Multi-energy complementary coordinated control method and device of photovoltaic system

Abstract

This application relates to the field of data processing technology and discloses a method and apparatus for multi-energy complementary coordinated control of a photovoltaic system. The method includes: sampling and processing the operating parameters of each component and energy storage system in the photovoltaic string using a multi-channel synchronous potential acquisition module to obtain system synchronous sampling data; performing operating state calculation processing on the system synchronous sampling data to obtain a system state parameter matrix; performing power prediction analysis processing on the system state parameter matrix to obtain system power balance target data; performing multi-energy coordinated optimization processing on the system power balance target data to obtain multi-energy system control command data; performing interleaved parallel control processing on the multi-energy system control command data to obtain real-time system operating data; and performing effect evaluation processing on the real-time system operating data to obtain system operating evaluation data. This application improves the efficiency and accuracy of multi-energy complementary coordinated control of photovoltaic systems.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and apparatus for multi-energy complementary coordinated control of a photovoltaic system. Background Technology

[0002] In current photovoltaic (PV) systems, PV modules are typically used in conjunction with energy storage systems to improve overall system performance and energy utilization efficiency. Existing PV systems usually connect PV panels to battery storage units, using batteries to store excess electricity to cope with power fluctuations between day and night. Traditional PV system control methods typically rely on independent power regulation and energy storage management systems. While effective, this approach has some limitations. For example, when there are significant fluctuations in the power output of PV modules, traditional systems struggle to optimize the relationship between PV power and energy storage in real time, easily leading to problems such as low PV cell power generation efficiency or over-discharge / charge of the energy storage system, especially under variable environmental conditions.

[0003] With the development of multi-energy complementary technologies, systems integrating multiple energy forms such as photovoltaics, wind power, and energy storage are gradually becoming the trend of future energy utilization. The coordinated control method for multi-energy complementary systems aims to coordinate the complementary effects between different energy sources through precise algorithms and real-time data acquisition, thereby achieving more efficient energy allocation and use. However, most existing multi-energy complementary systems suffer from insufficient precision in coordinating and optimizing the various subsystems, failing to dynamically adjust the operation of each system under real-time changing conditions, resulting in insufficient energy utilization efficiency and an inability to adapt to complex power grid operating environments. Summary of the Invention

[0004] This application provides a method and apparatus for multi-energy complementary coordinated control of a photovoltaic system, which is used to improve the efficiency and accuracy of multi-energy complementary coordinated control of the photovoltaic system.

[0005] Firstly, this application provides a multi-energy complementary coordinated control method for a photovoltaic system, the multi-energy complementary coordinated control method for the photovoltaic system comprising:

[0006] The operating parameters of each component and energy storage system in the photovoltaic string are sampled and processed by a multi-channel synchronous potential acquisition module to obtain system synchronous sampling data.

[0007] The system synchronous sampling data is processed to calculate the operating status, resulting in a system state parameter matrix that includes photovoltaic power generation efficiency, energy storage status, and environmental parameters;

[0008] The system state parameter matrix is ​​subjected to power prediction analysis to obtain the system power balance target data;

[0009] The system power balance target data is subjected to multi-energy coordination optimization processing to obtain multi-energy system control command data;

[0010] The control command data of the multi-functional system is processed by interleaved parallel control to obtain the real-time operation data of the system;

[0011] The real-time operating data of the system is processed to evaluate its effectiveness, resulting in system operation evaluation data.

[0012] Secondly, this application provides a multi-energy complementary coordination control device for a photovoltaic system, the multi-energy complementary coordination control device for the photovoltaic system comprising:

[0013] The acquisition module is used to sample and process the operating parameters of each component and energy storage system in the photovoltaic string through the multi-channel synchronous potential acquisition module to obtain system synchronous sampling data.

[0014] The calculation module is used to perform operational status calculation processing on the synchronously sampled data of the system to obtain a system status parameter matrix that includes photovoltaic power generation efficiency, energy storage status and environmental parameters;

[0015] The prediction module is used to perform power prediction analysis on the system state parameter matrix to obtain the system power balance target data;

[0016] The optimization module is used to perform multi-energy coordination optimization processing on the system power balance target data to obtain multi-energy system control command data;

[0017] The control module is used to perform interleaved parallel control processing on the control command data of the multi-energy system to obtain real-time operating data of the system.

[0018] The evaluation module is used to perform effect evaluation processing on the real-time operation data of the system to obtain system operation evaluation data.

[0019] The technical solution provided in this application ensures high precision and consistency of system data by accurately and synchronously acquiring and processing the real-time operating parameters of photovoltaic modules and energy storage systems, thus providing an accurate data foundation for subsequent state calculations and analyses. A multi-channel synchronous potential acquisition module, combined with IEEE 1588 clock synchronization technology, achieves time synchronization between the photovoltaic system and the energy storage system, avoiding data deviations caused by sampling time errors and greatly improving the reliability and accuracy of system operating data. Secondly, the system generates a system state parameter matrix, including photovoltaic power generation efficiency, energy storage status, and environmental parameters, by performing state calculations on the synchronously sampled data. This not only reflects the real-time operating status of the photovoltaic system and the energy storage system but also effectively integrates the impact of environmental factors on system operation, providing comprehensive support for subsequent power prediction and optimization. Furthermore, this method effectively predicts the system's power demand and supply through power prediction analysis and energy storage capacity constraint analysis, thereby achieving precise coordination between photovoltaic power generation and energy storage discharge in multi-energy complementary coordinated optimization, avoiding over-discharge or over-charging, and further improving the system's economy and stability. In terms of optimization, a multi-energy coordinated optimization algorithm was adopted to unify the scheduling of control commands for photovoltaic and energy storage systems. This not only improved the overall efficiency of photovoltaic power generation and energy storage systems but also optimized the efficiency of electricity utilization. Ultimately, through interleaved parallel control and grid synchronization, the output of the photovoltaic and energy storage systems was ensured to be stable and reliable, effectively reducing power fluctuations and improving the system's load adaptability and the continuity of energy supply. Finally, through real-time performance evaluation, potential problems in system operation can be quickly identified, and optimization strategies can be adjusted in a timely manner, thereby improving the overall system's operating efficiency and long-term stability. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of an embodiment of the multi-energy complementary coordinated control method for a photovoltaic system in this application.

