Method and system for local climate regulation and power generation gain for a desert photovoltaic power plant

Abstract

The present application relates to the field of photovoltaic power generation, and particularly relates to a local climate regulation and power generation gain method and system for a desert photovoltaic power station; environmental data and equipment state data in an internal area and an upwind area of the photovoltaic power station are collected in real time to generate standardized data; the standardized data is input into a coupled climate model for prediction to generate a prediction result; based on the prediction result, a global optimization target of maximizing a net present value of power generation gain is considered, power generation loss avoided by intervention, energy cost consumed by intervention, and loss cost of intervention actions on equipment life are comprehensively considered to generate an optimal control scheme; according to the optimal control scheme, a distributed array of environmental regulation equipment is dispatched to perform physical intervention; the present application realizes a change from passive operation and maintenance to active regulation and control of the desert photovoltaic power station, and significantly improves power generation efficiency and reduces operation and maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation, and more specifically to methods and systems for local climate regulation and power generation gain for desert photovoltaic power plants. Background Technology

[0002] Large-scale ground-mounted photovoltaic power plants, especially those built in desert areas, face frequent sandstorms and intense solar radiation, severely limiting their power generation efficiency and operational economics. Traditional passive operation and maintenance solutions are no longer sufficient to meet the demands for continuously improving power generation efficiency in such harsh environments.

[0003] Dust accumulation on the surface of photovoltaic modules is the main cause of significant power generation loss. At the same time, strong radiation in desert areas causes the modules to operate at excessively high temperatures, resulting in a high-temperature penalty effect. For a typical crystalline silicon module, the output power decreases by about 0.3% to 0.5% for every 1°C increase in temperature.

[0004] To address the aforementioned issues, existing technologies primarily focus on reactive, post-event intervention. For the problem of dust accumulation on modules, periodic manual cleaning or automated robotic cleaning is employed. By pre-setting fixed cleaning cycles or optimizing cleaning paths, dust particles adhering to the surface of photovoltaic modules are removed. For the problem of high temperature in modules, existing solutions mostly focus on improving the heat dissipation structure of the module backsheet, such as adding heat dissipation fins, using passive heat dissipation materials, or optimizing the airflow channels of the backsheet, in order to mitigate the adverse effects of high-temperature environments on power generation efficiency.

[0005] However, the aforementioned solutions are all localized, end-point interventions targeting the consequences of adverse environmental factors. Periodic cleaning methods suffer from drawbacks such as high water consumption, high operation and maintenance costs, and poor response timeliness, making them unsuitable for the frequent and sudden sandstorms in desert regions. Furthermore, cleaning operations often cause power outages or reduced power output. Component cooling solutions mostly employ passive heat dissipation structures, whose cooling effect is significantly constrained by external conditions such as ambient temperature, humidity, and wind speed, making effective temperature control difficult under extreme high-temperature conditions. More importantly, existing technologies do not consider the overall scale of the photovoltaic power plant, lacking the technical means to proactively identify and control adverse climate processes such as sandstorm transport and heat waves, thus failing to reduce power generation losses and improve overall system energy efficiency at the source. Summary of the Invention

[0006] In response to the problems mentioned in the prior art, this invention proposes a method and system for local climate regulation and power generation gain for desert photovoltaic power plants. It aims to solve the problems of low power generation efficiency and high operation and maintenance costs caused by sand and dust accumulation and high temperature performance degradation in desert photovoltaic power plants, and achieve the goal of preventing power generation loss from the source and significantly improving the overall energy efficiency and economy of the system.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: This invention relates to a method for local climate regulation and power generation gain in desert photovoltaic power plants, comprising the following steps: Real-time collection of environmental and equipment status data within the photovoltaic power plant area and upwind region; spatiotemporal matching and fusion processing of the collected environmental and equipment status data to generate standardized data; Standardized data is input into a coupled climate model for prediction, generating prediction results; the coupled climate model includes a mesoscale meteorological dust model and a computational fluid dynamics model. Based on the prediction results, with the maximization of the net present value of power generation gain as the global optimization objective, the optimal control scheme is generated by comprehensively considering the power generation loss avoided by intervention, the energy cost consumed by intervention, and the cost of equipment life loss caused by intervention. According to the optimal control scheme, a distributed array of environmental regulation equipment is scheduled to perform physical interventions to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station.

