Distributed energy storage monitoring system based on Internet of Things data platform

Abstract

The invention relates to the technical field of energy storage monitoring, in particular to a distributed energy storage monitoring system based on an Internet of Things data platform, which comprises a data acquisition module, a data transmission module, a data platform module, an application module and a security and privacy module. Based on the combination of internet-of-things data and distributed energy storage, the state of energy storage equipment can be monitored in real time, problems can be found and processed in time, big data and artificial intelligence technologies are utilized, the operation strategy of the energy storage equipment is optimized, remote control and management are supported, the operation and maintenance efficiency is improved, the system architecture is flexible and easy to expand and upgrade, redundancy design and a fault-tolerant mechanism are adopted, and the reliability of the system is improved. The stability and reliability of the system are ensured, the data management range can be expanded to the maximum extent, and the safety and stability of a user in the using process are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage monitoring, and in particular to a distributed energy storage monitoring system based on an Internet of Things data platform. Background Art

[0002] Distributed energy storage refers to small energy storage devices dispersed in the power system, which can operate independently or work together to improve the flexibility, reliability and efficiency of the power system. Distributed energy storage systems are usually deployed on the distribution network or user side, compared with traditional centralized energy storage.

[0003] After searching, based on the authorization announcement number CN108400651A, it discloses a distributed energy storage power station monitoring system and method, the monitoring system includes multiple monitoring nodes, the multiple monitoring nodes are distributedly arranged, and connected by wireless communication or wired communication; each of the monitoring nodes is connected to one or more energy storage branches, each of the monitoring nodes monitors the connected energy storage branches, collects and stores real-time data; the real-time data of each monitoring node is stored on another one or more monitoring nodes. The present invention monitors energy storage branches with a large amount of data through different distributed monitoring nodes, disperses the data processing pressure of the station control monitoring system, and reduces the data processing bottleneck; realizes the runtime configuration of the monitoring system, eliminates the service interruption time of the energy storage power station that is already in operation due to capacity expansion, and reduces the secondary development cost of the monitoring system caused by the capacity expansion of the energy storage power station.

[0004] At present, the correct operation of power energy storage is a key technology. In order to better realize the use of electric energy, it will be provided to users in the form of distributed energy storage. When users use it, in order to ensure the safety and stability of the user's use process, corresponding energy storage management is required. In most cases, the management of energy storage is localized, that is, the management scope is relatively small. At the same time, the management methods of some energy storage are relatively simple and cannot provide users with the best conditions. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a distributed energy storage monitoring system based on the Internet of Things data platform. The distributed energy storage monitoring system based on the Internet of Things data platform includes a data acquisition module, a data transmission module, a data platform module, an application module, and a security and privacy module;

[0006] The data acquisition module includes a sensor, a smart meter, and a communication unit;

[0007] The data transmission module includes a network infrastructure unit and an edge computing device;

[0008] The data platform module includes a data storage unit, a data processing unit, and a robot learning model;

[0009] The application module includes an operation interface, a main system interface, an alarm monitoring unit, an energy storage management unit, and a real-time monitoring unit;

[0010] The security and privacy module includes a data encryption unit, an access control unit and a privacy protection unit.

[0011] Furthermore, the robot learning model can predict equipment failures and optimize charging and discharging strategies through training and deployment.

[0012] Further, the specific method for optimizing the charging and discharging strategy is as follows:

[0013] A1. Optimization of electricity prices

[0014] A1.1. Peak-valley electricity price arbitrage: charging during low electricity price periods and discharging during peak electricity price periods to reduce electricity costs;

[0015] A1.2. Dynamic electricity price response: Real-time monitoring of electricity price changes, dynamic adjustment of charging and discharging strategies, and maximization of economic benefits;

[0016] A2. Load demand forecast

[0017] A2.1. Short-term forecasting: Use historical data and machine learning algorithms to predict load demand in the next few hours and adjust charging and discharging plans in advance;

[0018] A2.2. Long-term forecast: Combine seasonal changes and trend analysis to develop long-term charging and discharging strategies;

[0019] A3. Battery health management and life optimization

[0020] A3.1. Balanced charging: avoid overcharging and deep discharge, keep the battery in the best working range, and extend battery life;

[0021] A3.2 Temperature management: monitor battery temperature and take cooling or heating measures to prevent the battery from overheating or overcooling;

[0022] A3.3, Aging model: Establish a battery aging model to predict the remaining battery life and adjust the charging and discharging strategy accordingly;

[0023] A4. Multi-objective optimization

[0024] A4.1. Comprehensive optimization: Considering multiple objectives generated by economic benefits, battery life and system stability at the same time, a multi-objective optimization algorithm is used to find the optimal solution;

