A distributed photovoltaic power station intelligent integration system and method suitable for large roofs

Abstract

This application discloses an intelligent integrated system and method for distributed photovoltaic power stations on large rooftops, relating to the field of new energy power generation technology. It addresses the "weakest link" problem in existing large rooftop distributed photovoltaic power stations, as well as the systemic inefficiency caused by a lack of refined perception and dynamic optimization capabilities for complex microclimate environments. The system includes a photovoltaic array module, an intelligent optimization module, a power aggregation module, and a system control module. The photovoltaic array module includes photovoltaic subarrays; the intelligent optimization module includes subarray optimization units connected to the output terminals of their respective photovoltaic subarrays; the power aggregation module's input terminal is connected to the output terminals of all subarray optimization units, and its output terminal is used to connect to the power grid; the system control module is communicatively connected to both the subarray optimization units and the power aggregation module. This application effectively improves system efficiency and mitigates the weakest link problem.

Description

Technical Field

[0001] This application relates to the field of new energy power generation technology, and in particular to an intelligent integrated system and method for distributed photovoltaic power stations suitable for large rooftops. Background Technology

[0002] Distributed photovoltaic power stations are widely installed on the rooftops of industrial plants, commercial buildings, and other large structures, making them an important application scenario for clean energy.

[0003] However, the installation environment of large rooftops is complex and presents several inherent challenges. Firstly, rooftops typically have protrusions such as ventilation equipment, air conditioning units, and parapet walls, which create unpredictable localized shadows throughout the day as the sun's angle changes, leading to a sharp drop in the output of some photovoltaic modules. Secondly, large rooftops often contain multiple slopes facing different directions (e.g., south, east, west), with significant differences in the intensity and duration of solar radiation received by each side. Finally, production batches can affect the performance of even modules of the same model, resulting in slight variations in parameters such as power output and internal resistance. In traditional series string configurations, all modules are connected in a fixed series configuration, and the current in the entire string is limited by the worst-performing module, creating a severe "weakest link" effect.

[0004] Furthermore, traditional centralized or string inverter solutions lack the ability to finely perceive and dynamically respond to microclimate parameters such as irradiance, temperature, and wind speed in different roof zones. This results in the system operating at a suboptimal point for extended periods, with overall power generation efficiency significantly lower than the nominal theoretical value of the modules. While some optimization schemes exist in existing technologies, they mostly focus on module-level power electronics or simple series-parallel reconfiguration, failing to systematically address the global efficiency optimization problem under the aforementioned multi-factor coupling.

[0005] Therefore, there is an urgent need for an intelligent integrated system and method for distributed photovoltaic power stations on large rooftops, which can adapt to the complex working conditions of large rooftops and achieve systemic efficiency improvement, global optimization and intelligent collaborative solutions. Summary of the Invention

[0006] To address the "weakest link" problem in existing large-scale rooftop distributed photovoltaic power stations caused by shading, multiple orientations, and component mismatch, as well as the systemic inefficiency caused by the lack of refined perception and dynamic optimization capabilities for complex microclimate environments, this application provides an intelligent integrated system and method for distributed photovoltaic power stations suitable for large rooftops.

[0007] This application provides an intelligent integrated system and method for distributed photovoltaic power stations on large rooftops, employing the following technical solution: A smart integrated system for distributed photovoltaic power stations suitable for large rooftops includes a photovoltaic array module, a smart optimization module, a power collection module, and a system control module; The photovoltaic array module includes multiple photovoltaic sub-arrays. Each photovoltaic sub-array is divided based on the following criteria: an independent area on the roof with continuous microclimate characteristics, which are defined by at least irradiance uniformity and shading risk. The intelligent optimization module includes multiple sub-array optimization units, and each sub-array optimization unit is connected to the output terminal of the corresponding photovoltaic sub-array. The input terminal of the power aggregation module is connected to the output terminal of all subarray optimization units, and the output terminal of the power aggregation module is used to connect to the power grid or local load. The system control module is communicatively connected to each subarray optimization unit and power aggregation module.

[0008] Preferably, the subarray optimization unit further includes a subarray controller, and the photovoltaic array module further includes a series-parallel switch matrix. The photovoltaic subarray is internally equipped with photovoltaic modules, and each photovoltaic subarray is equipped with a first-stage DC bus for collecting the power of its internal photovoltaic modules. The photovoltaic modules are connected to the first-stage DC bus in an N×M matrix form through the series-parallel switch matrix, where N≥2 and M≥2. The series-parallel switch matrix is ​​controlled by the subarray controller and can dynamically switch between series, parallel, and hybrid connection topologies.

