An energy storage converter system for smart grid distribution areas

Abstract

This invention discloses an energy storage converter system applied to smart grid distribution areas, relating to the field of power grid analysis technology. It includes a converter control center, which is communicatively connected to a battery management system, an energy storage converter module, a data acquisition and monitoring module, a condition assessment module, a predictive maintenance module, and a control system module. The modules are electrically connected. The battery management system monitors and manages the status of the battery pack. This invention significantly improves the stability and reliability of the power grid through real-time monitoring and optimized control. By collecting relevant parameters in real time through the battery management system and the energy storage converter module, it ensures that the battery pack and converter operate in a safe and efficient state. The condition assessment module performs data analysis, calculates a condition assessment index, and promptly detects system anomalies, reducing the risk of failure. Furthermore, it optimizes the scheduling and control strategies of the energy storage converter based on the actual needs of the smart grid distribution area.

Description

Technical Field

[0001] This invention relates to the field of power grid analysis technology, and more specifically to an energy storage converter system applied to smart grid distribution areas. Background Technology

[0002] With the accelerated global energy transition, renewable energy sources such as solar and wind power have been widely adopted. Currently, the focus of electricity complaints is on the direct impact of power quality and reliability in low-voltage distribution substations on residents' normal production and lives. Due to the volatility and intermittency of power generation, energy storage systems are needed to balance power supply and demand. In terms of improving power quality in low-voltage distribution substations, the distribution of active and reactive power can significantly affect node voltage, which can lead to voltage exceeding limits and increased line losses. Unreasonable single-phase load distribution can cause three-phase imbalance in the substation, which can seriously affect electricity safety and reduce power factor. In addition, in terms of improving the reliability of power supply in low-voltage distribution substations, the intermittent and seasonal electricity consumption characteristics of low-voltage users can easily cause short-term heavy overload of distribution transformers, posing a hidden danger to the safe operation of distribution transformers.

[0003] For example, Chinese Patent Publication No. CN118232385A describes an energy storage converter system applied to a smart grid distribution area. The system includes a control loop circuit and a main circuit. The main circuit includes a DC-side protection circuit, a power conversion circuit, an AC-side protection circuit, and an AC-side isolation circuit connected in sequence.

[0004] In existing technologies, intelligent management of the charging and discharging of batteries in smart grid distribution areas can adapt to distribution areas of different sizes and types, thus solving the problem of not being able to design and optimize specifically for the actual operating conditions of distribution areas. However, because distribution grids are prone to long-term problems such as low voltage and periodic heavy overload in the distribution network, the power quality of low-voltage distribution areas is affected, leading to deviations in the state assessment of the energy storage converter system and affecting the system's operational safety. Therefore, there is an urgent need for an energy storage converter system applicable to smart grid distribution areas to accurately analyze the system's state and maintenance requirements, reduce the degree of deviation in the assessment process, and optimize the scheduling and control strategies of the energy storage converter according to the actual needs of the smart grid distribution area, thereby improving the system's response speed and control accuracy. Summary of the Invention

[0005] The purpose of this invention is to provide an energy storage converter system for use in smart grid distribution areas, in order to solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] An energy storage converter system for use in a smart grid distribution area includes a converter control center, which is communicatively connected to a battery management system, an energy storage converter module, a data acquisition and monitoring module, a status assessment module, a predictive maintenance module, and a control system module, wherein the modules are electrically connected to each other.

[0008] The battery management system is used to monitor and manage the status of the battery pack, including the battery's voltage, current, and temperature parameters, to ensure the battery operates safely and reliably. Through real-time monitoring and status assessment, it can prevent battery overcharging, over-discharging, and thermal runaway, extend battery life, improve system safety, and report the battery pack's health status and remaining capacity to the smart grid distribution area in real time, helping the distribution area optimize energy distribution and scheduling.

[0009] The energy storage converter module controls the switching states of current and voltage to achieve energy conversion and charging control. It converts the DC power from the battery pack into AC power for use in the power grid, and can also operate in reverse to convert AC power into DC power for charging. This enables efficient conversion of electrical energy between different forms, supports bidirectional energy flow in the power grid, improves the flexibility and reliability of the power grid, and balances the power grid supply and demand by combining the energy demand of the distribution area with the power grid status for charging and discharging operations.

[0010] The data acquisition and monitoring module monitors the energy storage converter system in real time, collects the operating data of the battery management system and the energy storage converter module, and obtains the energy storage data sequence.

[0011] The status assessment module assesses the operating status of the energy storage converter system based on the energy storage data sequence, analyzes the abnormal costs and failure risk trends during the operation of the energy storage converter system, reduces the bias of the status assessment, and improves the accuracy of the assessment.

[0012] The predictive maintenance module analyzes the energy loss rate of the energy storage converter system based on the state assessment results of the energy storage converter system, calculates the energy storage optimization coefficient, comprehensively analyzes the state optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, assists the distribution area in arranging maintenance work in advance, and reduces energy interruption and loss caused by system failure.

[0013] The control system module monitors the operating status in real time, and optimizes the scheduling and control of the energy storage converter system based on the predicted results of the current maintenance needs of the system, so as to ensure the efficient and stable operation of the energy storage converter system. At the same time, it works in coordination with the smart grid distribution area to realize distributed energy storage in the distribution area.