[0025] A4.2. Weight allocation: According to different application scenarios and user needs, the weights of each goal are reasonably allocated to achieve personalized optimization;

[0026] A5. Intelligent scheduling and control

[0027] A5.1. Real-time dispatch: Use real-time data and feedback control algorithms to dynamically adjust the charging and discharging power to ensure stable operation of the system;

[0028] A5.2. Predictive control: Combine load forecasting and electricity price forecasting to formulate forward-looking charging and discharging plans to improve system response speed and flexibility;

[0029] A6. Distributed coordinated control

[0030] A6.1. Multi-storage system coordination: Coordinate control among multiple energy storage systems to optimize the charging and discharging strategy of the overall system;

[0031] A6.2. Microgrid control: In a microgrid environment, coordinate the operation of the energy storage system and other distributed energy sources to achieve efficient use of energy;

[0032] A7. Economic Analysis

[0033] A7.1. Cost-benefit analysis: Evaluate the costs and benefits of different charging and discharging strategies and select the most economical solution;

[0034] A7.2. Calculation of investment payback period: Calculate the investment payback period of the energy storage system to help users make reasonable investment decisions.

[0035] Furthermore, the optimization algorithm in A4.1 uses the particle swarm optimization algorithm. PSO is initialized as a group of random particles and finds the optimal solution through iteration.

[0036] Furthermore, the operation interface is used to display the basic operation status of the equipment at each site in the energy storage system, including total active power, total reactive power and total apparent power.

[0037] Furthermore, the main system interface is used to display the wiring diagram of each site in the energy storage system, clearly present the electrical connection and wiring structure, and provide real-time data.

[0038] Furthermore, the alarm monitoring unit is divided into an early warning detection area, a real-time alarm area, a historical alarm area and an alarm statistics area.

[0039] Furthermore, the energy storage management unit includes a grid load area, an auxiliary equipment area, a charging and discharging record area, and a charging and discharging revenue area.

[0040] Furthermore, the real-time monitoring unit provides comprehensive performance analysis and management support by monitoring the operating data of each energy storage device in real time.

[0041] The beneficial effects of the present invention are as follows: 1. Based on the combination of IoT data and distributed energy storage, the status of energy storage equipment can be monitored in real time, problems can be discovered and handled in a timely manner, and the operation strategy of energy storage equipment can be optimized by using big data and artificial intelligence technology, remote control and management can be supported, and operation and maintenance efficiency can be improved. The system architecture is flexible and easy to expand and upgrade. Redundant design and fault-tolerant mechanism are adopted to ensure the stability and reliability of the system, and the scope of data management can be improved to the greatest extent, so that the safety and stability of users in the use process can be further improved;

[0042] 2. As described in 1, by optimizing the charging and discharging strategy, household users can charge during low electricity price periods and discharge during peak periods, thereby reducing electricity bills. At the same time, the stability and reliability of the power grid can be improved and the demand for backup power can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0044] Figure 1 This is a diagram of a distributed energy storage monitoring system based on an Internet of Things data platform of the present invention;

[0045] Figure 2 This is a flow chart of the optimized charge and discharge strategy of the present invention. DETAILED DESCRIPTION

[0046] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0047] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0048] like Figure 1-Figure 2 As shown, a distributed energy storage monitoring system based on the Internet of Things data platform includes a data acquisition module, a data transmission module, a data platform module, an application module, and a security and privacy module;

[0049] The data acquisition module includes sensors, smart meters, and communication units;

[0050] The data transmission module includes network infrastructure units and edge computing devices;

[0051] The data platform module includes a data storage unit, a data processing unit, and a robot learning model;

[0052] The application module includes operation interface, main system interface, alarm monitoring unit, energy storage management unit, and real-time monitoring unit;

[0053] The security and privacy module includes a data encryption unit, an access control unit and a privacy protection unit.

[0054] Specifically:

[0055] Sensors: Various sensors installed on energy storage devices, such as temperature sensors, current sensors, and voltage sensors, are used to monitor the status of the device in real time;

[0056] Smart meters: used to measure the consumption and generation of electrical energy;

[0057] Communication unit: transmits the collected data to the data platform by wireless or wired means;

[0058] Network infrastructure unit: including Wi-Fi, LoRa, NB-IoT wireless communication technologies and Ethernet wired communication technologies to achieve efficient data transmission;

[0059] Edge computing devices: perform preliminary data processing and analysis at locations close to data sources, reducing data transmission volume and latency;

[0060] Data storage unit: Use database technology (such as SQL, NoSQL) to store large amounts of sensor data and historical records;

[0061] Data processing unit: Use big data analysis technologies (such as Hadoop and Spark) to clean, aggregate and analyze data;

[0062] Machine Learning Models: Train and deploy machine learning models;

[0063] Data encryption unit: encrypts the transmitted and stored data to ensure data security;

[0064] Access control unit: set up a strict permission management mechanism to prevent unauthorized access;

[0065] Privacy protection unit: comply with relevant laws and regulations to protect users’ privacy information.

