Decentralized energy management system

Abstract

A distributed energy management system is electrically connected to a user-end device and at least one power-end device in a field. The distributed energy management system includes at least one server management unit and a user-end management unit. The server management unit and the user-end management unit are connected. The server management unit is connected to the power-end device to collect status data of the power-end device. The user-end management unit is connected to the user-end device and can compare the status data of the power-end device collected by the server management unit with the transaction requirements provided by the user-end device. If the requirements are met, the user-end management unit transmits setting parameters to the server management unit, and the server management unit adjusts the operation of the power-end device according to the setting parameters.

Description

Technical Field

[0001] This invention relates to a distributed energy management system, and more particularly to a system architecture that can be separately structured at the server end and the user end of an energy management system. This allows for dynamic and flexible adjustment of the user end scale based on the increase or decrease of the server end, making the expansion of the energy management system more flexible. Prior Technology

[0002] Currently, the main functions of energy management systems (EMS) on the market are to collect, control, manage, and alert on abnormalities related to energy storage battery modules, power control modules, meter modules, various sensor modules, monitor modules, and on-site control equipment. The setup of energy management system 1 is shown in Figure 1. On one end, it manages multiple sites 31, 32, and 33 (with internal power-end devices 312, 322, and 332) through data transmission units 311, 321, and 331 (DTU) (or remote control unit (RTU)). At the same time, it receives data from end-user devices 2 (data from power companies or aggregators) and provides real-time power services for related transactions.

[0003] Energy Management Systems (EMS) also have the following functions: (1) EMS can accept the assignment of managers or end devices to provide power dispatch and related services through contracts, bidding and other means, such as frequency regulation, power reserve and demand response. Some services need to provide corresponding real-time power management energy services in response to changes in power. (2) EMS can manage multiple battery storage cabinets, power control modules and other equipment in real time through data communication ports. That is, through the Ethernet, multiple energy storage sites in different locations can be managed in the cloud. (3) EMS must monitor the field in real time and respond to management measures in real time. This architecture requires a large amount of data transmission. That is, the more different fields EMS manages remotely through Ethernet, the more advanced network equipment and architecture it needs. At the same time, EMS computing equipment also needs to have a certain processing speed to respond to diverse services in real time. (4) Depending on the characteristics of different fields, EMS may also need to perform functions such as monitoring and managing generator modules and renewable energy modules.

[0004] In order to provide power services to end-user devices, an Energy Management System (EMS) needs to continuously monitor on-site energy storage and power status and adjust power parameters in real time to provide real-time power services. However, energy management systems are also prone to the following problems: (1) It is known that data collection in a single field increases with the size of the field, and the number of data collection units (DTUs) increases. If data collection is not carried out through a remote cloud, it is difficult to manage a large number of scattered data collection devices or to confirm abnormal problems on site at the same time. The management difficulty increases with the size of the field. (2) When the number of managed sites exceeds a certain limit, the EMS architecture must be redesigned with another identical EMS architecture. The reinvestment amount for redesign is very large. When two or more EMS cloud architectures are interconnected, there must be another interconnection mechanism. Such an architecture is also less flexible.

[0005] As mentioned above, with the increase in field size and the demand for real-time data transmission, conventional energy management systems face an increased load on data transmission over the network, thus placing higher demands on communication quality. However, this also means that system reliability becomes more difficult to maintain. Larger-scale energy management systems are more prone to failure when network quality degrades or is attacked.

[0006] Therefore, when the number of fields exceeds the data volume that the original energy system can handle and operate, a new energy management system must be added to cope with the newly added fields. This will inevitably involve a large investment in building the new system. At the same time, as multiple energy management systems and the new system work together, new mechanisms need to be established. As the number of energy systems increases, the complexity of managing multiple systems also increases. Therefore, in addition to the difficulty of expansion, the subsequent problems brought about by expansion are also troublesome.