[0022] Figure 2 This is a schematic diagram of one embodiment of the multi-energy complementary coordination control device for a photovoltaic system in this application. Detailed Implementation

[0023] This application provides a multi-energy complementary coordinated control method and apparatus for a photovoltaic system. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the multi-energy complementary coordinated control method for photovoltaic systems in this application includes:

[0025] Step S101: The operating parameters of each component and energy storage system in the photovoltaic string are sampled and processed by the multi-channel synchronous potential acquisition module to obtain the system synchronous sampling data;

[0026] Step S102: Perform operational status calculation processing on the system synchronous sampling data to obtain a system status parameter matrix containing photovoltaic power generation efficiency, energy storage status and environmental parameters;

[0027] Step S103: Perform power prediction analysis on the system state parameter matrix to obtain the system power balance target data;

[0028] Step S104: Perform multi-energy coordination optimization processing on the system power balance target data to obtain multi-energy system control command data;

[0029] Step S105: Perform interleaved parallel control processing on the control command data of the multi-functional system to obtain the real-time operating data of the system;

[0030] Step S106: Perform effect evaluation processing on the real-time system operation data to obtain system operation evaluation data.

[0031] It is understood that the executing entity of this application can be a multi-energy complementary coordinated control device for a photovoltaic system, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiments use a server as an example for illustration.

[0032] Specifically, the system samples and processes the operating parameters of each component and energy storage system in the photovoltaic string. The core of this process lies in accurately acquiring the operating data of each device through a multi-channel synchronous potential acquisition module, including the voltage and current of the photovoltaic modules, the charging and discharging status of the energy storage system, and remaining capacity. To ensure the synchronization of data sampling, the system uses a POWERBUS fieldbus master station to send synchronous acquisition broadcast commands, relying on the IEEE 1588 clock synchronization protocol to ensure time consistency of data acquisition. In this way, the acquired data includes the voltage signal of the photovoltaic modules, the current signal of the photovoltaic string, the capacity information and charging and discharging status of the energy storage system, as well as ambient temperature and light intensity. This data is transmitted to the system as the basis for subsequent data processing. The system's synchronously sampled data is used to calculate the operating status, resulting in the system's state parameter matrix. First, the photovoltaic power generation efficiency is calculated by comparing the actual power of the acquired components with the theoretical maximum power. This calculation allows for real-time determination of the photovoltaic power generation system's operating efficiency. If the power generation efficiency is low, it means that the photovoltaic modules are not operating at their maximum capacity, and the system may need adjustments. Next, the energy storage status assessment is completed using two sets of data: first, the remaining capacity of the energy storage system is normalized to a range of 0 to 1 for ease of subsequent operation; second, the health status and operational limitations of the energy storage system are assessed through its charge and discharge status. Environmental parameters are normalized by collecting temperature and light intensity data to ensure that data from different time periods can be adapted to a unified analysis model. Finally, the calculation results of photovoltaic power generation efficiency, energy storage status, and environmental parameters are compiled into a matrix as the overall system status information. The system status parameter matrix is ​​used for power prediction analysis to obtain power balance target data. This process mainly includes trend analysis of environmental parameters, i.e., predicting the changing trends of light intensity and temperature over a future period using historical data. Based on the predicted environmental changes and the photoelectric conversion characteristics of the photovoltaic system, the corresponding photovoltaic output power is calculated. In addition, future electricity demand can be predicted by analyzing historical load data, and this part of the load prediction data is also generated. By comparing the output power of the photovoltaic system with the load prediction data, the power difference data is obtained. The energy storage system then needs to adjust its charge and discharge strategies based on the power difference. If the photovoltaic (PV) output is insufficient to meet load demand, the energy storage system will provide additional power; if the PV output exceeds load demand, the excess power will be stored for later use. Ultimately, this information is aggregated into the system's power balance target data, which is used for the next optimization calculation. The power balance target data is used for multi-energy coordinated optimization processing to generate multi-energy system control command data. First, the system uses the Maximum Power Point Tracking (MPPT) algorithm to determine the optimal operating point of the PV system, thereby ensuring that the PV system's power generation efficiency is maximized.During the MPPT (Multi-Level Testing) process, the operating state of the photovoltaic system is adjusted to maintain its output power at an optimal level. Secondly, the power allocation of the energy storage system needs to be optimized based on the storage capacity, charging / discharging state, and power demand to ensure optimal charging and discharging efficiency. In this process, power limit constraints on energy storage are crucial to ensure the system operates within its capacity, avoiding overcharging or over-discharging. Finally, photovoltaic and energy storage regulation command data are combined and processed through duty cycle conversion to generate PWM (Pulse Width Modulation) control signals. These control signals are used to control the actual operation of the photovoltaic and energy storage systems.

[0033] The control command data of the multi-energy system enters the interleaved parallel control processing stage, generating real-time operating data for the system. First, phase difference calculations are performed on the control command data to determine the operating phases of the photovoltaic (PV) and energy storage systems, ensuring their coordinated operation does not conflict. Next, the interleaved signal data generates switching control signals via a driver, ensuring the equipment switches on and off according to predetermined time cycles, avoiding overload. During this process, the switching control signals undergo synchronous rectification to generate output power data for the PV system; the output data for the energy storage system is processed through power conversion. Finally, the output data of the PV and energy storage systems are synchronized through grid connection processing to ensure that the power output from both can be integrated into the grid, meeting the power system's needs. The real-time operating data undergoes performance evaluation processing to obtain the final system operation evaluation data. First, the power balance of the system is assessed by calculating the deviation between the system output power and the expected power. Second, the stability of the system output power is evaluated through fluctuation characteristic analysis to ensure that the system's overall efficiency is not affected by excessive fluctuations. Simultaneously, the charging and discharging efficiency of the PV and energy storage systems is evaluated to ensure optimal energy utilization. Finally, all the evaluation data are integrated to obtain the comprehensive operational evaluation results of the system, which will help with subsequent optimization and adjustments.

