Operation regulation and control method and system for distributed new energy photovoltaic power station

Abstract

The invention discloses an operation regulation and control method and system for a distributed new energy photovoltaic power station, and relates to the technical field of new energy, and the method comprises the steps: recognizing a power station, building a monitoring system, collecting and monitoring key equipment data through the system, so as to obtain historical and real-time operation data, and predicting photovoltaic output and load based on the data to obtain predicted values, calculating power deviation, inputting the deviation into an optimal scheduling model to obtain a strategy, and adaptively regulating and controlling the operation state of the power station according to the strategy. The technical problems of unbalanced power generation and power utilization, low operation efficiency, poor stability, high cost and poor power grid compatibility caused by the fact that efficient and self-adaptive optimal scheduling cannot be performed according to accurate photovoltaic output and load prediction in the prior art are solved, accurate balance of power generation and power utilization is achieved, and the power generation efficiency and the power utilization efficiency are improved. The operation efficiency and stability of the power station are remarkably improved, the operation cost is reduced, and the compatibility with a power grid is enhanced.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to an operation control method and system for a distributed new energy photovoltaic power station. Background Art

[0002] In the operation scenario of distributed new energy photovoltaic power stations, the randomness and volatility of photovoltaic output and the uncertainty and time-varying nature of user load demand are particularly prominent. The contradiction between efficient regulation of power station operation and accurate balance of power generation and power consumption is relatively more prominent. Achieving stable and efficient operation of power stations has become a crucial link in solving the development of distributed new energy photovoltaic power stations. The operation and regulation of traditional distributed new energy photovoltaic power stations are often extensive and localized, relying only on simple monitoring methods or limited experience judgments. There is insufficient control over the overall operation of the power station, lack of in-depth mining and analysis of historical operation data, and it is difficult to fully and accurately predict photovoltaic output and load changes. There are unreasonable situations in resource scheduling, resulting in poor operation of some key equipment and idle waste of some equipment. The formulation of operation and regulation strategies is relatively simple and fixed, and cannot cope well with the complex and changeable actual operation of the power station.

[0003] At the current stage, relevant technologies have the problem of being unable to perform efficient and adaptive optimization scheduling based on accurate photovoltaic output and load forecasts, which leads to technical problems such as imbalance between power generation and power consumption, low operating efficiency, poor stability, high costs and poor grid compatibility. Summary of the invention

[0004] The present application provides an operation and control method and system for a distributed new energy photovoltaic power station. The method identifies the power station and builds a monitoring system. The system collects and monitors data from key equipment to obtain historical and real-time operation data. The photovoltaic output and load are predicted based on these data to obtain predicted values. The power deviation is calculated, and the deviation is input into an optimization scheduling model to obtain a strategy. The operation status of the power station is adaptively controlled according to the strategy. The accurate prediction of photovoltaic output and load and efficient adaptive optimization scheduling are achieved, thereby achieving an accurate balance between power generation and power consumption, significantly improving the operating efficiency and stability of the power station, reducing operating costs, and enhancing the technical effect of compatibility with the power grid.

[0005] The present application provides an operation control method for a distributed new energy photovoltaic power station, including:

[0006] Identify a distributed new energy photovoltaic power station, and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station; collect data and monitor the key equipment of the distributed new energy photovoltaic power station in real time through the distributed photovoltaic monitoring system, and obtain the historical operation data and real-time operation data of the distributed new energy photovoltaic power station; predict the photovoltaic output and load of the distributed new energy photovoltaic power station based on the historical operation data and the real-time operation data, and obtain the photovoltaic output prediction value and the load prediction value in the future period; calculate the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, and the power deviation is the difference between the photovoltaic output prediction value and the load prediction value; input the power deviation into a preset optimization scheduling model for analysis, and obtain the optimization scheduling strategy of the distributed new energy photovoltaic power station, and the optimization scheduling strategy includes control instructions for the key equipment; according to the optimization scheduling strategy, the operation state of the distributed new energy photovoltaic power station is adaptively regulated.

[0007] This application also provides an operation and control system for a distributed new energy photovoltaic power station, including:

[0008] A monitoring system building module, which is used to identify distributed new energy photovoltaic power stations and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power stations; a data acquisition module, which is used to collect data and conduct real-time monitoring of key equipment of distributed new energy photovoltaic power stations through the distributed photovoltaic monitoring system, and obtain historical operation data and real-time operation data of distributed new energy photovoltaic power stations; a prediction module, which is used to predict the photovoltaic output and load of the distributed new energy photovoltaic power station based on the historical operation data and real-time operation data, and obtain the photovoltaic output prediction value and load prediction value in the future period; power deviation calculation Module, the power deviation calculation module is used to calculate the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, and the power deviation is the difference between the photovoltaic output prediction value and the load prediction value; the optimization scheduling strategy acquisition module, the optimization scheduling strategy acquisition module is used to input the power deviation into a preset optimization scheduling model for analysis, and obtain the optimization scheduling strategy of the distributed new energy photovoltaic power station, and the optimization scheduling strategy includes control instructions for the key equipment; the adaptive control module, the adaptive control module is used to adaptively control the operating state of the distributed new energy photovoltaic power station according to the optimization scheduling strategy.