[0066] The robot learning model is trained and deployed to predict equipment failures and optimize charging and discharging strategies.

[0067] The specific methods for optimizing the charging and discharging strategy are as follows:

[0068] A1. Optimization of electricity prices

[0069] A1.1. Peak-valley electricity price arbitrage: charging during low electricity price periods and discharging during peak electricity price periods to reduce electricity costs;

[0070] A1.2. Dynamic electricity price response: Real-time monitoring of electricity price changes, dynamic adjustment of charging and discharging strategies, and maximization of economic benefits;

[0071] A2. Load demand forecast

[0072] A2.1. Short-term forecasting: Use historical data and machine learning algorithms to predict load demand in the next few hours and adjust charging and discharging plans in advance;

[0073] A2.2. Long-term forecast: Combine seasonal changes and trend analysis to develop long-term charging and discharging strategies;

[0074] A3. Battery health management and life optimization

[0075] A3.1. Balanced charging: avoid overcharging and deep discharge, keep the battery in the best working range, and extend battery life;

[0076] A3.2 Temperature management: monitor battery temperature and take cooling or heating measures to prevent the battery from overheating or overcooling;

[0077] A3.3, Aging model: Establish a battery aging model to predict the remaining battery life and adjust the charging and discharging strategy accordingly;

[0078] A4. Multi-objective optimization

[0079] A4.1. Comprehensive optimization: Considering multiple objectives such as economic benefits, battery life and system stability at the same time, a multi-objective optimization algorithm is used to find the optimal solution;

[0080] A4.2. Weight allocation: According to different application scenarios and user needs, the weights of each goal are reasonably allocated to achieve personalized optimization;

[0081] A5. Intelligent scheduling and control

[0082] A5.1. Real-time dispatch: Use real-time data and feedback control algorithms to dynamically adjust the charging and discharging power to ensure stable operation of the system;

[0083] A5.2. Predictive control: Combine load forecasting and electricity price forecasting to formulate forward-looking charging and discharging plans to improve system response speed and flexibility;

[0084] A6. Distributed coordinated control

[0085] A6.1. Multi-storage system coordination: Coordinate control among multiple energy storage systems to optimize the charging and discharging strategy of the overall system;

[0086] A6.2. Microgrid control: In a microgrid environment, coordinate the operation of the energy storage system with other distributed energy sources (such as solar energy, wind energy, etc.) to achieve efficient use of energy;

[0087] A7. Economic Analysis

[0088] A7.1. Cost-benefit analysis: Evaluate the costs and benefits of different charging and discharging strategies and select the most economical solution;

[0089] A7.2. Calculation of investment payback period: Calculate the investment payback period of the energy storage system to help users make reasonable investment decisions.

[0090] The optimization algorithm in A4.1 uses the particle swarm optimization algorithm;

[0091] PSO is initialized as a group of random particles (random solutions), and then finds the optimal solution through iteration;

[0092] In each iteration, the particle updates itself by tracking two extreme values ​​(pbest, gbest). After finding these two optimal values, the particle updates its speed and position using the following formula;

[0093] Formula 1:

[0094] v i =v i +c i *rand()*(pbest i -x i )+c 2 *rand()*(gbest i -x i );

[0095] Formula 2:

[0096] x i =x i +v i ;

[0097] In formula 1 and formula 2, i = 1, 2, 3, ..., N, N is the total number of the particle group;

[0098] v i is the velocity of the particle group;

[0099] rang() A random number between (0,1);

[0100] x i is the current position of the particle;

[0101] c 1 and c 2is the learning factor, usually c 1 =c 2 =2;

[0102] v i The maximum value of v max (greater than 0), if v i Greater than v max , then v i =v max ;

[0103] The above formulas 1 and 2 constitute the standard form of PSO;

[0104] Formula 3:

[0105] v i =ω*v i +c 1 *rand()*(pbest i -x i )+c 2 *rand()*(gbest i -x i );

[0106] ω is the inertia factor, which is non-negative;

[0107] The larger its value, the stronger the global optimization ability is, and the weaker the local optimization ability is, and vice versa;

[0108] Dynamic ω can obtain better optimization results than fixed values. Dynamic ω can change linearly during the PSO search process. The linear decreasing weight is calculated as follows:

[0109] ω( t )=(ω ini -ω end )(G k -g) / G k +ω end ;

[0110] G k Maximum number of iterations;

[0111] ω ini is the initial inertia weight;

[0112] ω end is the inertia weight when iterating to the maximum evolutionary generation;

[0113] The above formula 2 and formula 3 are regarded as standard PSO algorithms.