[0034] In this embodiment, by accurately and synchronously acquiring and processing the real-time operating parameters of the photovoltaic module and the energy storage system, high precision and consistency of system data are ensured, thus providing an accurate data foundation for subsequent state calculations and analyses. A multi-channel synchronous potential acquisition module, combined with IEEE 1588 clock synchronization technology, achieves time synchronization between the photovoltaic system and the energy storage system, avoiding data deviations caused by sampling time errors and greatly improving the reliability and accuracy of system operating data. Secondly, the system generates a system state parameter matrix, including photovoltaic power generation efficiency, energy storage status, and environmental parameters, by performing state calculations on the synchronously sampled data. This not only reflects the real-time operating status of the photovoltaic system and the energy storage system but also effectively integrates the impact of environmental factors on system operation, providing comprehensive support for subsequent power prediction and optimization. Furthermore, this method effectively predicts the system's power demand and supply through power prediction analysis and energy storage capacity constraint analysis, thereby achieving precise coordination between photovoltaic power generation and energy storage discharge in multi-energy complementary coordinated optimization, avoiding over-discharge or over-charging, and further improving the system's economy and stability. In terms of optimization, a multi-energy coordinated optimization algorithm was adopted to unify the scheduling of control commands for photovoltaic and energy storage systems. This not only improved the overall efficiency of photovoltaic power generation and energy storage systems but also optimized the efficiency of electricity utilization. Ultimately, through interleaved parallel control and grid synchronization, the output of the photovoltaic and energy storage systems was ensured to be stable and reliable, effectively reducing power fluctuations and improving the system's load adaptability and the continuity of energy supply. Finally, through real-time performance evaluation, potential problems in system operation can be quickly identified, and optimization strategies can be adjusted in a timely manner, thereby improving the overall system's operating efficiency and long-term stability.

[0035] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0036] (1) Send a synchronous acquisition broadcast command to the POWERBUS fieldbus master station to perform time synchronization processing, obtain the IEEE1588 clock synchronization signal, and perform master-slave clock synchronization processing on the IEEE1588 clock synchronization signal to obtain slave station synchronization clock data.

[0037] (2) The slave station synchronization clock data is processed by clock discipline to obtain a 1MHz sampling frequency signal, and the 1MHz sampling frequency signal is divided to obtain a sampling control signal.

[0038] (3) The output voltage signal of the photovoltaic module is processed by high-precision analog-to-digital conversion to obtain the digital value of the module voltage, and the current signal of the photovoltaic string is processed by the shunt monitor to obtain the digital value of the string current.

[0039] (4) The charging and discharging status signal of the energy storage system is processed by status acquisition to obtain charging and discharging status data, and the remaining capacity signal of the energy storage system is processed by capacity measurement to obtain energy storage capacity data.

[0040] (5) The ambient temperature signal is processed by temperature sensing to obtain temperature parameter data, and the light intensity signal is processed by irradiance measurement to obtain light intensity data.

[0041] (6) The digital values ​​of component voltage, digital values ​​of string current, charging and discharging status data, energy storage capacity data, temperature parameter data and light intensity data are transmitted and processed through the MODBUS protocol to obtain the system synchronous sampling data.

[0042] Specifically, all devices in a photovoltaic system need to maintain strict time synchronization. This is achieved by sending synchronization acquisition broadcast commands from the POWERBUS fieldbus master station. The broadcast commands ensure that all devices start operating on the same time base. The IEEE 1588 clock synchronization protocol is used for high-precision time synchronization. Through this protocol, the system broadcasts the master station's time signal to each slave station (including each component in the photovoltaic string and the energy storage system). After receiving the synchronization signal, each slave station processes the master-slave clock synchronization data to obtain accurate slave station synchronization clock data. This ensures that all devices acquire data at the same point in time, avoiding time errors. After obtaining the slave station synchronization clock data, further stabilization and processing are required. Clock discipline is an adjustment process used to ensure that the slave station's clock is consistent with the master station's clock. This process ensures stable output by precisely adjusting the clock frequency. Through clock discipline, a standard 1MHz sampling frequency signal is obtained, which is high enough to meet the requirements of accurate sampling. Next, the frequency signal undergoes frequency division to convert it into a more suitable frequency value, ultimately resulting in a sampling control signal used for subsequent data acquisition control, ensuring that the signal is sampled at the correct time. The voltage signal output by the photovoltaic modules is processed by a high-precision analog-to-digital converter (ADC) to convert it into digital voltage values. These digital voltage values ​​represent the voltage level of each photovoltaic module and reflect its operating status. Then, the current signal from the photovoltaic string is processed by a current monitor to obtain a digital current value. The current monitor's function is to accurately measure the current and convert it into a digital signal for subsequent data processing and analysis.

[0043] The operational status of the energy storage system also requires precise data acquisition. During this process, the charging and discharging status signal of the energy storage system is processed to obtain charging and discharging status data. This data reflects whether the energy storage system is currently charging, discharging, or in standby mode. The remaining capacity signal of the energy storage system is processed through capacity measurement to obtain energy storage capacity data. Through capacity measurement, the system can determine the current remaining capacity of the energy storage device, understand its available power, and provide a basis for subsequent energy allocation and control. In this process, the ambient temperature signal is processed by a temperature sensor to obtain temperature parameter data. This data helps the system understand changes in the current ambient temperature, which has a direct impact on the performance of photovoltaic modules. The irradiance signal is processed by an irradiance measurement device to obtain irradiance data. Irradiance is a key factor affecting the power generation efficiency of photovoltaic modules; therefore, accurate irradiance data is crucial for optimizing system control strategies. After all sampling data (including module voltage, string current, energy storage status, remaining capacity, temperature, and irradiance) is collected, this data will be transmitted and processed via the MODBUS protocol. The MODBUS protocol is an industry-standard communication protocol used to transmit information between different devices. During this process, data is packaged and transmitted via the MODBUS protocol, ultimately yielding the system's synchronous sampling data. This synchronous sampling data will be sent to subsequent calculation and analysis modules, providing a foundation for system status assessment and optimized control.

