An energy storage system and method for low-voltage distribution networks based on big data.

Abstract

This invention discloses an energy storage system and method for low-voltage distribution networks based on big data, including a charging and discharging control module, a battery monitoring module, an energy sampling module, and a data uploading module. The charging and discharging control mechanism of the device in this invention can be configured as needed according to different application scenarios. Automatic switching can be set through relevant thresholds. Real-time viewing and monitoring of device operating conditions and energy storage battery data are possible. This project is based on an optimization algorithm, taking into account the day-ahead real-time electricity price curve and the future ancillary service price curve. The algorithm aims to maximize revenue in the electricity market environment, i.e., to make the optimal arrangement for the operation of the energy storage device under the electricity market environment. This invention can achieve online monitoring and management of the energy storage control device; realize full life-cycle management of the energy storage battery; implement a self-charging and discharging process control algorithm for energy storage; and realize data storage analysis and accident alarm management.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology for power distribution networks, specifically to an energy storage system and method for low-voltage power distribution networks based on big data. Background Technology

[0002] Low-voltage distribution networks supply power to end users. Under the premise of safe power supply, reliable, high-quality, and economical power supply are key concerns for distribution networks. Electrochemical energy storage systems applied to low-voltage distribution networks offer technical advantages such as power support, improved power supply reliability and power quality, and peak shaving and valley filling of grid load. With advancements in energy storage technology and cost reductions, as well as the evolution of demand, the widespread application of distributed energy storage in power systems is an inevitable trend in future grid development and a crucial way to break through traditional distribution network planning and operation methods. Traditional battery energy storage devices operate based on fixed-time charging and discharging, utilizing time-of-use pricing to charge during off-peak hours and discharge during peak hours, profiting from the peak-valley price difference.

[0003] With the development of the electricity market, especially the popularization of the electricity spot market, electricity prices change every 15 minutes. It is necessary to make full use of the fluctuations in electricity prices to realize the economic benefits of battery energy storage devices; the fixed-time charging and discharging method is no longer applicable. Summary of the Invention

[0004] The purpose of this invention is to provide an energy storage system and method for low-voltage distribution networks based on big data, so as to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an energy storage system for a low-voltage distribution network based on big data, comprising a charge / discharge control module, a battery monitoring module, an energy sampling module, a data upload module, and a central processing module. The charge / discharge control module, battery monitoring module, and energy sampling module are all connected to the central processing module via the data upload module. The charge / discharge control module performs charge / discharge control processing on the energy storage system; the battery monitoring module monitors the batteries in the energy storage system; the energy sampling module samples energy data from the devices in the system; the data upload module uploads the data from the system to the system platform; and the central processing module performs comprehensive processing and analysis of the data from the system.

[0006] Furthermore, the charging and discharging control module includes: a battery charging switch control unit and a battery discharging switch control unit. The battery charging switch control unit is used to control the charging switch of the battery, and the battery discharging switch control unit is used to control the discharging switch of the battery.

[0007] Furthermore, the battery monitoring module includes: a battery current monitoring unit, a battery voltage monitoring unit, and a battery power monitoring unit; the battery current monitoring unit is used to monitor and process the current of the energy storage battery, the battery voltage monitoring unit is used to monitor and process the voltage of the energy storage battery, and the battery power monitoring unit is used to monitor and process the power of the energy storage battery.

[0008] Furthermore, the power sampling module includes: a grid current sampling unit, a grid voltage sampling unit, and a grid electricity price data acquisition unit; the grid current sampling unit is used to sample the current data of the low-voltage distribution network, the grid voltage sampling unit is used to sample the voltage data of the low-voltage distribution network, and the grid electricity price data acquisition unit is used to acquire the electricity price data of the low-voltage distribution network.

[0009] Furthermore, the data upload module includes: a wireless data upload unit and a wired data upload unit, wherein the wireless data upload unit is used to upload data wirelessly, and the wired data upload unit is used to upload data wiredly.

[0010] Furthermore, the central processing module includes: a data storage unit, a data processing unit, and a data analysis unit; the data storage unit is used for storing and processing data from multiple systems, the data processing unit is used for processing the data, and the data analysis unit is used for analyzing and processing the data.

