A Photovoltaic Power Prediction and Control Method and System Based on an Energy Storage System

Abstract

The present invention provides a photovoltaic power prediction control method and system based on an energy storage system, which relates to the technical field of data science and intelligent analysis. The method includes: collecting photovoltaic data in real time, and calculating the current power generation data according to the collected data; obtaining a dynamically set and adjusted current power demand target according to the power generation data collected in real time; comparing the actual power generation power with the power demand target, and calculating the difference between the actual power generation power and the power demand target through difference detection to obtain a difference result; judging whether the current power generation power meets the preset power demand according to the dynamically set power demand. By collecting and processing photovoltaic system data in real time and dynamically adjusting the charge and discharge control of the energy storage system, the present invention improves the stability and efficiency of photovoltaic power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of data science and intelligent analysis, and particularly to a photovoltaic power prediction and control method and system based on an energy storage system. Background Art

[0002] Photovoltaic power generation meets the conditions of being clean, efficient, and renewable, and has developed rapidly globally. However, photovoltaic power generation is vulnerable to external environmental influences, and there are problems such as unstable output and large fluctuations. This instability poses challenges to the stability and reliability of the power system. Energy storage technology can reduce the power fluctuation range and smooth the power output. By storing and releasing electrical energy, the energy storage system can suppress the fluctuations of photovoltaic power and improve the stability and reliability of the power system. Traditional photovoltaic power prediction methods may have problems such as low prediction accuracy and insufficient utilization of the energy storage system. Therefore, a more efficient and accurate photovoltaic power prediction and control method and system based on an energy storage system are needed.

[0003] In response to the above problems, the industry has begun to explore a photovoltaic power prediction and control method and system based on an energy storage system. By comprehensively considering the power generation prediction of a photovoltaic power station, the state of the energy storage system, and the requirements of the power auxiliary service market, the effective suppression and accurate prediction of photovoltaic power fluctuations can improve the operation efficiency of the photovoltaic power station, reduce the dispatching difficulty of the power grid, and improve the stability and economy of the power system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a photovoltaic power prediction and control method and system based on an energy storage system. The present invention realizes the accurate prediction and efficient control of photovoltaic power by collecting and processing photovoltaic system data in real time and dynamically adjusting the charge and discharge control of the energy storage system, thereby improving the stability and efficiency of photovoltaic power generation. To solve the above technical problems, the technical solution of the present invention is as follows:

[0005] In a first aspect, a photovoltaic power prediction and control method based on an energy storage system, the method comprising:

[0006] Collect photovoltaic data in real time and calculate the current power generation data based on the collected data;

[0007] Based on the power generation data collected in real time, obtain and dynamically set and adjust the current power demand target;

[0008] Compare the actual power generation with the power demand target, and calculate the difference between the actual power generation and the power demand target through difference detection to obtain a difference result;

[0009] Based on the difference result, calculate the power generation of the energy storage system through momentum optimization to adjust the power generation;

[0010] Based on historical data, perform a multi-dimensional evaluation of the energy storage system before adjustment to obtain the evaluated energy storage system;

[0011] According to the power generation power, adjust the charge and discharge state of the evaluated energy storage system to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0012] Furthermore, collect data from the photovoltaic power station to obtain the operation data of the photovoltaic system;

[0013] Preprocess the operation data of the photovoltaic system to obtain the preprocessed operation data;

[0014] Pass the preprocessed operation data through

[0015] Perform calculations to obtain the power generation power data, where is the real-time power generation power, is the output voltage, I is the output current, α is the temperature coefficient, T is the real-time temperature, is the reference temperature, β is the exponential coefficient of the light intensity, L is the real-time light intensity, is the reference light intensity, γ is the aging coefficient, A is the service life of the photovoltaic cell, δ is the occlusion coefficient, is the fouling coefficient, S is the ratio of the total area of the photovoltaic cells to the area of the occluded photovoltaic cells, D is the ratio of the total area of the photovoltaic cells to the area of the occluded photovoltaic cells.

