Photovoltaic power prediction control method and system based on energy storage system

Abstract

The invention provides a photovoltaic power prediction control method and system based on an energy storage system, and relates to the technical field of data science and intelligent analysis, and the method comprises the steps: collecting photovoltaic data in real time, and calculating the current generation power data according to the collected data; according to the generated power data collected in real time, obtaining a target for dynamically setting and adjusting the current power demand; comparing the actual generated power with a 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; and judging whether the current generated power meets a preset power demand or not according to the dynamically set power demand. According to the invention, by collecting and processing the photovoltaic system data in real time and dynamically adjusting the charging and discharging control of the energy storage system, the stability and efficiency of photovoltaic power generation are improved.

Description

Technical Field

[0001] The present invention relates to the field of data science and intelligent analysis technology, and in particular 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 cleanliness, high efficiency, and renewable energy, and has been rapidly developed around the world. Photovoltaic power generation is easily affected by the external environment, and there are problems such as unstable output and large fluctuations. This instability poses a challenge to the stability and reliability of the power system. Energy storage technology can reduce the power fluctuation range and smooth power output. By storing and releasing electrical energy, the energy storage system can smooth out the fluctuation 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 energy storage systems. Therefore, a more efficient and accurate photovoltaic power prediction control method and system based on energy storage systems is needed.

[0003] In response to the above problems, the industry has begun to explore a photovoltaic power prediction control method and system based on energy storage systems. By comprehensively considering the power generation prediction of photovoltaic power stations, the status of energy storage systems, and the needs of the power auxiliary service market, the effective smoothing and accurate prediction of photovoltaic power fluctuations can improve the operating efficiency of photovoltaic power stations, reduce the difficulty of grid dispatching, 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 accurate prediction and efficient control of photovoltaic power by real-time acquisition and processing of photovoltaic system data and dynamic adjustment of 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 is provided, 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 real-time collected power generation data, the current power demand target is dynamically set and adjusted;

[0008] The actual power generation is compared with the power demand target, and the difference between the actual power generation and the power demand target is calculated through difference detection to obtain the difference result;

[0009] According to the difference results, the power generation of the energy storage system is calculated by momentum optimization to adjust the power generation;

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

[0011] 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.

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

[0013] Preprocess various operation data of the photovoltaic system to obtain various operation data after preprocessing;

[0014] The pre-processed operation data are 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 factor, S is the total area of ​​the photovoltaic cells divided by the area of ​​the photovoltaic cells that are shaded, and D is the total area of ​​the photovoltaic cells divided by the area of ​​the photovoltaic cells that are shaded.

[0015] Furthermore, real-time power generation data is analyzed to predict future power demand;

[0016] According to future power demand and current power generation data, the power output demand of the photovoltaic system is dynamically set to adjust the power output demand of the photovoltaic system.

[0017] Furthermore, by collecting data on actual power generation, data on power demand targets are obtained;

[0018] Clean and organize the collected actual power generation and power demand target data to obtain processed data;

[0019] Aligning the actual power generation data and the power demand target data in time to obtain aligned data;

[0020] The time-aligned actual power generation and the power demand target are subtracted point by point, and the difference between the actual power generation and the power demand target is calculated through subtraction to obtain the difference result.

[0021] Furthermore, by using 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, represents the influence coefficient of the nonlinear difference term, Indicates the time window size of the integration;

[0022] The power generation power is adjusted according to the power generation power of the energy storage system.

[0023] Furthermore, by collecting historical operation data of the energy storage system, the operating characteristics and laws of the energy storage system can be understood;

[0024] By understanding the operating characteristics and laws of energy storage systems, a multi-dimensional evaluation index system is constructed;

[0025] Through a multi-dimensional evaluation index system, each indicator is evaluated to obtain the evaluated energy storage system.

[0026] Furthermore, the power generation data can be obtained by real-time monitoring of the power generation of the photovoltaic system;

[0027] 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;

[0028] According to the adjustment direction of the energy storage system, the adjustment amount of the energy storage system is calculated, 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.

[0029] According to the power generation of the energy storage system, the power generation of the energy storage system is adjusted through the control of the energy storage system.