[0008] As a further improvement of the present invention, the environmental data includes wind speed, wind direction, solar irradiance, air temperature and humidity, upwind dust concentration and motion vector; The equipment status data includes the temperature of the photovoltaic module backsheet.

[0009] As a further improvement of the present invention, the mesoscale meteorological dust model is used to simulate the large-scale dust transport process and output the boundary conditions of wind speed, temperature, humidity and dust concentration to the boundary of the photovoltaic power station. The computational fluid dynamics model, based on the boundary conditions and the digital terrain model of the photovoltaic power station, predicts the spatiotemporal distribution of dust deposition inside the power station and the component temperature rise curve.

[0010] As a further improvement of the present invention, the environmental conditioning equipment array includes an atomizing device, an active flow guiding device, and a ground surface conditioning device.

[0011] As a further improvement of the present invention, the atomizing device can adjust the particle size distribution of the generated droplets according to the instruction, wherein the median particle size of the droplets in the first mode for dust suppression is greater than the median particle size of the droplets in the second mode for cooling.

[0012] As a further improvement of the present invention, the active flow guiding device is used to activate when the ambient natural wind speed is lower than a preset threshold, thereby generating directional induced airflow to enhance airflow inside the power plant.

[0013] As a further improvement of the present invention, the ground surface adjustment device is a high reflectivity film laid on the bare ground between the photovoltaic arrays, and the film has a reflectivity of ≥80% for visible light and near-infrared bands in the solar spectrum.

[0014] As a further improvement to the present invention, the following steps are also included: After the physical intervention ends, collect actual power generation data and resource consumption data after the intervention to calculate the energy yield. The data from the intervention process are fed back to the coupled climate model and the optimization algorithm used to generate the optimal control scheme, for model parameter correction and iterative optimization of decision-making strategies.

[0015] As a further improvement to the present invention, the energy yield is calculated as follows: Energy yield = (Electricity generated recovered by intervention - Electricity consumed by intervention) / Electricity consumed by intervention.

[0016] This invention proposes a local climate regulation and power generation gain system for desert photovoltaic power plants, which is used to achieve the above-mentioned method, including: The global perception layer is used to collect environmental data and equipment status data in real time within the photovoltaic power station area and the upwind area. The decision-making layer is communicatively connected to the global perception layer. It is used to receive data collected by the global perception layer, run a coupled climate model to make predictions, and generate the optimal control scheme with the goal of maximizing the net present value of power generation gain. The execution layer, which is communicatively connected to the decision-making layer, includes a distributed array of environmental regulation equipment for performing physical interventions according to the optimal control scheme to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station. The closed-loop optimization layer is communicatively connected to both the decision-making layer and the execution layer. It is used to schedule the precise execution layer to execute the optimal control scheme, collect actual power generation data and resource consumption data after intervention, and feed the evaluation results back to the decision-making layer to optimize subsequent decisions.

[0017] Compared with the prior art, the present invention achieves the following technical effects: This invention captures dust movement signals by collecting environmental data from within the power plant area and upwind regions, and combining spatiotemporal matching and fusion processing. Based on this, by running a coupled climate model (mesoscale meteorological dust model + computational fluid dynamics model), the spatiotemporal evolution of adverse climate processes can be predicted. Intervention plans can be formulated and implemented in advance based on the prediction results, allowing for control measures to be implemented before dust arrives or before high temperatures form. This suppresses dust deposition and reduces the operating temperature of the modules at the source, achieving a fundamental shift in photovoltaic power plant operation and maintenance from passive response and end-of-pipe treatment to predictive early warning and source intervention.

[0018] This invention takes maximizing the net present value of power generation gain as the global optimization objective, comprehensively considering power generation losses to be avoided through intervention, energy consumption costs, and equipment lifespan depreciation costs to generate an optimal control scheme. Based on this, it schedules a distributed array of environmental control equipment, including intelligent atomizing devices, active flow guiding devices, and surface conditioning devices, to work collaboratively, forming interventions that suppress dust, cool temperatures, and guide airflow. Compared to existing technologies, this invention, through systematic collaboration and global optimization, solves the problems of lack of system coordination in the independent operation of equipment such as cleaning robots and heat dissipation structures. It is expected to increase annual power generation by 3% to 8%, while significantly reducing the frequency of mechanical cleaning, lowering water consumption, and reducing operation and maintenance costs.