[0016] Furthermore, analyze the real-time power generation power data to predict future power demands;

[0017] According to the future power demands and the current power generation power data, dynamically set the power output demands of the photovoltaic system to adjust the power output demands of the photovoltaic system.

[0018] Furthermore, obtain the data of the power demand target by collecting the data of the actual power generation power;

[0019] Clean and organize the collected actual power generation power and power demand target data to obtain the processed data;

[0020] Align the actual power generation power data and the power demand target data in time to obtain the aligned data;

[0021] Subtract the time-aligned actual power generation power from the power demand target point by point, and calculate the difference value between the actual power generation power and the power demand target through subtraction operations to obtain the difference result.

[0022] Furthermore, through the difference result, through

[0023] The generated power of the energy storage system is calculated, which represents the amount of power adjustment at time ; represents the momentum coefficient, represents the previous amount of power adjustment, represents the difference between the current target power and the actual power; represents the influence coefficient of the difference change rate, represents the influence coefficient of the non - linear difference term, represents the size of the time window for integration;

[0024] According to the generated power of the energy storage system, the generated power is adjusted.

[0025] Furthermore, by collecting the historical operation data of the energy storage system, the operation characteristics and rules of the energy storage system are understood;

[0026] By understanding the operation characteristics and rules of the energy storage system, a multi - dimensional evaluation index system is constructed;

[0027] Through the multi - dimensional evaluation index system, each index is evaluated to obtain the evaluated energy storage system.

[0028] Furthermore, by real - time monitoring the generated power of the photovoltaic system, the generated power data is obtained;

[0029] Based on the generated power data and the difference result between the actual generated power and the power demand target, the adjustment direction of the energy storage system is determined;

[0030] According to the adjustment direction of the energy storage system, the adjustment amount of the energy storage system is calculated, and the charge - discharge state of the energy storage system is adjusted to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0031] According to the generated power of the energy storage system, its generated power is adjusted through the control of the energy storage system.

[0032] In a second aspect, a photovoltaic power prediction and control system based on an energy storage system includes:

[0033] An acquisition module that acquires photovoltaic data in real - time and calculates the current generated power data based on the acquired data; based on the real - time acquired generated power data, dynamically sets and adjusts the current power demand target;

[0034] A comparison module for comparing the actual generated power with the power demand target, calculating the difference between the actual generated power and the power demand target through difference detection to obtain a difference result; based on the difference result, calculating the generated power of the energy storage system through momentum optimization to adjust the generated power;

[0035] A processing module, configured to perform multi-dimensional evaluation on the energy storage system before adjustment according to historical data to obtain the energy storage system after evaluation; and adjust the charge and discharge state of the evaluated energy storage system according to the power generation power to achieve precise prediction and control of the total output power of the photovoltaic system.

[0036] In a third aspect, a computing device includes:

[0037] One or more processors;

[0038] A storage device for storing one or more programs, which when executed by the one or more processors cause the one or more processors to implement the method described above.

[0039] In a fourth aspect, a computer-readable storage medium stores a program that, when executed by a processor, implements the method described above.

[0040] The above solution of the present invention has at least the following beneficial effects:

[0041] By collecting photovoltaic data in real time and dynamically adjusting the power demand target, the photovoltaic system can be made to more intelligently adapt to environmental changes, thereby improving the power generation efficiency; through difference detection and optimization calculation, the power generation power of the energy storage system can be accurately adjusted to ensure that the photovoltaic system always operates in an optimal state. By performing multi-dimensional evaluation on the energy storage system, potential problems can be discovered and solved in a timely manner to avoid damage to the energy storage system due to overuse or improper use. Combining real-time power generation power data and historical data, through precise prediction and control algorithms, precise prediction and control of the total output power of the photovoltaic system can be achieved; by adjusting the charge and discharge state of the energy storage system, this method can effectively utilize and store solar energy and reduce energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 FIG. is a schematic flowchart of a method for predicting and controlling photovoltaic power based on an energy storage system provided by an embodiment of the present invention.

