A method and system for analyzing and optimizing energy consumption of distributed photovoltaic power stations

Abstract

The present invention discloses a method and system for energy consumption analysis and optimization management of a distributed photovoltaic power station, which relates to the field of energy consumption analysis and optimization technology, including: obtaining a first virtual model, which is a digital twin model for modeling the equipment and its operating status in the distributed photovoltaic power station through a first data set and a second data set, and the first virtual model includes a first virtual component and a second virtual component, each virtual component corresponding to a virtual representation of a device; obtaining a first operating parameter and constructing a second operating model through simulation, the first operating parameter is a device status parameter collected in real time, the second operating model is used to simulate the operation process of photovoltaic power station equipment, and dynamically optimize the first virtual component and the second virtual component according to the first operating parameter and the second data set. The present invention significantly optimizes the performance and management efficiency of photovoltaic power stations, reduces operating costs, and improves energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption analysis and management, and in particular to an energy consumption analysis and optimization management method and system for a distributed photovoltaic power station. Background Art

[0002] In recent years, with the transformation of the global energy mix and the promotion of sustainable development, distributed photovoltaic power plants have gained widespread adoption as a green energy solution. Photovoltaic power generation technology has experienced significant advancements in efficiency, cost, and reliability, particularly with the continuous improvement of photovoltaic module conversion efficiency and the optimization of system integration techniques. These technological advances have enabled photovoltaic power plants to excel in a wide range of applications, from residential to commercial to industrial, achieving high energy output and economic benefits.

[0003] However, with the popularity and expansion of photovoltaic power plants, the complexity of their operation and management has also increased. Current methods for analyzing and managing photovoltaic power plant energy consumption typically rely on traditional monitoring technologies and statistical analysis methods, which face several significant problems. First, traditional energy consumption analysis methods are often based on static data or periodically collected data, making it difficult to reflect the dynamic changes in the equipment's operating status in real time. Second, existing energy consumption optimization and management methods lack in-depth analysis of the complex interactions and environmental factors during equipment operation, resulting in optimization recommendations that may not be targeted and practical. In addition, many methods fail to fully utilize big data and real-time monitoring technologies, resulting in the potential for energy efficiency improvement not being fully tapped.

[0004] Against this backdrop, digital twin technology, as an advanced modeling and simulation tool, has begun to be introduced into energy consumption analysis and optimization management for photovoltaic power plants. Digital twin models create virtual replicas of physical devices, enabling real-time simulation and monitoring of device operating status. However, existing digital twin applications still face challenges, such as the accuracy of virtual model construction and the ability to update them in real time, as well as discrepancies between the model and the actual device operating status. These challenges limit the practical effectiveness of digital twin technology in optimizing photovoltaic power plant energy consumption. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, to solve the above technical problems, the present invention provides the following technical solution: a method for energy consumption analysis and optimization management of a distributed photovoltaic power station, comprising: obtaining a first virtual model, the first virtual model being a digital twin model that models equipment in the distributed photovoltaic power station and its operating status using a first data set and a second data set, the first virtual model including a first virtual component and a second virtual component, each virtual component corresponding to a virtual representation of a device;

[0007] Obtain a first operating parameter and construct a second operating model through simulation, wherein the first operating parameter is a device status parameter collected in real time, and the second operating model is used to simulate the operation process of the photovoltaic power station equipment, and dynamically optimize the first virtual component and the second virtual component according to the first operating parameter and the second data set.

[0008] As a preferred embodiment of the method for energy consumption analysis and optimization management of a distributed photovoltaic power station according to the present invention, wherein: the first data set is a real-time data set, including a first operating parameter, a second operating parameter, and a third operating parameter; the second operating parameter is an environmental impact parameter; the third operating parameter is a power interaction parameter; and the second data set is a historical data set, including equipment operation history and power market information;

[0009] The first virtual model further includes a grid interaction model, which is used to simulate the energy exchange between the distributed photovoltaic power station and the grid, and dynamically adjust the grid stability through reactive power compensation based on the first operating parameter and the third operating parameter.

