Distributed power supply station flexible transformation method, system and device based on optical storage flexible direct charging and storage medium

Abstract

The invention discloses a distributed power supply station flexible transformation method, system and device based on optical storage flexible direct charging and a storage medium, and relates to the technical field of distributed power supply flexible transformation, and the method comprises the steps: determining a transformation direction based on distributed power supply data, and integrating optical storage charging power supply; executing an energy management control strategy according to the determined transformation direction; and adjusting a control strategy in combination with the distributed power supply data and the prediction result. According to the method, stable power supply can still be kept under different weather and illumination conditions, the power utilization cost can be effectively reduced, the power supply reliability can be effectively improved, the system can better adapt to environment changes and requirements, dependence on external power purchase can be predicted and adjusted in advance, and therefore the optimal power supply effect is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed power supply flexible transformation, in particular to a flexible transformation method, system and device based on a "light storage flexible direct current charging" distributed power supply station and a storage medium. BACKGROUND

[0002] In traditional distributed power supply design in prisons, schools and rural and pastoral areas, most attention is paid to the external power supply system directly purchased from the power grid company, and there is a lack of self-generation of renewable energy utilization such as solar photovoltaic power generation and energy storage; the distributed power consumption subject cannot integrate and balance internal and external power supply resources, and effectively reduce the distributed power supply cost. In addition, the existing power supply system may not have a flexible energy management control strategy to adapt to different environments and changes in demand, and the distribution system has the phenomenon of closed-loop design and open-loop operation, and there are problems such as imbalance of feeder power in the distribution network, insufficient power supply capacity expansion capability, and reduced power supply reliability.

[0003] Therefore, a reliable "light storage flexible direct current charging" distributed power supply flexible transformation method and system are needed to solve the problems of insufficient balance and integration of various power supply resources (such as solar energy and purchased electricity) in the existing distributed power supply design method, how to maintain power supply stability and maximize energy utilization efficiency and effectively reduce power supply cost under the condition of changes in sunlight, and problems such as imbalance of feeder power in the distribution network, insufficient power supply capacity expansion capability, and reduced power supply reliability. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is that the existing distributed power supply design method has insufficient balance and integration of various power supply resources, effectively reduces the power supply cost, solves the problem of imbalance of feeder power in the distribution network, and how to maintain power supply stability under the condition of changes in sunlight.

[0006] To solve the above technical problems, the present application provides the following technical scheme: a flexible transformation method of a "light storage flexible direct current charging" distributed power supply station, including determining a transformation direction based on distributed power supply data and integrating light storage charging power supply; executing an energy management control strategy according to the determined transformation direction; adjusting the control strategy in combination with distributed power supply data and prediction results.

[0007] As a preferred scheme of the flexible transformation method of the "light storage flexible direct current charging" distributed power supply station, the determination of the transformation direction includes recording the actual output and the theoretical maximum output in the same time period, calculating the energy conversion efficiency, judging the stability of the sunlight if the energy conversion efficiency is less than a predetermined value, and evaluating the stability of the power grid frequency if the energy conversion efficiency is greater than a predetermined value.

[0008] As a preferred scheme of the flexible reconstruction method of the "light storage flexible direct charging" distributed power supply station based on the application, wherein: the evaluation of the power grid frequency stability comprises recording the power grid frequency data of the same time period, calculating the frequency deviation, if the deviation frequency and the data deviation are greater than or equal to the preset threshold, it is judged that the variable speed constant frequency reconstruction direction, if the deviation frequency exceeds the preset threshold, the data deviation does not exceed the preset threshold, the frequency sensor is checked, the resonance analysis is carried out, and the control strategy is adjusted;If the data deviation value exceeds the preset threshold, the deviation frequency does not exceed the preset threshold, the temporary fault or external disturbance is diagnosed, the power quality analysis is carried out, and the enhanced system stability adjustment strategy is executed;If the deviation is within the allowable range, secondary review is carried out, data is reexamined, it is judged whether there is omission or misjudgment, if all indicators meet the preset index requirements, routine maintenance is carried out.

