Photovoltaic power generation energy storage control method and system

Abstract

The invention discloses a photovoltaic power generation energy storage control method and system, and relates to the technical field of photovoltaic energy storage control, and the system calculates a net energy difference value, charging power and discharging power in real time through an energy storage self-balance optimization module by constructing an energy storage self-balance optimization algorithm model CNMX and utilizing a power generation storage data set extracted from a time sequence database. And the real-time energy storage balance analysis module further calculates an energy balance difference according to the outputs, performs preliminary comparison and evaluation with a preset balance threshold value, and analyzes the energy storage balance state of the current system. The optimized system is more accurate in calculation, regulation and control processes, energy storage and discharge strategies can be flexibly adjusted according to actual conditions, and reasonable distribution of energy among photovoltaic power generation, an energy storage system and a load is ensured. The system not only improves the energy utilization efficiency, but also enhances the adaptive capacity of the system in a complex and dynamic environment.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage control, and in particular to a photovoltaic power generation energy storage control method and system. Background Art

[0002] Photovoltaic power generation and energy storage control systems belong to the field of new energy, especially the application scope of solar power generation and energy storage technology. With the growing global demand for renewable energy, photovoltaic power generation has become an important source of clean energy. However, due to the instability of solar radiation, photovoltaic power generation is characterized by volatility and intermittency, resulting in poor stability in power supply. Therefore, in order to effectively utilize photovoltaic power generation and improve its stability, energy storage technology has gradually been applied to photovoltaic systems. Specifically, the role of the photovoltaic power generation and energy storage control system is to coordinate the energy management of photovoltaic power generation and energy storage devices to ensure that the system can stably supply power to the load when photovoltaic power generation fluctuates.

[0003] At present, the combination of photovoltaic power generation and energy storage systems faces a series of challenges. Existing photovoltaic energy storage systems usually have deficiencies in energy management, which is mainly reflected in the difficulty in maintaining the balance between energy supply and demand in real time, resulting in frequent energy waste or shortage. These systems often lack accurate analysis and real-time regulation of the energy balance state, and cannot make effective adaptive adjustments based on load demand, photovoltaic power generation, and the state of the energy storage system. In addition, when dealing with energy fluctuations, the existing system lacks intelligent self-balancing optimization capabilities, which makes it impossible to fully utilize the efficiency of the energy storage system and achieve the optimal energy utilization effect.

[0004] When the system cannot effectively cope with the fluctuation of photovoltaic power generation and the change of load demand, it will cause the energy storage system to overcharge or over-discharge, thus affecting the battery life and reducing the overall efficiency of the system. At the same time, the imbalance of energy management may also lead to the instability of the power grid, increase the maintenance cost of the system, and have a negative impact on the reliability of power supply. In summary, the self-balancing ability of the existing photovoltaic energy storage system needs to be improved in order to better cope with various challenges in energy management. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a photovoltaic power generation energy storage control method and system, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a photovoltaic data detection module, an energy storage self-balancing optimization module, a real-time energy storage balance analysis module, an energy storage adaptive control module and an optimization evaluation module;

[0007] The photovoltaic data detection module is integrated with sensors, photovoltaic power generation and energy storage systems, and smart meters to collect photovoltaic power generation and storage data in real time, and then transmits the collected photovoltaic power generation and storage data to the preprocessing unit for data processing, obtains power generation and storage data sets, and constructs a database for storage;

[0008] The energy storage self-balancing optimization module constructs an energy storage self-balancing optimization algorithm model CNMX, inputs the acquired power generation storage data set into the energy storage self-balancing optimization algorithm model CNMX, and outputs the net energy difference, charging power and discharging power;

[0009] The real-time energy storage balance analysis module is used to comprehensively calculate the output energy balance difference based on the output net energy difference, charging power and discharging power, and to perform preliminary comparative evaluation on the preset balance threshold and the output energy balance difference to analyze the energy storage balance of the current photovoltaic power generation energy storage system;

[0010] The energy storage adaptive control module is used to trigger execution according to the preliminary comparison and evaluation results, calculate the charge state index of the photovoltaic energy storage by constructing a charge state adjustment formula, and calculate and output the target charge state index according to the energy balance difference, and then perform a secondary comparison and evaluation on the obtained charge state index and the target charge state index, and generate an adaptive control mechanism;

[0011] The optimization evaluation module is used to update and optimize the energy balance difference obtained through a machine learning algorithm based on historical operation data, obtain the optimized energy storage balance difference, and calculate and output the energy storage self-balancing index according to the optimized energy storage balance difference, analyze the self-balancing effect according to the output result of the energy storage self-balancing index, and generate an early warning prompt.