[0011] Furthermore, it also includes: a smart terminal and an alarm module, the smart terminal and the alarm module being connected to the central processing module for communication and data connection, the smart terminal being used to view, set, and modify data in the system; the alarm module being used to process alarm notifications for abnormal data in the system.

[0012] This invention also provides an energy storage method for an energy storage system in a low-voltage distribution network based on big data, comprising the following steps:

[0013] S1. Start the system. The battery monitoring module in the system collects, monitors and processes the data of the energy storage battery system, and uploads the energy storage battery data to the central processing module in real time.

[0014] S2, the power energy sampling module is used to sample and process the current, voltage, and electricity price data in the power grid system in real time, and upload the power grid data to the central processing module in real time;

[0015] S3. The data upload module uploads on-site monitoring data to the system platform via wired or wireless means for users to monitor and manage in real time.

[0016] S4. The central processing module analyzes and processes the voltage, current, and power data of the energy storage battery system, performs real-time monitoring and management of the energy storage battery, and notifies relevant operation and maintenance personnel to perform inspection and maintenance when there is an abnormality or fault in the energy storage battery.

[0017] S5. The central processing module analyzes and integrates the current, voltage, and electricity price data in the power grid data, as well as the energy storage battery power data, to determine whether to perform charging and discharging operations.

[0018] The current is L i The voltage is U i The price of electricity is P. i Electricity price P i The corresponding time is T i The charge / discharge management assessment value is X. i The advanced value for charge / discharge management judgment is Y. i The remaining capacity of the energy storage battery is Z. i The energy storage battery's full capacity is Z. 满 The charging efficiency of the energy storage battery is A; the remaining percentage of the energy storage battery's charge is B. i ;

[0019] The data commands are sent to the charge / discharge control module, which then controls the charge / discharge switch of the energy storage battery.

[0020] Furthermore, in step S5,

[0021] Using the evaluation value X i And the advanced value Y i Establish a two-dimensional coordinate system curve;

[0022]

[0023] The real-time electricity price is analyzed and processed, and the assessed value X is evaluated. i Perform calculations to express the magnitude of changes between adjacent electricity prices;

[0024] When X i When the value is greater than 0, the price of electricity between adjacent prices is in an upward trend.

[0025] When X i When <0, the adjacent electricity prices are in a decreasing trend;

[0026] Evaluation value X i When the price is close to zero, it falls during peak or off-peak hours, and then is determined based on the previously assessed value X. i Judgment of changes;

[0027] When X i→When 0-, it enters the off-peak period for electricity prices;

[0028] When X i When →0+, the peak electricity price period begins;

[0029] Advanced value Y i This indicates the frequency of price fluctuations between adjacent electricity prices;

[0030] When X i >0, Y i When the value is ≥1, the price increases sharply between adjacent electricity prices.

[0031] When X i <0, Y i When the value is ≥1, the electricity price decreases sharply between adjacent prices.

[0032] When X i >0, Y i When the price is less than 1, the price increases gradually between adjacent prices.

[0033] When X i <0, Y i When the price is less than 1, the price decreases gradually between adjacent prices.

[0034] When U i <180V, B i When the battery level is ≤30%, the power supply switch for the energy storage battery will automatically close.

[0035] The optimal charging time is: when X i When →0-, the energy storage battery is automatically prioritized for power supply, and the charging time is T. 最 ;

[0036] High-performance charging time is: when X i <0, Y i When <1, the energy storage battery is automatically supplied with high-level power, and the charging time is T. 高 ;

[0037] The suboptimal charging time is: when X i >0, Y i When the value is less than 1, the energy storage battery is automatically supplied with secondary power, and the charging time is T. 次 ;

[0038] When X i When →0+, the energy storage battery is discharged.

[0039] Furthermore, the total charging time required is T. 总 ;

[0040] T 总 =f×T最 +g×T 高 +h×T 次 ;

[0041]

[0042]

[0043]

[0044] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0045] 1. This invention includes a charging / discharging control module, a battery monitoring module, an energy sampling module, and a data upload module. The charging / discharging control mechanism can be configured as needed according to different application scenarios. It can be set to automatically switch based on relevant thresholds, or it can be manually switched remotely as needed. The data upload module uploads on-site monitoring data to the system platform via wired or wireless means for real-time monitoring and management by users. Users can also view and monitor the device's operating status and energy storage battery data in real time via a mobile APP or mini-program. This project, based on an optimization algorithm and using the current daytime electricity price curve and future ancillary service price curve, develops software for scheduling the charging and discharging of the battery energy storage device. The algorithm aims to maximize revenue in the electricity market environment, i.e., to make the optimal arrangement for the operation of the energy storage device under the electricity market environment. This invention can achieve online monitoring and management of the energy storage control device; realize full life-cycle management of the energy storage battery; implement a self-charging and discharging process control algorithm for energy storage; and realize data storage analysis and accident alarm management.