[0009] As a preferred scheme of the flexible reconstruction method of the "light storage flexible direct charging" distributed power supply station based on the application, wherein: the integrated light storage charging power supply comprises obtaining historical sunshine data and power load data, using historical data to evaluate the maximum potential power generation of photovoltaic power generation, calculating the total energy supply at different time points, establishing a model with historical data to predict future power generation capacity and purchased power supply;Determine the optimal capacity required for the energy storage battery, determine the standby time T, consider the standby time, combine the photovoltaic and energy storage battery system, establish a comprehensive energy management system, and optimize the photovoltaic component configuration according to the power generation potential and load demand.

[0010] As a preferred scheme of the flexible reconstruction method of the "light storage flexible direct charging" distributed power supply station based on the application, wherein: the execution of the energy management control strategy comprises: when it is judged to manage and stabilize the sunshine, the sensor equipped in the main channel through the flow monitoring system, real-time monitoring of the sunshine intensity and the data wireless transmission to the central control system, using geographic information system to visualize the three-dimensional model of the sunshine, adjusting the photovoltaic power generation mode, applying neural network strategy, hydraulic drive system and multi-mode switching to cope with the change of sunshine intensity, combining with the sunshine monitoring system, when the fluctuation exceeds the threshold, through adjusting the photovoltaic panel angle control and purchased electricity to balance the power station demand, using machine learning algorithm to predict the future sunshine amount, and adjusting the photovoltaic panel angle strategy accordingly.

[0011] As a preferred scheme of the flexible reconstruction method of the "light storage flexible direct charging" based distributed power supply station, the adjustment control strategy comprises: when it is judged that the configuration of the photovoltaic panel is adjusted, the angle of the photovoltaic panel is adjusted through real-time sunshine data, when the sunshine is less than the threshold, the arrangement of the photovoltaic panel is optimized, the number of the photovoltaic panel is adjusted based on historical sunshine data and power demand; when it is judged that the constant frequency reconstruction direction is adopted, the frequency is fine-tuned by using the photovoltaic system output and the battery energy storage system; when it is judged that the frequency sensor is checked, the frequency sensor is calibrated and maintained periodically when the resonance analysis is performed, the control strategy is adjusted, and the accuracy of data is ensured; when the frequency anomaly is detected, the standby sensor is started and fault positioning is performed, the control algorithm is optimized, and the speed and output are adjusted according to real-time data; when it is judged that the temporary fault or external interference is diagnosed, the power quality is analyzed, the power quality is analyzed combined with the power grid state and user demand when the system stability adjustment strategy is executed to improve the power quality through the reactive power compensation equipment and the filter, the system is simulated and stress tested; when it is judged that the routine maintenance is performed, the data backtracking mechanism is established, the historical data is analyzed again, the fault prediction and fault diagnosis are performed combined with the artificial intelligence algorithm, and the standby sensor and the data source are introduced in the data analysis.

[0012] As a preferred scheme of the flexible reconstruction method of the "light storage flexible direct charging" based distributed power supply station, the frequency fine-tuning comprises: if the adjusted angle exceeds the upper threshold, the photovoltaic system is used as the basic output, and the frequency is fine-tuned by using the energy storage system; if the adjusted angle is lower than the lower threshold, the output of the photovoltaic system is reduced, and the amount of purchased electricity is increased to balance the stability of the system.

[0013] Another object of the present application is to provide a flexible reconstruction system of a "light storage flexible direct charging" based distributed power supply station, which can determine the reconstruction direction based on distributed power supply data, integrate light storage charging power supply, and solve the problem that the current distributed power supply design technology has insufficient utilization of balanced integration of multiple power supply resources.

[0014] As a preferred scheme of the flexible reconstruction system of the distributed power supply station based on "light storage flexible direct charging" of the application, wherein: including an energy collection module, an energy storage module, an intelligent scheduling module and a visual maintenance module; the energy collection module is used for collecting hydropower station data, processing information, determining the reconstruction direction and providing decision basis for the intelligent scheduling module; the energy collection module is used for collecting hydropower station data and prediction results, and executing energy management control strategy; the intelligent scheduling module is used for system scheduling according to data analysis and energy management strategy, and intelligent adjustment according to the stability of water flow and sunlight and the stability of power grid frequency; the visual maintenance module is used for providing user interface, allowing operators to view various data in real time, and being responsible for system test and verification after adjustment.