[0019] This invention is based on the microscopic simulation results of a computational fluid dynamics model. With maximizing the net present value of power generation gain as the core optimization objective, it comprehensively considers power generation revenue, energy costs, and equipment lifespan depreciation costs. Through optimization algorithms, it seeks to optimize the equipment start-up and shutdown sequence, working mode, and operating parameters, ensuring that every intervention action is executed at the optimal economic equilibrium point. This allows the system to obtain the maximum power generation revenue with the least resource consumption, effectively avoiding the problems of ineffective or excessive intervention common in existing technologies. In addition, the environmental control equipment array of this invention is specially designed for harsh desert environments, possessing characteristics such as wind and sand resistance, high temperature resistance, and corrosion resistance. It can operate stably for a long time in harsh environments such as deserts and Gobi, ensuring the reliability and durability of the system. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a schematic diagram of the workflow of the global perception layer of the present invention; Figure 4 This is a schematic diagram of the decision-making process of the present invention; Figure 5 This is a schematic diagram of the execution layer workflow of the present invention; Figure 6 This is a schematic diagram of the closed-loop optimization workflow of the present invention. Detailed Implementation

[0021] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0024] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a communication connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0026] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0027] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0028] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0029] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0030] See Figure 1 and Figure 2 As shown, this embodiment proposes a method for local climate regulation and power generation gain for desert photovoltaic power plants, including the following steps: Real-time collection of environmental and equipment status data within the photovoltaic power plant area and upwind region; spatiotemporal matching and fusion processing of the collected environmental and equipment status data to generate standardized data; Standardized data is input into a coupled climate model for prediction, generating prediction results; the coupled climate model includes a mesoscale meteorological dust model and a computational fluid dynamics model. Based on the prediction results, with the maximization of the net present value of power generation gain as the global optimization objective, the optimal control scheme is generated by comprehensively considering the power generation loss avoided by intervention, the energy cost consumed by intervention, and the cost of equipment life loss caused by intervention. According to the optimal control scheme, a distributed array of environmental regulation equipment is scheduled to perform physical interventions to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station.

[0031] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0032] Step 1, such as Figure 3As shown, this embodiment establishes a wide-coverage monitoring network to achieve comprehensive perception of the entire power plant and its surrounding environment. This network not only covers the power plant site but also extends upwind of the power plant, aiming to detect potential dust movement signals that could affect the power plant in advance. Specifically, the comprehensive perception layer consists of an upwind remote monitoring network, an internal monitoring network, and a data fusion center. Micropulse lidar stations can be deployed at 5 km, 10 km, and 20 km upwind of the power plant. These stations can monitor the optical characteristics, vertical distribution, and movement speed and direction of dust particles within a 20 km radius in real time, with data transmitted back in real time via a 5G private network.

[0033] Within the power plant area, various sensors are deployed in a grid-like manner. For example, a miniature weather station is placed every 20 acres, integrating sensors for wind speed, wind direction, air temperature, air humidity, air pressure, total solar radiation, and direct radiation. At least 10 infrared thermometers are deployed per megawatt of photovoltaic capacity to monitor the temperature field distribution on the backsheet of the photovoltaic modules in real time. PM2.5 and PM10 sensor arrays are deployed at the edges and central areas of the photovoltaic array to monitor real-time changes in airborne dust concentration. In addition, equipment status data includes the water level and operating status of the atomizing piles, the current speed of the guide fan, and the deployment and retraction position of the high-reflectivity film, reflecting the real-time status of the executing equipment and providing a basis for subsequent scheduling decisions.

[0034] The collected data comes from different devices, frequencies, and spatial locations. In order for this data to be used by subsequent prediction models, the data fusion center uses Kalman filtering and time series alignment algorithms to perform spatiotemporal matching and feature extraction on the data from various sensors. The scattered data is then uniformly processed into standardized data packets with precise timestamps and spatial coordinates, and pushed to the intelligent decision-making layer once per second. Through this processing, the originally scattered and disordered raw data is transformed into standardized data with clear structure and reliable quality.