[0030] In a second aspect, a photovoltaic power prediction control system based on an energy storage system comprises:

[0031] 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;

[0032] The 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 the difference result; according to the difference result, the power generation of the energy storage system is calculated through momentum optimization to adjust the power generation;

[0033] 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.

[0034] According to a third aspect, a computing device includes:

[0035] one or more processors;

[0036] The storage device is used to store 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 described.

[0037] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0038] The above solution of the present invention includes at least the following beneficial effects:

[0039] By collecting photovoltaic data in real time and dynamically adjusting power demand targets, the photovoltaic system can adapt to environmental changes more intelligently, thereby improving power generation efficiency; through difference detection and optimization calculations, the power generation power of the energy storage system can be accurately adjusted to ensure that the photovoltaic system always operates in the optimal state. By conducting a multi-dimensional evaluation of the energy storage system, potential problems can be discovered and resolved in a timely manner to avoid damage to the energy storage system due to excessive or improper use. Combining real-time power generation data and historical data, through precise prediction and control algorithms, the total output power of the photovoltaic system can be accurately predicted and controlled; 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

[0040] Figure 1 It is a flow chart of a photovoltaic power prediction and control method based on an energy storage system provided in an embodiment of the present invention.

[0041] Figure 2 It is a schematic diagram of a photovoltaic power prediction control system based on an energy storage system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The exemplary embodiments of the present disclosure are described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying 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, some embodiments are provided to enable a more thorough understanding of the present disclosure and to enable the scope of the present disclosure to be fully conveyed to those skilled in the art.

[0043] like Figure 1 As shown, an embodiment of the present invention provides a photovoltaic power prediction control method and system based on an energy storage system, the method comprising the following steps:

[0044] Step 11, collecting photovoltaic data in real time, and calculating the current power generation data based on the collected data;

[0045] Step 12, dynamically setting and adjusting the current power demand target according to the real-time collected power generation data;

[0046] Step 13, 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;

[0047] Step 14, according to the difference result, the power generation power of the energy storage system is calculated by momentum optimization to adjust the power generation power;

[0048] Step 15, based on historical data, a multi-dimensional evaluation is performed on the energy storage system before adjustment to obtain an evaluated energy storage system;

[0049] Step 16, according to the generated 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.

[0050] In an embodiment of the present invention, by collecting data from the photovoltaic system in real time, it is possible to quickly respond to environmental changes and changes in system status, and timely adjust the power generation and charging and discharging strategies of the energy storage system. By dynamically setting power requirements based on real-time data, it is possible to predict future power requirements, thereby planning the charging and discharging plan of the energy storage system. According to the real-time power generation data and the dynamically set power requirements, the generation, storage and use of energy are intelligently managed, the supply and demand of energy are balanced, and energy waste is reduced. Through the discharge control and charging control of the energy storage system, the grid load can be effectively balanced, grid fluctuations can be reduced, and the stability and reliability of the grid can be improved. Reasonable charging and discharging strategies can avoid excessive use or idleness of energy storage equipment, thereby extending its 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 dependence on traditional energy can be reduced.

[0051] In a preferred embodiment of the present invention, the above step 11 may include:

[0052] Step 111, collecting data from sensors at each node of the photovoltaic power station to obtain various operating data of the photovoltaic system;

[0053] Step 112, preprocessing various operation data of the photovoltaic system to obtain various operation data after preprocessing;

[0054] Step 113: The pre-processed operation data is 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 dirt 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.

[0055] In an embodiment of the present invention, by collecting sensors at each node of a photovoltaic power station, various operating data of the photovoltaic system can be fully acquired, which helps to realize real-time monitoring of the operating status of the photovoltaic power station and ensure the normal operation of the system. Preprocessing the collected raw data can remove noise, outliers and missing data to improve the quality of the data. Using the preprocessed data, combined with multiple parameters such as output voltage, output current, temperature coefficient, real-time temperature, light intensity, etc., the real-time power generation of the photovoltaic system can be accurately calculated. It helps to understand the actual power generation capacity of a photovoltaic power station. Through continuous monitoring and data analysis, potential faults or abnormal conditions in the photovoltaic system can be discovered. 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 replacement costs.