[0015] Still another object of the present application is to provide a flexible reconstruction equipment of distributed power supply station based on "light storage flexible direct charging", comprising a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps of the flexible reconstruction method of distributed power supply station based on "light storage flexible direct charging".

[0016] Still another object of the present application is to provide a flexible reconstruction storage medium of distributed power supply station based on "light storage flexible direct charging", which stores a computer program, and the computer program is executed by a processor to realize the steps of the flexible reconstruction method of distributed power supply station based on "light storage flexible direct charging".

[0017] The present application has the following advantages: The flexible reconstruction method of distributed power supply station based on "light storage flexible direct charging" provided by the present application integrates solar energy (photovoltaic) and purchased electricity to ensure stable power supply under different weather and electricity price conditions. The flexible energy management control strategy enables the system to better adapt to environmental changes and demands. The real-time monitoring and data analysis capability of the system can predict and adjust the dependence on solar energy in advance, thereby achieving optimal power supply effect, and adopting flexible interconnection device (FID) to realize flexible power flow control between multiple feeder lines, achieve balanced load, optimize power grid power supply capability, and improve power distribution network reliability and equipment utilization. The present application achieves better results in improving energy utilization efficiency, enhancing system stability and reducing power supply cost. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creating any creative labor.

[0019] Fig. 1A whole flow chart of a flexible reconstruction method based on a "light storage flexible direct current charging" distributed power supply station is provided for the embodiment 1 of the present application.

[0020] Fig. 2 A multi-terminal flexible controller topological structure diagram of a flexible reconstruction method based on a "light storage flexible direct current charging" distributed power supply station is provided for the embodiment 1 of the present application. DETAILED DESCRIPTION

[0021] In order to make the above objectives, characteristics and advantages of the present application more apparent, obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0022] Embodiment 1, refer to Fig. 1~Fig. 2 For an embodiment of the present application, a flexible reconstruction method based on a "light storage flexible direct current charging" distributed power supply station is provided, comprising:

[0023] S1: determining the reconstruction direction based on the distributed power supply data, integrating light storage charging power supply.

[0024] Specifically, determining the reconstruction direction comprises recording the actual output and the theoretical maximum output in the same time period, calculating the energy conversion efficiency, if the energy conversion efficiency is less than a predetermined value, judging the stability of the sunlight, if the energy conversion efficiency is greater than a predetermined value, evaluating the stability of the power grid frequency.

[0025] The distributed power supply data comprises the power and its peak and valley value of the distributed power supply, the electricity price, the energy conversion efficiency, the historical sunlight condition, the photovoltaic and storage cost.

[0026] Judging the stability of the sunlight, obtaining the sunlight intensity data in the same time period, analyzing the correlation between the sunlight and the actual power generation, if the sunlight intensity fluctuation is greater than or equal to a predetermined threshold value, judging that the sunlight is unstable, performing the sunlight management and the sunlight stabilization direction, if the sunlight data is lower than the predetermined threshold value, considering adjusting the photovoltaic panel configuration, adjusting the angle or considering the photovoltaic panel expansion direction.

[0027] Assessing grid frequency stability involves recording grid frequency data over the same time period, calculating frequency deviation, and determining if both the deviation frequency and data deviation are greater than or equal to preset thresholds. If the deviation frequency exceeds the preset threshold but the data deviation does not, the frequency sensor is checked, resonance analysis is performed, and the control strategy is adjusted. If the data deviation value exceeds the preset threshold but the deviation frequency does not, temporary faults or external interference are diagnosed, power quality analysis is conducted, and strategies to enhance system stability are implemented. If the deviation is within the allowable range, a second review is performed to re-examine the data and determine if there are any omissions or misjudgments. If all indicators meet the preset requirements, routine maintenance is performed.