[0010] As a preferred solution of the energy consumption analysis and optimization management method of the distributed photovoltaic power station described in the present invention, the first virtual component includes a photovoltaic array model, the second virtual component includes an inverter model, the photovoltaic array model is used to simulate the power generation process of the photovoltaic panel, and the inverter model is used to simulate the power conversion process.

[0011] As a preferred solution of the energy consumption analysis and optimization management method of the distributed photovoltaic power station described in the present invention, the second operating model simulates the impact of external environmental changes on the performance of the photovoltaic array and inverter through an environmental factor simulator based on historical data and real-time data. The simulation process includes the dynamic impact of solar radiation, temperature changes and shadow effects on power generation efficiency.

[0012] As a preferred solution of the energy consumption analysis and optimization management method of the distributed photovoltaic power station described in the present invention, the dynamic optimization includes adaptively optimizing the operating status of the first virtual component and the second virtual component by executing a first control strategy, and the first control strategy adjusts the power generation efficiency and energy consumption management of the photovoltaic power station based on real-time data and historical data.

[0013] As a preferred solution of the energy consumption analysis and optimization management method of the distributed photovoltaic power station described in the present invention, the first control strategy includes dynamically adjusting the inverter and photovoltaic array using a genetic algorithm or a particle swarm optimization algorithm, and the algorithm performs adaptive optimization based on real-time collected environmental and equipment data.

[0014] As a preferred solution of the energy consumption analysis and optimization management method of the distributed photovoltaic power station described in the present invention, the method also includes constructing a second feedback mechanism, which is used to monitor the difference between the simulation results of the second operation model and the actual equipment operation data, and iteratively optimize the first virtual component and the second virtual component by adjusting the optimization strategy.

[0015] Another object of the present invention is to provide an energy consumption analysis and optimization management system for a distributed photovoltaic power station. To solve the above technical problems, the present invention provides the following technical solutions: an energy consumption analysis and optimization management system for a distributed photovoltaic power station, comprising: a virtual model construction module, configured to obtain a first virtual model, wherein the first virtual model is a digital twin model that models the equipment in the distributed photovoltaic power station and its operating status using a first data set and a second data set, wherein the first virtual model includes a first virtual component and a second virtual component, each virtual component corresponding to a virtual representation of a device;

[0016] A simulation analysis and optimization module is used to obtain a first operating parameter and construct a second operating model through simulation. The first operating parameter is a device status parameter collected in real time. The second operating model is used to simulate the operation process of the photovoltaic power station equipment and dynamically optimize the first virtual component and the second virtual component based on the first operating parameter and the second data set.

[0017] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of the above-mentioned energy consumption analysis and optimization management method of the distributed photovoltaic power station.

[0018] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned method for energy consumption analysis and optimization management of distributed photovoltaic power stations are implemented.

[0019] Beneficial effects of the present invention: By implementing the distributed photovoltaic power station energy consumption analysis and optimization management method of the present invention, first, by establishing a detailed digital twin model, virtualized management of the equipment is achieved, reducing the cost and risk of actual testing and improving the efficiency of equipment management. Next, using a real-time data-driven simulation model, the equipment operating status is dynamically adjusted, improving the system's responsiveness and flexibility, and enhancing the overall performance and stability of the photovoltaic power station. By integrating real-time and historical data, the energy exchange between the power grid and the equipment is optimized, improving the efficiency of energy management and grid stability. Simultaneously, environmental factor simulation further enhances the adaptability and power generation efficiency of the photovoltaic power station under various environmental conditions. Dynamic adjustment through advanced optimization algorithms significantly improves the power generation efficiency and energy efficiency of the equipment, optimizes resource utilization, and reduces operating costs. Finally, a second feedback mechanism ensures real-time correction and adjustment of the optimization strategy, improves the consistency between the model and the actual equipment, and further enhances the accuracy and stability of the system. Overall, the present method significantly optimizes the performance and management efficiency of the photovoltaic power station, reduces operating costs, and improves energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 This is a schematic diagram of the structure of a terminal in the first embodiment of the present invention;

[0022] Figure 2 Schematic diagram of the process in the first embodiment of the present invention;

[0023] Figure 3 Schematic diagram of the structure of a computer device in the third embodiment of the present invention.