[0009] The operation and control method and system for distributed new energy photovoltaic power stations proposed in this application first identify the power station and build a monitoring system. The system collects and monitors key equipment data to obtain historical and real-time operation data. Based on these data, the photovoltaic output and load are predicted to obtain predicted values, the power deviation is calculated, and the deviation is input into the optimization scheduling model to obtain the strategy. The power station operation status is adaptively controlled according to the strategy. By realizing accurate prediction of photovoltaic output and load and efficient adaptive optimization scheduling, a precise balance between power generation and electricity consumption is achieved, which significantly improves the operating efficiency and stability of the power station, reduces operating costs, and enhances the technical effect of compatibility with the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic diagram of a flow chart of an operation control method for a distributed new energy photovoltaic power station provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the flow of an operation and control system for a distributed new energy photovoltaic power station provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: monitoring system building module 10, data acquisition module 20, prediction module 30, power deviation calculation module 40, optimization scheduling strategy acquisition module 50, adaptive control module 60. DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides an operation control method for a distributed new energy photovoltaic power station, such as Figure 1 As shown, the method includes:

[0018] Step S100, identify the distributed new energy photovoltaic power station, and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station. Specifically, the identification of the distributed new energy photovoltaic power station is carried out, and multiple factors are comprehensively analyzed. From the geographical location, it is necessary to clarify the specific location of the power station, including the region, topography and surrounding environment, etc., which is helpful to understand the external factors that may affect the operation of the power station, such as lighting conditions and climate characteristics. The installed capacity information is also a key identification point, including the number, specifications and overall power generation capacity of photovoltaic modules, which are directly related to the scale and potential power generation capacity of the power station. The way of accessing the power grid includes direct access to the large power grid and access through the microgrid. Different access methods will affect the transmission and distribution of electricity. The ownership information of the power station needs to be clear and clear, whether it belongs to an enterprise, a public institution or an individual, which affects the subsequent management and operation mode. After completing the comprehensive identification of the distributed new energy photovoltaic power station, the next step is to build a distributed photovoltaic monitoring system based on this. During the construction process, the architecture of the monitoring system must first be determined according to the scale and characteristics of the power station. For larger-scale power stations , it is necessary to adopt a hierarchical and distributed monitoring architecture to ensure effective coverage of various areas and equipment, and select appropriate hardware equipment, such as high-performance data collectors, stable communication modules, etc. The data collector must be able to accurately collect the operating parameters of various key equipment, and the communication module must ensure the timeliness and stability of data transmission. In terms of software, it is necessary to develop a powerful monitoring platform with functions such as data storage, analysis, display and early warning, and effectively organize and analyze the collected data, present it to management personnel in an intuitive form, and issue alarms in time when abnormal situations occur, ensuring that the monitoring system is seamlessly connected with various key equipment in the power station, and can accurately obtain the operating data of the equipment, and monitor and control it in real time. For example, in the construction process of a distributed new energy photovoltaic power station located in a mountainous area, considering the complex terrain and limited communication conditions, a communication module with strong signal penetration capability will be used, and a data analysis and early warning module specifically designed for the climate characteristics of the mountainous area will be set up in the monitoring platform.

[0019] In one possible implementation, a distributed new energy photovoltaic power station is identified, and a distributed photovoltaic monitoring system is built based on the distributed new energy photovoltaic power station. Step S100 further includes step S110, identifying the distributed new energy photovoltaic power station according to preset photovoltaic power station identification rules, and obtaining the photovoltaic power station identification result. The preset photovoltaic power station identification rules include the geographical location information, installed capacity information, grid access method and ownership information of the photovoltaic power station. Specifically, the identification work is carried out according to the pre-set photovoltaic power station identification rules. For geographical location information, it is necessary to accurately determine the latitude and longitude of the power station, the administrative region where it is located, and the topographical features. For example, if the power station is located in an area with sufficient sunlight and flat terrain, it means that it has good power generation conditions. For installed capacity information, it is necessary to understand in detail the total power of the photovoltaic components installed in the power station, the specifications and quantity of the solar panels, etc. For example, a power station with a larger installed capacity may mean that it has a stronger power generation capacity and can supply more electricity. In terms of the way of accessing the power grid, it is clear whether it is directly connected to the high-voltage transmission network, or through the regional distribution network, or connected to the microgrid. Different access methods will affect the transmission and allocation strategy of electricity. The ownership information of the power station is also crucial, whether it is state-owned, private, collectively owned or individually owned. This will determine the management and operation subject and the relevant responsibilities and rights. Through comprehensive analysis, a comprehensive and accurate photovoltaic power station identification result can be obtained.

[0020] Step S120, building a corresponding distributed photovoltaic monitoring system based on the photovoltaic power station identification result and characteristic parameters, wherein the characteristic parameters include the equipment type, equipment quantity, equipment parameters and operation mode of the distributed new energy photovoltaic power station. Specifically, after obtaining the identification results of the photovoltaic power station, the monitoring system is built based on the results and further combined with specific characteristic parameters. In terms of equipment type, it is determined whether the power station uses single-crystal photovoltaic modules or polycrystalline photovoltaic modules, and whether the inverter type is centralized or string-type. The number of equipment covers the total number of photovoltaic modules, the number of inverters, the number of energy storage devices, etc. The equipment parameters include the specific technical specifications of each device, such as the conversion efficiency of photovoltaic modules, the rated power and efficiency of inverters, the operation mode of the power station, such as self-generation and self-use or surplus power access to the grid, or both. Comprehensively analyze the characteristic parameters to build a distributed photovoltaic monitoring system that can adapt to the actual situation of the power station. For example, if the equipment types of the power station are diverse and numerous, then the monitoring system needs to have strong data processing capabilities and rich interfaces to be compatible with different types of equipment; if the operation mode of the power station is mainly surplus power access to the grid, the monitoring system needs to focus on the equipment operation status and power output of the part connected to the grid.