[0114] The operation interface is used to display the basic operation status of the equipment at each site in the energy storage system, including total active power, total reactive power and total apparent power, and intuitively presents the power curve in the form of a line graph;

[0115] At the same time, the operating status shows the health status of the equipment through color coding, and the communication status of PCS and BMS indicates the stability of the connection with the system. This information is combined in charts and lists to facilitate users to quickly obtain and analyze the operating status of the equipment.

[0116] The main system interface is used to display the wiring diagram of each site in the energy storage system, clearly present the electrical connection and wiring structure, and provide real-time data, including key parameters of current, voltage and power.

[0117] Users can view the status of each switch, such as closed or open, and monitor the status of the grounding knife to ensure system safety. In addition, this function supports remote control of the primary wiring diagram, allowing users to directly operate switches and grounding knives through the interface, improving management efficiency and response speed, and ensuring the flexibility and safety of system operation.

[0118] The alarm monitoring unit is divided into early warning detection area, real-time alarm area, historical alarm area and alarm statistics area;

[0119] The early warning monitoring area analyzes the selected models and reference standards through intelligent algorithms to achieve risk assessment of the equipment. The system will automatically identify and classify the equipment status, count the information of normal equipment, abnormal equipment and equipment with missing data, and generate detailed reports. This process not only considers historical data and operating parameters, but also combines real-time monitoring information to ensure the accuracy and comprehensiveness of the assessment.

[0120] In addition, users can intuitively understand the risk level of various types of equipment through the visual interface, so as to take corresponding measures in time and optimize equipment management and maintenance strategies, thereby improving the safety and reliability of the overall system;

[0121] The real-time alarm area filters the site information and displays the real-time alarm information that meets the filtering conditions, including the site, the number of alarms and the most recent occurrence time, and the filtered results can be exported;

[0122] The historical alarm area displays historical alarm information that meets the screening conditions by filling in the alarm number, loop name, and selecting site information, and supports exporting the screened results;

[0123] The alarm statistics area conducts in-depth analysis of the site's historical alarm information, and counts the alarm type, alarm time, alarm device, and alarm level to generate detailed reports. The system visualizes the data and displays the year-on-year and month-on-month changes in alarms, allowing users to intuitively identify trends and patterns. By comparing alarm data from different time periods, users can evaluate the security status of the site and identify potential risks in a timely manner. Based on these statistical information, the system also provides security assessment recommendations to help users develop effective maintenance and management strategies, thereby improving the overall safety and stability of operations.

[0124] The energy storage management unit includes the grid load area, auxiliary equipment area, charging and discharging record area, and charging and discharging revenue area;

[0125] The grid load area provides users with a comprehensive performance overview by real-time monitoring and displaying key electrical parameters of the equipment, including current, voltage, power factor, active power, reactive power and apparent power. Users can easily track the changing trends of various indicators and identify potential abnormalities in a timely manner, so as to make corresponding adjustments or maintenance decisions. Through this function, users can not only optimize the operating efficiency of the equipment, but also effectively reduce energy consumption and improve the reliability and safety of the overall system.

[0126] The auxiliary equipment area provides a comprehensive overview of equipment health and environmental conditions by monitoring the operating status of site equipment in real time and combining it with ambient temperature and humidity data. The ambient temperature and humidity data are also updated in real time to help users understand the impact of external conditions on equipment performance. In this way, users can promptly detect abnormal conditions and take necessary measures to make adjustments or maintenance, thereby ensuring that the equipment operates in the best environment and improving the reliability and safety of the overall system.

[0127] The charging and discharging record area provides users with in-depth analysis of power usage by systematically recording and displaying the charging and discharging data information of the site on a daily, monthly, and annual time scale. Users can intuitively view the charging and discharging amounts in different time periods, identify usage patterns and trends, and thus evaluate the performance and efficiency of the battery or energy storage system.

[0128] The charging and discharging revenue area uses reports to display the charging and discharging volume, charging costs, and discharging revenue of the site during the query time, and counts the cumulative revenue data of the site.