[0024] Figure 4 A system module diagram of a second embodiment of the present invention; DETAILED DESCRIPTION

[0025] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Example 1

[0028] Reference Figure 1-Figure 4 , which is an embodiment of the present invention, provides an energy consumption analysis and optimization management method for a distributed photovoltaic power station.

[0029] First, the energy consumption analysis and optimization management method of distributed photovoltaic power stations provided by this application can be applied to Figure 1 In the terminal shown. Figure 1 As shown, the terminal may include one or two ( Figure 1 Only one is shown) processor and memory for storing data, wherein the processor may include but is not limited to a processing system such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device and input and output devices for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0030] The memory can be used to store computer programs, such as the computer program corresponding to the method for analyzing and optimizing energy consumption in distributed photovoltaic power plants in this embodiment. The processor executes the computer program stored in the memory to perform various functional applications and data processing, thereby implementing the above-mentioned method. The memory can include high-speed random access memory and non-volatile memory, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] like Figure 2 As shown, the embodiment of the present invention provides a method for analyzing and optimizing the energy consumption of a distributed photovoltaic power station. Figure 1 The terminal in is used as an example to illustrate. In order to make this application clearer, some concepts and processing procedures mentioned in this application are first briefly introduced.

[0033] This application provides an effective solution to the above-mentioned problems. Next, we will describe in detail how to implement the energy consumption analysis and optimization management method of the distributed photovoltaic power station with reference to multiple embodiments, including the following steps:

[0034] S1: Obtain a first virtual model, where the first virtual model is a digital twin model that models devices in a distributed photovoltaic power station and their operating states using a first data set and a second data set. The first virtual model includes a first virtual component and a second virtual component, where each virtual component corresponds to a virtual representation of a device.

[0035] Furthermore, the first data set is a real-time data set, including a first operating parameter, a second operating parameter, and a third operating parameter; the second operating parameter is an environmental impact parameter; the third operating parameter is a power interaction parameter; and the second data set is a historical data set, including equipment operation history and power market information;

[0036] Furthermore, the first virtual component includes a photovoltaic array model, and the second virtual component includes an inverter model. The photovoltaic array model is used to simulate the power generation process of the photovoltaic panel, and the photovoltaic array output power model has a formula of:

[0037] ,

[0038] Among them, A is the surface area of ​​the photovoltaic panel, G is the light intensity, is the conversion efficiency of the photovoltaic panel. The effect of temperature on efficiency is modeled using a nonlinear regression model that includes a temperature correction coefficient.

[0039] The inverter model is used to simulate the power conversion process. By establishing a simulation model of the power electronic equipment, the inverter's conversion process from DC to AC is simulated, including switching loss, conduction loss, etc. A transformer loss model is established based on the Steiner formula to represent load loss and no-load loss. The calculation formula is as follows:

[0040] ,

[0041] in, is the no-load loss, I is the current, and R is the coil resistance.

[0042] S2: Obtain a first operating parameter and construct a second operating model through simulation, wherein the first operating parameter is a device status parameter collected in real time, and the second operating model is used to simulate the operation process of the photovoltaic power station equipment, and dynamically optimize the first virtual component and the second virtual component according to the first operating parameter and the second data set.

[0043] Furthermore, the first operating parameter is a device status parameter collected in real time, which includes but is not limited to the temperature of the photovoltaic panel (T pV ), solar radiation intensity (G), inverter output power (P inv ), PV array output current (I PV ), PV array output voltage (V pV );

[0044] The second operating model simulates the impact of external environmental changes on the performance of photovoltaic arrays and inverters through an environmental factor simulator based on historical data and real-time data. The simulation process includes the dynamic impact of solar radiation, temperature changes and shadow effects on power generation efficiency.