[0043] Figure 2 FIG. is a schematic diagram of a system for predicting and controlling photovoltaic power based on an energy storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0045] As Figure 1 shown, an embodiment of the present invention provides a photovoltaic power prediction control method and system based on an energy storage system. The method includes the following steps:

[0046] Step 11: Collect photovoltaic data in real time and calculate the current power generation data based on the collected data;

[0047] Step 12: Obtain and dynamically set and adjust the current power demand target according to the power generation data collected in real time;

[0048] Step 13: Compare the actual power generation with the power demand target, and calculate the difference between the actual power generation and the power demand target through difference detection to obtain a difference result;

[0049] Step 14: According to the difference result, calculate the power generation of the energy storage system through momentum optimization to adjust the power generation;

[0050] Step 15: Evaluate the energy storage system in multiple dimensions before adjustment according to historical data to obtain the evaluated energy storage system;

[0051] Step 16: Adjust the charge and discharge state of the evaluated energy storage system according to the power generation to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0052] In the embodiment of the present invention, by collecting data of the photovoltaic system in real time, it is possible to quickly respond to changes in the environment and system state, and timely adjust the power generation and the charge and discharge strategy of the energy storage system. Dynamically setting the power demand based on real-time data can predict future power demands, thereby planning the charge and discharge plan of the energy storage system. According to the real-time power generation data and the dynamically set power demand, intelligently manage the generation, storage and use of energy, balance the energy supply and demand, and reduce energy waste. Through the discharge control and charge control of the energy storage system, the grid load can be effectively balanced, the grid fluctuation can be reduced, and the stability and reliability of the grid can be improved. A reasonable charge and discharge strategy can avoid overuse or idleness of energy storage devices, thereby extending their service life and reducing maintenance costs. By optimizing the operation of the photovoltaic system, the utilization efficiency of renewable energy can be improved, the development of clean energy can be promoted, and the dependence on traditional energy can be reduced.

[0053] In a preferred embodiment of the present invention, the above step 11 may include:

[0054] Step 111: Collect sensors at each node of the photovoltaic power station to obtain various operation data of the photovoltaic system;

[0055] Step 112: Preprocess various operation data of the photovoltaic system to obtain the preprocessed various operation data;

[0056] Step 113, passing the pre - processed operation data items through

[0057] for calculation to obtain the power generation power data, where is the real - time power generation power, is the output voltage, I is the output current, α is the temperature coefficient, T is the real - time temperature, is the reference temperature, β is the exponential coefficient of the light intensity, L is the real - time light intensity, is the reference light intensity, γ is the aging coefficient, A is the service life of the photovoltaic cell, δ is the occlusion coefficient, is the dirt coefficient, S is the quantization value of the occlusion degree, D is the ratio of the total area of the photovoltaic cells to the area of the occluded photovoltaic cells.

[0058] In the embodiment of the present invention, by collecting data from sensors at each node of the photovoltaic power station, various operation data of the photovoltaic system can be comprehensively obtained, which helps to realize the real - time monitoring of the operation state of the photovoltaic power station and ensure the normal operation of the system. Pre - processing the collected original data can remove noise, outliers and missing data, and improve the quality of the data. Using the pre - processed data and combining multiple parameters such as output voltage, output current, temperature coefficient, real - time temperature, light intensity, etc., the real - time power generation power of the photovoltaic system can be accurately calculated, which helps to understand the actual power generation capacity of the photovoltaic power station. Through continuous monitoring and data analysis, potential faults or abnormal conditions in the photovoltaic system can be found. Through reasonable operation strategies and maintenance plans, unnecessary losses and aging of photovoltaic equipment can be reduced, thereby extending the service life of the equipment and reducing the replacement cost.