[0028] It should be noted that the integrated photovoltaic-storage-charging-power supply includes: acquiring historical sunshine data and power load data; using historical data to assess the maximum potential capacity of photovoltaic power generation; calculating the total energy supply at different points in time; building a model based on historical data to predict future power generation capacity and purchased power supply; determining the optimal capacity required for energy storage batteries; determining the standby time T; considering the standby time; combining photovoltaic and energy storage battery systems; establishing a comprehensive energy management system; and optimizing the configuration of photovoltaic modules based on power generation potential and load demand.

[0029] The "photovoltaic-storage-flexible-direct-charging" distributed power supply system selects photovoltaic modules based on historical sunshine conditions, optimizes their configuration and angle, selects photovoltaic panels based on actual sunshine and their stability, designs energy storage battery systems based on photovoltaic power generation capacity and actual electricity load, and integrates a hydro-photovoltaic complementary system.

[0030] The photovoltaic power generation capacity is expressed as follows: , in, Indicates total energy supply. Indicates time Photovoltaic power generation capacity This indicates the power supplied by externally purchased electricity.

[0031] The energy storage battery system design is represented as follows: , in, Indicates battery capacity, Indicates time Actual electricity load Indicates total energy supply. and Indicates the time frame under consideration.

[0032] The standby time T includes analyzing historical power load data, especially the load fluctuation in peak hours and off-peak hours. Identify the time period of power generation shortage in historical data, including the decrease of photovoltaic power generation due to weather changes, or the decrease of external power purchase due to market price increase. Evaluate the additional external power required to meet the load demand during these power generation shortage periods.

[0033] Based on the worst-case power generation shortage period, the standby time T is determined based on the frequency and duration of historical load fluctuations and power generation shortages to ensure that the energy storage battery can continue to supply power under similar conditions. According to the standby time T and the peak load demand, the required energy storage battery capacity is calculated.

[0034] The "light storage flexible direct current charging" distributed power supply system realizes multi-end flexible control through flexible interconnection device FID, such as Fig. 2 The architecture is shown in the figure, transformer 1 points to the power generation DC boost transformer, transformer 2 points to the external power purchase AC step-down transformer, and the middle is FID flexible device. The right side is the user side with energy storage and photovoltaic power generation, thereby forming a multi-end energy routing architecture.

[0035] It should also be noted that through analysis based on distributed power supply data, the direction of system transformation is determined, integrating photovoltaic, energy storage and charging systems to solve the technical problems of low power supply efficiency, sunlight fluctuation and unstable grid frequency, realize efficient and reliable energy management and optimal allocation, reduce operating costs, and promote efficient use of renewable energy.

[0036] S2: According to the determined transformation direction, execute the energy management control strategy.

[0037] Specifically, executing the energy management control strategy includes, when judging to proceed with the direction of sunlight management and sunlight stabilization, through the flow monitoring system equipped with sensors in the main channel, real-time monitoring of sunlight intensity and wireless transmission of data to the central control system, using geographic information system to visualize the three-dimensional model of sunlight, adjusting the working mode of photovoltaic power generation, applying neural network strategy, hydraulic drive system and multi-mode switching to cope with changes in sunlight intensity, combined with the sunlight monitoring system, when the fluctuation exceeds the threshold, through adjusting the photovoltaic panel angle control and external power purchase to balance the power station demand, using machine learning algorithm to predict future sunlight, and adjusting the photovoltaic panel angle strategy accordingly.

[0038] It should be noted that before executing the energy management control strategy, the photovoltaic component configuration is optimized, including collecting and analyzing historical sunlight data to evaluate the maximum potential capacity of photovoltaic and external power purchase, analyzing historical and forecast data of power load, especially peak and off-peak demand, using software tools to simulate different photovoltaic component configuration schemes to evaluate the efficiency in meeting power demand.

[0039] Considering geographical location, climate conditions, and seasonal variation factors, the best type, number, angle, and location of photovoltaic modules are selected based on simulation results.