[0021] Step S200, the distributed photovoltaic monitoring system collects data and conducts real-time monitoring on the key equipment of the distributed new energy photovoltaic power station, and obtains the historical operation data and real-time operation data of the distributed new energy photovoltaic power station. Specifically, the distributed photovoltaic monitoring system collects data and conducts real-time monitoring on the key equipment of the distributed new energy photovoltaic power station, and accurately obtains various key parameters through high-precision sensors and data acquisition devices installed on the key equipment. For photovoltaic modules, the output voltage, current, power and other data are collected to measure their power generation efficiency; for inverters, the input and output voltage, current, frequency, power factor and other parameters are collected to evaluate their conversion efficiency and working stability; for energy storage devices, the charging and discharging voltage, current, capacity, remaining power and other data are collected to understand their energy storage and release conditions; for grid connection equipment, the voltage, current, power, power quality and other parameters are collected to ensure a safe and stable connection with the grid. In the real-time monitoring stage, the collected data will be continuously transmitted to the central processing unit of the monitoring system at very short time intervals to analyze and process the real-time data. Once any parameter is found to deviate from the normal range, an alarm will be immediately issued and the relevant personnel will be notified. The staff can take countermeasures. For example, if the output power of the PV module suddenly drops significantly, the system will prompt possible problems such as module failure or shadow obstruction. The real-time data will be compared and analyzed with the historical operation data accumulated in the past. The historical operation data is important information retained during the long-term operation of the power station, covering the operation details of the equipment under different seasons, weather conditions and load conditions. Through comparative analysis, it is possible to more accurately judge whether the current operation status of the equipment is normal and whether there are potential problem trends. For example, by comparing the current and historical light intensity and the output power of the PV module, if it is found that the current output power is significantly lower than the historical level, it can be inferred that there may be problems such as module aging or dust accumulation, and maintenance and cleaning work needs to be arranged in time. The historical operation data and real-time operation data will be stored in a large-capacity database for subsequent more in-depth data analysis and mining, providing solid data support for the optimization of power station operation and maintenance decisions.

[0022] In a possible implementation, the distributed photovoltaic monitoring system is used to collect data and monitor in real time the key equipment of the distributed new energy photovoltaic power station, and obtain the historical operation data and real-time operation data of the distributed new energy photovoltaic power station. Step S200 further includes step S210, and the key equipment includes photovoltaic modules, inverters, energy storage devices and grid connection equipment in the distributed new energy photovoltaic power station. Specifically, photovoltaic modules convert solar energy into electrical energy. The modules are usually composed of multiple cells connected in series and in parallel. Their performance and quality directly determine the initial power generation capacity of the power station. In actual operation, attention is paid to the photoelectric conversion efficiency, output voltage and current stability of the photovoltaic modules, and their working performance under different light and temperature conditions. For example, when the light intensity is high, high-quality photovoltaic modules can maintain a high and stable output power. The inverter plays a key role in power conversion in the power station, converting the direct current generated by the photovoltaic modules into alternating current so as to connect to the power grid or for users to use. The performance indicators of the inverter include conversion efficiency, output power quality (such as harmonic content, voltage stability, etc.) and reliability. In order to ensure the normal operation of the inverter, it is necessary to monitor its input and output voltage, current, power and other parameters, as well as internal temperature and heat dissipation. For example, if the output power quality of the inverter does not meet the standards, it may have an adverse effect on the power grid and power-consuming equipment. The energy storage device can store electricity when the photovoltaic modules generate excess electricity, and release electricity when the power generation is insufficient or the power consumption is at peak, thereby improving the power supply stability and reliability of the power station. For the energy storage device, the key parameters include charging and discharging efficiency, capacity, cycle life, and self-discharge rate. Real-time monitoring of the charging and discharging status, remaining capacity, and battery health of the energy storage device will help to reasonably arrange energy storage and release and extend its service life. The grid connection equipment is responsible for safely and stably connecting the electricity generated by the power station to the grid, including transformers, switchgear, relay protection devices, etc. The performance of the equipment directly affects the interaction effect between the power station and the grid and the efficiency of power transmission. Attention should be paid to the voltage level adaptability, power transmission capacity, accuracy and timeliness of protection actions of the grid connection equipment. For example, when a fault occurs in the power grid, the relay protection device should be able to act quickly to isolate the power station from the grid to protect the safety of equipment and personnel.

[0023] In a possible implementation, the distributed photovoltaic monitoring system is used to collect data and monitor in real time the key equipment of the distributed new energy photovoltaic power station, and the historical operation data and real-time operation data of the distributed new energy photovoltaic power station are obtained. Step S200 further includes step S220, in which a data acquisition unit is installed on the key equipment of the distributed new energy photovoltaic power station, and the data acquisition unit includes a data collector and a communication module. Specifically, the data acquisition unit is installed for the key equipment in the distributed new energy photovoltaic power station, such as photovoltaic modules, inverters, energy storage devices, and grid connection devices. The data acquisition unit consists of two parts: a data collector and a communication module. The data collector is responsible for obtaining various operating parameters from the equipment, such as the voltage, current and power of the photovoltaic modules, the input and output parameters of the inverter, the charge and discharge status of the energy storage device, etc., and the communication module is responsible for transmitting the collected data.

[0024] Step S230, the operating parameters of the key equipment are collected in real time by the data collector, and the collected operating parameters are transmitted in real time to the monitoring center server of the distributed photovoltaic monitoring system by the communication module. Specifically, during the operation of the equipment, the data collector collects the operating parameters of the key equipment in real time with extremely high frequency and accuracy. The parameters reflect the immediate working status and performance of the equipment. At the same time, the communication module uses a wireless network, a wired network or other communication methods to continuously transmit the collected real-time operating parameters to the monitoring center server of the distributed photovoltaic monitoring system to ensure the timeliness and integrity of the data, so that the monitoring center can obtain the latest equipment operation information as soon as possible.

[0025] Step S240, the monitoring center server receives and stores the operating parameters of the key equipment, generates the historical operating data of the distributed new energy photovoltaic power station, and processes and analyzes the received real-time operating parameters to generate the real-time operating data of the distributed new energy photovoltaic power station. Specifically, after receiving the operating parameters transmitted from each key device, the monitoring center server first receives and stores the data. The continuously accumulated data forms the historical operating data of the distributed new energy photovoltaic power station, which provides a basis for subsequent analysis and trend prediction. The server uses powerful computing power and data analysis algorithms to process and deeply analyze the real-time operating parameters just received. By comparing with preset thresholds, standard models or historical data, key information and potential problems are extracted, thereby generating real-time operating data that can reflect the current operating status of the power station. The real-time operating data can provide immediate decision support for operation and maintenance personnel, so that timely measures can be taken to optimize the operation of the power station, troubleshoot or perform preventive maintenance.