[0059] In a specific embodiment of the present invention, the specific steps include:

[0060] Step 111, according to the layout and design of the photovoltaic power station, determine the key nodes to be monitored, such as photovoltaic modules, inverters, busbars, etc. Install corresponding sensors on each key node, such as temperature sensors, light sensors, current sensors and voltage sensors, etc., and the connection between the sensors and the data acquisition system is stable. Real - time collect various operation data of the photovoltaic system through the sensors, including temperature, light intensity, output voltage and current, etc. The collected data is transmitted to the data center or control system through data lines or wireless networks.

[0061] Step 112, store the original data collected from the sensors into the database of the data center or control system. Remove duplicate, invalid or abnormal data, and screen out the data related to power generation power calculation as needed, such as temperature, light intensity, output voltage and current, etc. Convert the data collected by different sensors into a unified format and unit. Standardize the data to eliminate the influence of different dimensions and orders of magnitude on data analysis.

[0062] Step 113: Use the preprocessed data to calculate the real-time power generation. The real-time power generation is equal to the output voltage multiplied by the output current multiplied by the comprehensive correction factor. Among them, the comprehensive correction factor takes into account the influence of various factors on the power generation. The comprehensive correction factor is equal to the temperature correction factor multiplied by the light intensity correction factor multiplied by the aging correction factor multiplied by the shading correction factor multiplied by the fouling correction factor.

[0063] In a preferred embodiment of the present invention, the above step 12 may include:

[0064] Step 121: Analyze the real-time power generation data to predict the future power demand;

[0065] Step 122: Dynamically set the power output demand of the photovoltaic system according to the future power demand and the current power generation data, so as to adjust the power output demand of the photovoltaic system.

[0066] In the embodiment of the present invention, by predicting the future power demand and dynamically adjusting the power output of the photovoltaic system, the photovoltaic system operates in an optimal manner, improving the energy utilization efficiency. Dynamically adjusting according to the predicted power demand helps to reduce the fluctuations and instability factors of the power grid, thereby enhancing the stability of the power grid. Through prediction and adjustment, the situations of over-generation or under-generation can be avoided, thus reducing energy waste. The optimized power output can improve the economic benefits of the photovoltaic system and reduce the operating costs. The dynamic adjustment strategy makes the management of the photovoltaic system more intelligent and automated, reducing the need for manual intervention.

[0067] In a specific embodiment of the present invention, the specific steps include:

[0068] Step 121: Collect the power generation data of the photovoltaic system in real time, including the current power generation, daily power generation, monthly power generation, etc. Sort out the historical power generation data to ensure the accuracy and integrity of the data. Use the time series analysis method to analyze the trends and periodic changes of the historical power generation data. Identify and consider the external factors affecting the power generation, such as weather conditions and equipment status. Based on the results of the time series analysis, predict the power generation demand in the future for a period of time (such as the next few hours or days).

[0069] Step 122: Compare the predicted future power demand with the current actual power generation data. Analyze the differences between the two, including the increasing or decreasing trends and magnitudes of the power demand. Based on the results of the comparative analysis, formulate an adjustment strategy for the power output of the photovoltaic system. If the predicted future power demand is higher than the current power generation, consider increasing the power output of the photovoltaic system; if the predicted future power demand is lower than the current power generation, consider reducing the power output of the photovoltaic system. According to the formulated adjustment strategy, dynamically set the power output requirement of the photovoltaic system by adjusting the parameters of the inverter in the photovoltaic system, changing the working angle or connection method of the photovoltaic panels, etc. After adjusting the power output of the photovoltaic system, monitor the operating status and power output of the system in real time.

[0070] In a preferred embodiment of the present invention, the above step 13 may include:

[0071] Step 131: Obtain the data of the power demand target by collecting the data of the actual power generation.

[0072] Step 132: Clean and sort the collected actual power generation and power demand target data to obtain the processed data.