[0012] Preferably, the photovoltaic data detection module includes an integrated acquisition unit, a data transmission unit, a data processing unit and a data storage unit;

[0013] The integrated collection unit is integrated with the photovoltaic power generation sensor, the photovoltaic power generation energy storage system and the smart meter, and sets the collection frequency at a time interval of thirty minutes to collect photovoltaic power generation storage data in real time;

[0014] The photovoltaic power generation sensor includes a photovoltaic inverter and a reflectivity sensor;

[0015] The photovoltaic power generation and energy storage system includes an energy management system EMS and a battery management system BMS;

[0016] The photovoltaic power generation storage data includes photovoltaic power generation data, energy storage data and load data;

[0017] The photovoltaic power generation data includes photovoltaic power generation power and bottom edge reflectivity;

[0018] The energy storage data includes the current stored energy of the energy storage system, the state of charge of the energy storage system, the battery internal resistance, the battery external resistance and the battery temperature gradient;

[0019] The battery management system BMS of the energy storage system is usually equipped with a power metering module, which can calculate the storage energy stored in the battery in real time;

[0020] The measurement module integrated in the battery management system BMS can calculate through historical charge and discharge data and real-time measurement data;

[0021] The battery management system BMS usually integrates the internal resistance measurement function, and an external impedance analyzer can also be used to regularly measure the battery internal resistance;

[0022] If the temperature sensors are installed at different locations of the battery pack, the battery temperature gradient can be calculated by the temperature difference;

[0023] The load data includes load demand power and power demand fluctuation index;

[0024] Monitor the load demand power in real time through the smart meter on the load side;

[0025] By collecting load power data over a long period of time and calculating the standard deviation or fluctuation range, we can measure demand volatility and obtain power demand fluctuation indicators;

[0026] The data transmission unit transmits the collected photovoltaic power generation storage data to the data processing unit through the wireless communication LoRa protocol.

[0027] Preferably, the data processing unit is used to receive photovoltaic power generation storage data in real time, and use linear interpolation on the photovoltaic power generation storage data to infer missing values ​​based on existing photovoltaic power generation storage data, and then normalize the photovoltaic power generation storage data and summarize them to obtain a power generation storage data set;

[0028] The data storage unit is used to construct a time series database, mark the power generation storage data set obtained after preprocessing with a timestamp, and store it in the time series database according to a time series.

[0029] Preferably, the energy storage self-balancing optimization module is used to construct an energy storage self-balancing optimization algorithm model CNMX, and extract the power generation storage data set stored in the time series database in real time, input it into the energy storage self-balancing optimization algorithm model CNMX, and output the net energy difference, charging power and discharging power in turn.

[0030] Preferably, the real-time energy storage balance analysis module includes a real-time energy storage balance calculation unit and an energy storage balance analysis unit;

[0031] The real-time energy storage balance calculation unit is used to comprehensively calculate the output energy balance difference based on the output net energy difference, charging power and discharging power;

[0032] The energy storage balance analysis unit performs a preliminary comparative evaluation of the preset balance threshold value P by the user and the output energy balance difference to analyze the current photovoltaic power generation energy storage balance state. The specific evaluation contents are as follows;

[0033] When the energy balance difference is greater than the balance threshold, it indicates that the energy is sufficient and an energy storage adjustment instruction is sent;

[0034] When the energy balance difference = balance threshold, it means that the energy is normal and no adjustment instruction is needed;

[0035] When the energy balance difference is less than the balance threshold, it indicates that the energy is insufficient and an energy storage supplement adjustment instruction is sent.

[0036] Preferably, the energy storage adaptive control module includes a new state of charge calculation unit, a target state of charge calculation unit and a dynamic adjustment unit;

[0037] The new state of charge calculation unit is triggered by receiving an energy storage adjustment instruction and an energy storage supplement adjustment instruction. By constructing a state of charge adjustment formula, the obtained charging power and discharging power are input into the algorithm formula to calculate and output the state of charge index.

[0038] Preferably, the target state of charge calculation unit calculates and outputs a target state of charge index by combining the energy balance difference;

[0039] The dynamic adjustment unit is used to perform a secondary comparative evaluation on the acquired state of charge index and the target state of charge index, and generate an adaptive control instruction to adjust the energy flow between the photovoltaic power generation, energy storage system and the load. The specific evaluation content is as follows;

[0040] When the state of charge index is greater than the target state of charge index, a first adaptive control instruction is sent to increase the discharge power by 50%, stop charging at the same time, and gradually reduce the state of charge index of the energy storage system to meet the load demand. Through timely adjustment, the energy storage system is prevented from being overcharged, thereby avoiding efficiency loss or potential battery damage caused by battery overcharging;

[0041] When the state of charge index = the target state of charge index, there is no need to perform adaptive control;

[0042] When the state of charge index is less than the target state of charge index, a second adaptive control instruction is sent to increase the charging power by 50% and stop discharging at the same time. By reducing discharge or increasing charging, the energy storage system is prevented from over-discharging and performance degradation or damage caused by over-discharge of the battery is avoided.

[0043] Preferably, the optimization and evaluation module includes an energy self-balancing optimization unit and a self-balancing capability evaluation unit;

[0044] The self-balancing optimization unit is used to analyze the historical operation data of photovoltaic power generation and energy storage, and to update and optimize the energy balance difference obtained through a machine learning algorithm, and output the optimized energy storage balance difference.