[0009] Preferably, the subarray optimization unit further includes a DC / DC converter array, which includes a DC / DC converter, a second-stage linear bus, and a first-stage MPPT controller. The subarray controller is connected to the series-parallel switch matrix control; Each input terminal of the DC / DC converter is independently connected to a string or a group of components in the photovoltaic sub-array, and the output terminals are connected in parallel to form a stable second-stage DC bus. The first-stage MPPT controller is a multi-channel independent MPPT controller, which controls each input channel of the DC / DC converter.

[0010] Preferably, it also includes a distributed sensor network, which includes visual sensors fixedly installed at key feature points on the roof, distributed irradiance sensors, and temperature sensors; the visual sensors are used to identify and predict the trajectory and range of moving shadows on the roof, and the distributed sensor network is communicatively connected to the system control module.

[0011] Preferably, the subarray controller is configured to perform the following operations: receive shadow prediction data from a visual sensor, and before a shadow covers a specific string within the photovoltaic subarray, dynamically disconnect the string that is about to be covered by shadow from the series topology by controlling the string parallel switch matrix, and switch it to an independent parallel branch or a low-voltage operating mode to reduce the impact on the overall output of the subarray.

[0012] Preferably, the power aggregation module includes one or more DC / AC aggregation inverters with a wide voltage range input; the system control module has a built-in global optimization algorithm, which takes real-time and predicted microclimate data obtained from a distributed sensor network as input, wherein the microclimate data includes dynamic parameters identified by a visual sensor for predicting shadow effects, and the real-time output characteristic curves of each subarray optimization unit as another input, dynamically allocating and setting the tracking target points of each first-level MPPT controller and the operating voltage range of the aggregation inverter.

[0013] Preferably, the global optimization algorithm adopts a model predictive control framework. In the optimization problem of this model predictive control framework, firstly, based on the shadow dynamic parameters identified and extracted in real time by the visual sensor, including the shadow's movement speed, coverage area, and occlusion height, a shadow influence weight factor is calculated for each subarray optimization unit. This shadow influence weight factor is used to quantify the degree of shadow influence on the corresponding subarray, and its value is positively correlated with the degree of influence. Subsequently, this weight factor is introduced as a key parameter into the optimization problem of model predictive control, specifically by adjusting the weight of the affected subarray optimization unit in the global power optimization cost function.

[0014] A smart integration method for distributed photovoltaic power stations suitable for large rooftops includes: System initialization phase: Based on the 3D model of the roof and historical climate data, the roof is divided into multiple photovoltaic sub-arrays with similar microclimate characteristics, and the baseline topology of the series-parallel switch matrix in each photovoltaic sub-array is pre-configured. Real-time operation phase: a) Real-time acquisition and prediction of microclimate conditions and shadow movement in various areas of the roof through distributed sensor networks, especially visual sensors; b) Based on the data from step a), the system control module runs a global optimization algorithm to generate an optimized parameter set that includes string topology switching instructions, multi-channel MPPT target values ​​at the sub-array level, and the input voltage range of the convergence inverter; c) Distribute the optimized parameter set to the corresponding subarray controller and power aggregation module; d) Each subarray controller operates the series-parallel switch matrix according to the topology switching command, and coordinates with multiple independent MPPT controllers to execute the optimized tracking target; e) The power collection module adjusts its operating point according to the set optimal input voltage range to achieve power conversion and collection.

[0015] In summary, this application includes at least one of the following beneficial effects: 1. This application, through the dynamic reconfiguration capability of the "series and parallel switch matrix", can separate the series links of strings whose performance has degraded or are covered by shadows, so that they can operate with low loss in independent or parallel mode, thereby completely breaking the constraints of the traditional fixed series architecture and providing a basis for global optimization at the hardware topology level. 2. This application also constructs a "distributed sensor network", which in particular uses visual sensors to identify and predict the trajectory and range of moving shadows. Combined with data from other sensors, it forms a real-time and predictive digital twin of the roof microclimate, providing accurate data input for system-level intelligent decision-making. 3. This application constructs a two-layer optimization architecture that coordinates local optimization at the subarray level with local optimization at the multi-path MPPT level and global optimization algorithm at the system level. The global optimization adopts a model predictive control framework and introduces a visual shadow influence weight factor, which can quantify and predict the dynamic influence of shadows, thereby dynamically adjusting the optimization weight of each photovoltaic subarray at the algorithm level to achieve a forward-looking, non-uniform optimal power allocation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of the intelligent integrated system for distributed photovoltaic power stations in Embodiment 1 of this application; Figure 2 This is the subarray dynamic topology control diagram of this embodiment of the present application; Figure 3 This is a control diagram of the local optimization layer and the global optimization layer in Embodiment 1 of this application; Figure 4 This is a real-time operation flowchart of the intelligent integration method for distributed photovoltaic power stations in Embodiment 2 of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application. Example 1