[0073] Step 133: Align the actual power generation data and the power demand target data in terms of time to obtain the aligned data.

[0074] Step 134: Subtract the time-aligned actual power generation from the power demand target point by point, and calculate the difference value between the actual power generation and the power demand target through subtraction operation to obtain the difference result.

[0075] In the embodiments of the present invention, by analyzing the differences between the actual power generation and the power demand target, deficiencies in the power generation process can be discovered and corrected in a timely manner, the energy utilization efficiency can be improved to meet the actual power demand. By reducing waste and unnecessary losses in the power generation process, the cost of power production can be reduced. Adjusting the power generation strategy to match the power demand helps to maintain the stable operation of the power system and reduce risks such as power outages caused by imbalance between supply and demand.

[0076] In a specific embodiment of the present invention, the specific steps include:

[0077] Step 131: Obtain the actual power generation data and the power demand target data from the power grid monitoring system, smart meters, energy management systems, etc. Determine the data collection frequency as needed, such as collecting data once per minute, per hour, or per day. Use data acquisition software to automatically collect the actual power generation data and the power demand target data from the data sources. The collected data is stored in a secure and reliable database.

[0078] Step 132, Remove duplicate data, identify and handle missing values, identify and handle missing values by interpolation, deletion, or estimation, identify and handle outliers, such as extremely large or small values. Ensure that all data is in a unified format, such as the unification of date and time formats. Convert data types, compare with other reliable data sources, and verify the accuracy of the data.

[0079] Step 133, Check whether the timestamps of the two sets of data are consistent to ensure that they are recorded at the same time point. If the timestamps are inconsistent, interpolation or resampling is required to align the data. If the data sources are from different time zones, they need to be converted to a unified time zone for comparison. Verify whether the two sets of data are collected at the same time interval for fair comparison.

[0080] Step 134, Ensure that the actual power generation data and the power demand target data at each time point are correctly paired. For each time point, subtract the power demand target from the actual power generation to obtain the power difference at that time point. The power difference at each time point is recorded, which can form difference time series data. Analyze the difference results to find the time points with large power differences or large fluctuations.

[0081] In a preferred embodiment of the present invention, the above step 14 may include:

[0082] Step 141, Through the difference result,

[0083] Calculate the power generation of the energy storage system, represents the adjustment power amount at time ; represents the momentum coefficient, represents the previous adjustment power amount, represents the difference between the current target power and the actual power, represents the influence coefficient of the difference change rate, represents the influence coefficient of the non - linear difference term, represents the size of the time window for integration;

[0084] Step 142, Adjust the power generation according to the power generation of the energy storage system.

[0085] In the embodiments of the present invention, by carefully studying the previous power difference results, a more accurate power generation adjustment target can be set for the energy storage system, thereby reducing unnecessary power fluctuations and improving the stability of the power grid. A reasonable power generation adjustment target can reduce the overcharging and discharging of the energy storage system, extend the equipment life, and reduce the maintenance cost. Collecting energy storage system data provides a solid data foundation, making the adjustment more scientific and reasonable. By comprehensively considering various dynamic factors, the energy storage system can adjust the power generation more flexibly to adapt to the changing power grid demands. Complex calculation formulas ensure that the energy storage system can quickly respond to the power fluctuations of the power grid and maintain the stability of the power grid. The introduction of considerations such as non-linear difference terms and sign functions makes the adjustment strategy more refined and can better handle complex and changeable power grid conditions.

[0086] In a specific embodiment of the present invention, the specific steps include:

[0087] Step 141, according to the collected energy storage system data, through a series of calculations, obtain the power generation of the energy storage system. Clean and format the collected energy storage system data. Using the collected data and the previously set adjustment target, calculate the amount of power that needs to be adjusted at a certain time. Consider the momentum factor, that is, the impact of the previous adjustment power amount on the current adjustment. Calculate the difference between the current target power and the actual power. Introduce the influence coefficient of the difference change rate to reflect the speed and direction of the difference change. Consider the change rate of the difference over time t, and use the influence coefficient of the non-linear difference term to adjust the power generation. Apply the sign function to handle the positive and negative situations of the difference and adjust the correctness of the direction. Calculate the α-th power of the absolute value of the difference to adjust the influence of the difference size on the adjustment amount.