[0045] Preferably, the self-balancing ability evaluation unit includes a self-balancing effect calculation unit and a self-balancing effect evaluation unit;

[0046] The self-balancing effect calculation unit calculates and obtains a self-balancing index by combining the obtained optimized energy storage balance difference and the energy balance difference;

[0047] The self-balancing effect evaluation unit is used to evaluate and analyze the energy self-balancing ability according to the result output by the self-balancing index, and the specific evaluation content is as follows;

[0048] When the self-balancing index is ≥1, it means that the self-balancing effect is normal and the control strategy is effective;

[0049] When the self-balancing index is less than 1, it indicates that the self-balancing effect is abnormal. At this time, an early warning is generated to prompt the staff to check and maintain the energy storage system.

[0050] A photovoltaic power generation energy storage control method comprises the following steps:

[0051] S1. By integrating with sensors, photovoltaic power generation storage systems and smart meters, photovoltaic power generation storage data is collected in real time, and then the collected photovoltaic power generation storage data is transmitted to the pre-processing unit for data processing, the power generation storage data set is obtained, and a database is constructed for storage;

[0052] S2. By constructing an energy storage self-balancing optimization algorithm model CNMX, the acquired power generation storage data set is input into the energy storage self-balancing optimization algorithm model CNMX, and the net energy difference, charging power and discharging power are output;

[0053] S3. Based on the output net energy difference, charging power and discharging power, the output energy balance difference is comprehensively calculated, and a preliminary comparative evaluation is performed between the preset balance threshold and the output energy balance difference to analyze the energy storage balance of the current photovoltaic power generation energy storage system;

[0054] S4. Trigger execution according to the preliminary comparative evaluation results, calculate the charge state index of the photovoltaic energy storage by constructing a charge state adjustment formula, and calculate and output the target charge state index according to the energy balance difference, then conduct a secondary comparative evaluation on the obtained charge state index and the target charge state index, and generate an adaptive control mechanism;

[0055] S5. Based on historical operation data, the energy balance difference obtained is updated and optimized through machine learning algorithm to obtain the optimized energy storage balance difference. According to the optimized energy storage balance difference, the energy storage self-balancing index is calculated and output. According to the output result of the energy storage self-balancing index, the self-balancing effect is analyzed and an early warning prompt is generated.

[0056] The present invention provides a photovoltaic power generation energy storage control method and system. It has the following beneficial effects:

[0057] (1) The system realizes the real-time collection of photovoltaic power generation storage data through the integration of photovoltaic power generation sensors, energy storage systems and smart meters through the photovoltaic data detection module. Through the data transmission unit, the data is quickly transmitted to the pre-processing unit for processing, such as linear interpolation to fill missing data and normalization processing, and then constructed and stored in the time series database. The optimized data processing process greatly improves the accuracy and timeliness of data processing, enabling the system to respond to photovoltaic power generation fluctuations and load demand changes in a timely manner, ensuring high-quality data transmission and storage, and providing reliable data support for subsequent energy management and control. Through these improvements, the overall response speed and operating efficiency of the system have been significantly improved.

[0058] (2) The system constructs the energy storage self-balancing optimization algorithm model CNMX through the energy storage self-balancing optimization module, and uses the power generation storage data set extracted from the time series database to calculate the net energy difference, charging power and discharging power in real time. The real-time energy storage balance analysis module further calculates the energy balance difference based on these outputs, and makes a preliminary comparative evaluation with the preset balance threshold to analyze the energy storage balance status of the current system. The optimized system is more accurate in the calculation and regulation process, and can flexibly adjust the energy storage and discharge strategies according to actual conditions to ensure the reasonable distribution of energy between photovoltaic power generation, energy storage system and load. By reducing unnecessary energy waste and supplementing deficiencies, the system not only improves energy utilization efficiency, but also enhances its adaptability in the face of complex and dynamic environments.

[0059] (3) The system automatically generates adaptive control instructions through the energy storage adaptive control module based on the real-time energy balance difference and charge state index, and by comparing with the target charge state index, and dynamically adjusts the energy flow between photovoltaic power generation, energy storage system and load. This adaptive control mechanism ensures that the system can automatically adjust to avoid overcharging or over-discharging when responding to fluctuations in energy supply and demand. At the same time, the optimization and evaluation module continuously updates and optimizes the control strategy based on historical operation data through machine learning algorithms, outputs the optimized energy storage balance difference and energy storage self-balancing index, and generates early warning prompts based on the evaluation results. The optimized system can not only adjust the current energy management strategy in real time, but also realize the continuous improvement of the control strategy through long-term data accumulation and analysis, thereby ensuring the efficient operation of the energy storage system while extending its service life and improving the overall stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of a photovoltaic power generation and energy storage control system according to the present invention;

[0061] Figure 2 The figure is a schematic diagram of the steps of a photovoltaic power generation energy storage control method of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Example 1

[0064] See also Figure 1 The present invention provides a photovoltaic power generation energy storage control system. To achieve the above purpose, the present invention is implemented through the following technical solutions: including a photovoltaic data detection module, an energy storage self-balancing optimization module, a real-time energy storage balance analysis module, an energy storage adaptive control module and an optimization evaluation module;