[0014] A further improvement to the technical solution of this invention lies in the following: the process of balancing the power grid supply and demand in the energy storage converter module is as follows:

[0015] The energy storage converter receives DC power from the energy storage system (battery pack). After receiving the DC power, the energy storage converter converts the DC power into high-frequency AC power through an internal DC / AC bidirectional converter, making it easier for the power to be transmitted and used in the power grid. The high-frequency AC power after conversion by the DC / AC bidirectional converter is then filtered by the output filter to remove high-frequency harmonic components and obtain AC power that meets the requirements, preventing harmonic pollution to the power grid.

[0016] During the charging process, the energy storage converter converts the AC power from the grid into DC power through a rectification process to supply the battery pack with charging power, ensuring that the battery pack can receive electrical energy safely and efficiently.

[0017] When there is excess electrical energy in the power grid, the energy storage converter stores the excess energy in the battery pack for backup power. When there is insufficient electrical energy in the power grid, the energy storage converter releases electrical energy from the battery pack to supplement the energy shortage in the power grid. Based on the real-time demand of the power grid and the status information of the battery pack, the energy storage converter performs intelligent control and management through the control system module to achieve efficient use of electrical energy, balance the supply and demand relationship of the power grid, ensure that the power grid can maintain stable operation under any circumstances, and avoid power grid failures caused by imbalance between power supply and demand.

[0018] A further improvement to the technical solution of this invention lies in the following: the process of acquiring the energy storage data sequence in the data acquisition and monitoring module is as follows:

[0019] When the energy storage converter system starts up, the data acquisition and monitoring module starts up simultaneously and enters the initialization phase, then loads the preset configuration parameters, including the data acquisition frequency, data type and storage location.

[0020] Relevant operational data are collected from the battery management system and the energy storage converter module. Specifically, the battery management system collects real-time status data of the battery pack, including voltage, current, temperature, and state of charge parameters. The energy storage converter module obtains the output power, efficiency, and fault status of the energy storage converter. The collected data is then preliminarily verified to ensure its accuracy and completeness. Any abnormal or missing data is marked.

[0021] The collected raw data is preprocessed, including filtering, noise reduction, data cleaning and format conversion. The preprocessed data is then integrated to generate an energy storage data sequence, which is stored in the database of the converter control center.

[0022] A further improvement to the technical solution of this invention lies in the following: the process of evaluating the operating status of the energy storage converter system in the status evaluation module is as follows:

[0023] The energy storage data sequence is obtained from the database of the converter control center, and the voltage, current, temperature, and state of charge data of the battery pack, as well as the output power, efficiency, and fault status data of the energy storage converter are extracted.

[0024] Based on the operational requirements of the energy storage converter system, the characteristics of the battery pack and the energy storage converter are selected, namely the voltage, current, temperature, and state of charge characteristics of the battery pack, and the output power, efficiency, and fault state characteristics of the energy storage converter. The normal threshold of each characteristic is determined by using historical fault data.

[0025] Based on the normal thresholds of various characteristics of the battery pack, combined with the data of various characteristics of the battery pack, the battery pack evaluation index is comprehensively analyzed and calculated to analyze the operating status of the battery pack. Based on the normal thresholds of various characteristics of the energy storage converter, combined with the data of various characteristics of the energy storage converter, the converter evaluation index is comprehensively analyzed and calculated to analyze the performance of the energy storage converter.

[0026] By combining battery pack evaluation indicators and converter evaluation indicators, the state evaluation index of the entire energy storage system is calculated, and the abnormal cost and failure risk trends of the energy storage converter system during operation are analyzed.

[0027] A further improvement to the technical solution of this invention is that the expression for the battery pack evaluation index is:

[0028]

[0029] Among them, BI is the battery pack evaluation metric, and V d This is the actual measured battery pack voltage, V. t This is the normal threshold voltage of the battery pack, I d This is the actual measured battery pack current, I. t This is the normal threshold for battery pack current, T d This is the actual measured battery pack temperature, T. t This is the normal threshold temperature for the battery pack, S d This is the actual measured state of charge (S) of the battery pack. t The BI value is the normal threshold for the state of charge of the battery pack. The BI value ranges from 0 to 1. As the actual measured value of each feature approaches its normal threshold, the BI value increases, indicating that the battery pack is in better condition.

[0030] The expression for the converter evaluation index is as follows:

[0031]

[0032] Among them, IAI is the converter evaluation index, P d It is the actual measured output power of the energy storage converter, P. t This is the normal threshold value for the output power of the energy storage converter, η.d It is the actual measured efficiency of the energy storage converter, η. t This is the normal threshold for the efficiency of energy storage converters, F. d This is a fault condition of the energy storage converter, where F d =0 indicates no fault, F d =0 indicates the presence of a fault. The value of IAI ranges from 0 to 1. As the actual measured value of each feature approaches its normal threshold and no fault occurs, the value of IAI increases, indicating that the converter is in better condition.