[0088] Step 142, based on the power generation calculated in Step 141, generate corresponding adjustment instructions. Send the adjustment instructions to the control system of the energy storage system. The control system adjusts the power generation of the energy storage system according to the instructions to ensure that the actual output matches the calculated power generation. Continuously monitor the state and actual output power of the energy storage system during the adjustment process.

[0089] In a preferred embodiment of the present invention, the above Step 15 may include:

[0090] Step 151, according to the historical operation data of the collected energy storage system, understand the operation characteristics and laws of the energy storage system;

[0091] Step 152, by understanding the operation characteristics and laws of the energy storage system, construct a multi-dimensional evaluation index system;

[0092] Step 153, through the multi-dimensional evaluation index system, evaluate each index to obtain the evaluated energy storage system.

[0093] In the embodiments of the present invention, emphasis is placed on collecting and analyzing the historical operating data of the energy storage system to gain an in-depth understanding of the actual operating conditions and characteristics of the energy storage system. Data-based decisions can reflect the real needs and potential problems of the system and improve the effectiveness and pertinence of optimization measures. By constructing a multi-dimensional evaluation index system, the performance of the energy storage system can be evaluated in all aspects, and various problems and bottlenecks in the system can be identified, including efficiency, reliability, economy and other aspects. By evaluating each indicator, the specific performance of the energy storage system in various aspects can be obtained. After this series of steps of evaluation and optimization, the overall performance of the energy storage system is expected to be significantly improved, which can not only improve the operating efficiency and reliability of the system, but also reduce operating costs. Through in-depth evaluation and optimization of the energy storage system, strong data support and experience reference can be provided for future system planning and expansion.

[0094] In a specific embodiment of the present invention, the specific steps include:

[0095] Step 151, comprehensively collect the operation data of the energy storage system in the past period of time. These data include but are not limited to charge and discharge efficiency, charge and discharge times, energy loss, temperature change, capacity decay, etc. By analyzing these data, we can understand the operation performance of the energy storage system under different conditions, such as performance changes under different environmental factors such as charge and discharge rates, temperature, humidity, etc.

[0096] Step 152, after understanding the operating characteristics and rules of the energy storage system, a multi-dimensional evaluation index system is constructed, which should cover all aspects of the energy storage system, such as efficiency, reliability, safety, economy, etc. Multiple specific evaluation indicators can be set up under each dimension, including charging and discharging efficiency, energy conversion efficiency, etc., reliability dimension, mean trouble-free operation time, fault recovery time, etc., and safety dimension, including overheat protection performance, overcharge and over-discharge protection performance.

[0097] Step 153, use the evaluation index system constructed above to comprehensively evaluate the energy storage system. For each evaluation index, a corresponding algorithm or formula can be used for calculation. For example, the charge and discharge efficiency can be calculated by comparing the amount of electricity charged and discharged, where the efficiency is equal to the amount of electricity discharged divided by the amount of electricity charged multiplied by 100%. The mean trouble-free operation time can be calculated by counting the average operation time of the energy storage system between two faults. After completing the evaluation of all indicators, an evaluation report that comprehensively reflects the performance of the energy storage system can be obtained.

[0098] In a preferred embodiment of the present invention, the above step 16 may include:

[0099] Step 161, acquiring power generation data by real-time monitoring of the power generation of the photovoltaic system;

[0100] Step 162: Determine the adjustment direction of the energy storage system based on the power generation data and the difference between the actual power generation and the power demand target.