[0045] The second operating model is a simulation model used to simulate the operation of photovoltaic power station equipment. Its construction process includes obtaining real-time data from sensors and monitoring systems and establishing a model based on environmental factors. Environmental factors include solar radiation, which affects the power generation efficiency of the photovoltaic array; temperature changes, which affect the performance of photovoltaic panels and inverters; and shadow effects, which simulate the impact of shadows on power generation efficiency under different time periods and environmental conditions. The calculation formula of the environmental factor simulator is as follows:

[0046] ,

[0047] in, is the power generated by the photovoltaic array, is the area of ​​the photovoltaic array, G is the solar radiation intensity, is the photovoltaic panel temperature, is the reference temperature (usually 25°C).

[0048] Furthermore, the dynamic optimization includes adaptively optimizing the operating states of the first virtual component and the second virtual component by executing a first control strategy, wherein the first control strategy adjusts the power generation efficiency and energy consumption management of the photovoltaic power station based on real-time data and historical data.

[0049] Furthermore, the first control strategy includes dynamically adjusting the inverter and photovoltaic array using a genetic algorithm and a particle swarm optimization algorithm. The algorithm performs adaptive optimization based on real-time collected environmental and equipment data. The first control strategy considers the optimization of the power generation and energy consumption of the photovoltaic power station. The specific steps of the genetic algorithm include:

[0050] Generate multiple random control strategies as the initial population;

[0051] The fitness of each strategy is evaluated based on its power generation efficiency and energy consumption management. The fitness function formula used is as follows:

[0052] ,

[0053] in, is the photovoltaic array power generated based on the input variable x, is the average power generated by the photovoltaic array, is the standard deviation of the photovoltaic array power generation, is the energy consumption calculated based on the input variable x, is the mean energy consumption, is the standard deviation of energy consumption, α and β are adjustment parameters, It is a function used to evaluate the fitness of each individual in the genetic algorithm.

[0054] Select a strategy with higher fitness for crossover and mutation operations;

[0055] By repeating the above steps, new strategies are continuously generated and evaluated until the optimal strategy is found.

[0056] The specific steps of the particle swarm optimization algorithm include:

[0057] Generate an initial set of particles and randomly assign their velocities and positions;

[0058] Update the position and velocity of each particle based on its historical best position and the best position of the group;

[0059] The objective function of the particle swarm optimization algorithm is as follows:

[0060] ,

[0061] in, is the objective function used to evaluate each particle in the particle swarm optimization algorithm, is the particle position in particle swarm optimization, representing the current solution or set of variables, At the particle position The photovoltaic array power generation power calculated below is: is the predicted photovoltaic array power generation, is the standard deviation of generated power, used for normalization, At the particle position The energy consumption calculated below is is the predicted energy consumption, is the standard deviation of energy consumption, used for normalization, and is an adjustment factor used to control the impact of power generation and energy consumption on the objective function;

[0062] Evaluate the fitness of each particle and update the particle's velocity and position to find the optimal control strategy;

[0063] Apply the optimized control strategy to the operation of photovoltaic power plants, and improve power generation efficiency and reduce energy consumption by adjusting the working status of inverters and photovoltaic arrays;

[0064] Control strategies include adjusting the angle of the photovoltaic array, changing the operating mode of the inverter, and optimizing energy distribution.

[0065] Furthermore, the method also includes constructing a second feedback mechanism, which is used to monitor the difference between the simulation results of the second operation model and the actual equipment operation data, and iteratively optimize the first virtual component and the second virtual component by adjusting the optimization strategy.

[0066] Example 2

[0067] Reference Figure 4 , as one embodiment of the present invention, provides an energy consumption analysis and optimization management system for a distributed photovoltaic power station, comprising:

[0068] a virtual model construction module, configured to obtain a first virtual model, wherein the first virtual model is a digital twin model that models equipment in a distributed photovoltaic power station and its operating status using a first data set and a second data set, wherein the first virtual model includes a first virtual component and a second virtual component, each virtual component corresponding to a virtual representation of a device;

[0069] A simulation analysis and optimization module is used to obtain a first operating parameter and construct a second operating model through simulation. The first operating parameter is a device status parameter collected in real time. The second operating model is used to simulate the operation process of the photovoltaic power station equipment and dynamically optimize the first virtual component and the second virtual component based on the first operating parameter and the second data set.