[0033] A further improvement to the technical solution of this invention lies in that: the state assessment index is obtained based on the battery pack assessment index and the converter assessment index, and its expression is:

[0034]

[0035] Wherein, S is the condition assessment index, BI is the battery pack assessment index, and IAI is the inverter assessment index. It should be noted that the value of S ranges from 0 to 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in good condition. Conversely, if the value of BI or IAI decreases, the value of S will decrease significantly, indicating that the energy storage system is in poor condition.

[0036] A further improvement to the technical solution of this invention lies in the following: the analysis process of the energy storage converter system state optimization trend in the predictive maintenance module is as follows:

[0037] Based on the state assessment results of the energy storage converter system, battery pack assessment indicators, converter assessment indicators, and state assessment indices are obtained to analyze the current state and performance of the system.

[0038] The system monitors energy input and output within a fixed period, analyzes energy transmission loss factors, including battery pack internal resistance loss, line loss, and energy storage converter loss, to determine the proportion of energy loss, and then calculates the energy loss rate of the energy storage converter system during operation, analyzes the changing trend of the energy loss rate, and identifies potential energy consumption problems.

[0039] By combining the condition assessment index and energy loss rate, the energy storage optimization coefficient is calculated, the energy storage optimization coefficient is analyzed, and the energy storage efficiency and performance optimization potential of the system are comprehensively evaluated.

[0040] Based on the value of the energy storage optimization coefficient, the operating status of the energy storage converter system is divided into different status levels, namely excellent status level, good status level and poor status level, and corresponding optimization evaluation thresholds are matched for each status level.

[0041] Based on the energy storage optimization coefficient and the set state level, analyze the future state of the energy storage converter system, determine the maintenance requirements of the energy storage converter system, clarify the maintenance priority, and assist the distribution area in arranging maintenance work in advance.

[0042] A further improvement to the technical solution of this invention is that the expression for the energy storage optimization coefficient is:

[0043]

[0044] Where SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the condition assessment index, and E in E is the total energy input within a fixed period. out State of Charge (SOC) is the total energy output within a fixed period, where n is the number of loss factors, including battery internal resistance loss, cable loss, converter loss, etc. The SOC value ranges from 0 to 1. As the State of Charge (SOC) increases and the energy loss rate decreases, the value of the energy storage optimization coefficient increases, indicating that the energy storage system has greater optimization potential.

[0045] A further improvement of the technical solution of the present invention lies in that: the multiple state levels correspond to multiple optimization evaluation thresholds, wherein the state level and the optimization evaluation threshold correspond one-to-one, specifically as follows:

[0046] State of Excellence (SOC) H ≤SOC<1;

[0047] Good condition level: SOC L ≤SOC <SOC H ;

[0048] Poor state level: 0 <SOC<SOC L ;

[0049] Where SOC is the energy storage optimization coefficient, SOC H SOC is the lower threshold for the excellent state level and the upper threshold for the good state level. L SOC is the lower threshold for a good state level and the upper threshold for a poor state level. H =0.8, SOC L =0.6.

[0050] A further improvement to the technical solution of this invention lies in the following: In the control system module, the process of optimizing the scheduling and control of the energy storage converter system is as follows:

[0051] Through the data acquisition and monitoring module, various operating data of the energy storage converter system are collected in real time, including but not limited to the voltage, current, temperature, and state of charge of the battery pack, as well as the output power, efficiency, and fault status of the energy storage converter. The grid area problems are also identified, including the long-term three-phase load imbalance and low voltage problems at the end of the grid area and the long-term heavy overload conditions of the transformers in the grid area.

[0052] Based on real-time monitoring data and the status assessment results of the predictive maintenance module, as well as the predicted maintenance needs, the control system module optimizes the scheduling and control strategies of the energy storage converter system, including formulating appropriate charging and discharging plans and adjusting the control parameters of the energy storage converter, to ensure that the system can operate in a highly efficient and stable state.

[0053] The control system module intelligently controls the output of the energy storage converter system based on the energy demand and grid status of the smart grid distribution area, achieving a balance between grid supply and demand. Specifically, for long-term three-phase load imbalance and low voltage issues at the end of the grid distribution area, the energy storage system is connected to the distribution end of the area, and the low voltage and three-phase imbalance mitigation mode is activated. The mitigation effect is achieved by adjusting the reactive and active outputs of the three phases of the energy storage converter. For long-term heavy overload conditions of the transformers in the grid distribution area, resulting in problems such as excessive transformer temperature and burnout, the energy storage system is connected to the front end of the transformers in the area, and the overload mitigation mode is activated. The output of the energy storage system is adjusted by detecting the real-time load power, discharging when the load power is too high and charging when the load power is low, effectively reducing the transformer load rate.

[0054] Based on the characteristics of distributed energy storage, a small energy storage system is connected to the low-voltage side of the distribution transformer. Power compensation is performed in combination with the operating load of the power grid area to alleviate problems such as temporary overload of the distribution transformer and low voltage at the outlet of the power grid area. The energy storage system in the area is connected to the distribution terminal of the area, and the low voltage and three-phase imbalance treatment mode is activated. The treatment effect is achieved by adjusting the reactive / active output of the three phases of the energy storage converter, thus completing the distributed energy storage in the area.