[0026] Step S300, based on the historical operation data and real-time operation data, predict the photovoltaic output and load of the distributed new energy photovoltaic power station to obtain the photovoltaic output forecast value and load forecast value in the future period of time. Specifically, the historical operation data and real-time operation data of the distributed new energy photovoltaic power station are obtained. The historical operation data contains the photovoltaic output and load changes of the photovoltaic power station in different time periods in the past, covering a variety of weather conditions, seasonal changes, peak and trough electricity consumption and other rich information. The real-time operation data reflects the current status of the power station. The data are processed using data analysis technology and prediction algorithms. For example, time series analysis methods can be used to mine the periodicity and trend in historical data. At the same time, meteorological data, such as the impact of factors such as sunshine duration, solar radiation intensity, temperature, humidity, wind speed, etc. on photovoltaic output, as well as the impact of factors such as user behavior, economic activities, holidays, etc. on load, are considered. Machine learning algorithms, such as neural networks, support vector machines, etc., are used to establish a prediction model. The model learns the patterns and laws in historical data and combines them with The latest information of real-time operation data is used to predict the photovoltaic output and load in the future. During the prediction process, the model will be continuously adjusted and optimized to improve the accuracy of the prediction. For example, if the prediction result deviates greatly from the actual situation, the quality and completeness of the input data will be re-evaluated, the parameters of the model will be adjusted, or new characteristic variables will be introduced. Finally, the photovoltaic output and load forecast values ​​for the future period are obtained. The forecast value can be an estimate of the next few hours, day, week or even longer. For example, by analyzing the photovoltaic output and load data of the historical high temperature period in summer, combined with the current real-time temperature and electricity consumption, the peak photovoltaic output at noon in the next day and the load value at the peak of electricity consumption in the afternoon are predicted. For example, according to the electricity consumption patterns of historical holidays and current real-time data, the load change trend during the upcoming National Day holiday is predicted.

[0027] In one possible implementation, the photovoltaic output and load of the distributed new energy photovoltaic power station are predicted based on the historical operation data and the real-time operation data to obtain the photovoltaic output prediction value and the load prediction value in the future period, and step S300 further includes step S310, constructing a photovoltaic output prediction model and a load prediction model, and the photovoltaic output prediction model and the photovoltaic output prediction model are constructed based on the historical operation data. Specifically, a large amount of historical operation data of the distributed new energy photovoltaic power station is collected, and the data includes photovoltaic output conditions under different weather conditions, load changes in different time periods, etc., and the photovoltaic output prediction model and the load prediction model are constructed based on the historical data using data analysis and mathematical modeling methods. In the construction process, statistical principles, machine learning algorithms, etc. are applied to extract hidden patterns and laws in the data.

[0028] Step S320, using the photovoltaic output prediction model to predict the photovoltaic output of the distributed new energy photovoltaic power station according to the real-time operation data, and obtain the photovoltaic output prediction value in the future period of time. Specifically, after the model is built, when it is necessary to predict the photovoltaic output, obtain the current real-time operation data, such as the current light intensity, temperature, humidity and other environmental parameters, and input the real-time data into the constructed photovoltaic output prediction model. The model will calculate and analyze according to the input real-time data and the rules learned from the historical data, so as to give the photovoltaic output prediction value in the future period of time (such as the next few hours, a day or longer).

[0029] Step S330, using the load prediction model to predict the load of the distributed new energy photovoltaic power station according to the real-time operation data, and obtain the load prediction value in the future period. Specifically, for load prediction, first obtain the current real-time operation data, such as the current time, season, user electricity consumption behavior and other related information, and input the real-time data into the load prediction model. The model will predict the load prediction value of the distributed new energy photovoltaic power station in the future period based on its internal algorithm and the rules summarized from the historical load data, providing an important reference for the operation and scheduling of the power station. For example, on a sunny morning, the current real-time light intensity, temperature and real-time power consumption of the power station are obtained. These data are respectively input into the photovoltaic output prediction model and the load prediction model, so as to obtain the photovoltaic output prediction value and load prediction value of the afternoon of the same day, so as to prepare for power allocation and equipment management in advance.

[0030] Step S400, calculate the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, wherein the power deviation is the difference between the photovoltaic output prediction value and the load prediction value. Specifically, the photovoltaic output prediction value and the load prediction value of the distributed new energy photovoltaic power station are obtained through the prediction model. In order to calculate the power deviation, it is necessary to perform a simple subtraction operation on the photovoltaic output prediction value and the load prediction value. When performing the subtraction, it is necessary to ensure that the units and time scales of the two are consistent. For example, if the photovoltaic output prediction value is expressed in kilowatts per hour, then the load prediction value should also use the same unit and time scale. If the photovoltaic output prediction value is greater than the load prediction value, then the power deviation value obtained is a positive number, indicating that the power station is expected to generate more electricity than demand, and there may be surplus electricity or need to be transmitted to the power grid. Conversely, if the photovoltaic output prediction value is less than the load prediction value, the power deviation value is positive, indicating that the power station is expected to generate more electricity than demand, and there may be surplus electricity or need to be transmitted to the power grid. Conversely, if the photovoltaic output prediction value is less than the load prediction value, the power deviation value is positive, indicating that the power station is expected to generate more electricity than demand, and there may be surplus electricity or need to be transmitted to the power grid. A negative deviation value means that the power plant is expected to generate insufficient electricity to meet demand, and it may be necessary to discharge from energy storage devices, purchase electricity from the grid, or take energy-saving measures to balance supply and demand. For example, assuming that the photovoltaic output forecast value is 500 kilowatts in a certain period of time, and the load forecast value is 400 kilowatts, by calculating 500-400=100 kilowatts, the power deviation value is 100 kilowatts, which indicates that there is an estimated surplus of 100 kilowatts of electricity in this period of time. If the photovoltaic output forecast value is 300 kilowatts and the load forecast value is 450 kilowatts, the power deviation of -150 kilowatts is calculated, which means that there is an estimated electricity gap of 150 kilowatts in this period of time, and corresponding measures need to be taken to supplement the electricity.