[0035] Step 2: Input standardized data into the coupled climate model for prediction and generate prediction results; the coupled climate model includes a mesoscale meteorological dust model and a computational fluid dynamics model.

[0036] like Figure 4As shown, in this step, after the standardized data is pushed to the decision-making level, the system will start the coupled climate model for predictive calculations. The coupled climate model in this embodiment combines two models of different scales. First, a mesoscale meteorological dust model is run; the WRF-Chem model is preferred in this embodiment. This model, based on real-time standardized data and combined with short-term numerical weather forecasts, can simulate the atmospheric circulation patterns and dust transport processes over a large area over the next 2 to 48 hours. Therefore, the system can calculate the time when dust clouds reach the power station boundary, the dust concentration distribution, and the accompanying boundary conditions such as wind speed, temperature, and humidity over a period of time. Subsequently, the next calculation is performed using a computational fluid dynamics model. The model reads the power station boundary conditions calculated by the mesoscale model and imports them into a high-precision digital terrain model of the photovoltaic power station. This digital terrain model includes terrain information such as the precise layout of the photovoltaic array, the tilt angle of the modules, and the height of the support structures. Based on this, the computational fluid dynamics model performs high-resolution simulations of the microclimate field inside the power station, predicting the deposition rate and cumulative thickness of dust at different locations after entering the power station area, as well as the spatiotemporal variation curves of photovoltaic module temperature under different meteorological conditions.

[0037] By coupling the above-mentioned mesoscale meteorological dust model with the fluid dynamics model, we can not only grasp the evolution trend of dust events, but also obtain the specific impact on each component inside the power station, thus realizing the prediction of when the dust will arrive at the dust landing location and how much impact it will have.

[0038] Step 3: Based on the prediction results, with the maximization of the net present value of power generation gain as the global optimization objective, the optimal control scheme is generated by comprehensively considering the power generation loss avoided by intervention, the energy cost consumed by intervention, and the cost of equipment life loss caused by intervention.

[0039] like Figure 5 As shown, the system makes decisions based on the prediction results generated in step two. The core objective of the optimization decision is to maximize the net present value of power generation gain, comprehensively considering the global optimization objective of benefits and costs. Specifically, based on the prediction results, the system calculates the power generation loss caused by dust deposition and high temperatures if no intervention measures are taken; this loss represents the potential recoverable benefits. The system evaluates the resource consumption of each possible intervention strategy, including the water and electricity consumed by the atomizing device, the electricity consumed by the flow guide fan, the electricity consumed by the deployment and retraction of the high-reflectivity film, and the equipment lifespan depreciation costs caused by frequent equipment operation or long-term operation.

[0040] The optimization algorithm in this embodiment preferably employs a sequential quadratic programming algorithm. This algorithm quantifies the benefits and costs and establishes a mathematical model. It seeks the optimal solution among different combinations of equipment start-up and shutdown sequences, operating modes, and running parameters, maximizing the net present value (NPV) obtained by subtracting the various costs incurred during intervention from the power generation gain. Through the optimization process, the system outputs an optimal control scheme containing specific instructions. This optimal control scheme clarifies which areas' atomizing piles need to be activated at what time, whether to use dust suppression or cooling mode, how to adjust the atomization intensity, which guide fans need to be activated and at what speed, and which blocks' high-reflectivity films need to be expanded or retracted, etc. This ensures that each intervention action finds the optimal balance between resource consumption and power generation benefits, avoiding the problems of over-intervention or under-intervention common in traditional schemes.

[0041] S4. According to the optimal control scheme, the distributed environmental regulation equipment array is scheduled to perform physical intervention to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station.

[0042] like Figure 5 As shown, once the optimal control scheme is generated, the system will parse and execute the scheme through the central controller in the closed-loop optimization layer. The central controller preferably adopts an industrial-grade programmable automation controller, running a real-time operating system, which can decompose the instructions in the scheme into instruction sequences with precise timestamps, and synchronously distribute them to each execution device through industrial Ethernet and wireless mesh network.