[0065] The photovoltaic data detection module integrates with sensors, photovoltaic power generation storage systems and smart meters to collect photovoltaic power generation storage data in real time, and then transmits the collected photovoltaic power generation storage data to the pre-processing unit for data processing, obtains power generation storage data sets, and builds a database for storage;

[0066] The energy storage self-balancing optimization module constructs the energy storage self-balancing optimization algorithm model CNMX, inputs the acquired power generation storage data set into the energy storage self-balancing optimization algorithm model CNMX, and outputs the net energy difference Enet, charging power Pcha and discharge power Pdis;

[0067] The real-time energy storage balance analysis module is used to comprehensively calculate the output energy balance difference △Eba based on the output net energy difference Enet, charging power Pcha and discharge power Pdis, and to perform preliminary comparative evaluation between the preset balance threshold P and the output energy balance difference △Eba to analyze the energy storage balance of the current photovoltaic power generation energy storage system;

[0068] The energy storage adaptive control module is used to trigger execution according to the preliminary comparative evaluation results. By constructing the state of charge adjustment formula, the state of charge index SOCnew of the photovoltaic energy storage is calculated. At the same time, according to the energy balance difference △Eba, the target state of charge index SOCtarget is calculated and output. Then, the obtained state of charge index SOCnew is compared and evaluated with the target state of charge index SOCtarget for a second time, and an adaptive control mechanism is generated.

[0069] The optimization and evaluation module is used to update and optimize the energy balance difference △Eba obtained through machine learning algorithm based on historical operation data, obtain the optimized energy storage balance difference △Eop, and calculate and output the energy storage self-balancing index Ebala according to the optimized energy storage balance difference △Eop. The self-balancing effect is analyzed according to the output result of the energy storage self-balancing index Ebala, and an early warning prompt is generated.

[0070] In this embodiment, the photovoltaic data detection module of the system collects and processes photovoltaic power generation data, energy storage data and load data in real time through integration with sensors, photovoltaic power generation and energy storage systems and smart meters, and builds a reliable database to provide accurate data support for subsequent optimization and analysis. Then, the energy storage self-balancing optimization module uses the optimization algorithm model CNMX to process the acquired power generation and storage data set, and outputs the net energy difference Enet, charging power Pcha and discharge power Pdis to ensure that energy is reasonably distributed between power generation, energy storage and load. The real-time energy storage balance analysis module analyzes the energy balance state of the current system by comprehensively calculating the energy balance difference △Eba based on the key parameters of the above output, and compares it with the preset balance threshold P. Such real-time analysis enables the system to quickly identify and respond to energy imbalance. Then, the energy storage adaptive control module calculates the new state of charge SOCnew and the target state of charge SOCtarget through the state of charge adjustment formula based on the analysis results, and generates an adaptive control mechanism by comparing these two states, dynamically adjusts the charging and discharging strategy of the energy storage system, prevents overcharging or over-discharging, and thus improves the safety and operation efficiency of the system. Finally, the optimization and evaluation module uses historical operation data to continuously update and optimize the energy balance difference △Eba through machine learning algorithms, and outputs the optimized energy storage balance difference △Eop. This module calculates the energy storage self-balancing index Ebala, and by analyzing the output results of this index, evaluates the self-balancing effect of the system and generates early warning prompts. Through the coordinated work of this series of modules, the system achieves continuous optimization of energy management. Compared with traditional technologies, it significantly improves the accuracy and adaptability of energy management, reduces energy waste, and enhances the reliability and stability of the system. In the end, the system achieves optimal energy flow control, realizes efficient energy utilization between photovoltaic power generation and energy storage systems, and improves the performance and long-term operation capabilities of the overall system.

[0071] Example 2

[0072] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the photovoltaic data detection module includes an integrated acquisition unit, a data transmission unit, a data processing unit and a data storage unit;

[0073] The integrated collection unit is integrated with the photovoltaic power generation sensor, photovoltaic power generation energy storage system and smart meter, and sets the collection frequency at a time interval of thirty minutes to collect photovoltaic power generation storage data in real time;

[0074] The photovoltaic power generation sensor includes a photovoltaic inverter and a reflectivity sensor;

[0075] Photovoltaic power generation and energy storage system includes energy management system EMS and battery management system BMS;

[0076] Photovoltaic power generation storage data includes photovoltaic power generation data, energy storage data and load data;

[0077] Photovoltaic power generation data include photovoltaic power generation power Ppv and bottom edge reflectivity Dbf;

[0078] Energy storage data includes the current stored energy Est of the energy storage system, the state of charge SOC of the energy storage system, the battery internal resistance Rin, the battery external resistance Rex and the battery temperature gradient ▽Tba;

[0079] The battery management system BMS of the energy storage system is usually equipped with a power metering module, which can calculate the storage energy Est stored in the battery in real time;

[0080] The measurement module integrated in the battery management system BMS can calculate the SOC through historical charge and discharge data and real-time measurement data;

[0081] The battery management system BMS usually integrates the internal resistance measurement function, and an external impedance analyzer can also be used to regularly measure the battery internal resistance Rin;

[0082] If the temperature sensor is installed at different positions of the battery pack, the battery temperature gradient ▽Tba is calculated by the temperature difference;

[0083] Load data includes load demand power Plo and power demand fluctuation index Bdz;

[0084] The load demand power Plo is monitored in real time through the smart meter on the load side;

[0085] By collecting load power data for a long time and calculating the standard deviation or fluctuation range, the demand volatility is measured to obtain the power demand fluctuation index Bdz;

[0086] The data transmission unit transmits the collected photovoltaic power generation storage data to the data processing unit through the wireless communication LoRa protocol.