[0031] Step S500, input the power deviation into a preset optimization scheduling model for analysis, and obtain the optimization scheduling strategy of the distributed new energy photovoltaic power station, wherein the optimization scheduling strategy includes control instructions for the key equipment. Specifically, the power deviation of the distributed new energy photovoltaic power station obtained by calculation is obtained, and the power deviation is passed as input data to a preset optimization scheduling model. The optimization scheduling model is constructed based on complex mathematical algorithms and actual operating experience. After receiving the power deviation data, the model will comprehensively analyze many factors, including but not limited to: performance parameters of key equipment, current operating status, energy storage conditions of the power station, grid demand and restrictions, and operating costs, etc. Through comprehensive evaluation of factors and complex calculations, the model will generate an optimization scheduling strategy for the distributed new energy photovoltaic power station. The strategy specifically includes detailed control instructions for key equipment. For example, if the power deviation shows excess power, the control instructions may include reducing the photovoltaic components. Working efficiency, reducing the output power of the inverter, increasing the charging power of the energy storage device, or increasing the power transmitted to the grid. On the contrary, if the power deviation shows insufficient power, the control instruction may be to improve the efficiency of the photovoltaic modules, increase the output power of the inverter, reduce the discharge of the energy storage device, or purchase electricity from the grid. For a specific example, assuming that the power deviation is positive and large, the optimization scheduling model may generate a control instruction to require that some photovoltaic modules be properly shaded to reduce the power generation, and at the same time increase the charging speed of the energy storage device to store excess electrical energy. For another example, if the power deviation is negative and large, the model may issue an instruction to let the inverter run at full power, quickly release the electrical energy in the energy storage device, and apply to the grid for power purchase to meet the load demand.

[0032] In a possible implementation, the power deviation is input into a preset optimization scheduling model for analysis to obtain an optimization scheduling strategy for the distributed new energy photovoltaic power station, the optimization scheduling strategy includes control instructions for the key equipment, and step S500 further includes step S510, establishing an optimization scheduling model for a distributed new energy photovoltaic power station, and the objective function of the optimization scheduling model is to minimize the power deviation. Specifically, an optimization scheduling model for a distributed new energy photovoltaic power station is constructed, and the model comprehensively considers the characteristics of various equipment in the power station, operating constraints, and external environmental factors, and the objective function is set to minimize the power deviation, in order to minimize the difference between the photovoltaic output forecast value and the load forecast value, so as to achieve the best balance between power generation and power consumption.

[0033] Step S520, input the power deviation into the optimization scheduling model, solve the optimization scheduling model, and obtain the optimal operating state of the distributed new energy photovoltaic power station. Specifically, the calculated power deviation data is input into the established optimization scheduling model, and the model is solved using a mathematical optimization algorithm, such as linear programming, nonlinear programming or intelligent optimization algorithm. In the process of solving, the model will continuously adjust and optimize related variables under the premise of satisfying various constraints (such as equipment operation restrictions, power grid regulations, etc.) to find the optimal solution that can minimize the power deviation. The optimal solution represents the optimal operating state of the distributed new energy photovoltaic power station, including the ideal operating parameters and collaborative working methods of each key device.

[0034] Step S530, based on the optimal operating state of the distributed new energy photovoltaic power station, generate control instructions for the key equipment in the distributed new energy photovoltaic power station as the optimization scheduling strategy. Specifically, according to the optimal operating state obtained by solving, specific control instructions are generated for the key equipment in the power station. The control instructions clarify the working voltage and current of the photovoltaic module, the output power and frequency of the inverter, the charging and discharging power and duration of the energy storage device, and the operation mode of the grid connection device, etc. The control instructions together constitute the optimization scheduling strategy, which is used to guide the actual operation of the power station to achieve the best power generation efficiency, power supply stability and economic benefits. For example, assuming that the power deviation is large and positive, the optimal operating state obtained by the model solution may be to reduce the power generation power of the photovoltaic module and increase the charging power of the energy storage device. Based on the above information, the generated control instruction may be to adjust the tilt angle of the photovoltaic module or partially block it, and to increase the charging current of the energy storage device. For example, if the power deviation is negative and large, the optimal operating state may be to let the inverter run at full power and quickly release the electric energy of the energy storage device. The corresponding control instruction is to adjust the output power of the inverter to the maximum value and start the discharge mode of the energy storage device.

[0035] Step S600, according to the optimization scheduling strategy, the operating state of the distributed new energy photovoltaic power station is adaptively regulated. Specifically, the generated optimization scheduling strategy is obtained, which includes specific control instructions for key equipment in the distributed new energy photovoltaic power station. According to the control instructions, the operating state of the power station is adaptively regulated. For photovoltaic components, their working state is dynamically adjusted according to the instructions. For example, if the instruction requires improving the power generation efficiency, the tilt angle of the photovoltaic component may be adjusted to allow it to receive sunlight more fully; or the surface of the component may be cleaned to reduce the dust blocking the light, thereby increasing its output power. For the inverter, its output power is accurately adjusted according to the control instruction. According to the current power generation and power consumption, the output voltage and current of the inverter are increased or decreased to ensure that the output power quality and power meet the requirements. For the energy storage device, its charging and discharging power is dynamically adjusted according to the strategy. When there is excess power, the charging power is increased to store the excess power; and when there is insufficient power, the discharge power is increased to supplement the power supply. For the grid-connected equipment, its operating parameters are optimized according to the instructions, for example, the voltage, frequency and other parameters of the equipment are adjusted to ensure To ensure stable connection and efficient interaction with the power grid, during the entire adaptive control process, the actual operating status of the power station is monitored in real time and compared with the previous predicted status. If there is a deviation between the actual operating status and the predicted status, it will be fed back to the control system in a timely manner. The system will re-evaluate and adjust the control instructions to ensure that the actual operating status of the photovoltaic power station can match the predicted status. For example, at noon, the predicted photovoltaic output is high and the load is relatively low. The optimized scheduling strategy may instruct the energy storage device to increase the charging power, but in actual operation, it is found that the charging speed of the energy storage device does not meet expectations. At this time, the system will automatically adjust the relevant parameters and increase the charging current to ensure that the actual operation is consistent with the prediction. In the evening, the predicted load gradually increases and the photovoltaic output decreases. The strategy instructs the inverter to increase the output power, but in practice it is found that the power increase is insufficient. The system will immediately increase the control voltage of the inverter to ensure that the output power meets the demand. Through adaptive control, the operating efficiency and stability of the power station can be continuously optimized to achieve efficient utilization and reliable supply of energy.