[0046] 2. In this invention, real-time electricity prices are analyzed and processed. The evaluation value expresses the magnitude of the change between adjacent electricity prices; the progressive value expresses the frequency of the change between adjacent electricity prices. When the evaluation value is greater than zero and the progressive value is greater than or equal to 1, the adjacent electricity prices are in a state of rapid increase. When the evaluation value is less than zero and the progressive value is greater than or equal to 1, the adjacent electricity prices are in a state of rapid decrease. When the evaluation value is greater than zero and the progressive value is less than 1, the adjacent electricity prices are in a state of gradual increase. When the evaluation value is less than zero and the progressive value is less than 1, the adjacent electricity prices are in a state of gradual decrease. When the evaluation value is close to zero, the electricity price is in a peak period or a valley period. Then, based on the previous evaluation value changes, if the evaluation value gradually approaches zero from a state of less than zero, it enters a valley period of electricity prices; if the evaluation value gradually approaches zero from a state of greater than zero, it enters a peak period of electricity prices. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a schematic diagram of the module connections in this invention;

[0049] Figure 2 This is a schematic diagram of the charge / discharge control module in this invention;

[0050] Figure 3 This is a schematic diagram of the battery monitoring module in this invention;

[0051] Figure 4 This is a schematic diagram of the electrical energy sampling module in this invention;

[0052] Figure 5 This is a schematic diagram of the data upload module in this invention;

[0053] Figure 6 This is a schematic diagram of the central processing module in this invention;

[0054] In the diagram: 1. Charge / discharge control module; 2. Battery monitoring module; 3. Energy sampling module; 4. Data upload module; 5. Central processing module. Detailed Implementation

[0055] 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, and 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.

[0056] Please see Figures 1-6 This invention provides a technical solution: an energy storage system for a low-voltage distribution network based on big data, comprising a charge / discharge control module 1, a battery monitoring module 2, an energy sampling module 3, a data upload module 4, and a central processing module 5. The charge / discharge control module 1, battery monitoring module 2, and energy sampling module 3 are all connected to the central processing module 5 via the data upload module 4. The charge / discharge control module 1 is used for charge / discharge control processing of the energy storage system; the battery monitoring module 2 is used for monitoring the batteries in the energy storage system; the energy sampling module 3 is used for sampling energy data from the devices in the system; the data upload module 4 is used for uploading the data from the system to the system platform; and the central processing module 5 is used for comprehensive processing and analysis of the data from the system.

[0057] The charging and discharging control module 1 includes: a battery charging switch control unit and a battery discharging switch control unit. The battery charging switch control unit controls the charging switch of the battery, and the battery discharging switch control unit controls the discharging switch of the battery. The battery monitoring module 2 includes: a battery current monitoring unit, a battery voltage monitoring unit, and a battery power monitoring unit. The battery current monitoring unit monitors the current of the battery, the battery voltage monitoring unit monitors the voltage of the battery, and the battery power monitoring unit monitors the power of the battery. The energy sampling module 3 includes: a grid current sampling unit, a grid voltage sampling unit, and a grid electricity price data acquisition unit. The grid current sampling unit samples the current data of the low-voltage distribution network, and the grid voltage sampling unit samples the current data of the low-voltage distribution network. The voltage data is sampled, and the power grid price data acquisition unit is used to collect the price data of the low-voltage distribution network; the data upload module 4 includes: a wireless data upload unit and a wired data upload unit. The wireless data upload unit is used to upload data wirelessly, and the wired data upload unit is used to upload data wiredly; the central processing module 5 includes: a data storage unit, a data processing unit, and a data analysis unit. The data storage unit is used to store and process data from multiple systems, the data processing unit is used to process the data, and the data analysis unit is used to analyze and process the data; it also includes: a smart terminal and an alarm module. The smart terminal and the alarm module are respectively connected to the central processing module 5 for communication and data connection. The smart terminal is used to view, set, and modify the data in the system; the alarm module is used to provide alarm notifications for abnormal data in the system.