[0087] The data processing unit is used to receive photovoltaic power generation storage data in real time, and use linear interpolation for the photovoltaic power generation storage data, infer missing values ​​based on the existing photovoltaic power generation storage data, and summarize the photovoltaic power generation storage data after normalization to obtain a power generation storage data set;

[0088] The data storage unit is used to construct a time series database, mark the power generation storage data set obtained after preprocessing with a timestamp, and store it in the time series database according to the time series.

[0089] In this embodiment, the integrated acquisition unit of the system integrates with photovoltaic power generation sensors, energy storage systems and smart meters to collect photovoltaic power generation data in real time at a collection frequency of thirty minutes. The data transmission unit uses the LoRa protocol to efficiently transmit the collected photovoltaic power generation data, ensuring the reliability and low power consumption of photovoltaic power generation data in long-distance transmission. The data processing unit performs linear interpolation and normalization on the received data, solving the problems of data missing and standardization, thereby generating a high-quality power generation storage data set. Ultimately, these data are stored in a time series database and marked with timestamps, providing a solid data foundation for subsequent energy management and optimization analysis.

[0090] Example 3

[0091] This embodiment is explained in Example 2. Please refer to Figure 1 Specifically: the energy storage self-balancing optimization module is used to construct the energy storage self-balancing optimization algorithm model CNMX, and extract the power generation storage data set stored in the time series database in real time, input it into the energy storage self-balancing optimization algorithm model CNMX, and output the net energy difference Enet, charging power Pcha and discharge power Pdis in turn;

[0092] The output net energy difference Enet, charging power Pcha and discharging power Pdis are output through the following energy storage self-balancing optimization algorithm model CNMX;

[0093]

[0094] In the formula, Est max Represents the peak capacity of the energy storage system, η st Indicates the energy storage efficiency, min indicates the limit is exceeded, and the limit selects the minimum value.

[0095] The real-time energy storage balance analysis module includes a real-time energy storage balance calculation unit and an energy storage balance analysis unit;

[0096] The real-time energy storage balance calculation unit is used to comprehensively calculate the output energy balance difference △Eba based on the output net energy difference Enet, the charging power Pcha and the discharging power Pdis;

[0097] The energy balance difference △Eba is calculated by the following algorithm formula;

[0098] ΔEba=Enet-(Pcha+Pdis);

[0099] The energy storage balance analysis unit performs a preliminary comparative evaluation of the preset balance threshold value P by the user and the output energy balance difference △Eba to analyze the current balance state of photovoltaic power generation and energy storage. The specific evaluation contents are as follows;

[0100] When the energy balance difference △Eba> the balance threshold P, it means that the energy is sufficient and an energy storage adjustment instruction is sent;

[0101] When the energy balance difference △Eba = the balance threshold P, it means that the energy is normal and no adjustment command needs to be sent;

[0102] When the energy balance difference △Eba is less than the balance threshold P, it indicates that the energy is insufficient and an energy storage supplement adjustment instruction is sent.

[0103] In this embodiment, the system extracts the power generation storage data set stored in the time series database in real time through the self-balancing optimization module, and uses the optimization algorithm model CNMX to output the net energy difference Enet, charging power Pcha and discharge power Pdis in sequence. By accurately calculating the peak capacity and energy storage efficiency of the energy storage system, and applying the min function to limit excess operations, the efficiency and safety of the system during energy storage and release are ensured. Next, the real-time energy storage balance analysis module further calculates the energy balance difference △Eba based on these outputs, and compares and evaluates it with the user-preset balance threshold P, and dynamically analyzes the energy balance state of the current photovoltaic power generation and energy storage system. When the energy balance difference △Eba exceeds or falls below the preset balance threshold P, the system can automatically send energy storage or replenishment adjustment instructions to ensure the balance of energy supply and demand.

[0104] Example 4

[0105] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the energy storage adaptive control module includes a new state of charge calculation unit, a target state of charge calculation unit and a dynamic adjustment unit;

[0106] The new state of charge calculation unit is triggered by receiving the energy storage adjustment instruction and the energy storage supplement adjustment instruction. By constructing the state of charge adjustment formula, the obtained charging power Pcha and discharging power Pdis are input into the algorithm formula to calculate and output the state of charge indicator SOCnew;

[0107] The state of charge indicator SOCnew is calculated by the following state of charge adjustment formula;

[0108]

[0109] Where SOCcurr represents the current state of charge indicator.