[0056] In a specific embodiment of the present invention, the specific steps include:

[0057] Step 111, according to the layout and design of the photovoltaic power station, determine the key nodes that need to be monitored, such as photovoltaic modules, inverters, junction boxes, 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. The sensors collect various operating data of the photovoltaic system in real time, including temperature, light intensity, output voltage and current, etc. The collected data is transmitted to the data center or control system via data lines or wireless networks.

[0058] Step 112, the raw data collected from the sensor is stored in the database of the data center or control system. Duplicate, invalid or abnormal data is removed and data related to power generation calculation is screened out as needed, such as temperature, light intensity, output voltage and current, etc. Data collected by different sensors are converted into a unified format and unit. The data is standardized to eliminate the impact of different dimensions and orders of magnitude on data analysis.

[0059] 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 coefficient, wherein the comprehensive correction coefficient takes into account the influence of various factors on the power generation, and the comprehensive correction coefficient is equal to the temperature correction coefficient multiplied by the light correction coefficient multiplied by the aging correction coefficient multiplied by the shading correction coefficient multiplied by the dirt correction coefficient.

[0060] In a preferred embodiment of the present invention, the above step 12 may include:

[0061] Step 121, analyzing the real-time power generation data to predict future power demand;

[0062] Step 122 , dynamically setting 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.

[0063] In the embodiment of the present invention, by predicting future power demand and dynamically adjusting the power output of the photovoltaic system, the photovoltaic system operates in an optimal manner to improve energy utilization efficiency. Dynamic adjustment based on the predicted power demand helps to reduce the fluctuation and instability of the power grid, thereby enhancing the stability of the power grid. Through prediction and adjustment, over-generation or under-generation can be avoided, thereby reducing energy waste. Optimized power output can improve the economic benefits of the photovoltaic system and reduce operating costs. The dynamic adjustment strategy makes the management of the photovoltaic system more intelligent and automated, reducing the need for manual intervention.

[0064] In a specific embodiment of the present invention, the specific steps include:

[0065] 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. Organize the historical power generation data to ensure the accuracy and completeness of the data. Use the time series analysis method to analyze the trend and periodic changes of the historical power generation data. Identify and consider the external factors that affect 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 (such as the next few hours or days).

[0066] Step 122, compare the predicted future power demand with the current actual power generation data. Analyze the difference between the two, including the increase and decrease trend and amplitude of the power demand. According to 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, the power output demand of the photovoltaic system is dynamically set by adjusting the parameters of the inverter in the photovoltaic system, changing the working angle or connection method of the photovoltaic panel, etc. After adjusting the power output of the photovoltaic system, monitor the operating status and power output of the system in real time.

[0067] In a preferred embodiment of the present invention, the above step 13 may include:

[0068] Step 131, acquiring data of power demand target by collecting data of actual power generation;

[0069] Step 132, cleaning and sorting the collected actual power generation and power demand target data to obtain processed data;

[0070] Step 133, aligning the actual power generation data and the power demand target data in time to obtain aligned data;

[0071] Step 134 , subtracting the time-aligned actual generated power from the power demand target point by point, calculating the difference between the actual generated power and the power demand target by subtraction operation, and obtaining a difference result.

[0072] In the embodiment of the present invention, by analyzing the difference 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, and 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 maintain the stable operation of the power system and reduce the risk of power outages caused by imbalance between supply and demand.

[0073] In a specific embodiment of the present invention, the specific steps include:

[0074] Step 131, obtain actual power generation data and power demand target data from the power grid monitoring system, smart meter, energy management system, etc., and determine the data collection frequency as needed, such as collecting data once every minute, every hour, or every day. Use data acquisition software to automatically collect actual power generation data and power demand target data from data sources. The collected data is stored in a secure and reliable database.

[0075] 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 values ​​that are too large or too small. Ensure that all data is in a unified format, such as the unified date and time format. Convert data types and compare with other reliable data sources to verify the accuracy of the data.