[0055] The optimized scheduling and control commands are sent from the control system module to the converter control center, and the execution of the commands is tracked in real time. The control system module continuously monitors the system's operating status and control effect, collects feedback data, fine-tunes and further optimizes the control strategy, and formulates detailed maintenance plans based on prediction results and actual operating conditions. The maintenance plans are then executed to repair and maintain the system, ensuring that the system can always maintain optimal operating conditions.

[0056] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows:

[0057] 1. This invention provides an energy storage converter system for smart grid distribution areas. Through real-time monitoring and optimized control, it significantly improves the stability and reliability of the power grid. By collecting relevant parameters in real time through the battery management system and the energy storage converter module, it ensures that the battery pack and converter operate in a safe and efficient state. The system uses a state assessment module to perform data analysis, calculate the state assessment index, detect system anomalies in a timely manner, reduce the risk of failure, and further optimize the scheduling and control strategies of the energy storage converter based on the actual needs of the smart grid distribution area.

[0058] 2. This invention provides an energy storage converter system for smart grid distribution areas. Based on the condition assessment results and energy loss rate, it calculates the energy storage optimization coefficient, evaluates the system's energy storage efficiency and performance optimization potential, and then predicts maintenance needs, guides the system to operate in the best state, reduces unexpected downtime, and combines real-time monitoring with predictive maintenance, enabling the grid to supply power more stably. This not only reduces energy loss but also lowers costs by reducing unnecessary energy waste, thus improving economic efficiency. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0060] Figure 1 This is a block diagram of the present invention;

[0061] Figure 2 This is a flowchart illustrating the process of evaluating the operating status of an energy storage converter system according to the present invention;

[0062] Figure 3 This is a flowchart illustrating the state optimization trend analysis of the energy storage converter system of the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1, as Figure 1 , Figure 2As shown, the present invention provides an energy storage converter system for use in smart grid distribution areas, including a converter control center. The converter control center is communicatively connected to a battery management system, an energy storage converter module, a data acquisition and monitoring module, a status assessment module, a predictive maintenance module, and a control system module, wherein the modules are electrically connected to each other.

[0065] The battery management system is used to monitor and manage the status of the battery pack, including battery voltage, current, and temperature parameters, to ensure the safe and reliable operation of the battery. Through real-time monitoring and status assessment, it can prevent battery overcharging, over-discharging, and thermal runaway, extend battery life, improve system safety, and report the health status and remaining capacity of the battery pack to the distribution area in real time in the smart grid, helping the distribution area to optimize energy distribution and scheduling.

[0066] The energy storage converter module controls the switching states of current and voltage to achieve energy conversion and charging control. It converts the DC power from the battery pack into AC power for grid use, and can also operate in reverse to convert AC power back to DC power for charging. This achieves efficient conversion between different forms of energy, supports bidirectional energy flow in the grid, and improves the grid's flexibility and reliability. It balances grid supply and demand by combining the energy demand of the distribution area with the grid conditions for charging and discharging operations. The energy storage converter receives DC power from the energy storage system (battery pack). After receiving the DC power, the converter converts it into high-frequency AC power through an internal DC / AC bidirectional converter, making it easier for the energy to be transmitted and used in the grid. The high-frequency AC power after conversion by the DC / AC bidirectional converter then passes through an output filter... The system performs filtering to remove high-frequency harmonic components, obtaining AC power that meets the requirements and preventing harmonic pollution to the power grid. During charging, the energy storage converter converts the AC power from the power grid into DC power through rectification to supply power to the battery pack for charging, ensuring that the battery pack can safely and efficiently receive power. When there is excess power in the power grid, the energy storage converter stores the excess power in the battery pack for backup power. When there is insufficient power in the power grid, the energy storage converter releases power from the battery pack to supplement the energy shortage in the power grid. Based on the real-time demand of the power grid and the status information of the battery pack, the energy storage converter performs intelligent control and management through the control system module to achieve efficient use of power, balance the supply and demand of the power grid, ensure that the power grid can maintain stable operation under any circumstances, and avoid power grid failures caused by power supply and demand imbalance.

[0067] The data acquisition and monitoring module performs real-time monitoring of the energy storage converter system, collecting operational data from the battery management system and the energy storage converter module to obtain an energy storage data sequence. Upon startup of the energy storage converter system, the data acquisition and monitoring module starts synchronously and enters the initialization phase, loading preset configuration parameters, including data acquisition frequency, data type, and storage location. It collects relevant operational data from the battery management system and the energy storage converter module. Specifically, the battery management system collects real-time status data of the battery pack, including voltage, current, temperature, and state of charge parameters. The energy storage converter module obtains the output power, efficiency, and fault status of the energy storage converter. Preliminary data verification is performed on the collected data to ensure accuracy and completeness; any abnormal or missing data is marked. The collected raw data undergoes preprocessing, including filtering, noise reduction, data cleaning, and format conversion. The preprocessed data is then integrated to generate an energy storage data sequence, which is stored in the database of the converter control center.