[0129] The real-time monitoring unit provides comprehensive performance analysis and management support by monitoring the operating data of each energy storage device in real time.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A distributed energy storage monitoring system based on the Internet of Things data platform, characterized by: The distributed energy storage monitoring system based on the IoT data platform includes data acquisition module, data transmission module, data platform module, application module, and security and privacy module; The data acquisition module includes a sensor, a smart meter, and a communication unit; The data transmission module includes a network infrastructure unit and an edge computing device; The data platform module includes a data storage unit, a data processing unit, and a robot learning model; The application module includes an operation interface, a main system interface, an alarm monitoring unit, an energy storage management unit, and a real-time monitoring unit; The security and privacy module includes a data encryption unit, an access control unit and a privacy protection unit.

2. A distributed energy storage monitoring system based on the IoT data platform according to claim 1, characterized in that: The robot learning model predicts equipment failures and optimizes charging and discharging strategies through training and deployment.

3. A distributed energy storage monitoring system based on the IoT data platform according to any one of claims 1-2, characterized in that: The specific methods for optimizing the charging and discharging strategy are as follows: A1. Optimization of electricity prices A1.

1. Peak-valley electricity price arbitrage: charging during low electricity price periods and discharging during peak electricity price periods to reduce electricity costs; A1.

2. Dynamic electricity price response: Real-time monitoring of electricity price changes, dynamic adjustment of charging and discharging strategies, and maximization of economic benefits; A2. Load demand forecast A2.

1. Short-term forecasting: Use historical data and machine learning algorithms to predict load demand in the next few hours and adjust charging and discharging plans in advance; A2.

2. Long-term forecast: Combine seasonal changes and trend analysis to develop long-term charging and discharging strategies; A3. Battery health management and life optimization A3.

1. Balanced charging: avoid overcharging and deep discharge, keep the battery in the best working range, and extend battery life; A3.2 Temperature management: monitor battery temperature and take cooling or heating measures to prevent the battery from overheating or overcooling; A3.3, Aging model: Establish a battery aging model to predict the remaining battery life and adjust the charging and discharging strategy accordingly; A4. Multi-objective optimization A4.

1. Comprehensive optimization: Considering multiple objectives generated by economic benefits, battery life and system stability at the same time, a multi-objective optimization algorithm is used to find the optimal solution; A4.

2. Weight allocation: According to different application scenarios and user needs, the weights of each goal are reasonably allocated to achieve personalized optimization; A5. Intelligent scheduling and control A5.

1. Real-time dispatch: Use real-time data and feedback control algorithms to dynamically adjust the charging and discharging power to ensure stable operation of the system; A5.

2. Predictive control: Combine load forecasting and electricity price forecasting to formulate forward-looking charging and discharging plans to improve system response speed and flexibility; A6. Distributed coordinated control A6.

1. Multi-storage system coordination: Coordinate control among multiple energy storage systems to optimize the charging and discharging strategy of the overall system; A6.

2. Microgrid control: In a microgrid environment, coordinate the operation of the energy storage system and other distributed energy sources to achieve efficient use of energy; A7. Economic Analysis A7.

1. Cost-benefit analysis: Evaluate the costs and benefits of different charging and discharging strategies and select the most economical solution; A7.

2. Calculation of investment payback period: Calculate the investment payback period of the energy storage system to help users make reasonable investment decisions.

4. A distributed energy storage monitoring system based on the IoT data platform according to claim 3, characterized in that: The optimization algorithm in A4.1 uses the particle swarm optimization algorithm. PSO is initialized as a group of random particles and finds the optimal solution through iteration.

5. A distributed energy storage monitoring system based on the IoT data platform according to claim 1, characterized in that: The operation interface is used to display the basic operation status of the equipment at each site in the energy storage system, including total active power, total reactive power and total apparent power.

6. A distributed energy storage monitoring system based on the IoT data platform according to claim 1, characterized in that: The main system interface is used to display the wiring diagram of each site in the energy storage system, clearly present the electrical connection and wiring structure, and provide real-time data.

7. A distributed energy storage monitoring system based on the Internet of Things data platform according to claim 1, characterized in that: The alarm monitoring unit is divided into an early warning detection area, a real-time alarm area, a historical alarm area and an alarm statistics area.

8. The distributed energy storage monitoring system based on the IoT data platform according to claim 1 is characterized in that: The energy storage management unit includes a power grid load area, an auxiliary equipment area, a charging and discharging record area, and a charging and discharging revenue area.

9. The distributed energy storage monitoring system based on the IoT data platform according to claim 1 is characterized in that: The real-time monitoring unit provides comprehensive performance analysis and management support by monitoring the operating data of each energy storage device in real time.

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