[0070] The specific limitations of the distributed photovoltaic power station energy consumption analysis and optimization management system can be found in the limitations of the distributed photovoltaic power station energy consumption analysis and optimization management method described above and will not be repeated here. Each module in the distributed photovoltaic power station energy consumption analysis and optimization management system can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the modules can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the operations corresponding to each of the modules.

[0071] Example 3

[0072] Reference Figure 3 , which is the third embodiment of the present invention. Based on the first two embodiments, the present invention provides a computer device, which can be a server, and its internal structure diagram can be as shown below. Figure 3 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium.

[0073] The computer device's database is used to store motion detection data. The computer device's network interface is used to communicate with an external terminal via a network connection. When executed by a processor, the computer program implements the steps of any of the above-mentioned sparse tensor operation acceleration method embodiments.

[0074] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0075] In one embodiment, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned sparse tensor operation acceleration method embodiments are implemented.

[0076] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the embodiments provided herein may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0077] Example 4

[0078] As an embodiment of the present invention, a method for analyzing and optimizing the energy consumption of a distributed photovoltaic power station is provided. To verify the beneficial effects of the present invention, economic benefit calculations and simulation experiments are used for scientific demonstration. A photovoltaic power station is selected as the test object, and the implementation process is as follows:

[0079] A digital twin model is constructed using real-time and historical data from distributed photovoltaic power plants. This data includes the PV array's generated power, inverter conversion efficiency, and electricity market prices. The model includes a primary virtual component (the PV array model), a secondary virtual component (the inverter model), and a grid interaction model.

[0080] Collect real-time data, such as the power generation of the photovoltaic array, the efficiency of the inverter, and environmental impact parameters; as well as historical data, such as the long-term operation history of the equipment and market electricity price fluctuation information.

[0081] The system simulates the impact of environmental factors (such as solar radiation, temperature changes, and shadow effects) on the performance of photovoltaic arrays and inverters. A second operating model is constructed to dynamically adjust the operating status of the equipment and perform optimization simulations.

[0082] Genetic algorithms (GA) and particle swarm optimization (PSO) are used to optimize the photovoltaic array and inverter. During the optimization process, the power generation efficiency and energy consumption management strategies of the photovoltaic power station are adjusted based on real-time and historical data.

[0083] A second feedback mechanism is constructed to monitor the difference between the simulation results of the second operation model and the actual equipment operation data, and the optimization strategy is adjusted through the feedback mechanism to achieve iterative optimization.

[0084] During the experiment, various operating parameters and performance indicators were recorded under different optimization strategies. The data is recorded in the following three tables for detailed comparison and analysis.

[0085] Table 1: PV array power generation and inverter efficiency

[0086] ,

[0087] Table 2: System operating costs and electricity market prices

[0088] ,

[0089] Table 3: Impact of environmental factors on power generation efficiency

[0090] ,

[0091] Analysis of the data in the three tables above clearly demonstrates the technical effectiveness and innovative nature of the present invention. Table 1 shows the impact of different optimization strategies and feedback mechanisms on PV array power generation and inverter efficiency. The application of the optimization strategies (GA and PSO) and feedback mechanisms significantly improves PV array power generation and inverter efficiency. In particular, the combination of the feedback mechanism with the optimization strategies (GA + feedback and PSO + feedback) further improves power generation and inverter efficiency, while significantly reducing energy losses. This demonstrates that the dynamic optimization and feedback mechanisms significantly improve the operational performance of the PV power station. Table 2 shows the impact of different optimization strategies and feedback mechanisms on system operating costs and electricity market prices. The optimization strategies (GA and PSO) and feedback mechanisms effectively reduce system operating costs. In particular, the combination of PSO optimization and feedback mechanisms achieves the lowest operating costs and the highest overall cost savings, demonstrating the significant advantages of the optimization strategies in reducing operating costs. Furthermore, the optimization strategies also improve power generation efficiency and energy utilization efficiency, demonstrating the significant effectiveness of the present invention in optimizing economic benefits. Table 3 records the impact of environmental factors on power generation efficiency. Different optimization strategies and feedback mechanisms enhance the adaptability of power generation efficiency to environmental changes. For example, the PSO optimization strategy, combined with a feedback mechanism, demonstrated improved power generation efficiency under varying temperatures and solar radiation, demonstrating the method's strong adaptability to environmental changes. Furthermore, the effects of temperature and radiation on power generation efficiency were effectively controlled using the optimization strategy, further demonstrating the advantages of optimization methods in improving system stability.