[0068] The condition assessment module, based on energy storage data sequences, evaluates the operating status of the energy storage converter system. It analyzes abnormal costs and failure risk trends during system operation, reducing bias and improving accuracy. The module retrieves energy storage data sequences from the converter control center's database, extracting voltage, current, temperature, and state of charge (SOC) data for the battery pack, as well as output power, efficiency, and fault status data for the energy storage converter. Based on the system's operational requirements, it selects specific characteristics for both the battery pack and the energy storage converter, including voltage, current, temperature, and SOC characteristics for the battery pack, and SOC characteristics for the energy storage converter. The output power, efficiency, and fault state characteristics of the inverter are analyzed. The normal threshold for each characteristic is determined using historical fault data. Based on the normal thresholds of each characteristic of the battery pack, and combined with the data of each characteristic of the battery pack, the battery pack evaluation index is comprehensively analyzed and calculated to analyze the operating status of the battery pack. Based on the normal thresholds of each characteristic of the energy storage inverter, and combined with the data of each characteristic of the energy storage inverter, the inverter evaluation index is comprehensively analyzed and calculated to analyze the performance of the energy storage inverter. Combining the battery pack evaluation index and the inverter evaluation index, the state evaluation index of the entire energy storage system is calculated, and the abnormal costs and fault risk trends of the energy storage inverter system during operation are analyzed.

[0069] Furthermore, the expression for the battery pack evaluation index is as follows:

[0070]

[0071] Among them, BI is the battery pack evaluation metric, and V d This is the actual measured battery pack voltage, V. t This is the normal threshold voltage of the battery pack, I d This is the actual measured battery pack current, I. tThis is the normal threshold for battery pack current, T d This is the actual measured battery pack temperature, T. t This is the normal threshold temperature for the battery pack, S d This is the actual measured state of charge (S) of the battery pack. t The BI value is the normal threshold for the state of charge of the battery pack. The BI value ranges from 0 to 1. As the actual measured value of each feature approaches its normal threshold, the BI value increases, indicating that the battery pack is in better condition. Conversely, if the actual measured value of any feature deviates significantly from its normal threshold, the BI value will decrease significantly, indicating that the battery pack is in poor condition.

[0072] The expression for the converter evaluation index is:

[0073]

[0074] Among them, IAI is the converter evaluation index, P d It is the actual measured output power of the energy storage converter, P. t This is the normal threshold value for the output power of the energy storage converter, η. d It is the actual measured efficiency of the energy storage converter, η. t This is the normal threshold for the efficiency of energy storage converters, F. d This is a fault condition of the energy storage converter, where F d =0 indicates no fault, F d =0 indicates the presence of a fault. The value of IAI ranges from 0 to 1. As the actual measured value of each feature approaches its normal threshold and no fault occurs, the value of IAI increases, indicating that the converter is in better condition. Conversely, if the actual measured value of any feature deviates significantly from its normal threshold or a fault occurs, the value of IAI will decrease significantly, indicating that the converter is in poor condition.

[0075] Furthermore, the condition assessment index is obtained based on battery pack assessment metrics and inverter assessment metrics, and its expression is:

[0076]

[0077] Where S is the condition assessment index, BI is the battery pack assessment index, and IAI is the inverter assessment index. The product of BI and IAI is adjusted using an exponential function. This part of the product is close to 1 when both BI and IAI are close to 1, and decreases when either assessment is low. The evaluation index is further adjusted using logarithmic functions and radicals, taking into account the combined effects of BI and IAI. When both are high, the value inside the logarithmic function is close to 0, and the value of the entire expression is close to 1. When either evaluation index is low, the value inside the logarithmic function increases, and the value of the entire expression decreases. It should be noted that the value of S ranges from 0 to 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in good condition. Conversely, if the values ​​of BI or IAI decrease, the value of S will decrease significantly, indicating that the energy storage system is in poor condition.

[0078] The predictive maintenance module analyzes the energy loss rate of the energy storage converter system based on the state assessment results of the energy storage converter system, calculates the energy storage optimization coefficient, comprehensively analyzes the state optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, assists the distribution area in arranging maintenance work in advance, and reduces energy interruption and loss caused by system failure.

[0079] The control system module monitors the operating status in real time, and optimizes the scheduling and control of the energy storage converter system based on the predicted results of the current maintenance needs of the system, so as to ensure the efficient and stable operation of the energy storage converter system. At the same time, it works in conjunction with the smart grid distribution area to realize distributed energy storage in the distribution area.

[0080] Example 2, as Figure 3 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, in the predictive maintenance module, the analysis process of the energy storage converter system state optimization trend is as follows:

[0081] Based on the state assessment results of the energy storage converter system, battery pack assessment indicators, converter assessment indicators, and state assessment indices are obtained to analyze the current state and performance of the system. Energy input and output of the system are monitored within a fixed period, and energy transmission loss factors, including battery pack internal resistance loss, line loss, and energy storage converter loss, are analyzed to determine the energy loss ratio. The energy loss rate of the energy storage converter system during operation is then calculated, and the changing trend of the energy loss rate is analyzed to identify potential energy consumption problems. Combining the state assessment index and energy loss rate, the energy storage optimization coefficient is calculated and analyzed. A comprehensive assessment of the system's energy storage efficiency and performance optimization potential is conducted. Based on the value of the energy storage optimization coefficient, the operating state of the energy storage converter system is divided into different state levels: excellent, good, and poor. Corresponding optimization evaluation thresholds are matched to each state level. Based on the energy storage optimization coefficient and the set state levels, the future state of the energy storage converter system is analyzed, and the maintenance requirements of the energy storage converter system are determined. Maintenance priorities are clarified to assist the distribution area in arranging maintenance work.