[0036] In a possible implementation, according to the optimization scheduling strategy, the operation state of the distributed new energy photovoltaic power station is adaptively controlled, and step S600 further includes step S610, extracting the control instructions of the key equipment in the optimization scheduling strategy, and sending the control instructions to the microgrid controller of the distributed new energy photovoltaic power station, controlling the key equipment to perform corresponding adjustment actions, and adjusting the operation state of the distributed new energy photovoltaic power station. Specifically, the detailed control instructions for key equipment (such as photovoltaic components, inverters, energy storage devices and grid connection devices) are accurately extracted from the optimization scheduling strategy, and the control instructions are quickly sent to the microgrid controller of the distributed new energy photovoltaic power station through an efficient and reliable communication channel. After receiving the instructions, the microgrid controller immediately sends corresponding control signals to the key equipment, prompting these devices to perform specific adjustment actions, such as changing the angle of the photovoltaic component, adjusting the output parameters of the inverter, controlling the charge and discharge rate of the energy storage device, or changing the operation mode of the grid connection device, so as to make targeted adjustments to the operation state of the distributed new energy photovoltaic power station and make it develop in a more optimized direction.

[0037] Step S620, continuously monitor the actual operating status parameters of the distributed new energy photovoltaic power station. When the deviation between the actual operating status parameters and the optimization scheduling strategy exceeds a preset threshold, the optimization scheduling model is triggered to regenerate an updated optimization scheduling strategy, and the operating status is re-regulated according to the updated optimization scheduling strategy. Specifically, during the entire operation process, various sensors and monitoring equipment are continuously used to collect and monitor the actual operating status parameters of the distributed new energy photovoltaic power station in real time. The parameters include but are not limited to power generation, power consumption, equipment temperature, voltage and current, etc. The actual operating status parameters are compared with the parameters expected by the previously formulated optimization scheduling strategy. Once it is found that the deviation between the actual operating status parameters and the optimization scheduling strategy exceeds a preset threshold, the optimization scheduling model will be triggered to recalculate and analyze, and an updated optimization scheduling strategy will be generated again according to the latest actual operation data and related constraints. The operating status of the power station is regulated again according to the new strategy, and the key parameters are adjusted by adjusting the key parameters. The control instructions of key devices can make the actual operating status of the power station return to the optimized track as soon as possible, ensuring that the power station always maintains efficient and stable operation. For example, assuming the preset threshold is 10%, the optimized scheduling strategy expects the photovoltaic output to be 100 kilowatts at a certain moment, but the actual monitoring is only 85 kilowatts, and the deviation exceeds the threshold. At this time, the model will be triggered to regenerate the strategy, which may increase the light-receiving area of ​​the photovoltaic module or improve the efficiency of the inverter to increase the photovoltaic output. The original strategy predicts that the discharge power of the energy storage device at a certain moment is 50 kilowatts, but the actual discharge power reaches 60 kilowatts, exceeding the threshold. The model will recalculate and generate a new strategy, which may reduce the discharge power to ensure the reasonable use and life of the energy storage device.

[0038] The embodiment of the present application adopts the method of identifying power stations and building a monitoring system. The system collects and monitors data from key equipment to obtain historical and real-time operating data. Based on these data, photovoltaic output and load are predicted to obtain predicted values, power deviation is calculated, and the deviation is input into the optimization scheduling model to obtain a strategy. The operating status of the power station is adaptively controlled according to the strategy. By realizing accurate prediction of photovoltaic output and load and efficient adaptive optimization scheduling, a precise balance between power generation and power consumption is achieved, which significantly improves the operating efficiency and stability of the power station, reduces operating costs, and enhances the technical effect of compatibility with the power grid.

[0039] In the above, refer to Figure 1 The operation control method for a distributed new energy photovoltaic power station according to an embodiment of the present invention is described in detail. Figure 2 An operation control system for a distributed new energy photovoltaic power station according to an embodiment of the present invention is described.

[0040] The operation and control system for distributed new energy photovoltaic power stations according to the embodiments of the present invention is used to solve the technical problems that the prior art cannot perform efficient and adaptive optimization scheduling based on accurate photovoltaic output and load prediction, which leads to imbalance between power generation and power consumption, low operating efficiency, poor stability, high cost and poor grid compatibility. By realizing accurate prediction of photovoltaic output and load and efficient adaptive optimization scheduling, a precise balance between power generation and power consumption is achieved, which significantly improves the operating efficiency and stability of the power station, reduces operating costs, and enhances the technical effect of compatibility with the grid. The operation and control system for distributed new energy photovoltaic power stations includes: a monitoring system construction module 10, a data acquisition module 20, a prediction module 30, a power deviation calculation module 40, an optimization scheduling strategy acquisition module 50, and an adaptive control module 60.