[0092] Overall, this invention, through the application of optimization algorithms and feedback mechanisms, significantly improves the power generation efficiency of photovoltaic power plants, reduces operating costs, and enhances the system's adaptability to environmental factors. Compared with traditional methods, this invention demonstrates significant advantages in system optimization and economic benefits, demonstrating its innovativeness and effectiveness in practical applications.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for energy consumption analysis and optimization management of a distributed photovoltaic power station, characterized in that: include: Acquire a first virtual model, where the first virtual model is a digital twin model that models the equipment in the distributed photovoltaic power station and its operating status through a first data set and a second data set, where the first virtual model includes a first virtual component and a second virtual component, and each virtual component corresponds to a virtual representation of a device; Acquire a first operating parameter and construct a second operating model through simulation, wherein the first operating parameter is a device state parameter collected in real time, the second operating model is used to simulate the operation process of the photovoltaic power station equipment, and dynamically optimize the first virtual component and the second virtual component according to the first operating parameter and the second data set; The first data set is a real-time data set, including a first operating parameter, a second operating parameter and a third operating parameter; the second operating parameter is an environmental impact parameter; the third operating parameter is a power interaction parameter; the second data set is a historical data set, including equipment operation history and power market information; The first virtual model further includes a power grid interaction model, which is used to simulate energy exchange between the distributed photovoltaic power station and the power grid, and dynamically adjust the stability of the power grid through reactive power compensation based on the first operating parameter and the third operating parameter; The second operation model simulates the impact of external environmental changes on the performance of the photovoltaic array and the inverter through an environmental factor simulator based on historical data and real-time data, and the simulation includes the dynamic impact of solar radiation, temperature changes and shadow effects on power generation efficiency; The dynamic optimization includes adaptively optimizing the operating states of the first virtual component and the second virtual component by executing a first control strategy, wherein the first control strategy adjusts the power generation efficiency and energy consumption management of the photovoltaic power station based on real-time data and historical data; The method also includes constructing a second feedback mechanism, which is used to monitor the difference between the simulation results of the second operation model and the actual equipment operation data, and iteratively optimize the first virtual component and the second virtual component by adjusting the optimization strategy.

2. The method for optimizing the energy consumption analysis of a distributed photovoltaic power station according to claim 1, characterized in that: The first virtual component includes a photovoltaic array model, and the second virtual component includes an inverter model. The photovoltaic array model is used to simulate a power generation process of a photovoltaic panel, and the inverter model is used to simulate a power conversion process.

3. The method for optimizing the energy consumption analysis of a distributed photovoltaic power station according to claim 2, characterized in that: The first control strategy includes dynamically adjusting the inverter and the photovoltaic array using a genetic algorithm or a particle swarm optimization algorithm, and the algorithm performs adaptive optimization based on real-time collected environmental and equipment data.

4. A system using the energy consumption analysis and optimization management method of a distributed photovoltaic power station as claimed in any one of claims 1 to 3, characterized in that: include: A virtual model building module is used to obtain a first virtual model, where the first virtual model is a digital twin model that models the equipment in the distributed photovoltaic power station and its operating status through a first data set and a second data set, and the first virtual model includes a first virtual component and a second virtual component, each virtual component corresponding to a virtual representation of a device; A simulation analysis and optimization module is used to obtain a first operating parameter and construct a second operating model through simulation, wherein the first operating parameter is a device status parameter collected in real time, and the second operating model is used to simulate the operation process of photovoltaic power station equipment, and dynamically optimize the first virtual component and the second virtual component according to the first operating parameter and the second data set.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method for energy consumption analysis and optimization management of a distributed photovoltaic power station according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for energy consumption analysis and optimization management of a distributed photovoltaic power station according to any one of claims 1 to 3 are implemented.

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