[0044] For example, suppose a photovoltaic system contains several photovoltaic modules, energy storage devices, and environmental sensors. The system sends a synchronization acquisition command through the POWERBUS master station. The clocks of each device are synchronized via the IEEE 1588 protocol to ensure they begin data acquisition simultaneously. Assume that at a certain moment, the photovoltaic module voltage is 30V, the current is 5A, the energy storage system has 80% remaining capacity, and the solar irradiance is 1000W / m². 2 The ambient temperature was 25℃. After the above processing, these data were converted into digital signals (e.g., 30V voltage converted to a digital value of 300, and 5A current converted to a digital value of 50), and then synchronously transmitted to the central processing system via the MODBUS protocol. Through this precise synchronous acquisition method, the system ensures that all data is processed under a unified time reference, avoiding data inconsistencies caused by time errors, thus guaranteeing the efficient operation of the system.

[0045] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0046] (1) Perform a product operation on the component voltage and current in the system synchronous sampling data to obtain the actual power data of the component, and perform a ratio calculation on the actual power data of the component and the theoretical maximum power to obtain the photovoltaic power generation efficiency;

[0047] (2) The energy storage capacity data in the system synchronous sampling data is normalized to obtain the capacity status data, and the power limit is calculated on the charge and discharge status data in the system synchronous sampling data to obtain the energy storage status.

[0048] (3) Normalize the temperature parameter data and light intensity data in the synchronous sampling data of the system to obtain environmental parameters, and combine the photovoltaic power generation efficiency, energy storage status and environmental parameters through matrix combination to obtain the system state parameter matrix.

[0049] Specifically, the voltage and current data of the photovoltaic (PV) module are used to calculate its actual power. Specifically, the voltage and current values ​​of the PV module are multiplied to obtain the actual power. Actual power data is a key parameter describing the power generation capacity of a PV module, reflecting the effective power output at present. Subsequently, the actual power is compared with the module's theoretical maximum power to obtain the PV power generation efficiency. The theoretical maximum power is usually determined by the rated parameters of the PV module, representing the maximum power the module can achieve under ideal environmental conditions. This ratio calculation measures the efficiency of the PV module under current conditions. A higher power generation efficiency indicates that the module is close to its maximum output power under current conditions. The capacity data of the energy storage system also needs processing. First, the energy storage capacity data is normalized to per-unit values. The purpose of per-unit normalization is to standardize the capacity data, eliminating differences between different devices and capacity ranges, thereby facilitating the comparison and evaluation of different energy storage devices. The normalized capacity data is called capacity status data, reflecting the ratio between the current available capacity of the energy storage system and its total capacity. In addition, the charge / discharge status data of the energy storage system also needs processing. This data reflects the current charging and discharging mode of the energy storage system. The system determines the power state of the energy storage system by calculating power limits based on the charging and discharging state data. This calculation ensures that the charging and discharging process of the energy storage system is within a safe and efficient range, avoiding overload or excessive discharge, thereby guaranteeing the long-term stable operation of the system.

[0050] Next, the environmental data in the system also needs to be processed. First, temperature and light intensity data are normalized. The purpose of normalization is to convert different parameters to the same dimension, eliminating the influence of different units of measurement, so that these parameters can be directly used for subsequent comprehensive analysis. Through normalization, the obtained temperature and light intensity data reflect the impact of the current environment on the performance of the photovoltaic system. Photovoltaic power generation efficiency, energy storage status, and environmental parameters are combined into a matrix, which is the system state parameter matrix. Through matrix combination processing, the system can integrate operational data from different dimensions, thereby comprehensively evaluating the operating status of the entire photovoltaic system. The system state parameter matrix contains multi-dimensional data reflecting the power generation efficiency of photovoltaic modules, the operating status of the energy storage system, and environmental influencing factors, providing crucial input for subsequent power prediction and system optimization.

[0051] For example, assuming a photovoltaic module has a voltage of 30V and a current of 6A, its actual power is 180W. If the module's theoretical maximum power is 200W, then its power generation efficiency is 90%. Assuming the energy storage system has a capacity of 50kWh, after per-unit normalization, its capacity status data is 0.8, indicating that the energy storage system currently has 80% of its capacity available. Simultaneously, the energy storage system's charging status data indicates that it is currently charging, and after calculation using power limits, the charging power of the energy storage system is 5kW. Regarding environmental data, the temperature is 30℃, and the illuminance is 800W / m². 2 After normalization, these environmental data are transformed into unified standard values ​​for subsequent analysis. This processed data will then be integrated into a system state parameter matrix, serving as an accurate representation of the overall system operating status.

[0052] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] (1) Perform trend analysis on the environmental parameters in the system state parameter matrix to obtain environmental change prediction data, and then perform photoelectric conversion calculation on the environmental change prediction data to obtain photovoltaic output power prediction data.

[0054] (2) Perform load characteristic analysis on historical electricity load data to obtain load forecast data, and perform difference calculation on photovoltaic output power forecast data and load forecast data to obtain power difference data.

[0055] (3) Perform energy storage capacity constraint analysis on the power difference data to obtain the system power balance target data.

[0056] Specifically, trend analysis is performed on environmental parameters in the system state parameter matrix to predict environmental change trends. Environmental parameters typically include data such as temperature and light intensity, which directly affect the power generation efficiency of photovoltaic (PV) modules. Trend analysis reveals the patterns of environmental parameter changes over time, thus predicting environmental changes in the future. For example, based on past light intensity and temperature data, trend analysis can identify periodic or abrupt trends in environmental changes. After obtaining the environmental change prediction data, this data is processed through photoelectric conversion calculations to predict the output power of the PV system. Photoelectric conversion calculations, based on environmental data such as light intensity and temperature, combined with the conversion efficiency of the PV modules, calculate the predicted output power of the PV modules. This prediction data helps the system predict future power generation, serving as a reference for power dispatch and energy storage management. Load characteristic analysis is also performed on historical electricity load data. Electricity load data typically comes from historical electricity records, reflecting fluctuations in load demand over time. Load characteristic analysis extracts patterns in load demand, such as peak load periods and electricity consumption trends. This analysis helps predict future electricity demand, yielding load forecast data. Load forecasting data is an important basis for determining the output power demand of energy storage and photovoltaics, and helps the system assess changes in grid load.