[0041] A monitoring system building module 10, wherein the monitoring system building module 10 is used to identify a distributed new energy photovoltaic power station and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station;

[0042] A data acquisition module 20, which is used to collect data and conduct real-time monitoring of key equipment of a distributed new energy photovoltaic power station through the distributed photovoltaic monitoring system, and obtain historical operation data and real-time operation data of the distributed new energy photovoltaic power station;

[0043] A prediction module 30, the prediction module 30 is used to predict the photovoltaic output and load of the distributed new energy photovoltaic power station based on the historical operation data and the real-time operation data, and obtain a photovoltaic output prediction value and a load prediction value within a future period of time;

[0044] A power deviation calculation module 40, the power deviation calculation module 40 is used to calculate the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, the power deviation being the difference between the photovoltaic output prediction value and the load prediction value;

[0045] An optimization scheduling strategy acquisition module 50, wherein the optimization scheduling strategy acquisition module 50 is used to input the power deviation into a preset optimization scheduling model for analysis, and obtain an optimization scheduling strategy for the distributed new energy photovoltaic power station, wherein the optimization scheduling strategy includes control instructions for the key equipment;

[0046] The adaptive control module 60 is used to adaptively control the operating state of the distributed new energy photovoltaic power station according to the optimization scheduling strategy.

[0047] The specific configuration of the monitoring system building module 10 will be described in detail below. As described above, the distributed new energy photovoltaic power station is identified, and a distributed photovoltaic monitoring system is built based on the distributed new energy photovoltaic power station. The monitoring system building module 10 further includes: an identification result acquisition unit, the identification result acquisition unit is used to identify the distributed new energy photovoltaic power station according to the preset photovoltaic power station identification rules, and obtain the photovoltaic power station identification results. The preset photovoltaic power station identification rules include the geographical location information, installed capacity information, access to the power grid, and ownership information of the photovoltaic power station; a photovoltaic monitoring system building unit, the photovoltaic monitoring system building unit is used to build a corresponding distributed photovoltaic monitoring system based on the photovoltaic power station identification results and characteristic parameters. The characteristic parameters include the equipment type, equipment quantity, equipment parameters, and operation mode of the distributed new energy photovoltaic power station.

[0048] The specific configuration of the data acquisition module 20 will be described in detail below. As described above, the distributed photovoltaic monitoring system collects data and monitors the key equipment of the distributed new energy photovoltaic power station in real time to obtain the historical operation data and real-time operation data of the distributed new energy photovoltaic power station. The data acquisition module 20 further includes: a key equipment component unit, which is used for the key equipment including photovoltaic components, inverters, energy storage devices and grid connection equipment in the distributed new energy photovoltaic power station.

[0049] Among them, the data acquisition module 20 further includes: an installation unit, which is used to install a data acquisition unit on the key equipment of the distributed new energy photovoltaic power station, and the data acquisition unit includes a data collector and a communication module; a real-time transmission unit, which is used to collect the operating parameters of the key equipment in real time through the data collector, and transmit the collected operating parameters to the monitoring center server of the distributed photovoltaic monitoring system in real time through the communication module; a historical operation data generation unit, which is used for the monitoring center server to receive and store the operating parameters of the key equipment, generate historical operation data of the distributed new energy photovoltaic power station, and process and analyze the received real-time operation parameters to generate real-time operation data of the distributed new energy photovoltaic power station.

[0050] The specific configuration of the prediction module 30 will be described in detail below. As described above, based on the historical operation data and the real-time operation data, the photovoltaic output and load of the distributed new energy photovoltaic power station are predicted to obtain the photovoltaic output prediction value and load prediction value for a period of time in the future. The prediction module 30 further includes: a prediction model construction unit, the prediction model construction unit is used to construct a photovoltaic output prediction model and a load prediction model, the photovoltaic output prediction model and the photovoltaic output prediction model are constructed based on the historical operation data; a photovoltaic output prediction value acquisition unit, the photovoltaic output prediction value acquisition unit is used to use the photovoltaic output prediction model to predict the photovoltaic output of the distributed new energy photovoltaic power station according to the real-time operation data, and obtain the photovoltaic output prediction value for a period of time in the future; a load prediction unit, the load prediction unit is used to use the load prediction model to predict the load of the distributed new energy photovoltaic power station according to the real-time operation data, and obtain the load prediction value for a period of time in the future.

[0051] The specific configuration of the optimization scheduling strategy acquisition module 50 will be described in detail below. As described above, the power deviation is input into the preset optimization scheduling model for analysis to obtain the optimization scheduling strategy of the distributed new energy photovoltaic power station, the optimization scheduling strategy includes control instructions for the key equipment, and the optimization scheduling strategy acquisition module 50 further includes: an optimization scheduling model construction unit, the optimization scheduling model construction unit is used to establish an optimization scheduling model for the distributed new energy photovoltaic power station, and the objective function of the optimization scheduling model is to minimize the power deviation; an optimal operating state acquisition unit, the optimal operating state acquisition unit is used to input the power deviation into the optimization scheduling model, solve the optimization scheduling model, and obtain the optimal operating state of the distributed new energy photovoltaic power station; a control instruction generation unit, the control instruction generation unit is used to generate control instructions for the key equipment in the distributed new energy photovoltaic power station based on the optimal operating state of the distributed new energy photovoltaic power station as the optimization scheduling strategy.

[0052] The specific configuration of the adaptive control module 60 will be described in detail below. As described above, according to the optimization scheduling strategy, the operating state of the distributed new energy photovoltaic power station is adaptively controlled, and the adaptive control module 60 further includes: a control instruction extraction unit, the control instruction extraction unit is used to extract the control instructions of the key equipment in the optimization scheduling strategy, and send the control instructions to the microgrid controller of the distributed new energy photovoltaic power station to control the key equipment to perform corresponding adjustment actions and adjust the operating state of the distributed new energy photovoltaic power station; an updated optimization scheduling strategy generation unit, the updated optimization scheduling strategy generation unit is used to continuously monitor the actual operating state parameters of the distributed new energy photovoltaic power station, when the deviation between the actual operating state parameters and the optimization scheduling strategy exceeds the preset threshold, the optimization scheduling model is triggered to generate an updated optimization scheduling strategy again, and the operating state is re-regulated according to the updated optimization scheduling strategy.