[0076] Step 133, check whether the timestamps of the two sets of data are consistent, and ensure that they are recorded at the same time point. If the timestamps are inconsistent, the data needs to be aligned by interpolation or resampling. 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 in the same time interval and the comparison is fair.

[0077] Step 134 ensures that the actual power generation data and the power demand target data at each time point are correctly matched. For each time point, the actual power generation minus the power demand target is used to obtain the power difference at that time point. The power difference at each time point is recorded to form a difference time series data. The difference results can be analyzed to find the time points with large power differences or large fluctuations.

[0078] In a preferred embodiment of the present invention, the above step 14 may include:

[0079] Step 141, through the difference results, through 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, represents the influence coefficient of the nonlinear difference term, Indicates the time window size of the integration;

[0080] Step 142, adjusting the power generation according to the power generation of the energy storage system.

[0081] In an embodiment 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. Reasonable power generation adjustment targets can reduce excessive charging and discharging of the energy storage system, extend equipment life, and reduce maintenance costs. Collecting energy storage system data provides a solid data foundation, making adjustments more scientific and reasonable. By comprehensively considering a variety of dynamic factors, the energy storage system can more flexibly adjust the power generation to adapt to the ever-changing power grid needs. Complex calculation formulas ensure that the energy storage system can quickly respond to power fluctuations in the power grid and maintain the stability of the power grid. The introduction of nonlinear difference terms and sign functions makes the adjustment strategy more sophisticated and better able to handle complex and changeable power grid conditions.

[0082] In a specific embodiment of the present invention, the specific steps include:

[0083] Step 141, based on the collected energy storage system data, the generated power of the energy storage system is obtained through a series of calculations to 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 time. Consider the momentum factor, that is, the impact of the last power adjustment on this 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 rate of change of the difference over time t, and use the influence coefficient of the nonlinear difference term to adjust the generated power. Apply the sign function to handle the positive and negative situations of the difference and adjust the correctness of the direction. Calculate the absolute value of the difference to the αth power to adjust the impact of the difference size on the adjustment amount.

[0084] Step 142: Generate a corresponding adjustment instruction based on the power generation calculated in step 141. The adjustment instruction is sent to the control system of the energy storage system. The control system adjusts the power generation of the energy storage system according to the instruction to ensure that the actual output matches the calculated power generation. The state and actual output power of the energy storage system are continuously monitored during the adjustment process.

[0085] In a preferred embodiment of the present invention, the above step 15 may include:

[0086] Step 151, understanding the operating characteristics and laws of the energy storage system based on the collected historical operating data of the energy storage system;

[0087] Step 152, building a multi-dimensional evaluation index system by understanding the operating characteristics and laws of the energy storage system;

[0088] Step 153, evaluate each indicator through a multi-dimensional evaluation indicator system to obtain an evaluated energy storage system.

[0089] 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.

[0090] In a specific embodiment of the present invention, the specific steps include:

[0091] 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.

[0092] 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.

[0093] 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.

[0094] In a preferred embodiment of the present invention, the above step 16 may include:

[0095] Step 161, acquiring power generation data by real-time monitoring of the power generation of the photovoltaic system;

[0096] Step 162, determining the adjustment direction of the energy storage system according to the power generation data and the difference between the actual power generation and the power demand target;

[0097] Step 163, according to the adjustment direction of the energy storage system, calculate the adjustment amount of the energy storage system, and adjust the charging and discharging state of the energy storage system to achieve accurate prediction and control of the total output power of the photovoltaic system.

[0098] In an embodiment of the present invention, the rapid response capability 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 quality of power supplied to the load or the power grid. In the event of a power outage due to a power grid failure or natural disaster, a fully charged energy storage system can quickly provide backup power and continuous power supply to the load, thereby improving the emergency response capability of the system. The energy storage system can discharge during peak hours to reduce demand on the grid, while charging 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, and 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.