[0040] The selected configuration is integrated into the photovoltaic storage flexible direct charging system and tested to ensure stable operation under various conditions.

[0041] In the photovoltaic storage flexible direct charging system, high-efficiency photovoltaic modules are used, and the configuration is optimized based on historical sunshine data to maximize the photovoltaic conversion efficiency. The installation angle is adjusted according to the geographical location and seasonal changes to achieve the best sunlight reception effect. The system selects photovoltaic modules of appropriate specifications based on actual sunlight and its stability to ensure optimal power conversion efficiency under different sunlight conditions. Based on the predicted capacity of photovoltaic and purchased electricity and the actual electricity load of the power station, an expandable and high-cycle-life energy storage battery system is selected to ensure continuous power supply and provide additional energy during peak demand. The "photovoltaic storage charging flexible direct charging" system further adjusts and optimizes in real time through the integration of a control unit to achieve efficient use of energy and ensure the stability of the power distribution network.

[0042] It should also be noted that by integrating real-time sunlight monitoring, three-dimensional modeling, neural network, and machine learning algorithms, the angle of the photovoltaic panel is dynamically adjusted and the purchased electricity strategy is optimized to solve the problem of unstable power supply and load matching caused by sunlight fluctuations, achieve efficient energy conversion and precise balance between supply and demand, improve the system's adaptive ability and power supply reliability, reduce dependence on external power grids, and extend the service life of equipment.

[0043] S3: Adjust the control strategy based on distributed power supply data and prediction results.

[0044] Specifically, adjusting the control strategy includes adjusting the angle of the photovoltaic panel based on real-time sunlight data when considering adjusting the photovoltaic panel configuration, adjusting the angle, or considering the expansion direction of the photovoltaic panel, optimizing the arrangement of the photovoltaic panel during periods of insufficient sunlight, and adjusting the number of photovoltaic panels based on historical sunlight data and power demand.

[0045] Equipped with solar radiation sensors and sunlight time monitoring systems, real-time sunlight intensity and angle are obtained.

[0046] Using an automatic tracking system, the angle of the photovoltaic panel is automatically adjusted based on sunlight data and seasonal changes to ensure it always faces the sun and achieves the best irradiation effect. Through machine learning algorithms, future solar trajectories are predicted based on historical sunlight data to develop automatic adjustment strategies and optimize sunlight capture efficiency. Based on the monitored sunlight intensity, the optimal distance between photovoltaic panels is determined to reduce the impact of shading and reflection.

[0047] Using optical simulation software, simulate the configuration of photovoltaic panels, further optimize their arrangement and angle to achieve the best light receiving effect, adjust the inclination angle of the photovoltaic panels during periods of insufficient sunlight to obtain more sunlight time, calculate the required photovoltaic panel area based on historical sunlight data, current power demand and system efficiency, analyze geographic location, weather patterns and power demand fluctuations, evaluate whether to increase the number of photovoltaic panels, design expansion plans, determine the location, arrangement and connection method of new photovoltaic panels based on the existing system configuration and land use, integrate all the data of the above steps into the central control system, use advanced data analysis and algorithms to automatically determine and execute the best photovoltaic panel configuration, angle adjustment and expansion strategy, and ensure that the system always operates in the best state.

[0048] When it is determined to be a constant frequency modification direction, the speed adjustment strategy is implemented through the frequency converter to adapt to the real-time load changes of the power grid and the water flow conditions, and the frequency is fine-tuned using the photovoltaic system output and the battery energy storage system; When it is determined to check the frequency sensor, calibrate and maintain the frequency sensor periodically to ensure the accuracy of the data, and when the frequency anomaly is detected, start the backup sensor and perform fault location, optimize the control algorithm, and adjust the speed and output according to the real-time data.

[0049] When it is determined to diagnose temporary faults or external interference, perform power quality analysis, and execute enhanced system stability adjustment strategy, perform power quality analysis in combination with power grid state and user demand, improve power quality through reactive power compensation equipment and filters, and simulate and stress test the system.