[0018] The present application discloses an intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops, including a photovoltaic array module, an intelligent optimization module, a power collection module, and a system control module. These four modules are closely coupled physically and logically, forming a complete closed loop from energy harvesting, local optimization, global decision-making to power collection.

[0019] The photovoltaic array module is intelligently divided into multiple photovoltaic sub-arrays based on the roof's microclimate characteristics. Each photovoltaic sub-array is independently managed by a sub-array optimization unit within an intelligent optimization module. The system control module acquires and predicts the entire power station's operating environment in real time through a distributed sensor network covering the roof. Based on this, the system control module runs a global optimization algorithm to calculate the optimal operating strategy (including string topology, MPPT operating point, inverter voltage, etc.) for the current and future period, and sends instructions to each sub-array optimization unit and the power aggregation module. Each sub-unit executes the instructions to maximize the extraction and adaptation of local energy, and finally, the power aggregation module converts the optimized DC power into AC power for grid connection or supply to local loads.

[0020] Furthermore, the division of photovoltaic array modules is based on intelligent zoning according to microclimate characteristics. Specifically, during the system planning phase, the roof is divided into multiple zones using a 3D roof model and historical meteorological data, employing algorithms such as cluster analysis. Key indicators for this division are irradiance uniformity and shading risk. For example, a flat, unshaded south-facing slope can be designated as a photovoltaic sub-array; while an area partially shaded by ventilation ducts and parapet walls, and which experiences moving shadows in the afternoon, should be designated as an independent photovoltaic sub-array, even if it is small. This division method ensures a relatively uniform environment within each photovoltaic sub-array.

[0021] Furthermore, the photovoltaic subarray includes photovoltaic modules arranged in an N×M (N≥2, M≥2) matrix and connected to the first-stage DC bus via a series-parallel switch matrix. This series-parallel switch matrix is ​​composed of semiconductor power switches (such as MOSFETs) and is precisely controlled by the subarray controller.

[0022] Furthermore, in this embodiment, the subarray controller can dynamically change the connection relationships between components or strings according to instructions. For example: Series mode: In the morning when the light is uniform, all components are connected in series to obtain high voltage and reduce transmission loss.

[0023] Parallel or hybrid mode: When the visual sensor predicts that a moving cloud shadow will cover one of the series links (let's say link A), the subarray controller can control the switching matrix to "cut off" link A from the main series link before the shadow arrives, turning it into an independent parallel branch. This way, the current drop in link A caused by the shadow will not "drag down" other well-lit series branches, effectively suppressing the "weakest link effect." After the shadow passes, the controller can reconnect it to the series link.

[0024] Decoupling mode: For individual components whose performance has been severely degraded due to aging, their group string can be decoupled, and their voltage can be boosted separately by a DC / DC converter before being connected to the second-stage straight bus to avoid affecting the overall output.

[0025] Furthermore, the intelligent optimization module is the core of the optimization strategy, employing a two-layer architecture of "local optimization and global coordination." Local optimization is a sub-array level architecture, where each sub-array optimization unit contains a multi-port modular DC / DC converter array. This array includes DC / DC converters, a second-stage linear bus, and a first-stage MPPT controller. Each DC / DC converter's input is independently connected to a string or group of components, and its outputs are connected in parallel to form the second-stage DC bus. The first-stage MPPT controller is a multi-channel independent MPPT controller, capable of simultaneously tracking the maximum power point (MPPT) of multiple inputs. This means that even within a single sub-array, strings with different orientations or varying degrees of shading can operate independently at their respective optimal points, achieving preliminary refined energy management.

[0026] Global optimization is a system-level architecture that receives comprehensive data from a distributed sensor network: irradiance sensors provide light intensity for each area, temperature sensors monitor the operating temperature of components, and key vision sensors (such as high-definition cameras installed at high points on the roof) continuously scan the roof, using image recognition algorithms to locate fixed obstructions such as ventilation equipment and air conditioning units in real time, and track the trajectory of moving cloud shadows, calculate their coverage area and movement speed, and thus predict the distribution of shadows in the next few minutes.