[0101] Step 163: Calculate the adjustment amount of the energy storage system according to the adjustment direction of the energy storage system, and adjust the charge and discharge state of the energy storage system to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0102] In the embodiment of the present invention, the fast response ability of the energy storage system can help correct voltage fluctuations and frequency deviations in the output of the photovoltaic system, thereby maintaining or improving the power quality supplied to the load or the power grid. In the case of a power outage caused by a grid fault or natural disaster, the fully charged energy storage system can quickly provide backup power, continuously supply power to the load, and improve the emergency response ability of the system. The energy storage system can discharge during peak hours to reduce the demand on the grid, and at the same time charge during low-demand hours to balance the grid load and support the demand-side management strategy of the power system. By intelligently managing the energy storage system, the photovoltaic system can more easily meet the grid connection standards, promote the optimization of distributed energy resources. The operation of the photovoltaic system and the energy storage system can reduce the demand for fossil fuel power generation and reduce greenhouse gas emissions.

[0103] In a specific embodiment of the present invention, the specific steps include:

[0104] Step 161: Obtain the real-time power generation data of the photovoltaic system through the direct measurement method. The direct measurement method directly reads the current and voltage values by installing measuring equipment at the output end of the photovoltaic system, and calculates the real-time power accordingly. For the direct measurement method, the accuracy and reliability of the equipment, as well as the real-time data transmission ability, are required, which includes steps such as data cleaning, sorting, visualization, and detection and processing of outliers.

[0105] Step 162: Compare and analyze the real-time monitored power generation data of the photovoltaic system with the preset power demand target. This involves the real-time processing and analysis ability of power data to timely detect the difference between the power output and the demand target. According to the comparison result, analyze the specific reasons for the difference between the power output and the demand target. These reasons may include changes in weather conditions, performance fluctuations of photovoltaic modules, charge and discharge states of the energy storage system, etc. Determine the adjustment direction. Based on the analysis result of the difference reasons, determine the adjustment direction of the energy storage system. For example, if the power output is lower than the demand target, adjust the energy storage system to increase the discharge amount to make up for the power gap. On the contrary, if the power output is higher than the demand target, it may be necessary to adjust the energy storage system to increase the charge amount to store the excess electric energy.

[0106] Step 163: Based on the determined adjustment direction and actual requirements, evaluate the current state of the energy storage system and predict the changes in power demand over a period of time in the future, and calculate the specific amount that the energy storage system needs to be adjusted. According to the calculated adjustment amount, perform corresponding charge and discharge operation adjustments on the energy storage system. During and after the adjustment process, continuously monitor the power generation of the photovoltaic system and the state changes of the energy storage system, and make necessary further adjustments according to the actual situation.

[0107] As Figure 2 shown, an embodiment of the present invention also provides a photovoltaic power prediction and control system based on an energy storage system, including:

[0108] An acquisition module that acquires photovoltaic data in real time and calculates the current power generation data based on the acquired data; based on the power generation data acquired in real time, dynamically sets and adjusts the current power demand target.

[0109] A comparison module for comparing the actual power generation with the power demand target, calculating the difference between the actual power generation and the power demand target through difference detection to obtain a difference result; according to the difference result, calculating the power generation of the energy storage system through momentum optimization to adjust the power generation.