[0082] Furthermore, the expression for the energy storage optimization coefficient is as follows:

[0083]

[0084] Where SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the condition assessment index, and E in E is the total energy input within a fixed period. out ELR represents the total energy output over a fixed period, where 'n' is the number of loss factors, including battery internal resistance losses, cable losses, and converter losses. A smaller ELR value indicates higher energy efficiency and lower losses in the energy storage converter system; conversely, a larger ELR value indicates higher energy losses and lower efficiency. The impact of energy loss rate is adjusted using an exponential function. When the condition assessment index is high, the value of the exponential function increases, thereby improving the energy storage optimization coefficient. The logarithmic function is used to further adjust the energy storage optimization coefficient. This part analyzes the combined effect of the state assessment index and the energy loss rate. When S is high and ELR is low, the value inside the logarithmic function increases and the value of the entire expression decreases, thereby improving the energy storage optimization coefficient. The value of SOC ranges from 0 to 1. As the state assessment index increases and the energy loss rate decreases, the value of the energy storage optimization coefficient increases, indicating that the optimization potential of the energy storage system is greater. Conversely, if S decreases or ELR increases, the value of SOC will decrease, indicating that the optimization potential of the system decreases.

[0085] Furthermore, multiple state levels correspond to multiple optimization evaluation thresholds, where each state level and optimization evaluation threshold has a one-to-one correspondence, specifically as follows:

[0086] State of Excellence (SOC) H ≤SOC<1 indicates that the system's operating efficiency and performance have reached their optimal state, with low energy loss rate and high state assessment index, indicating that the system is in a highly efficient operating state with low maintenance requirements and low maintenance priority. Regular inspections and preventive maintenance are recommended.

[0087] Good condition level: SOC L ≤SOC <SOC H The system operates efficiently and performs well, with a low energy loss rate and a high condition assessment index, indicating that the system is in good operating condition. However, some preventative maintenance may be required. It is of medium maintenance priority and regular diagnostic testing and maintenance of some components are recommended.

[0088] Poor state level: 0 <SOC<SOC L The system has poor operating efficiency and performance, high energy loss rate, and low condition assessment index, indicating that there may be problems with the system. It needs timely maintenance and optimization, and is a high maintenance priority. It is recommended to conduct a detailed system inspection and necessary repairs immediately.

[0089] Where SOC is the energy storage optimization coefficient, SOC H SOC is the lower threshold for the excellent state level and the upper threshold for the good state level. L SOC is the lower threshold for a good state level and the upper threshold for a poor state level. H =0.8, SOC L =0.6;

[0090] In the control system module, the process of optimizing the scheduling and control of the energy storage converter system is as follows:

[0091] The data acquisition and monitoring module collects real-time operational data of the energy storage converter system, including but not limited to battery voltage, current, temperature, and state of charge, as well as the output power, efficiency, and fault status of the energy storage converter. It also identifies grid area issues, such as persistent three-phase load imbalance and low voltage at the grid distribution terminal, and long-term heavy overload conditions on the grid transformers. Based on real-time monitoring data, the status assessment results of the predictive maintenance module, and predicted maintenance needs, the control system module optimizes the scheduling and control strategies of the energy storage converter system. This includes developing appropriate charging and discharging plans and adjusting the control parameters of the energy storage converter to ensure efficient and stable system operation. The control system module intelligently controls the output of the energy storage converter system based on the energy demand and grid status of the smart grid area, achieving a balance between grid supply and demand. Specifically, for persistent three-phase load imbalance and low voltage at the grid distribution terminal, the energy storage system is connected to the distribution terminal, and a low voltage and three-phase imbalance mitigation mode is activated. This involves adjusting the three phases of the energy storage converter separately. The system achieves effective energy management through active / active power output. Addressing the long-term overload conditions of transformers in power grid distribution areas, which lead to excessively high transformer temperatures and burnout, an energy storage system is connected to the transformer's upstream end. In overload management mode, the system adjusts its output by monitoring real-time load power, discharging when the load power is too high and charging when the load power is too low, effectively reducing the transformer load rate. Leveraging the flexibility and rapid response of distributed energy storage, a small energy storage system is connected to the low-voltage side of the distribution transformer. Power compensation is then performed based on the power grid distribution area's operating load, alleviating temporary transformer overloads and low voltage at the grid distribution area's outlet, thus realizing distributed energy storage in the distribution area. Optimized scheduling and control commands are sent from the control system module to the converter control center, with real-time tracking of command execution. The control system module continuously monitors the system's operating status and control effectiveness, collects feedback data, fine-tunes and further optimizes the control strategy, and develops detailed maintenance plans based on predictions and actual operating conditions. These plans are then executed to repair and maintain the system, ensuring it remains in optimal operating condition.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy storage converter system applied to a smart grid distribution area, comprising a converter control center, characterized in that: The converter control center is communicatively connected to a battery management system, an energy storage converter module, a data acquisition and monitoring module, a condition assessment module, a predictive maintenance module, and a control system module, wherein the modules are connected by electrical signals. The battery management system is used to monitor and manage the status of the battery pack, and report the health status and remaining capacity of the battery pack to the smart grid area in real time, thereby optimizing the energy distribution and scheduling of the area. The energy storage converter module controls the switching state of current and voltage, and performs charging and discharging operations in combination with the energy demand of the power grid area and the power grid status to balance the power grid supply and demand relationship. The data acquisition and monitoring module monitors the energy storage converter system in real time, collects the operating data of the battery management system and the energy storage converter module, and obtains the energy storage data sequence. The status assessment module, based on energy storage data sequences, assesses the operating status of the energy storage converter system, analyzes the abnormal costs and failure risk trends during the system's operation, and the process of assessing the operating status of the energy storage converter system is as follows: The energy storage data sequence is obtained from the database of the converter control center, and the voltage, current, temperature, and state of charge data of the battery pack, as well as the output power, efficiency, and fault status data of the energy storage converter are extracted. Based on the operational requirements of the energy storage converter system, the characteristics of the battery pack and the energy storage converter are selected, namely the voltage, current, temperature, and state of charge characteristics of the battery pack, and the output power, efficiency, and fault state characteristics of the energy storage converter. The normal threshold of each characteristic is determined by using historical fault data. Based on the normal thresholds of various characteristics of the battery pack, combined with the data of various characteristics of the battery pack, the battery pack evaluation index is comprehensively analyzed and calculated to analyze the operating status of the battery pack. Based on the normal thresholds of various characteristics of the energy storage converter, combined with the data of various characteristics of the energy storage converter, the converter evaluation index is comprehensively analyzed and calculated to analyze the performance of the energy storage converter. By combining battery pack evaluation indicators and converter evaluation indicators, the state evaluation index of the entire energy storage system is calculated, and the abnormal cost and failure risk trends of the energy storage converter system during operation are analyzed. The expression for the battery pack evaluation index is as follows: Among them, BI is the battery pack evaluation metric, and V d This is the actual measured battery pack voltage, V. t This is the normal threshold voltage of the battery pack, I d This is the actual measured battery pack current, I. t This is the normal threshold for battery pack current, T d This is the actual measured battery pack temperature, T. t This is the normal threshold temperature for the battery pack, S d This is the actual measured state of charge (S) of the battery pack. t This is the normal threshold for the state of charge of the battery pack; The expression for the converter evaluation index is as follows: Among them, IAI is the converter evaluation index, P d It is the actual measured output power of the energy storage converter, P. t This is the normal threshold value for the output power of the energy storage converter, η. d It is the actual measured efficiency of the energy storage converter, η. t This is the normal threshold for the efficiency of energy storage converters, F. d This is a fault condition of the energy storage converter, where F d =0 indicates no fault; The state assessment index is obtained based on the battery pack assessment index and the converter assessment index, and its expression is: Wherein, S is the condition assessment index, BI is the battery pack assessment index, and IAI is the converter assessment index. It should be noted that the value of S ranges from 0 to 1. As the values ​​of BI and IAI increase, the value of S increases, indicating that the energy storage system is in good condition. If the value of BI or IAI decreases, the value of S decreases significantly, indicating that the energy storage system is in poor condition. The predictive maintenance module analyzes the energy loss rate of the energy storage converter system based on the state assessment results of the energy storage converter system, calculates the energy storage optimization coefficient, comprehensively analyzes the state optimization trend of the energy storage converter system, predicts the current maintenance needs of the system, and assists the power grid area in arranging maintenance work. The control system module monitors the operating status in real time, optimizes the system's scheduling and control based on maintenance demand forecasts, and works in conjunction with smart grid distribution areas to achieve distributed energy storage in the distribution areas.

2. The energy storage converter system applied to a smart grid distribution area according to claim 1, characterized in that: In the energy storage converter module, the process of balancing the power grid supply and demand is as follows: The energy storage converter receives DC power from the battery pack. After receiving the DC power, the energy storage converter converts the DC power into high-frequency AC power through an internal DC / AC bidirectional converter. The high-frequency AC power after conversion by the DC / AC bidirectional converter is then filtered by the output filter to remove high-frequency harmonic components and obtain AC power that meets the requirements. During the charging process, the energy storage converter converts the AC power from the grid into DC power through a rectification process to supply power to the battery pack for charging. When there is excess electrical energy in the power grid, the energy storage converter stores the excess electrical energy in the battery pack for use as a backup power source. When there is insufficient electrical energy in the power grid, the energy storage converter releases electrical energy from the battery pack to supplement the energy shortage in the power grid. The energy storage converter performs intelligent control and management through the control system module based on the real-time demand of the power grid and the status information of the battery pack, thereby balancing the supply and demand relationship of the power grid.