[0058] This invention also provides an energy storage method for an energy storage system in a low-voltage distribution network based on big data, comprising the following steps:

[0059] S1. Start the system. The battery monitoring module 2 in the system collects, monitors and processes the data of the energy storage battery system and uploads the energy storage battery data to the central processing module 5 in real time.

[0060] S2, the power energy sampling module 3 is used to sample and process the current, voltage and electricity price data in the power grid system in real time, and upload the power grid data to the central processing module 5 in real time;

[0061] S3, Data Upload Module 4 uploads on-site monitoring data to the system platform via wired or wireless means for users to monitor and manage in real time;

[0062] S4 and the central processing module 5 analyze and process the voltage, current and power data of the energy storage battery system, and monitor and manage the energy storage battery in real time. When there is an abnormality or fault in the energy storage battery, the relevant operation and maintenance personnel are notified through alarm to carry out inspection and maintenance.

[0063] S5, the central processing module 5, analyzes and integrates the current, voltage, and electricity price data in the power grid data, as well as the power data of the energy storage battery, to determine whether to perform charging and discharging operations.

[0064] The current is L i The voltage is U i The price of electricity is P. i Electricity price P i The corresponding time is T i The charge / discharge management assessment value is X. i The advanced value for charge / discharge management judgment is Y. i The remaining capacity of the energy storage battery is Z. i The energy storage battery's full capacity is Z. 满 The charging efficiency of the energy storage battery is A; the remaining percentage of the energy storage battery's charge is B. i ;

[0065]

[0066] Using the evaluation value X i And the advanced value Y i Establish a two-dimensional coordinate system curve;

[0067]

[0068] The real-time electricity price is analyzed and processed, and the assessed value X is evaluated. i Perform calculations to express the magnitude of changes between adjacent electricity prices;

[0069] When X i When the value is greater than 0, the price of electricity between adjacent prices is in an upward trend.

[0070] When X i When <0, the adjacent electricity prices are in a decreasing trend;

[0071] Evaluation value X i When the price is close to zero, it falls during peak or off-peak hours, and then is determined based on the previously assessed value X. i Judgment of changes;

[0072] When X i →When 0-, it enters the off-peak period for electricity prices;

[0073] When X i When →0+, the peak electricity price period begins;

[0074] Advanced value Y i This indicates the frequency of price fluctuations between adjacent electricity prices;

[0075] When X i >0, Y i When the value is ≥1, the price increases sharply between adjacent electricity prices.

[0076] When X i <0, Y i When the value is ≥1, the electricity price decreases sharply between adjacent prices.

[0077] When X i >0, Y i When the price is less than 1, the price increases gradually between adjacent prices.

[0078] When X i <0, Y i When the price is less than 1, the price decreases gradually between adjacent prices.

[0079] When U i <180V, B i When the battery level is ≤30%, the power supply switch for the energy storage battery will automatically close.

[0080] The optimal charging time is: when X i When →0-, the energy storage battery is automatically prioritized for power supply, and the charging time is T. 最 ;

[0081] High-performance charging time is: when X i <0, Y i When <1, the energy storage battery is automatically supplied with high-level power, and the charging time is T. 高 ;

[0082] The suboptimal charging time is: when X i >0, Y i When the value is less than 1, the energy storage battery is automatically supplied with secondary power, and the charging time is T. 次 ;

[0083] When X i When →0+, the energy storage battery is discharged.

[0084] The total charging time required is T. 总 ;

[0085] T 总 =f×T 最 +g×T 高 +h×T 次 ;

[0086]

[0087]

[0088]

[0089] The data command is sent to the charge / discharge control module 1, which controls the charge / discharge switch of the energy storage battery.

[0090] This invention addresses the problem that with the development of the electricity market, especially the widespread adoption of the electricity spot market, electricity prices change every 15 minutes, necessitating full utilization of price fluctuations to achieve economic benefits for battery energy storage devices; fixed-time charging and discharging methods are no longer applicable.