[0110] A processing module for performing multi-dimensional evaluation on the energy storage system before adjustment based on historical data to obtain the evaluated energy storage system; adjusting the charge and discharge state of the evaluated energy storage system according to the power generation to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0111] The above is the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A photovoltaic power prediction and control method based on an energy storage system, characterized in that: The method comprises: Collect photovoltaic data in real time and calculate the current power generation data based on the collected data, including: Collect data from the photovoltaic power station to obtain the operating data of the photovoltaic system; pre-process the various operating data of the photovoltaic system to obtain the pre-processed operating data; and Calculate and obtain the power generation data, where is the real-time power generation, is the output voltage, I is the output current, α is the temperature coefficient, T is the real-time temperature, is the reference temperature, β is the exponential coefficient of light intensity, L is the real-time light intensity, is the reference light intensity, γ is the aging coefficient, A is the service life of the photovoltaic cell, δ is the shading coefficient, is the fouling coefficient, S is the quantitative value of the degree of shading, and D is the total area of ​​the photovoltaic cells divided by the area of ​​the photovoltaic cells that are shaded; Dynamically set and adjust the current power demand target based on the real-time collected power generation data, including analyzing the real-time power generation data to predict future power demand; dynamically set the power output demand of the photovoltaic system based on the future power demand and the current power generation data to adjust the power output demand of the photovoltaic system; Comparing the actual generated power with the power demand target, and calculating the difference between the actual generated power and the power demand target through difference detection to obtain a difference result, including: obtaining the power demand target data by collecting the actual generated power data; cleaning and sorting the collected actual generated power and power demand target data to obtain processed data; aligning the actual generated power data and the power demand target data in time to obtain aligned data; subtracting the time-aligned actual generated power from the power demand target point by point, calculating the difference value between the actual generated power and the power demand target through subtraction operation, and obtaining a difference result; According to the difference results, the power generation of the energy storage system is calculated by momentum optimization to adjust the power generation, including: according to the difference results, The power generation power of the energy storage system is calculated. Indicates at time The amount of power adjustment, represents the momentum coefficient, Indicates the last adjusted power. Indicates the difference between the current target power and the actual power; The influence coefficient representing the rate of change of the difference, Indicates the influence coefficient of the nonlinear difference term; the power generation is adjusted according to the power generation of the energy storage system; Based on historical data, a multi-dimensional evaluation is conducted on the energy storage system before adjustment to obtain an evaluated energy storage system; According to the generated power, the charging and discharging status of the evaluated energy storage system is adjusted to achieve accurate prediction and control of the total output power of the photovoltaic system.

2. A photovoltaic power prediction and control method based on an energy storage system according to claim 1, characterized in that: Based on historical data, a multi-dimensional evaluation of the energy storage system is conducted before adjustment to obtain the evaluated energy storage system, including: Obtain the operating characteristics and laws of the energy storage system by collecting historical operating data of the energy storage system; Construct a multi-dimensional evaluation index system based on the operating characteristics and laws of the energy storage system; Through a multi-dimensional evaluation index system, each indicator is evaluated to obtain the evaluated energy storage system.

3. A photovoltaic power prediction and control method based on an energy storage system according to claim 2, characterized in that: According to the generated power, the charging and discharging status of the evaluated energy storage system is adjusted to achieve accurate prediction and control of the total output power of the photovoltaic system, including: Obtain power generation data by real-time monitoring of the power generation of the photovoltaic system; Determine the adjustment direction of the energy storage system based on the power generation data and the difference between the actual power generation and the power demand target; According to the adjustment direction of the energy storage system, the adjustment amount of the energy storage system is obtained, and the charging and discharging state of the energy storage system is adjusted to achieve accurate prediction and control of the total output power of the photovoltaic system.

4. A photovoltaic power prediction control system based on an energy storage system, the system implementing the method as claimed in any one of claims 1 to 3, characterized in that: include: The acquisition module collects photovoltaic data in real time and calculates the current power generation data based on the collected data; based on the real-time collected power generation data, the current power demand target is dynamically set and adjusted; A comparison module is used to compare the actual power generation with the power demand target, and calculate the difference between the actual power generation and the power demand target through difference detection to obtain a difference result; based on the difference result, the power generation of the energy storage system is calculated through momentum optimization to adjust the power generation; The processing module is used to conduct a multi-dimensional evaluation of the energy storage system before adjustment based on historical data to obtain the energy storage system after evaluation; according to the power generation power, the charging and discharging state of the energy storage system after evaluation is adjusted to achieve accurate prediction and control of the total output power of the photovoltaic system.

5. A computing device, characterized in that: include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 3 when executed by a processor.

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