[0057] Next, the difference between the photovoltaic output power forecast data and the load forecast data is calculated. The purpose of this process is to calculate the difference between photovoltaic power generation and electrical load, i.e., the power difference data. The power difference data reflects the difference between the photovoltaic system's power generation and load demand. If the photovoltaic output power is greater than the load demand, the difference is positive, meaning there is excess electrical energy that can be stored or fed into the grid; if the difference is negative, it indicates that photovoltaic power generation is insufficient to meet load demand, requiring reliance on energy storage systems or external grid power. This calculation step is crucial for achieving power balance. Finally, energy storage capacity constraint analysis is performed on the power difference data. The purpose of energy storage capacity constraint analysis is to assess the charging and discharging capabilities of the energy storage system under the current power difference conditions and ensure that the energy storage system does not exceed its maximum capacity. In this step, the system considers the current charging state, maximum capacity, and charging / discharging power limitations of the energy storage devices, analyzing how to rationally allocate and schedule electrical energy to obtain the system's power balance target data. This target data guides the energy storage system to maintain balance during charging and discharging while meeting load demand, avoiding overcharging or over-discharging, and ensuring the long-term stability of the system.

[0058] For example, suppose that during a certain period, after environmental trend analysis and photoelectric conversion processing, the system predicts a photovoltaic output power of 10kW, while the load prediction data obtained through load characteristic analysis is 8kW. In this case, the power difference is 2kW, indicating that the photovoltaic system's power generation exceeds the electricity load. The system can store the 2kW of excess energy in the energy storage device. Assuming the energy storage system currently has a remaining capacity of 50kWh and a maximum capacity of 100kWh, after energy storage capacity constraint analysis, the system determines that storing the 2kW power difference in the energy storage device will not exceed the device's capacity limit. The final power balance target data is the energy storage charging command and the grid output command, thus ensuring a stable power supply and efficient operation of the energy storage system.

[0059] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0060] (1) The system power balance target data is processed by MPPT operating point calculation to obtain photovoltaic regulation command data, and the photovoltaic regulation command data is processed by duty cycle conversion to obtain PWM control data;

[0061] (2) The system power balance target data is processed by energy storage power allocation to obtain energy storage command data, and the energy storage command data is processed by power limit constraint to obtain energy storage control data;

[0062] (3) The PWM control data and energy storage control data are processed by instruction synthesis to obtain the multi-energy system control instruction data.

[0063] Specifically, the system performs MPPT (Maximum Power Point Tracking) operating point calculations on the power balance target data to obtain photovoltaic (PV) regulation command data. The MPPT algorithm adjusts the PV system's operating state based on current environmental conditions (such as light intensity and temperature) to maximize its output power. The MPPT calculation process typically uses the voltage and current characteristic curves of the PV modules to find the maximum power point and uses this information as the PV regulation command data. Through this command, the system can adjust the PV power generation operating point in real time, ensuring that the PV modules maintain optimal power output under different environmental conditions. The PV regulation command data is then processed through duty cycle conversion to generate PWM (Pulse Width Modulation) control data. PWM control is a technique used to adjust the output power of power electronic switches (such as inverters or regulators). By adjusting the duty cycle, i.e., the ratio of "on" to "off" signals, the output power can be controlled. The purpose of duty cycle conversion is to convert the PV regulation command into a specific PWM signal, controlling the inverter's switching frequency and timing, thereby precisely regulating the PV system's output power. For example, when the system needs to increase its output power, the duty cycle of the PWM signal will increase, thereby enabling the inverter to output more electrical energy.

[0064] Secondly, the system power balance target data is processed for energy storage power allocation to obtain energy storage command data. The purpose of energy storage power allocation processing is to determine the charging and discharging requirements of the energy storage system based on the system power difference (i.e., the difference between photovoltaic power generation and load). When the power difference is positive, the energy storage system needs to charge; when the power difference is negative, the energy storage system needs to discharge. Energy storage power allocation processing matches the system's load demand with the photovoltaic power generation and calculates the required energy storage charging and discharging power, ultimately deriving energy storage command data. These commands will guide the energy storage devices on how to operate to maintain power balance. After obtaining the energy storage command data, the system also needs to undergo power limit constraint processing to ensure that the charging and discharging power of the energy storage devices is within their operating range and does not exceed the safe operating limits of the energy storage system. Energy storage devices typically have maximum charging and discharging power limits, so the energy storage command data must be appropriately adjusted to ensure that these limits are not exceeded. After power limit constraint processing, the resulting energy storage control data can accurately guide the charging and discharging process of the energy storage system, ensuring the safety of the equipment and the stable operation of the system.

[0065] Finally, the PWM control data and energy storage control data are processed through instruction synthesis to obtain the final multi-energy system control instruction data. Instruction synthesis combines the regulation requirements of the photovoltaic system and the energy storage system to form a unified control instruction. This control instruction ensures the coordinated operation of the photovoltaic system and the energy storage system, meeting load demands while maintaining system power balance. The synthesized multi-energy system control instruction data will be used to control the photovoltaic power generation and energy storage charging and discharging processes in real time, thereby achieving optimized operation of the overall system. For example, suppose that at a certain time, the photovoltaic system's power balance target data indicates that the photovoltaic system needs to provide 10kW of power, while the load demand is 8kW, and the system's energy storage equipment has sufficient capacity to store the remaining 2kW of electrical energy. The system first calculates the photovoltaic regulation instruction data through MPPT, assuming that the optimal operating point of the photovoltaic modules at this time is 20V and 5A, capable of generating 10kW of power. After duty cycle conversion, the photovoltaic regulation instruction data yields a PWM signal, instructing the inverter to output 10kW of power. Simultaneously, the energy storage power allocation processing instructs the energy storage system to charge at 2kW. After power limit constraint processing, the energy storage command data yields energy storage control data, guiding the energy storage system to perform charging operations. Finally, the regulation commands from the photovoltaic and energy storage systems are merged into multi-energy system control command data through command synthesis processing, ensuring the overall high efficiency and stability of the system operation.

[0066] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0067] (1) The control command data of the multi-energy system is processed by phase difference calculation to obtain interleaved signal data, and the interleaved signal data is processed by the driver to obtain switch control data;

[0068] (2) The switch control data is processed by synchronous rectification to obtain photovoltaic output data, and the multi-energy system control command data is processed by power conversion to obtain energy storage output data;

[0069] (3) Photovoltaic output data and energy storage output data are processed synchronously through grid connection to obtain real-time system operation data.