[0043] The environmental control equipment array of this invention mainly includes three types of equipment: atomizing devices, active airflow guiding devices, and surface conditioning devices. As a specific implementation, the atomizing devices preferably employ atomizing cooling and dust suppression piles, which integrate an ultrasonic atomizer, a directional nozzle, an embedded miniature meteorological sensor, a water supply module, and an independent photovoltaic power supply unit. During deployment, one pile is deployed every 15 meters along the upwind boundary of the power station; inside the power station, based on the airflow channels and dust-prone areas identified by computational fluid dynamics models, they are deployed in a 50m × 50m grid pattern.

[0044] The device has two operating modes: dust suppression and cooling. For dust suppression scenarios, the system will deploy atomizing piles on the upwind boundary of the power station and in the internal airflow channels, operating them in dust suppression mode. In this mode, the median droplet size of the atomizing device is controlled between 30 and 50 μm. These droplets can collide with dust particles in the air, causing dust to agglomerate and thus accelerating dust settling, effectively preventing dust from entering the core area of ​​the power station. The effective coverage radius of a single pile can reach 15 to 20 meters. For high-temperature mitigation scenarios, the system will deploy the atomizing piles in cooling mode. In this mode, the median droplet size is controlled between 10 and 20 μm. These tiny droplets can evaporate rapidly in the air, absorbing a large amount of heat and achieving localized cooling.

[0045] As a specific implementation method, the active airflow guiding device preferably adopts an active airflow guiding fan array. These fans are arranged at key airflow nodes in the photovoltaic array that are prone to forming calm wind zones or heat island effects. When the ambient natural wind speed is less than 1.5 m / s, the system will start these fans according to the command, generating directional induced airflow of 2 to 3 m / s. The effective disturbance distance of a single fan can reach more than 10 m, thereby effectively enhancing the airflow inside the power station, timely carrying away the cold air formed by atomization cooling, and forming effective convective heat dissipation.

[0046] As a specific implementation method, the surface regulation device preferably employs a high-reflectivity surface device. This device is laid on the exposed ground between the photovoltaic arrays and is specifically a rollable high-reflectivity thin film material, equipped with an automatic unfolding and retracting mechanical mechanism with a response time of no more than 2 minutes. This film can achieve a reflectivity of over 80% for visible and near-infrared wavelengths of the solar spectrum, significantly reducing the absorption of solar radiation by the surface and lowering the surface temperature. Under complex conditions where sandstorms and high temperatures occur simultaneously, the system can also dynamically switch between the above modes in different areas and at different times based on the priority of optimization decisions, achieving comprehensive regulation.

[0047] During the aforementioned intervention process, the system can also flexibly adjust the specific implementation of the equipment according to the actual working conditions. For example, for the upwind long-distance dust monitoring station in the all-domain sensing layer, in addition to using micro-pulse lidar, other monitoring equipment such as visibility meters can also be selected. Besides atomizing cooling and dust suppression piles, high-pressure fine water mist nozzles or centrifugal atomizers can also be used to adapt to different working conditions, depending on the different wind and sand environment requirements.

[0048] After the intervention is completed, the method of the present invention further includes a closed-loop optimization step. For example... Figure 6 As shown, the method further includes the following steps: after the physical intervention ends, collecting actual power generation data and resource consumption data after the intervention, and calculating the energy yield; feeding back the data from the intervention process to the coupled climate model and the optimization algorithm used to generate the optimal control scheme, for model parameter correction and iterative optimization of the decision strategy. The energy yield is calculated as follows: the energy yield equals the power generation recovered by the intervention minus the power consumed by the intervention, divided by the power consumed by the intervention.

[0049] Through this closed-loop optimization process, after each adverse weather event, the system retrieves the actual power generation curve from the SCADA system, obtains water and electricity consumption data from resource meters, and calls upon historical similar weather databases or runs a no-intervention benchmark simulation to calculate the reduced power generation loss, resource costs, and final energy return rate. Subsequently, the knowledge base self-learning system associates and stores the evaluation results with data from the entire chain, from perception, prediction, decision-making to execution and evaluation. Employing an incremental learning mechanism, it fine-tunes the prediction model's weights every 24 hours using new data from the past week through online gradient descent, and every month uses accumulated intervention cases to update and optimize the cost weights and policy functions in the decision engine through deep reinforcement learning algorithms. This ensures that the system's prediction accuracy and decision-making effectiveness continuously improve with the accumulation of operational data.