3. The energy storage converter system applied to a smart grid distribution area according to claim 2, characterized in that: In the data acquisition and monitoring module, the process of acquiring the energy storage data sequence is as follows: When the energy storage converter system starts up, the data acquisition and monitoring module starts up simultaneously and enters the initialization phase, then loads the preset configuration parameters, including the data acquisition frequency, data type and storage location. Relevant operational data are collected from the battery management system and the energy storage converter module. Specifically, the battery management system collects real-time status data of the battery pack, including voltage, current, temperature, and state of charge parameters. The energy storage converter module obtains the output power, efficiency, and fault status of the energy storage converter and performs preliminary data verification on the collected data. The collected raw data is preprocessed, including filtering, noise reduction, data cleaning and format conversion. The preprocessed data is then integrated to generate an energy storage data sequence, which is stored in the database of the converter control center.

4. The energy storage converter system applied to a smart grid distribution area according to claim 1, characterized in that: In the predictive maintenance module, the analysis process for the state optimization trend of the energy storage converter system is as follows: Based on the state assessment results of the energy storage converter system, battery pack assessment indicators, converter assessment indicators, and state assessment indices are obtained to analyze the current state and performance of the system. The system monitors the energy input and output of the system within a fixed period, analyzes the loss factors of energy transmission, including battery pack internal resistance loss, line loss and energy storage converter loss, in order to determine the proportion of energy loss, and then calculates the energy loss rate of the energy storage converter system during operation, analyzes the changing trend of the energy loss rate, and identifies potential energy consumption problems. By combining the condition assessment index and energy loss rate, the energy storage optimization coefficient is calculated, the energy storage optimization coefficient is analyzed, and the energy storage efficiency and performance optimization potential of the system are comprehensively evaluated. Based on the value of the energy storage optimization coefficient, the operating status of the energy storage converter system is divided into different status levels, namely excellent status level, good status level and poor status level, and corresponding optimization evaluation thresholds are matched for each status level. Based on the energy storage optimization coefficient and the set state level, analyze the future state of the energy storage converter system, determine the maintenance requirements of the energy storage converter system, clarify the maintenance priority, and assist the substation in arranging maintenance work.

5. The energy storage converter system applied to a smart grid distribution area according to claim 4, characterized in that: The expression for the energy storage optimization coefficient is: Where SOC is the energy storage optimization coefficient, ELR is the energy loss rate, S is the condition assessment index, and E in E is the total energy input within a fixed period. out It is the total energy output within a fixed period, where n is the number of loss factors, and the value of SOC ranges from 0 to 1.

6. The energy storage converter system applied to a smart grid distribution area according to claim 5, characterized in that: Multiple state levels correspond to multiple optimization evaluation thresholds, wherein each state level and each optimization evaluation threshold corresponds one-to-one, specifically as follows: State of Excellence (SOC) H ≤SOC<1; Good condition level: SOC L ≤SOC <SOC H ; Poor state level: 0 <SOC<SOC L ; Where SOC is the energy storage optimization coefficient, SOC H SOC is the lower threshold for the excellent state level and the upper threshold for the good state level. L SOC is the lower threshold for a good state level and the upper threshold for a poor state level. H =0.8, SOC L =0.

6.

7. The energy storage converter system applied to a smart grid distribution area according to claim 6, characterized in that: In the control system module, the process of optimizing the scheduling and control of the energy storage converter system is as follows: Through the data acquisition and monitoring module, various operating data of the energy storage converter system are collected in real time, including the voltage, current, temperature, and state of charge of the battery pack, as well as the output power, efficiency, and fault status of the energy storage converter. The grid area problems are also identified, including the long-term three-phase load imbalance and low voltage problems at the end of the grid area and the long-term heavy overload conditions of the transformers in the grid area. Based on real-time monitoring data and the status assessment results of the predictive maintenance module, as well as the predicted maintenance needs, the control system module optimizes the scheduling and control strategy of the energy storage converter system and adjusts the control parameters of the energy storage converter. The control system module intelligently controls the output of the energy storage converter system based on the energy demand and grid status of the smart grid distribution area, thereby achieving a balance between grid supply and demand. Specifically, for long-term three-phase load imbalance and low voltage issues at the end of the grid distribution area, the energy storage system is connected to the distribution end of the area, and the low voltage and three-phase imbalance mitigation mode is activated. The mitigation effect is achieved by adjusting the reactive and active power outputs of the three phases of the energy storage converter. For long-term heavy overload conditions of the transformers in the grid distribution area, the energy storage system is connected to the front end of the transformers, and the overload mitigation mode is activated. The output of the energy storage system is adjusted by detecting the real-time load power, discharging when the load power is too high and charging when the load power is low, thereby reducing the transformer load rate. Based on the characteristics of distributed energy storage, small energy storage systems are connected to the low-voltage side of distribution transformers, and power compensation is performed in combination with the operating load of the power grid area to realize distributed energy storage in the area. The optimized scheduling and control commands are sent from the control system module to the converter control center, and the execution of the commands is tracked in real time. The control system module continuously monitors the system's operating status and control effect, collects feedback data, fine-tunes and further optimizes the control strategy, and formulates detailed maintenance plans based on the predicted results and actual operating conditions, and then executes the maintenance plans to repair and maintain the system.

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