[0070] Specifically, phase difference calculations are performed on the control command data of the multi-energy system to obtain interleaved signal data. The purpose of phase difference calculation is to distribute the control command data of the multi-energy system to different control units to achieve interleaved operation. The core of this process is to prevent the simultaneous start-up or shutdown of various control units through the phase difference of the control signals, thereby reducing instantaneous load fluctuations and improving system stability. For example, in a system composed of multiple photovoltaic modules and energy storage units, using phase difference control signals can ensure that different units operate at different times, avoiding excessively high power peaks or current fluctuations in a short period of time. In this way, the system can more efficiently coordinate the operation between various units, balance the load, and reduce the impact on the power grid. The interleaved signal data is processed by a driver to obtain switching control data. The driver's role is to adjust the switching state of each switching device according to the interleaved signal, thereby controlling the specific output of the photovoltaic and energy storage units. For example, the inverters or switching equipment of the photovoltaic and energy storage systems use switching control data to regulate the rise and fall rates of output power. In this way, the system ensures that the output of each unit is smooth, without power fluctuations or equipment overload. The adjustment accuracy of the driver is crucial to the stability of the entire system, enabling precise power scheduling and ensuring that the equipment does not operate under overload.

[0071] Switching control data is processed through synchronous rectification to obtain photovoltaic output data. The purpose of synchronous rectification is to improve current conversion efficiency and reduce power loss by optimizing the operation of switching devices. Synchronous rectification technology utilizes the reverse conductivity of switching devices to ensure more efficient power conversion in the system, thereby increasing the output power of the photovoltaic system. For example, in the inverter of a photovoltaic system, the rectifier realizes the conversion of positive and negative current through switching control signals, ultimately achieving an efficient process of converting DC power into AC power. After synchronous rectification, the system can utilize photovoltaic power generation more efficiently and reduce energy loss. Simultaneously, multi-energy system control command data is processed through power conversion to obtain energy storage output data. The main function of power conversion is to convert the input electrical energy into voltage and current suitable for storage or output according to the charging and discharging requirements of the energy storage system. Energy storage systems typically use DC-DC converters or DC-AC inverters to appropriately convert electrical energy to match the needs of the energy storage devices or supply it to the grid. Power conversion ensures that the energy storage system can perform charging or discharging operations efficiently and accurately.

[0072] Finally, the photovoltaic (PV) output data and energy storage output data are processed through grid-connected synchronization to obtain the system's real-time operating data. The purpose of grid-connected synchronization is to ensure that the electrical energy output from the PV power generation and energy storage devices matches the frequency and phase of the power grid, avoiding reverse flow or power imbalance. Through grid-connected synchronization, the system ensures that the electrical energy output from the PV and energy storage devices can be seamlessly connected to the grid, thereby achieving coordinated operation of the system. For example, when the system generates excess power, the PV modules transmit electricity to the grid; when the PV output is insufficient, the energy storage system will automatically discharge to supplement the grid's power demand. Through grid-connected synchronization, the system can adjust the output of each unit in real time according to the grid's demand, ensuring a balanced and stable flow of electrical energy.

[0073] For example, suppose the photovoltaic system outputs 12kW, while the load demand is 10kW, and the energy storage system has sufficient remaining capacity. In this case, the system, according to multi-energy system control commands, calculates the phase difference to ensure staggered operation of each unit, reducing load fluctuations. The staggered signal control driver precisely adjusts the inverter's switching state, enabling the photovoltaic system to output 12kW, while the energy storage system begins to provide 2kW of supplemental power to the grid. After synchronous rectification, the photovoltaic system's output power is effectively converted into AC power, and power conversion ensures the energy storage system's output also meets grid requirements. Finally, after grid synchronization, the photovoltaic and energy storage systems are successfully connected to the grid, and the system manages power flow smoothly and effectively while ensuring load demand. This control method ensures the coordinated operation of the photovoltaic and energy storage systems, enabling the overall system to operate efficiently and smoothly, reducing energy waste and improving system stability.

[0074] In one specific embodiment, the process of executing step S106 may specifically include the following steps:

[0075] (1) The real-time operating data of the system is processed by deviation calculation to obtain balance evaluation data, and the real-time operating data of the system is processed by fluctuation characteristic analysis to obtain fluctuation evaluation data;

[0076] (2) The real-time operating data of the system is processed by efficiency calculation to obtain photovoltaic evaluation data, and the real-time operating data of the system is processed by charge and discharge efficiency calculation to obtain energy storage evaluation data;

[0077] (3) The balance assessment data, fluctuation assessment data, photovoltaic assessment data and energy storage assessment data are integrated and processed to obtain system operation assessment data.

[0078] Specifically, the real-time operating data of the system is processed through deviation calculation to obtain balance assessment data. Deviation calculation mainly assesses the system's balance by comparing the difference between the actual output of the current system and the expected target. For example, assuming the expected power output of the system is 10kW, while the actual output is 9.8kW, then the deviation is -0.2kW. Deviation calculation processing quantifies the errors in the system's load scheduling, photovoltaic power generation, and energy storage release processes by statistically analyzing this difference. A smaller deviation value means that the system executes scheduling commands more accurately and the system's operation is closer to the ideal state; conversely, it may mean that the system is unbalanced and needs adjustment or optimization. Through this calculation, a quantitative "balance assessment data" can be obtained to determine whether the system is operating stably. Then, the real-time operating data of the system is processed through fluctuation characteristic analysis to obtain fluctuation assessment data. Fluctuation characteristic analysis mainly focuses on the power fluctuations during the system's operation. For example, the output power of the photovoltaic system is affected by the intensity of sunlight and may fluctuate in the short term. Fluctuations may also occur during the charging and discharging process of the energy storage system, such as due to fluctuations in charging efficiency or changes in the discharging load. Fluctuation characteristic analysis assesses a system's ability to maintain stable output by calculating statistical data such as the standard deviation of power and peak-to-valley values. Large fluctuations indicate poor system stability, potentially requiring improvements in the charging and discharging management of the energy storage system or the power dispatching methods of the photovoltaic system. Fluctuation assessment data helps identify which components might cause fluctuations, thus providing data support for subsequent optimization.