[0050] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0051] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for local climate regulation and power generation gain in desert photovoltaic power plants, characterized in that, Includes the following steps: Real-time collection of environmental and equipment status data within the photovoltaic power plant area and upwind region; spatiotemporal matching and fusion processing of the collected environmental and equipment status data to generate standardized data; Standardized data is input into a coupled climate model for prediction, generating prediction results. The coupled climate model includes a mesoscale meteorological dust model and a computational fluid dynamics model; Based on the prediction results, with the maximization of the net present value of power generation gain as the global optimization objective, the optimal control scheme is generated by comprehensively considering the power generation loss avoided by intervention, the energy cost consumed by intervention, and the cost of equipment life loss caused by intervention. According to the optimal control scheme, a distributed array of environmental regulation equipment is scheduled to perform physical interventions to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station.

2. The method for local climate regulation and power generation gain for desert photovoltaic power stations according to claim 1, characterized in that, The environmental data includes wind speed, wind direction, solar irradiance, air temperature and humidity, upwind dust concentration and motion vector; The equipment status data includes the temperature of the photovoltaic module backsheet.

3. The method for local climate regulation and power generation gain for desert photovoltaic power stations according to claim 1, characterized in that, The mesoscale meteorological dust model is used to simulate large-scale dust transport processes and outputs the boundary conditions of wind speed, temperature, humidity and dust concentration to the photovoltaic power station boundary. The computational fluid dynamics model, based on the boundary conditions and the digital terrain model of the photovoltaic power station, predicts the spatiotemporal distribution of dust deposition inside the power station and the component temperature rise curve.

4. The method for local climate regulation and power generation gain for desert photovoltaic power stations according to claim 1, characterized in that, The environmental conditioning equipment array includes an atomizing device, an active flow guiding device, and a ground surface conditioning device.

5. The method for local climate regulation and power generation gain for desert photovoltaic power plants according to claim 4, characterized in that, The atomizing device can adjust the particle size distribution of the generated droplets according to instructions, wherein the median particle size of the droplets in the first mode used for dust suppression is greater than the median particle size of the droplets in the second mode used for cooling.

6. The method for local climate regulation and power generation gain for desert photovoltaic power plants according to claim 4, characterized in that, The active airflow guiding device is used to activate when the ambient natural wind speed is lower than a preset threshold, generating directional induced airflow to enhance airflow inside the power plant.

7. The method for local climate regulation and power generation gain for desert photovoltaic power plants according to claim 4, characterized in that, The surface conditioning device is a high-reflectivity thin film laid on the exposed ground between the photovoltaic arrays. The thin film has a reflectivity of ≥80% for visible and near-infrared light in the solar spectrum.

8. The method for local climate regulation and power generation gain for desert photovoltaic power plants according to claim 1, characterized in that, It also includes the following steps: After the physical intervention ends, collect actual power generation data and resource consumption data after the intervention to calculate the energy yield. The data from the intervention process are fed back to the coupled climate model and the optimization algorithm used to generate the optimal control scheme, for model parameter correction and iterative optimization of decision-making strategies.

9. The method for local climate regulation and power generation gain for desert photovoltaic power plants according to claim 8, characterized in that, The formula for calculating the energy yield is as follows: Energy yield = (Electricity generated recovered by intervention - Electricity consumed by intervention) / Electricity consumed by intervention.

10. A local climate regulation and power generation gain system for desert photovoltaic power plants, characterized in that, To implement the method according to any one of claims 1 to 9, comprising: The global perception layer is used to collect environmental data and equipment status data in real time within the photovoltaic power station area and the upwind area. The decision-making layer is communicatively connected to the global perception layer. It is used to receive data collected by the global perception layer, run a coupled climate model to make predictions, and generate the optimal control scheme with the goal of maximizing the net present value of power generation gain. The execution layer, which is communicatively connected to the decision-making layer, includes a distributed array of environmental regulation equipment for performing physical interventions according to the optimal control scheme to actively regulate the physical state of the local atmosphere and surface of the photovoltaic power station. The closed-loop optimization layer is communicatively connected to both the decision-making layer and the execution layer. It is used to schedule the precise execution layer to execute the optimal control scheme, collect actual power generation data and resource consumption data after intervention, and feed the evaluation results back to the decision-making layer to optimize subsequent decisions.

Images