[0050] When it is determined to be routine maintenance, a data backtracking mechanism is established to re-analyze historical data, combine artificial intelligence algorithms for fault prediction and diagnosis, and introduce backup sensors and data sources into data analysis to reduce the impact of single-point failures.

[0051] It should be noted that frequency fine-tuning includes, if the adjusted angle exceeds the upper threshold, the photovoltaic system will be used as the basic output, and the energy storage system will be used for frequency fine-tuning; if the adjusted angle is lower than the lower threshold, the photovoltaic system reduces output and increases the amount of purchased electricity to balance the stability of the system.

[0052] Adaptive adjustment of weight parameters using fuzzy logic, the system adjusts weight parameters based on historical operation data, current real-time data and external environmental factors, adjusts energy management control strategy according to changes in frequency and power parameters; When an abnormality occurs in system operation, start the fault detection module to locate and diagnose the fault, and use backup sensors and advanced control algorithms to handle and correct the fault.

[0053] It should also be noted that by fusing real-time monitoring data with machine learning predictions, the system utilizes automatic tracking, fuzzy logic adaptive algorithms, and frequency fine-tuning strategies to dynamically optimize photovoltaic panel angles and operating modes, addressing power instability caused by sunlight fluctuations and grid frequency deviations, achieving efficient energy capture and regulation, and improving power quality and overall energy efficiency.

[0054] Example 2, as an embodiment of the present application, provides a flexible transformation method for a "light storage flexible direct current charging" distributed power supply station. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0055] Firstly, two experimental sites (Site A and Site B) are selected in the experiment. Site A uses a "light storage flexible direct current charging" power supply system, while Site B uses a traditional power supply system. The experiment lasts for three months, covering different sunlight conditions and peak electricity demand periods. Site A and Site B are located in similar geographical locations with similar climate conditions and sunlight resources. Site A is equipped with a light storage flexible direct current charging power supply system, including high-efficiency photovoltaic components, energy storage batteries, real-time monitoring sensors, and an energy management system. Site B uses a traditional purchased electricity system, equipped with sensors to record total power supply, self-generation, purchased electricity, energy conversion efficiency, system operation stability (number of faults), and comprehensive power supply cost.

[0056] Daily records of total power supply, self-generation ratio, and purchased electricity of distributed power supply are recorded. Energy conversion efficiency and comprehensive power supply cost are calculated respectively. Site A adjusts photovoltaic panel angles, energy storage battery charging and discharging strategies, and predicts future sunlight intensity to optimize energy distribution. Site B maintains traditional power supply methods. Under high load electricity or low sunlight conditions, the number of faults and response time of both systems are recorded to evaluate system stability. Refer to Table 1 for experimental data recording and analysis.

[0057] Table 1 Experimental data recording table Test subject Total power supply (kWh) Purchased power (kWh) Self-generated power (kWh) Energy conversion efficiency (%) System stability (number of failures) Comprehensive power supply cost (yuan / kWh) Site A 1200 400 800 85 0 0.32 Site B 1200 1000 200 60 3 0.45 Site A 1350 300 1050 87 0 0.3 Site B 1350 1000 350 65 4 0.48 Site A 1100 350 750 84 0 0.33 Site B 1100 1000 100 62 2 0.47

[0058] The data shows that the comprehensive power supply cost of site A is lower than that of site B under all experimental conditions, for example, when the total power supply is 1200kWh, the unit cost of site A is 0.32 yuan / kWh, and that of site B is 0.45 yuan / kWh, the cost is reduced by about 28.9%, which shows that the light storage flexible charging system reduces the energy use cost by optimizing the self-generation ratio, and the energy conversion efficiency of site A system is always kept above 85%, while the highest efficiency of site B is only 65%, the difference is due to the optimization and real-time adjustment ability of the light storage flexible charging system to the photovoltaic module configuration, during the experiment, site A did not have any failure, while site B had 3, 4 and 2 failures respectively, which shows that the light storage flexible charging system improves the system stability through intelligent regulation and control, avoids power interruption caused by sunlight fluctuation or load change, the light storage flexible charging system uses historical data for machine learning prediction, dynamically adjusts the photovoltaic panel angle and energy storage strategy, improves the resource utilization rate, under the condition of 1350kWh power supply, the self-generation of site A reaches 1050kWh, while that of site B is only 350kWh, which proves the innovativeness of the application, site A reduces the dependence on external power purchase, reduces carbon emissions and environmental burden, and meets the requirements of sustainable development.