[0079] Photovoltaic (PV) assessment data is obtained by processing real-time system operating data through efficiency calculations. This assessment data reflects the gap between the actual and theoretical power generation efficiency of the PV system. Specifically, the efficiency of a PV system is typically calculated as the ratio of actual output power to the theoretical maximum power of the PV module. For example, if a PV module has a maximum output power of 5kW but only outputs 4.8kW in actual operation, then the PV efficiency is \frac{4.8}{5} = 0.96\, or 96%. In this way, the PV assessment data reflects the power generation efficiency of the PV system in actual operation and provides an important basis for judging the health status of the PV system. Lower efficiency may be due to factors such as shading, pollution, or aging of the PV modules. Simultaneously, energy storage assessment data is obtained by processing real-time system operating data through charge / discharge efficiency calculations. The charge / discharge efficiency of an energy storage system reflects the efficiency with which the system stores and releases electrical energy from the battery. For example, if an energy storage system is charging at an actual charging power of 5kW, but due to charging losses, the effective amount of energy ultimately stored in the battery is 4.7kWh, then the charging efficiency is \(\frac{4.7}{5}=0.94\). Energy storage assessment data helps evaluate the performance of an energy storage system by analyzing energy losses during charging and discharging. A low charging and discharging efficiency may indicate performance degradation or mismanagement of the energy storage system.

[0080] Finally, the balance assessment data, fluctuation assessment data, photovoltaic assessment data, and energy storage assessment data are integrated and processed to obtain system operation assessment data. This data integration process involves summarizing and comprehensively analyzing the different types of assessment data to arrive at a comprehensive system operation assessment result. This process mainly involves weighted averaging or multi-dimensional combination analysis of data from multiple assessment dimensions. For example, the system's photovoltaic efficiency may be high at a certain time but fluctuate significantly; while the energy storage system's charging and discharging efficiency may be low. This comprehensive assessment helps identify areas where the system has problems and provides a basis for further optimization.

[0081] For example, assuming the system's photovoltaic power output on a certain day is 10kW, while the expected output is 9.5kW, the deviation calculation result is +0.5kW, indicating that the system is slightly over-generating power. In the fluctuation analysis, the system's power fluctuation on that day is ±1.5kW, which may be due to large fluctuations caused by changes in sunlight. The fluctuation assessment results indicate that the system needs optimization to reduce power fluctuations. The actual efficiency of the photovoltaic system is 93%, which is slightly lower than the theoretical maximum, indicating some loss in power generation efficiency. The energy storage system exhibits a charging efficiency of 90% during the charging process, but also shows some losses. Through the integration of evaluation data, the final system operation evaluation data shows that the system is operating stably overall, but improvements are needed in the power generation management of the photovoltaic system and the charging efficiency of the energy storage system to improve overall performance.

[0082] The above describes the multi-energy complementary coordinated control method for photovoltaic systems in the embodiments of this application. The following describes the multi-energy complementary coordinated control device for photovoltaic systems in the embodiments of this application. Please refer to [link to relevant documentation]. Figure 2 One embodiment of the multi-energy complementary coordination control device for a photovoltaic system in this application includes:

[0083] The acquisition module 201 is used to sample and process the operating parameters of each component and energy storage system in the photovoltaic string through the multi-channel synchronous potential acquisition module to obtain system synchronous sampling data.

[0084] The calculation module 202 is used to perform operational status calculations on the system synchronous sampling data to obtain a system status parameter matrix that includes photovoltaic power generation efficiency, energy storage status, and environmental parameters.

[0085] Prediction module 203 is used to perform power prediction analysis on the system state parameter matrix to obtain system power balance target data;

[0086] Optimization module 204 is used to perform multi-energy coordination optimization processing on the system power balance target data to obtain multi-energy system control command data;

[0087] Control module 205 is used to perform interleaved parallel control processing on the control command data of the multi-energy system to obtain real-time operating data of the system;

[0088] Evaluation module 206 is used to perform effect evaluation processing on the real-time operation data of the system to obtain system operation evaluation data.

[0089] Through the collaborative efforts of the aforementioned components, and by precisely and synchronously acquiring and processing the real-time operating parameters of the photovoltaic modules and energy storage system, high precision and consistency of system data are ensured, thus providing an accurate data foundation for subsequent state calculations and analysis. A multi-channel synchronous potential acquisition module, combined with IEEE 1588 clock synchronization technology, achieves time synchronization between the photovoltaic system and the energy storage system, avoiding data deviations caused by sampling time errors and greatly improving the reliability and accuracy of system operating data. Secondly, the system generates a system state parameter matrix, including photovoltaic power generation efficiency, energy storage status, and environmental parameters, by processing the synchronously sampled data. This not only reflects the real-time operating status of the photovoltaic and energy storage systems but also effectively integrates the impact of environmental factors on system operation, providing comprehensive support for subsequent power prediction and optimization. Furthermore, this method effectively predicts the system's power demand and supply through power prediction analysis and energy storage capacity constraint analysis, thereby achieving precise coordination between photovoltaic power generation and energy storage discharge in multi-energy complementary coordinated optimization, avoiding over-discharge or over-charging, and further improving the system's economy and stability. In terms of optimization, a multi-energy coordinated optimization algorithm was adopted to unify the scheduling of control commands for photovoltaic and energy storage systems. This not only improved the overall efficiency of photovoltaic power generation and energy storage systems but also optimized the efficiency of electricity utilization. Ultimately, through interleaved parallel control and grid synchronization, the output of the photovoltaic and energy storage systems was ensured to be stable and reliable, effectively reducing power fluctuations and improving the system's load adaptability and the continuity of energy supply. Finally, through real-time performance evaluation, potential problems in system operation can be quickly identified, and optimization strategies can be adjusted in a timely manner, thereby improving the overall system's operating efficiency and long-term stability.