[0110] The target state of charge calculation unit calculates and outputs the target state of charge indicator SOCtarget by combining the energy balance difference △Eba;

[0111] The target state of charge indicator SOCtarget is calculated by the following algorithm formula;

[0112] SOCtarget=SOCopt+k×ΔEba;

[0113] In the formula, SOCopt represents the optimal state of charge index, which is calculated based on historical data and current operating status, and k represents the adaptive coefficient;

[0114] The dynamic adjustment unit is used to conduct a secondary comparative evaluation of the acquired state of charge indicator SOCnew and the target state of charge indicator SOCtarget, and generate an adaptive control instruction to adjust the energy flow between photovoltaic power generation, energy storage system and load. The specific evaluation contents are as follows;

[0115] When the state of charge indicator SOCnew> the target state of charge indicator SOCtarget, send the first adaptive control instruction to increase the discharge power Pdis by 50%, stop charging at the same time, and gradually reduce the state of charge indicator SOCnew of the energy storage system to meet the load demand. Through timely adjustment, the energy storage system is prevented from overcharging, and the efficiency reduction or potential battery damage caused by battery overcharging is avoided;

[0116] When the state of charge indicator SOCnew = the target state of charge indicator SOCtarget, there is no need for adaptive control;

[0117] When the state of charge indicator SOCnew is less than the target state of charge indicator SOCtarget, a second adaptive control instruction is sent to increase the charging power Pcha by 50% and stop discharging at the same time. By reducing discharge or increasing charging, the energy storage system is prevented from over-discharging and performance degradation or damage caused by over-discharge of the battery is avoided.

[0118] In this embodiment, after receiving the energy storage or replenishment adjustment instruction, the system calculates the current state of charge indicator SOCnew through the real-time data of the charging power Pcha and the discharging power Pdis through the new state of charge calculation unit to ensure accurate monitoring of the state of the energy storage system. Then, the target state of charge calculation unit combines the energy balance difference △Eba and historical data to calculate the target state of charge indicator SOCtarget, thereby setting a scientific and reasonable operating goal for the energy storage system. Finally, the dynamic adjustment unit generates an adaptive control instruction in real time by comparing SOCnew and SOCtarget. When SOCnew exceeds or falls below SOCtarget, the system automatically adjusts the charging and discharging strategy to avoid overcharging or over-discharging, thereby ensuring the safety and efficient operation of the energy storage system.

[0119] Example 5

[0120] This embodiment is explained in Example 6. Please refer to Figure 1,Specifically: the optimization and evaluation module includes an energy self-balancing optimization unit and a self-balancing ,capacity evaluation unit;

[0121] The self-balancing optimization unit is used to analyze the historical operation data of photovoltaic power generation and energy storage, and update and optimize the energy balance difference △Eba obtained through machine learning algorithms, and output the optimized energy storage balance difference △Eop;

[0122] The optimized energy storage balance difference △Eop is calculated and obtained through the following machine learning algorithm;

[0123]

[0124] In the formula, n represents the number of time periods, which determines the sample size of the calculation, △Eba i Represents the energy balance difference △Eba in the i-th cycle.

[0125] The self-balancing ability evaluation unit includes a self-balancing effect calculation unit and a self-balancing effect evaluation unit;

[0126] The self-balancing effect calculation unit combines the obtained optimized energy storage balance difference △Eop with the energy balance difference △Eba to calculate and obtain the self-balancing index Ebala;

[0127] The self-balancing indicator Ebala is calculated by the following algorithm formula;

[0128]

[0129] The self-balancing effect evaluation unit is used to evaluate and analyze the energy self-balancing ability based on the output results of the self-balancing indicator Ebala. The specific evaluation contents are as follows;

[0130] When the self-balancing index Ebala ≥ 1, it means that the self-balancing effect is normal and the control strategy is effective;

[0131] When the self-balancing indicator Ebala is less than 1, it indicates that the self-balancing effect is abnormal. At this time, an early warning is generated to prompt the staff to check and maintain the energy storage system.

[0132] In this embodiment, the energy self-balancing optimization unit of the system uses a machine learning algorithm to deeply analyze and process the historical operating data of the photovoltaic power generation and energy storage system, and calculates the optimized energy storage balance difference △Eop by updating and optimizing the energy balance difference △Eba. This optimization based on historical data enables the system to dynamically adjust the control strategy to cope with possible changes in energy demand in the future, thereby improving the operating efficiency of the system. Next, the self-balancing capability evaluation unit calculates the self-balancing index Ebala by combining the optimized energy storage balance difference △Eop and the original energy balance difference △Eba, which is used to quantify the energy balance capability of the system. Through the evaluation of the self-balancing index Ebala, the system can promptly identify changes in the self-balancing effect. When the self-balancing effect is found to be abnormal, an early warning prompt is automatically generated to remind the staff to perform necessary inspections and maintenance.