[0027] The system control module's built-in global optimization algorithm employs a model predictive control (MPC) framework. This algorithm periodically executes the following steps: Prediction: Based on real-time and predictive data provided by visual sensors and other sources, construct a future microclimate scenario within an optimized time domain.

[0028] Modeling: Build a refined output model for each subarray that includes its current string topology, component parameters, and prediction environment input.

[0029] Command issuance: A set of optimal control sequences is obtained by solving the problem, including: the switching commands of the series and parallel switching matrix of each photovoltaic subarray, the tracking target voltage or current value of the multiple MPPTs in each photovoltaic subarray, and the optimal input voltage operating range of the power aggregation module (DC / AC aggregation inverter). These commands are issued in real time.

[0030] Furthermore, the power aggregation module includes multiple DC / AC aggregation inverters with wide voltage range inputs. Their operating points (primarily the input voltage) are no longer passively adapted to the output of the photovoltaic subarrays, but are actively set by the global optimization algorithm of the system control module. This global optimization algorithm seeks an optimal voltage range that allows all photovoltaic subarrays to operate efficiently at their respective optimized operating points, while also considering the inverters' own conversion efficiency. This coordination ensures optimal overall link efficiency from the DC side to the AC side.

[0031] Furthermore, the global optimization algorithm employs a model predictive control framework (MMC) and, based on dynamic data from visual sensor data, generates a shadow impact weighting factor for each subarray unit within the MMC optimization problem. This shadow impact weighting factor is incorporated into the power output cost term of the affected subarray optimization unit to dynamically adjust its weight in the global power optimization. In addition, this shadow impact weighting factor dynamically adjusts the power output cost function of the affected subarray optimization unit according to the shadow's movement speed, coverage area, and occlusion height, thereby achieving a forward-looking, non-uniform power allocation strategy to ensure power output in areas unaffected by shadows. Example 2

[0032] A smart integration method for distributed photovoltaic power stations suitable for large rooftops mainly includes a system initialization phase and a real-time operation phase.

[0033] The system initialization phase involves importing a 3D model of the roof and historical climate data, and using a microclimate zoning algorithm to divide the roof into multiple photovoltaic sub-arrays with similar microclimate characteristics. Hardware installation is then completed, including photovoltaic modules, switch matrices, sensors, controllers, etc., and a baseline topology is pre-configured for the series-parallel switch matrices of each sub-array.

[0034] Real-time operation phase: a) The distributed sensor network (especially visual sensors) works continuously to collect real-time data on irradiance, temperature and high-definition images of various areas of the roof. The image processing unit identifies and predicts the dynamics of shadows. b) The system control module collects all data and runs a global optimization algorithm based on MPC (Model Predictive Control). The global optimization algorithm comprehensively considers factors such as real-time power, predicted shading, and component temperature to calculate the optimal set of operating parameters for the next control cycle. c) The optimized parameter set (topology command, MPPT target, inverter voltage range) is sent to the corresponding subarray controller and power pooling module controller respectively; d) Each subarray controller operates the series and parallel switch matrix according to the instructions, dynamically reconstructs the electrical topology, and multiple independent MPPT controllers drive the DC / DC converter, so that each input branch can quickly and accurately track the newly set optimized operating point; e) The DC / AC aggregation inverter in the power aggregation module adjusts its operating point to the set optimal voltage range, efficiently converting the optimized DC power from each subarray into AC power to complete grid connection.

[0035] This application addresses the complex efficiency loss problem of large-scale rooftop photovoltaic power plants through a three-pronged approach: dynamically reconfigurable hardware topology, fine perception integrating visual prediction, and two-layer intelligent optimization based on model predictive control. It not only responds quickly and mitigates the impact of shading, but also predicts and avoids losses in advance, achieving a leap from passive adaptation to proactive optimization and significantly improving the power generation revenue throughout the entire lifecycle of the photovoltaic power plant.

[0036] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A smart integrated system for distributed photovoltaic power stations suitable for large rooftops, characterized in that, It includes a photovoltaic array module, an intelligent optimization module, a power collection module, and a system control module; The photovoltaic array module includes multiple photovoltaic sub-arrays. Each photovoltaic sub-array is divided based on the following criteria: an independent area on the roof with continuous microclimate characteristics, which are defined by at least irradiance uniformity and shading risk. The intelligent optimization module includes multiple sub-array optimization units, and each sub-array optimization unit is connected to the output terminal of the corresponding photovoltaic sub-array. The input terminal of the power aggregation module is connected to the output terminal of all subarray optimization units, and the output terminal of the power aggregation module is used to connect to the power grid or local load. The system control module is communicatively connected to each subarray optimization unit and power aggregation module.

2. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 1, characterized in that: The subarray optimization unit further includes a subarray controller, and the photovoltaic array module further includes a series-parallel switch matrix. The photovoltaic subarray is internally equipped with photovoltaic modules, and each photovoltaic subarray is equipped with a first-stage DC bus for collecting the power of its internal photovoltaic modules. The photovoltaic modules are connected to the first-stage DC bus in an N×M matrix form through the series-parallel switch matrix, where N≥2 and M≥2. The series-parallel switch matrix is ​​controlled by the subarray controller and can dynamically switch between series, parallel, and hybrid connection topologies.

3. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 2, characterized in that: The subarray optimization unit also includes a multi-port modular DC / DC converter array, which includes a DC / DC converter, a second-stage linear bus, and a first-stage MPPT controller. The subarray controller is connected to the series-parallel switch matrix control; Each input terminal of the DC / DC converter is independently connected to a string or a group of components in the photovoltaic sub-array, and the output terminals are connected in parallel to form a stable second-stage DC bus. The first-stage MPPT controller is a multi-channel independent MPPT controller, which controls each input channel of the DC / DC converter.

4. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 1, characterized in that: It also includes a distributed sensor network, which comprises visual sensors fixedly installed at key feature points on the roof, distributed irradiance sensors, and temperature sensors; the visual sensors are used to identify and predict the trajectory and range of moving shadows on the roof, and the distributed sensor network is communicatively connected to the system control module.

5. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 3, characterized in that: The subarray controller is configured to perform the following operations: receive shadow prediction data from a visual sensor, and before a shadow covers a specific string within the photovoltaic subarray, dynamically disconnect the string that is about to be covered by shadow from the series topology by controlling the string parallel switch matrix, and switch it to an independent parallel branch or a low-voltage operating mode to reduce the impact on the overall output of the subarray.

6. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 5, characterized in that: The power aggregation module includes one or more DC / AC aggregation inverters with a wide voltage range input; the system control module has a built-in global optimization algorithm, which takes real-time and predicted microclimate data obtained from a distributed sensor network as input, wherein the microclimate data includes dynamic parameters identified by a visual sensor for predicting the shadow effect, and the real-time output characteristic curves of each subarray optimization unit as another input, dynamically allocating and setting the tracking target points of each first-level MPPT controller and the operating voltage range of the aggregation inverter.

7. The intelligent integrated system for distributed photovoltaic power stations suitable for large rooftops according to claim 6, characterized in that: The global optimization algorithm adopts a model predictive control framework. In the optimization problem of this model predictive control framework, firstly, based on the shadow dynamic parameters identified and extracted in real time by the visual sensor, including the shadow's movement speed, coverage area, and occlusion height, a shadow influence weight factor is calculated for each subarray optimization unit. This shadow influence weight factor is used to quantify the degree of shadow influence on the corresponding subarray, and its value is positively correlated with the degree of influence. Subsequently, this weight factor is introduced as a key parameter into the optimization problem of model predictive control, which is manifested as adjusting the weight of the affected subarray optimization unit in the global power optimization cost function.

8. A smart integration method for distributed photovoltaic power stations suitable for large rooftops, characterized in that, The method, applied to the system of any one of claims 1 to 7, comprises: System initialization phase: Based on the 3D model of the roof and historical climate data, the roof is divided into multiple photovoltaic sub-arrays with similar microclimate characteristics, and the baseline topology of the series-parallel switch matrix in each photovoltaic sub-array is pre-configured. Real-time operation phase: a) Real-time acquisition and prediction of microclimate conditions and shadow movement in various areas of the roof through distributed sensor networks, especially visual sensors; b) Based on the data from step a), the system control module runs a global optimization algorithm to generate an optimized parameter set that includes string topology switching instructions, multi-channel MPPT target values ​​at the sub-array level, and the input voltage range of the convergence inverter; c) Distribute the optimized parameter set to the corresponding subarray controller and power aggregation module; d) Each subarray controller operates the series-parallel switch matrix according to the topology switching command, and coordinates with multiple independent MPPT controllers to execute the optimized tracking target; e) The power collection module adjusts its operating point according to the set optimal input voltage range to achieve power conversion and collection.

Images