[0091] Working principle of the invention:

[0092] Refer to the instruction manual appendix Figure 1This invention includes a charging / discharging control module, a battery monitoring module, an energy sampling module, and a data upload module. The charging / discharging control mechanism can be configured as needed according to different application scenarios. It can be set to automatically switch via relevant thresholds or manually controlled remotely as needed. The data upload module uploads on-site monitoring data to the system platform via wired or wireless means for real-time monitoring and management by users. Users can also view and monitor the device's operating status and energy storage battery data in real time via a mobile app or mini-program. The invention analyzes and processes real-time electricity prices, calculating the evaluation value between adjacent prices on the real-time electricity price curve to express the magnitude of price changes. When the evaluation value is greater than zero, the adjacent prices are in an upward trend. When the estimated value is less than zero, the adjacent electricity price is in a downward trend; when the estimated value is close to zero, the electricity price is in a peak or valley period, and then judged based on the previous changes in the estimated value; when the estimated value gradually approaches zero from a state of less than zero, it enters a valley period; when the estimated value gradually approaches zero from a state of greater than zero, it enters a peak period; the increment value expresses the frequency of change between adjacent electricity prices; when the estimated value is greater than zero and the increment value is greater than or equal to 1, the adjacent electricity price is in a sharp upward trend; when the estimated value is less than zero and the increment value is greater than or equal to 1, the adjacent electricity price is in a sharp downward trend; when the estimated value is greater than zero and the increment value is less than 1, the adjacent electricity price is in a gradual upward trend; when the estimated value is less than zero and the increment value is less than 1, the adjacent electricity price... The price decreases gradually over time; when the assessed value is close to zero, the electricity price is in either peak or valley period. Based on previous assessed value changes, if the assessed value gradually approaches zero from a state less than zero, it enters a valley period; if the assessed value gradually approaches zero from a state greater than zero, it enters a peak period. The remote control contacts of this device are installed on the energy storage control switch to monitor the switch's open state in real time and to collect and monitor data from the energy storage battery system in real time. It also monitors and manages the battery's operating status in real time, notifying relevant maintenance personnel of any abnormalities or malfunctions. Simultaneously, it monitors electrical parameters such as current and voltage in the power system in real time, automatically closing the power supply when the voltage is too low (e.g., below 180V). The battery power supply switch initiates power supply, ensuring its stability. When the battery discharges to 30%, the power supply switch automatically switches, determining whether it's during peak or off-peak hours. If it's during off-peak hours (e.g., late at night), the charging switch automatically switches to begin charging the battery for energy storage. All charging and discharging operation records are automatically stored in the system platform for future accident and process analysis. This project, based on an optimization algorithm and using the current daytime electricity price curve and future ancillary service price curve, develops software to schedule the charging and discharging of the battery energy storage device. The algorithm aims to maximize revenue in the electricity market environment, i.e., to make the optimal arrangement for the operation of the energy storage device under the electricity market conditions. This invention enables online monitoring and management of the energy storage control device.Achieve full lifecycle management of energy storage batteries; implement self-charging and discharging process control algorithms for energy storage; and implement data storage, analysis, and accident alarm management.