[0133] Example 6

[0134] See also Figure 1 and Figure 2 , a photovoltaic power generation energy storage control method, comprising the following steps:

[0135] S1. By integrating with sensors, photovoltaic power generation storage systems and smart meters, photovoltaic power generation storage data is collected in real time, and then the collected photovoltaic power generation storage data is transmitted to the pre-processing unit for data processing, the power generation storage data set is obtained, and a database is constructed for storage;

[0136] S2. By constructing an energy storage self-balancing optimization algorithm model CNMX, the acquired power generation storage data set is input into the energy storage self-balancing optimization algorithm model CNMX, and the net energy difference Enet, charging power Pcha and discharging power Pdis are output;

[0137] S3. Based on the output net energy difference Enet, charging power Pcha and discharging power Pdis, a comprehensive calculation is performed on the output energy balance difference △Eba, and a preliminary comparative evaluation is performed between the preset balance threshold P and the output energy balance difference △Eba to analyze the energy storage balance of the current photovoltaic power generation energy storage system;

[0138] S4. Trigger execution according to the preliminary comparison and evaluation results, calculate the state of charge index SOCnew of the photovoltaic energy storage by constructing a state of charge adjustment formula, and calculate and output the target state of charge index SOCtarget according to the energy balance difference △Eba, then perform a secondary comparison and evaluation on the obtained state of charge index SOCnew and the target state of charge index SOCtarget, and generate an adaptive control mechanism;

[0139] S5. Based on the historical operation data, the energy balance difference △Eba is updated and optimized through the machine learning algorithm to obtain the optimized energy storage balance difference △Eop, and according to the optimized energy storage balance difference △Eop, the energy storage self-balancing index Ebala is calculated and output, and the self-balancing effect is analyzed according to the output result of the energy storage self-balancing index Ebala, and an early warning prompt is generated.

[0140] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A photovoltaic power generation energy storage control system, characterized in that: It includes photovoltaic data detection module, energy storage self-balancing optimization module, real-time energy storage balance analysis module, energy storage adaptive control module and optimization evaluation module; The photovoltaic data detection module is integrated with sensors, photovoltaic power generation and energy storage systems, and smart meters to collect photovoltaic power generation and storage data in real time, and then transmits the collected photovoltaic power generation and storage data to the preprocessing unit for data processing, obtains power generation and storage data sets, and constructs a database for storage; The energy storage self-balancing optimization module constructs an energy storage self-balancing optimization algorithm model CNMX, inputs the acquired power generation storage data set into the energy storage self-balancing optimization algorithm model CNMX, and outputs the net energy difference, charging power and discharging power; The real-time energy storage balance analysis module is used to comprehensively calculate the output energy balance difference based on the output net energy difference, charging power and discharging power, and to perform preliminary comparative evaluation on the preset balance threshold and the output energy balance difference to analyze the energy storage balance of the current photovoltaic power generation energy storage system; The energy storage adaptive control module is used to trigger execution according to the preliminary comparison and evaluation results, calculate the charge state index of the photovoltaic energy storage by constructing a charge state adjustment formula, and calculate and output the target charge state index according to the energy balance difference, and then perform a secondary comparison and evaluation on the obtained charge state index and the target charge state index, and generate an adaptive control mechanism; The optimization evaluation module is used to update and optimize the energy balance difference obtained through a machine learning algorithm based on historical operation data, obtain the optimized energy storage balance difference, and calculate and output the energy storage self-balancing index according to the optimized energy storage balance difference, analyze the self-balancing effect according to the output result of the energy storage self-balancing index, and generate an early warning prompt.

2. A photovoltaic power generation energy storage control system according to claim 1, characterized in that: The photovoltaic data detection module includes an integrated acquisition unit, a data transmission unit, a data processing unit and a data storage unit; The integrated collection unit is integrated with the photovoltaic power generation sensor, the photovoltaic power generation energy storage system and the smart meter, and sets the collection frequency at a time interval of thirty minutes to collect photovoltaic power generation storage data in real time; The photovoltaic power generation sensor includes a photovoltaic inverter and a reflectivity sensor; The photovoltaic power generation and energy storage system includes an energy management system EMS and a battery management system BMS; The photovoltaic power generation storage data includes photovoltaic power generation data, energy storage data and load data; The photovoltaic power generation data includes photovoltaic power generation power and bottom edge reflectivity; The energy storage data includes the current stored energy of the energy storage system, the state of charge of the energy storage system, the battery internal resistance, the battery external resistance and the battery temperature gradient; If the temperature sensors are installed at different locations of the battery pack, the battery temperature gradient can be calculated by the temperature difference; The load data includes load demand power and power demand fluctuation index; The data transmission unit transmits the collected photovoltaic power generation storage data to the data processing unit through the wireless communication LoRa protocol.

3. A photovoltaic power generation energy storage control system according to claim 2, characterized in that: The data processing unit is used to receive photovoltaic power generation storage data in real time, and use linear interpolation on the photovoltaic power generation storage data to infer missing values ​​based on the existing photovoltaic power generation storage data, and then normalize the photovoltaic power generation storage data and summarize them to obtain a power generation storage data set; The data storage unit is used to construct a time series database, mark the power generation storage data set obtained after preprocessing with a timestamp, and store it in the time series database according to a time series.

