically correct the heat transfer coefficient in the transient heat balance equation or the evapotranspiration coefficient in the moisture balance equation.

7. A synergistic enhancement method based on the system according to any one of claims 1-6, characterized in that, Includes the following steps: S1: Real-time data on solar panel temperature, vegetation layer humidity, irradiance, and air temperature are obtained through the monitoring module; S2: Calculate the power loss of the solar panel due to temperature rise based on the solar panel temperature, irradiance, and air temperature data, and estimate the latent heat flux of the vegetation layer based on the vegetation layer humidity, irradiance, air temperature data and vegetation parameters. S3: Construct a predictive model that includes the thermal dynamics of the solar panel and the wet dynamics of the vegetation layer; S4: With the goal of maximizing the comprehensive benefit function, solve for the optimal control quantity that satisfies the upper limit of temperature and the humidity range in the prediction time domain. The control quantity includes the cooling medium flow rate and the irrigation water volume. S5: Convert the solved cooling medium flow rate and irrigation water volume into control commands to drive the first power pump and water flow regulator to execute; S6: Based on the error between the measured state and the model prediction state, adjust the key parameters in the prediction model online.

8. The method according to claim 7, characterized in that, The formula for calculating the power loss in step S2 is as follows: P loss (t) = A pv ·G(t)·η ref ·β·(T pv (t)-T a (t)); Among them, A pv For the area of ​​the photovoltaic panel, η ref β represents the standard test efficiency, and β is the power temperature coefficient.

9. The method according to claim 7, characterized in that, The calculation process of latent heat flux in step S2: Calculate the reference crop evapotranspiration ET0(t); According to the soil moisture stress coefficient K s (t) and crop coefficient Kc are used to calculate the actual evapotranspiration ET. c (t): ETc(t) = Ks(t)·Kc·ET0(t); Calculate the latent heat flux: LE(t) = (λ·ρ) w ·ETc(t)) / 3600, where λ is the latent heat of vaporization of water, ρ w This is the density of water.

10. The method according to claim 7, characterized in that, The formula for calculating the comprehensive benefit function in step S4 is as follows: J = w1·P elec - w2·E pump - w3·V water ; Among them, P elec To predict power generation revenue, E pump For pumping energy consumption, V water For irrigation water consumption, w1, w2, and w3 are configurable weighting coefficients.

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