[0053] The operation and control system for a distributed new energy photovoltaic power station provided by an embodiment of the present invention can execute the operation and control method for a distributed new energy photovoltaic power station provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0055] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An operation and control method for a distributed new energy photovoltaic power station, characterized in that: The method comprises: Identify a distributed new energy photovoltaic power station, and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station; The distributed photovoltaic monitoring system collects data and monitors the key equipment of the distributed new energy photovoltaic power station in real time to obtain the historical operation data and real-time operation data of the distributed new energy photovoltaic power station; Based on the historical operation data and the real-time operation data, the photovoltaic output and load of the distributed new energy photovoltaic power station are predicted to obtain a photovoltaic output prediction value and a load prediction value within a future period of time; Calculating the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, wherein the power deviation is the difference between the photovoltaic output prediction value and the load prediction value; Inputting the power deviation into a preset optimization scheduling model for analysis to obtain an optimization scheduling strategy for the distributed new energy photovoltaic power station, wherein the optimization scheduling strategy includes control instructions for the key equipment; According to the optimization scheduling strategy, the operation status of the distributed new energy photovoltaic power station is adaptively controlled.

2. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: The key equipment includes photovoltaic modules, inverters, energy storage devices and grid connection equipment in distributed new energy photovoltaic power stations.

3. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: Identify a distributed new energy photovoltaic power station, and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station, including: According to the preset photovoltaic power station identification rules, the distributed new energy photovoltaic power station is identified, and the photovoltaic power station identification result is obtained, wherein the preset photovoltaic power station identification rules include the geographical location information, installed capacity information, access mode to the power grid, and ownership information of the photovoltaic power station; Based on the identification result of the photovoltaic power station, a corresponding distributed photovoltaic monitoring system is built in combination with characteristic parameters. The characteristic parameters include the equipment type, equipment quantity, equipment parameters and operation mode of the distributed new energy photovoltaic power station.

4. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: The distributed photovoltaic monitoring system collects data and monitors the key equipment of the distributed new energy photovoltaic power station in real time, and obtains the historical operation data and real-time operation data of the distributed new energy photovoltaic power station, including: Installing a data acquisition unit on key equipment of the distributed new energy photovoltaic power station, wherein the data acquisition unit includes a data collector and a communication module; The operating parameters of the key equipment are collected in real time by the data collector, and the collected operating parameters are transmitted in real time to the monitoring center server of the distributed photovoltaic monitoring system by the communication module; The monitoring center server receives and stores the operating parameters of the key equipment, generates historical operating data of the distributed new energy photovoltaic power station, and processes and analyzes the received real-time operating parameters to generate real-time operating data of the distributed new energy photovoltaic power station.

5. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: Based on the historical operation data and the real-time operation data, the photovoltaic output and load of the distributed new energy photovoltaic power station are predicted to obtain the photovoltaic output prediction value and load prediction value in the future period, including: Constructing a photovoltaic output prediction model and a load prediction model, wherein the photovoltaic output prediction model and the photovoltaic output prediction model are constructed based on the historical operation data; Using the photovoltaic output prediction model to predict the photovoltaic output of the distributed new energy photovoltaic power station according to the real-time operation data, to obtain a photovoltaic output prediction value within a future period of time; The load prediction model is used to predict the load of the distributed new energy photovoltaic power station according to the real-time operation data to obtain a load prediction value for a period of time in the future.

6. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: The power deviation is input into a preset optimization scheduling model for analysis to obtain an optimization scheduling strategy for the distributed new energy photovoltaic power station, including: Establishing an optimization scheduling model for a distributed new energy photovoltaic power station, wherein the objective function of the optimization scheduling model is to minimize the power deviation; Inputting the power deviation into the optimization scheduling model, solving the optimization scheduling model, and obtaining the optimal operating state of the distributed new energy photovoltaic power station; Based on the optimal operating state of the distributed new energy photovoltaic power station, control instructions for the key equipment in the distributed new energy photovoltaic power station are generated as the optimization scheduling strategy.

7. The operation control method for a distributed new energy photovoltaic power station according to claim 1, characterized in that: According to the optimization scheduling strategy, the operation state of the distributed new energy photovoltaic power station is adaptively regulated, including: Extracting control instructions of the key equipment in the optimization scheduling strategy, and sending the control instructions to the microgrid controller of the distributed new energy photovoltaic power station, controlling the key equipment to perform corresponding adjustment actions, and adjusting the operating state of the distributed new energy photovoltaic power station; Continuously monitor the actual operating status parameters of the distributed new energy photovoltaic power station. When the deviation between the actual operating status parameters and the optimization scheduling strategy exceeds a preset threshold, trigger the optimization scheduling model to regenerate an updated optimization scheduling strategy, and re-regulate the operating status according to the updated optimization scheduling strategy.

8. An operation and control system for a distributed new energy photovoltaic power station, characterized in that: The system is used to implement the operation control method for a distributed new energy photovoltaic power station according to any one of claims 1 to 7, and the system comprises: A monitoring system building module, the monitoring system building module is used to identify a distributed new energy photovoltaic power station and build a distributed photovoltaic monitoring system based on the distributed new energy photovoltaic power station; A data acquisition module, which is used to collect data and conduct real-time monitoring of key equipment of a distributed new energy photovoltaic power station through the distributed photovoltaic monitoring system, and obtain historical operation data and real-time operation data of the distributed new energy photovoltaic power station; A prediction module, wherein the prediction module is used to predict the photovoltaic output and load of the distributed new energy photovoltaic power station based on the historical operation data and the real-time operation data, and obtain a photovoltaic output prediction value and a load prediction value within a future period of time; A power deviation calculation module, the power deviation calculation module is used to calculate the power deviation of the distributed new energy photovoltaic power station according to the photovoltaic output prediction value and the load prediction value, the power deviation being the difference between the photovoltaic output prediction value and the load prediction value; An optimization scheduling strategy acquisition module, the optimization scheduling strategy acquisition module is used to input the power deviation into a preset optimization scheduling model for analysis, and obtain an optimization scheduling strategy for the distributed new energy photovoltaic power station, the optimization scheduling strategy including control instructions for the key equipment; An adaptive control module is used to adaptively control the operating state of the distributed new energy photovoltaic power station according to the optimization scheduling strategy.

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