[0007] In addition to expansion issues, energy management systems also face transmission problems, as explained below: (1) In the prior art, after the data of each module is collected by the data transmission or remote control unit, it is transmitted through the data channel connected to the EMS. The EMS then performs storage and analysis after collecting the data. Under such an architecture, the data channel is easily affected by changes in transmission quality, which can affect the real-time monitoring and management of each module by the EMS. When some important data is congested, it is easy to experience delays in receiving or loss. (2) For example, when the battery experiences an overvoltage or overtemperature alarm or a short circuit in the power control, if the data channel quality is poor, the EMS may be delayed in receiving the overvoltage or power control abnormality signal, or an important signal may be lost. (3) In response to the above transmission problems, EMS needs to provide immediate power services to external parties, which will further increase the burden on the system.

[0008] To address the aforementioned issues related to the expansion and transmission of energy management systems, this invention proposes a distributed architecture to solve the problem of the inability to dynamically and flexibly adjust the scale of users. Furthermore, this invention employs priority data channel transmission control to resolve issues arising from data congestion. Therefore, this invention should be considered the optimal solution. Summary of the Invention

[0009] This invention discloses a distributed energy management system electrically connected to a user-end device and at least one power-end device within a field. The distributed energy management system includes at least one server management unit electrically connected to one or more power-end devices. Each server management unit includes at least one first communication management module for receiving and / or uploading data. This first communication management module manages the priority order of uploading and receiving data based on a data channel priority setting. A task execution module is connected to the first communication management module and receives task execution data through it. The task execution data contains at least a device code and a setting parameter. The task execution module outputs the setting parameter to the power-end device corresponding to the device code based on the content of the task execution data. A device status acquisition module is connected to the first communication management module. The system comprises: a first communication management module for collecting status data of the power terminal equipment, wherein status data of different power terminal equipment are transmitted through the first communication management module; a data storage module connected to the first communication management module, the task execution module, and the equipment status acquisition module for storing setting parameters and status data of different power terminal equipment; a user terminal management unit electrically connected to the user terminal equipment and at least one server terminal management unit, wherein the server terminal management unit includes at least one second communication management module connected to the first communication management module, the second communication management module for receiving and / or uploading data, and the second communication management module for managing the priority order of uploading and receiving data according to the data channel priority setting value; and a data backup module connected to the second communication management module for receiving status data of different power terminal equipment through the second communication management module.

[0010] More specifically, the power terminal equipment is one or more of a distributed power generation module, a battery module, a power control module, an electricity meter module, various sensor modules, a monitor module, and a field control device.

[0011] More specifically, the task execution module is also capable of storing at least one task execution record file, which the task execution module can send back to the user terminal management unit.

[0012] More specifically, the server management unit further includes an anomaly handling module, which is connected to the first communication management module and the data storage module. The anomaly handling module sends an alarm notification to the user management unit through the first communication management module based on the status data of different power terminal devices and an alarm judgment standard value. The user management unit then sends back an emergency management command, and the anomaly handling module processes the data according to the emergency management command.

[0013] More specifically, the anomaly handling module can establish an emergency contact file, which contains the communication information of a system administrator or an emergency contact person. The anomaly handling module can also send the warning notification information to the system administrator and / or the emergency contact person based on the content of the emergency contact file.

[0014] More specifically, the server management unit further includes an analysis and prediction module, which is connected to the first communication management module and the data storage module to aggregate the status data of different power terminal devices into an analysis data file. The analysis data file is divided into normal data and abnormal data, and the analysis data file is transmitted to the user management unit through the first communication management module.

[0015] More specifically, the user-end management unit can perform machine learning based on the status data of different power-end devices to train an analysis and judgment training module. This analysis and judgment training module operates within the analysis and prediction module. The analysis and judgment training module can monitor the normalized data, perform data summarization or data abstraction processing on the normalized data, and schedule the upload to the user-end management unit for backup.

[0016] More specifically, the user-end management unit further includes a maintenance module, which is connected to the second communication management module and the data backup module to analyze an alarm notification to generate emergency maintenance task data and routine maintenance task data. The emergency maintenance task data or the routine maintenance task data is transmitted to the server-end management unit through the second communication management module. The emergency maintenance task data includes at least an emergency management instruction, and the routine maintenance task data is a troubleshooting task scheduling instruction, an inspection task scheduling instruction, a maintenance task scheduling instruction, or a repair task scheduling instruction.