[0059] Embodiment 3 is an embodiment of the application, which provides a flexible transformation system based on a "light storage flexible charging" distributed power supply station, including an energy collection module, an energy storage module, an intelligent scheduling module and a visual maintenance module.

[0060] The energy collection module is used to collect hydropower station data and process information to determine the transformation direction and provide decision basis for the intelligent scheduling module.

[0061] The energy storage module is used to execute energy management control strategy according to the collected hydropower station data and prediction results.

[0062] The intelligent scheduling module is used to perform system scheduling according to data analysis and energy management strategy, and intelligently adjust according to the stability of water flow and sunlight and the stability of power grid frequency.

[0063] The visual maintenance module is used to provide a user interface to allow operators to view various data in real time, and is responsible for system testing and verification after adjustment.

[0064] The embodiment also provides a computer device, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the flexible transformation method of the "light storage flexible charging" distributed power supply station as proposed in the above embodiment.

[0065] The embodiment also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the flexible reconstruction method of the distributed power supply station based on the light storage flexible direct current filling as proposed in the above embodiment.

[0066] The functions described above can be implemented in hardware or software stored on storage medium. Based on such understanding, the technical solutions of the present application or the part of the technical solutions that make essential contributions to the prior art can be embodied in the form of software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0067] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch and execute instructions from the instruction execution system, device or apparatus), or in conjunction with these instructions execution system, device or apparatus. For the purpose of this specification, the "computer readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by an instruction execution system, device or apparatus or in conjunction with these instructions execution system, device or apparatus.

[0068] More specific examples (a non-exhaustive list) of the computer readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CD ROM). In addition, the computer readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by electronic conversion, interpretation or processing, if necessary, in other suitable ways, and then stored in a computer memory.

[0069] It should be understood that portions of the present application can be implemented in hardware, software, firmware, or combinations thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0070] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A method for flexible retrofitting of distributed power stations based on "photovoltaic-storage-flexible-direct-charging" is characterized by, include: Based on distributed power supply data, the direction of transformation is determined, and photovoltaic, energy storage, charging and power supply are integrated; Implement energy management and control strategies according to the determined transformation direction; Adjust the control strategy based on distributed power supply data and forecast results.

2. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible direct charging" as described in claim 1, characterized in that: The determination of the direction of modification includes, Record the actual output and theoretical maximum output for the same time period, calculate the energy conversion efficiency, and if the energy conversion efficiency is less than the predetermined value, determine the solar radiation stability; if the energy conversion efficiency is greater than the predetermined value, assess the grid frequency stability.

3. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible direct charging" as described in claim 2, characterized in that: The assessment of power grid frequency stability includes, Record the power grid frequency data for the same time period, calculate the frequency deviation, and if both the deviation frequency and the data deviation are greater than or equal to the preset threshold, it is determined to be a direction for variable speed constant frequency transformation. If the deviation frequency exceeds the preset threshold but the data deviation does not exceed the preset threshold, check the frequency sensor, perform resonance analysis, and adjust the control strategy. If the data deviation value exceeds the preset threshold, but the deviation frequency does not exceed the preset threshold, perform a diagnosis of temporary faults or external interference, conduct power quality analysis, and implement an adjustment strategy to enhance system stability. If the deviation is within the allowable range, a second review is conducted to re-examine the data and determine whether there are any omissions or misjudgments. If all indicators meet the preset requirements, routine maintenance is performed.

4. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible direct charging" as described in any one of claims 1 to 3, characterized in that: The integrated photovoltaic energy storage charging power supply includes, Acquire historical sunshine data and power load data, use historical data to assess the maximum potential capacity of photovoltaic power generation, calculate the total energy supply at different points in time, build models based on historical data, and predict future power generation capacity and purchased power supply. Determine the optimal capacity required for the energy storage battery, determine the backup time T, consider the backup time, combine the photovoltaic and energy storage battery systems, establish an integrated energy management system, and optimize the configuration of photovoltaic modules based on power generation potential and load demand.

5. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible direct charging" as described in claim 4, characterized in that: The implementation of the energy management and control strategy includes, When determining the direction of sunshine management and stabilization, sensors equipped with a flow monitoring system on the main channels monitor sunshine intensity in real time and wirelessly transmit the data to the central control system. A three-dimensional model of sunshine is visualized using a geographic information system to adjust the photovoltaic power generation mode. Neural network strategies, hydraulic drive systems, and multi-mode switching are applied to cope with changes in sunshine intensity. Combined with the sunshine monitoring system, when fluctuations exceed the threshold, the power plant's demand is balanced by adjusting the angle control of the photovoltaic panels and purchasing external electricity. Machine learning algorithms are used to predict future sunshine amount and adjust the photovoltaic panel angle strategy accordingly.

6. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible direct charging" as described in any one of claims 1 to 3 and 5, characterized in that: The adjustment control strategy includes: When it is determined that adjustments to the configuration, angle, or expansion direction of photovoltaic panels should be considered, the angle of the photovoltaic panels should be adjusted based on real-time sunshine data. During periods when sunshine is below the threshold, the arrangement of photovoltaic panels should be optimized, and the number of photovoltaic panels should be adjusted based on historical sunshine data and electricity demand. When the direction is determined to be constant frequency transformation, the frequency is fine-tuned by using the output of the photovoltaic system and the battery energy storage system. When it is determined that the frequency sensor needs to be checked, resonance analysis should be performed, and control strategy should be adjusted, the frequency sensor should be calibrated and maintained regularly to ensure the accuracy of the data. When a frequency abnormality is detected, the backup sensor should be activated and the fault location should be performed. The control algorithm should be optimized and the speed and output should be adjusted according to real-time data. When it is determined that a temporary fault or external interference is being diagnosed, power quality analysis is performed, and strategies to enhance system stability are implemented. Power quality analysis is combined with grid conditions and user needs. Power quality is improved through reactive power compensation equipment and filters. The system is then simulated and stress-tested. When the maintenance is determined to be routine, a data backtracking mechanism is established to re-analyze historical data, combine it with artificial intelligence algorithms to perform fault prediction and fault diagnosis, and introduce backup sensors and data sources into the data analysis.

7. The flexible transformation method for distributed power stations based on "photovoltaic-storage-flexible-direct-charging" as described in claim 6, characterized in that: The frequency fine-tuning includes, If the adjusted angle exceeds the upper limit threshold, the photovoltaic system will serve as the basic output, and the energy storage system will be used for frequency fine-tuning. If the adjusted angle is lower than the lower threshold, the photovoltaic system will reduce its output and increase the amount of electricity purchased from outside the system to balance the system's stability.

8. A flexible retrofit system for distributed power stations based on "photovoltaic-storage-flexible-direct-charging" technology, employing the flexible retrofit method for distributed power stations based on "photovoltaic-storage-flexible-direct-charging" as described in any one of claims 1 to 7, characterized in that: It includes an energy acquisition module, an energy storage module, an intelligent scheduling module, and a visual maintenance module; The energy acquisition module is used to collect data and process information from the hydropower station, providing a basis for determining the direction of transformation and for the intelligent dispatch module to make decisions. The energy storage module is used to execute energy management and control strategies based on the collected hydropower station data and forecast results; The intelligent scheduling module is used to perform system scheduling based on data analysis and energy management strategies, and to make intelligent adjustments based on the stability of water flow and sunshine as well as the stability of power grid frequency. The visualization maintenance module provides a user interface that allows operators to view various data in real time and is responsible for system testing and verification after adjustments.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the flexible transformation method for distributed power stations based on "photovoltaic storage flexible direct charging" as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the flexible transformation method for distributed power stations based on "photovoltaic storage flexible direct charging" as described in any one of claims 1 to 7.

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