[0093] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy storage system for a low-voltage distribution network based on big data, comprising a charge / discharge control module (1), a battery monitoring module (2), an energy sampling module (3), a data upload module (4), and a central processing module (5), characterized in that: The charging and discharging control module (1), battery monitoring module (2), and energy sampling module (3) are all connected to the central processing module (5) via the data upload module (4). The charging and discharging control module (1) is used to control the charging and discharging of the energy storage system. The battery monitoring module (2) is used to monitor the batteries in the energy storage system. The energy sampling module (3) is used to sample the energy data of the devices in the system. The data upload module (4) is used to upload the data in the system to the system platform. The central processing module (5) is used to comprehensively organize and analyze the data in the system. An energy storage method for a low-voltage distribution network based on big data includes the following steps: S1. Start the system. The battery monitoring module (2) in the system collects and monitors the data of the energy storage battery system and uploads the energy storage battery data to the central processing module (5) in real time. S2, the power energy sampling module (3) is used to sample and process the current, voltage and electricity price data in the power grid system in real time, and upload the power grid data to the central processing module (5) in real time. S3, Data Upload Module (4) uploads on-site monitoring data to the system platform via wired or wireless means for users to monitor and manage in real time; S4, Central processing module (5) analyzes and processes the voltage, current and power data of the energy storage battery system, monitors and manages the energy storage battery in real time, and notifies relevant maintenance personnel to perform inspection and maintenance when there is an abnormality or fault in the energy storage battery. S5, the central processing module (5) analyzes and integrates the current, voltage, electricity price data and energy storage battery power data in the power grid data to determine whether to perform charging and discharging operations; The current is L i The voltage is U i The price of electricity is P. i Electricity price P i The corresponding time is T i The charge / discharge management assessment value is X. i The advanced value for charge / discharge management judgment is Y. i The remaining capacity of the energy storage battery is Z. i The energy storage battery's full capacity is Z. 满 The charging efficiency of the energy storage battery is A; the remaining percentage of the energy storage battery's charge is B. i ; The data command is sent to the charge and discharge control module (1), which controls the charge and discharge switch of the energy storage battery. Using the evaluation value X i And the advanced value Y i Establish a two-dimensional coordinate system curve; The real-time electricity price is analyzed and processed, and the assessed value X is evaluated. i Perform calculations to express the magnitude of changes between adjacent electricity prices; When X i When the value is greater than 0, the price of electricity between adjacent prices is in an upward trend. When X i When <0, the adjacent electricity prices are in a decreasing trend; Evaluation value X i When the price is close to zero, it falls during peak or off-peak hours, and then is determined based on the previously assessed value X. i Judgment of changes; When X i →When 0-, it enters the off-peak period for electricity prices; When X i When →0+, the peak electricity price period begins; Advanced value Y i This indicates the frequency of price fluctuations between adjacent electricity prices; When X i >0, Y i When the value is ≥1, the price increases sharply between adjacent electricity prices. When X i <0, Y i When the value is ≥1, the electricity price decreases sharply between adjacent prices. When X i >0, Y i When the price is less than 1, the price increases gradually between adjacent prices. When X i <0, Y i When the price is less than 1, the price decreases gradually between adjacent prices. When U i <180V, B i When the battery level is ≤30%, the power supply switch for the energy storage battery will automatically close. The optimal charging time is: when X i When →0-, the energy storage battery is automatically prioritized for power supply, and the charging time is T. 最 ; High-performance charging time is: when X i <0, Y i When <1, the energy storage battery is automatically supplied with high-level power, and the charging time is T. 高 ; The suboptimal charging time is: when X i >0, Y i When the value is less than 1, the energy storage battery is automatically supplied with secondary power, and the charging time is T. 次 ; When X i When →0+, the energy storage battery is discharged. The total charging time required is T. 总 ; T 总 =f×T 最 +g×T 高 +h×T 次 ; 2. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: The charging and discharging control module (1) includes: a battery charging switch control unit and a battery discharging switch control unit. The battery charging switch control unit is used to control the charging switch of the battery, and the battery discharging switch control unit is used to control the discharging switch of the battery.

3. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: The battery monitoring module (2) includes: a battery current monitoring unit, a battery voltage monitoring unit, and a battery power monitoring unit; the battery current monitoring unit is used to monitor and process the current of the energy storage battery, the battery voltage monitoring unit is used to monitor and process the voltage of the energy storage battery, and the battery power monitoring unit is used to monitor and process the power of the energy storage battery.

4. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: The power sampling module (3) includes: a power grid current sampling unit, a power grid voltage sampling unit, and a power grid electricity price data acquisition unit; the power grid current sampling unit is used to sample the current data of the low-voltage distribution network, the power grid voltage sampling unit is used to sample the voltage data of the low-voltage distribution network, and the power grid electricity price data acquisition unit is used to acquire the electricity price data of the low-voltage distribution network.

5. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: The data upload module (4) includes: a wireless data upload unit and a wired data upload unit. The wireless data upload unit is used to upload data wirelessly, and the wired data upload unit is used to upload data wiredly.

6. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: The central processing module (5) includes: a data storage unit, a data processing unit, and a data analysis unit; the data storage unit is used for storing and processing data from multiple systems, the data processing unit is used for processing the data, and the data analysis unit is used for analyzing and processing the data.

7. The energy storage system for a low-voltage distribution network based on big data according to claim 1, characterized in that: Also includes: The intelligent terminal and the warning module are respectively connected to the central processing module (5) for communication and data connection. The intelligent terminal is used to view, set and modify the data in the system; the warning module is used to process alarm notifications for abnormal data in the system.

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