[0090] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-energy complementary coordinated control method for a photovoltaic system, characterized in that, The multi-energy complementary coordinated control method for the photovoltaic system includes: The operating parameters of each component and energy storage system in the photovoltaic string are sampled and processed by a multi-channel synchronous potential acquisition module to obtain system synchronous sampling data. The system synchronous sampling data is processed to calculate the operating status, resulting in a system state parameter matrix that includes photovoltaic power generation efficiency, energy storage status, and environmental parameters; The system state parameter matrix is ​​subjected to power prediction analysis to obtain the system power balance target data; The system power balance target data is subjected to multi-energy coordination optimization processing to obtain multi-energy system control command data; The control command data of the multi-functional system is processed by interleaved parallel control to obtain the real-time operation data of the system; The real-time operating data of the system is processed to evaluate its effectiveness, resulting in system operation evaluation data.

2. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of sampling and processing operating parameters for each component and energy storage system in the photovoltaic string using a multi-channel synchronous potential acquisition module yields system synchronous sampling data, including: The POWERBUS fieldbus master station sends a synchronous acquisition broadcast command to perform time synchronization processing to obtain the IEEE1588 clock synchronization signal. The IEEE1588 clock synchronization signal is then processed by master-slave clock synchronization to obtain slave station synchronous clock data. The slave station synchronization clock data is processed by clock discipline to obtain a 1MHz sampling frequency signal, and the 1MHz sampling frequency signal is divided to obtain a sampling control signal. The output voltage signal of the photovoltaic module is processed by a high-precision analog-to-digital converter to obtain the digital value of the module voltage, and the current signal of the photovoltaic string is processed by a shunt monitor to obtain the digital value of the string current. The charging and discharging status signals of the energy storage system are processed through status acquisition to obtain charging and discharging status data, and the remaining capacity signal of the energy storage system is processed through capacity measurement to obtain energy storage capacity data. The ambient temperature signal is processed by temperature sensing to obtain temperature parameter data, and the light intensity signal is processed by irradiance measurement to obtain light intensity data. The digital values ​​of the component voltage, the digital values ​​of the string current, the charge / discharge status data, the energy storage capacity data, the temperature parameter data, and the light intensity data are transmitted and processed via the MODBUS protocol to obtain the system synchronous sampling data.

3. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of calculating the operating status of the synchronously sampled data of the system yields a system state parameter matrix containing photovoltaic power generation efficiency, energy storage status, and environmental parameters, including: The component voltage and current in the synchronously sampled data of the system are multiplied to obtain the actual power data of the component, and the ratio of the actual power data of the component to the theoretical maximum power is calculated to obtain the photovoltaic power generation efficiency. The energy storage capacity data in the synchronous sampling data of the system is normalized to obtain capacity status data, and the power limit is calculated on the charge and discharge status data in the synchronous sampling data of the system to obtain the energy storage status. The temperature parameter data and light intensity data in the synchronous sampling data of the system are normalized to obtain environmental parameters. The photovoltaic power generation efficiency, the energy storage state and the environmental parameters are combined by matrix to obtain the system state parameter matrix.

4. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of performing power prediction analysis on the system state parameter matrix to obtain system power balance target data includes: Trend analysis is performed on the environmental parameters in the system state parameter matrix to obtain environmental change prediction data, and the environmental change prediction data is then processed by photoelectric conversion calculation to obtain photovoltaic output power prediction data. Historical electricity load data is analyzed and processed to obtain load forecast data. The difference between the photovoltaic output power forecast data and the load forecast data is calculated to obtain power difference data. The power difference data is subjected to energy storage capacity constraint analysis to obtain the system power balance target data.

5. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of performing multi-energy coordination optimization on the system power balance target data to obtain multi-energy system control command data includes: The system power balance target data is processed by MPPT operating point calculation to obtain photovoltaic regulation command data, and the photovoltaic regulation command data is processed by duty cycle conversion to obtain PWM control data; The system power balance target data is processed by energy storage power allocation to obtain energy storage command data, and the energy storage command data is processed by power limit constraints to obtain energy storage control data. The PWM control data and the energy storage control data are processed by instruction synthesis to obtain the control instruction data of the multi-energy system.

6. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of interleaving and paralleling the control command data of the multi-energy system to obtain real-time system operation data includes: The control command data of the multi-energy system is processed by phase difference calculation to obtain interleaved signal data, and the interleaved signal data is processed by a driver to obtain switch control data; The switch control data is processed by synchronous rectification to obtain photovoltaic output data, and the multi-energy system control command data is processed by power conversion to obtain energy storage output data; The photovoltaic output data and the energy storage output data are processed synchronously through grid connection to obtain the real-time operating data of the system.

7. The multi-energy complementary coordinated control method for a photovoltaic system according to claim 1, characterized in that, The process of evaluating the real-time operating data of the system to obtain system operating evaluation data includes: The real-time operating data of the system is processed by deviation calculation to obtain balance evaluation data, and the real-time operating data of the system is processed by fluctuation characteristic analysis to obtain fluctuation evaluation data; The real-time operating data of the system is processed through efficiency calculation to obtain photovoltaic evaluation data, and the real-time operating data of the system is processed through charge and discharge efficiency calculation to obtain energy storage evaluation data; The system operation assessment data is obtained by integrating and processing the balance assessment data, the fluctuation assessment data, the photovoltaic assessment data, and the energy storage assessment data.

8. A multi-energy complementary coordinated control device for a photovoltaic system, used to implement the multi-energy complementary coordinated control method for a photovoltaic system as described in any one of claims 1-7, characterized in that, The multi-energy complementary coordination control device of the photovoltaic system includes: The acquisition module is used to sample and process the operating parameters of each component and energy storage system in the photovoltaic string through the multi-channel synchronous potential acquisition module to obtain system synchronous sampling data. The calculation module is used to perform operational status calculation processing on the synchronously sampled data of the system to obtain a system status parameter matrix that includes photovoltaic power generation efficiency, energy storage status and environmental parameters; The prediction module is used to perform power prediction analysis on the system state parameter matrix to obtain the system power balance target data; The optimization module is used to perform multi-energy coordination optimization processing on the system power balance target data to obtain multi-energy system control command data; The control module is used to perform interleaved parallel control processing on the control command data of the multi-energy system to obtain real-time operating data of the system. The evaluation module is used to perform effect evaluation processing on the real-time operation data of the system to obtain system operation evaluation data.

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