4. A photovoltaic power generation energy storage control system according to claim 3, characterized in that: The energy storage self-balancing optimization module is used to construct the energy storage self-balancing optimization algorithm model CNMX, and extract the power generation storage data set stored in the time series database in real time, input it into the energy storage self-balancing optimization algorithm model CNMX, and output the net energy difference, charging power and discharging power in turn.

5. A photovoltaic power generation energy storage control system according to claim 4, characterized in that: The real-time energy storage balance analysis module includes a real-time energy storage balance calculation unit and an energy storage balance analysis unit; The real-time energy storage balance calculation unit is used to comprehensively calculate the output energy balance difference based on the output net energy difference, charging power and discharging power; The energy storage balance analysis unit performs a preliminary comparative evaluation of the preset balance threshold by the user and the output energy balance difference to analyze the current balance state of photovoltaic power generation and energy storage. The specific evaluation contents are as follows; When the energy balance difference is greater than the balance threshold, it indicates that the energy is sufficient and an energy storage adjustment instruction is sent; When the energy balance difference = balance threshold, it means that the energy is normal and no adjustment instruction is needed; When the energy balance difference is less than the balance threshold, it indicates that the energy is insufficient and an energy storage supplement adjustment instruction is sent.

6. A photovoltaic power generation energy storage control system according to claim 4, characterized in that: The energy storage adaptive control module includes a new state of charge calculation unit, a target state of charge calculation unit and a dynamic adjustment unit; The new state of charge calculation unit is triggered by receiving an energy storage adjustment instruction and an energy storage supplement adjustment instruction. By constructing a state of charge adjustment formula, the obtained charging power and discharging power are input into the algorithm formula to calculate and output the state of charge index.

7. A photovoltaic power generation energy storage control system according to claim 6, characterized in that: The target state of charge calculation unit calculates and outputs a target state of charge index by combining the energy balance difference; The dynamic adjustment unit is used to perform a secondary comparative evaluation on the acquired state of charge index and the target state of charge index, and generate an adaptive control instruction to adjust the energy flow between the photovoltaic power generation, energy storage system and the load. The specific evaluation content is as follows; When the state of charge index is greater than the target state of charge index, a first adaptive control instruction is sent to increase the discharge power by 50% and stop charging at the same time; When the state of charge index = the target state of charge index, there is no need to perform adaptive control; When the state of charge index is less than the target state of charge index, a second adaptive control instruction is sent to increase the charging power by 50% and stop discharging.

8. A photovoltaic power generation energy storage control system according to claim 1, characterized in that: The optimization and evaluation module includes an energy self-balancing optimization unit and a self-balancing capability evaluation unit; The self-balancing optimization unit is used to analyze the historical operation data of photovoltaic power generation and energy storage, and to update and optimize the energy balance difference obtained through a machine learning algorithm, and output the optimized energy storage balance difference.

9. A photovoltaic power generation energy storage control system according to claim 8, characterized in that: The self-balancing ability evaluation unit includes a self-balancing effect calculation unit and a self-balancing effect evaluation unit; The self-balancing effect calculation unit calculates and obtains a self-balancing index by combining the obtained optimized energy storage balance difference and the energy balance difference; The self-balancing effect evaluation unit is used to evaluate and analyze the energy self-balancing ability according to the result output by the self-balancing index, and the specific evaluation content is as follows; When the self-balancing index is ≥1, it means that the self-balancing effect is normal and the control strategy is effective; When the self-balancing index is less than 1, it indicates that the self-balancing effect is abnormal. At this time, an early warning is generated to prompt the staff to check and maintain the energy storage system.

10. A photovoltaic power generation energy storage control method, applied to a photovoltaic power generation energy storage control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. By integrating with sensors, photovoltaic power generation storage systems and smart meters, photovoltaic power generation storage data is collected in real time, and then the collected photovoltaic power generation storage data is transmitted to the pre-processing unit for data processing, the power generation storage data set is obtained, and a database is constructed for storage; S2. By constructing an energy storage self-balancing optimization algorithm model CNMX, the acquired power generation storage data set is input into the energy storage self-balancing optimization algorithm model CNMX, and the net energy difference, charging power and discharging power are output; S3. Based on the output net energy difference, charging power and discharging power, the output energy balance difference is comprehensively calculated, and a preliminary comparative evaluation is performed between the preset balance threshold and the output energy balance difference to analyze the energy storage balance of the current photovoltaic power generation energy storage system; S4. Trigger execution according to the preliminary comparative evaluation results, calculate the charge state index of the photovoltaic energy storage by constructing a charge state adjustment formula, and calculate and output the target charge state index according to the energy balance difference, then conduct a secondary comparative evaluation on the obtained charge state index and the target charge state index, and generate an adaptive control mechanism; S5. Based on historical operation data, the energy balance difference obtained is updated and optimized through machine learning algorithm to obtain the optimized energy storage balance difference. According to the optimized energy storage balance difference, the energy storage self-balancing index is calculated and output. According to the output result of the energy storage self-balancing index, the self-balancing effect is analyzed and an early warning prompt is generated.

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