[0099] In a specific embodiment of the present invention, the specific steps include:

[0100] Step 161, obtain the real-time power generation data of the photovoltaic system through direct measurement method. The direct measurement method directly reads the current and voltage values ​​by installing measurement equipment at the output end of the photovoltaic system, and calculates the real-time power based on this. For the direct measurement method, the accuracy and reliability of the equipment and the real-time transmission capability of the data are required, which includes steps such as data cleaning, sorting, visualization, and detection and processing of abnormal values.

[0101] Step 162, compare and analyze the real-time monitored photovoltaic system power generation data with the preset power demand target. This involves the ability to process and analyze power data in real time, so as to promptly discover the difference between the power output and the demand target, and analyze the specific reasons for the difference between the power output and the demand target based on the comparison results. These reasons may include changes in weather conditions, performance fluctuations of photovoltaic components, and the charging and discharging status of the energy storage system. Determine the adjustment direction, based on the analysis results of the reasons for the difference, 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 to make up for the power gap, conversely, if the power output is higher than the demand target, it may be necessary to adjust the energy storage system to increase the charging capacity to store excess electrical energy.

[0102] Step 163, based on the determined adjustment direction and actual needs, the current state of the energy storage system is evaluated and the power demand change in the future is predicted, and the specific value of the energy storage system that needs to be adjusted is calculated. Based on the calculated adjustment value, the energy storage system is adjusted for charging and discharging operations accordingly. During and after the adjustment, the power generation of the photovoltaic system and the state changes of the energy storage system are continuously monitored, and further adjustments are made as necessary according to the actual situation.

[0103] like Figure 2 The embodiment of the present invention further provides a photovoltaic power prediction control system based on an energy storage system, comprising:

[0104] 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;

[0105] The 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 the difference result; according to the difference result, the power generation of the energy storage system is calculated through momentum optimization to adjust the power generation;

[0106] 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.

[0107] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection 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; Dynamically set and adjust the current power demand target based on the real-time collected power generation data; The actual power generation is compared with the power demand target, and the difference between the actual power generation and the power demand target is calculated through difference detection to obtain the 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; 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: Collect photovoltaic data in real time and calculate the current power generation data based on the collected data, including: Collect data from photovoltaic power stations to obtain the operating data of the photovoltaic system; Preprocess various operation data of the photovoltaic system to obtain various operation data after preprocessing; The pre-processed operation data are 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 factor, S is the total area of ​​the photovoltaic cells divided by the area of ​​the photovoltaic cells that are shaded, and D is the total area of ​​the photovoltaic cells divided by the area of ​​the photovoltaic cells that are shaded.

3. A photovoltaic power prediction and control method based on an energy storage system according to claim 2, characterized in that: Based on the real-time collected power generation data, the current power demand is dynamically set and adjusted: Analyze real-time power generation data to predict future power demand; According to future power demand and current power generation data, the power output demand of the photovoltaic system is dynamically set to adjust the power output demand of the photovoltaic system.

4. A photovoltaic power prediction and control method based on an energy storage system according to claim 3, characterized in that: The actual power generation is compared with the power demand target, and the difference between the actual power generation and the power demand target is calculated through difference detection to obtain the difference results, including: By collecting data on actual power generation, data on power demand targets can be obtained; Clean and organize the collected actual power generation and power demand target data to obtain processed data; Aligning the actual power generation data and the power demand target data in time to obtain aligned data; The time-aligned actual power generation and the power demand target are subtracted point by point, and the difference between the actual power generation and the power demand target is calculated through subtraction to obtain the difference result.

5. A photovoltaic power prediction and control method based on an energy storage system according to claim 4, characterized in that: According to the difference results, the power generation of the energy storage system is calculated through momentum optimization to adjust the power generation, including: According to the difference results, The power generation 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, represents the influence coefficient of the nonlinear difference term, Indicates the time window size of the integration; The power generation power is adjusted according to the power generation power of the energy storage system.

6. A photovoltaic power prediction and control method based on an energy storage system according to claim 5, 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.

7. A photovoltaic power prediction and control method based on an energy storage system according to claim 6, 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.

8. A photovoltaic power prediction control system based on an energy storage system, the system implementing the method according to any one of claims 1 to 7, 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; The 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 the difference result; according to 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.

9. 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 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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