[0017] More specifically, the user terminal management unit further includes a third communication management module, which is connected to the user terminal device to receive transaction service work data. The transaction service work data has at least one transaction requirement. The data backup module is connected to the third communication management module, and the data backup module is used to receive the transaction service work data through the third communication management module.

[0018] More specifically, the user-end management unit further includes a resource integration module connected to the data backup module. This resource integration module determines whether the transaction requirements are met based on the status data of different power-end devices and generates service resource aggregation data.

[0019] More specifically, the user terminal management unit further includes a task dispatch module connected to the second communication management module, the data backup module, and the resource integration module, for analyzing the status data of different power terminal devices and / or the service resource aggregation data to generate the task execution data, and the task execution data is transmitted through the second communication management module.

[0020] More specifically, the user-end management unit further includes a transaction service analysis module connected to the data backup module. The transaction service analysis module is used to perform transaction analysis processing on the transaction service work data. This analysis processing includes power service data analysis, market strategy formulation analysis, transaction execution processing analysis, or transaction report processing analysis.

[0021] More specifically, the server management unit and the user management unit can be located in the same server device or in different server devices.

[0022] More specifically, the server management unit can use virtual hosting, hyperconverged architecture, and Docker technologies to virtually integrate and centrally manage multiple server devices.

[0023] More specifically, the user-end management unit is a software framework that uses distributed storage of big data to flexibly adjust the number and architecture of the server devices, directly increasing the scale of the service devices.

[0024] More specifically, the first communication management module and the second communication management module can manage the priority order of data channel scheduling according to the data channel priority setting value. The data backup module can also provide the data channel scheduling buffer function of the first communication management module, and the data storage module can also provide the data channel scheduling buffer function of the second communication management module.

[0025] More specifically, the data channel between the first communication management module and the second communication management module can use a parallel communication architecture and a high-availability seamless backup protocol in terms of communication architecture.

[0026] More specifically, the data channel between the first communication management module and the second communication management module can use Kafka technology in the software framework to control the transmission order of different data through channel priority. Simple Explanation of the Diagram

[0027] [Figure 1] is a schematic diagram of the architecture of a conventional energy management system. [Figure 2A] is a schematic diagram of the architecture of the distributed energy management system of the present invention. [Figure 2B] is a schematic diagram of the architecture of the server management unit of the distributed energy management system of the present invention. [Figure 2C] is a schematic diagram of the architecture of the user-end management unit of the distributed energy management system of the present invention. [Figure 3A] is a schematic diagram of the architecture of the distributed energy management system of the present invention, which is set up on different hardware. [Figure 3B] is a schematic diagram of the architecture of the distributed energy management system of the present invention, which is installed on the same hardware. [Figure 4] is a schematic diagram of the architecture of the distributed energy management system of the present invention applied to multiple energy fields. Implementation

[0028] Other technical contents, features and effects of the present invention will be clearly presented in the following detailed description of the preferred embodiments with reference to the accompanying drawings.

[0029] Please refer to Figures 2A-2C, which are schematic diagrams of the architecture of the distributed energy management system, the server management unit, and the user management unit of the present invention. As can be seen from the figures, the distributed energy management system is electrically connected to a user device 2 and at least one power terminal device 51 in the field 5.

[0030] The aforementioned field is an energy field, that is, a site area related to electrical energy. The power terminal equipment in this field can be one or more of the following: a distributed power generation module (e.g., renewable energy or small generator), a battery module, a power control module, an electricity meter module, various sensor modules (e.g., fire sensor), a monitor module, and a field control device (e.g., fire alarm, sprinkler system, and environmental control device (air conditioning, heating and cooling, and exhaust fan)).

[0031] The aforementioned end-user equipment belongs to companies related to the power and energy industry, such as power companies, aggregators, and other related industries, and is mainly used to provide power services for related transactions in real time.

[0032] As shown in Figure 2A, the distributed energy management system includes at least one server management unit 5221 and one user management unit 421. The server management unit 5221 is electrically connected to one or more power-end devices 51. (To avoid confusion in the diagram, the server management unit 5221 and the power-end device 51 are not connected by lines. However, the server management unit 5221 and the power-end device 51 are connected by physical wires or wirelessly to transmit data.)

[0033] The server management unit 5221 is located near the power equipment (energy storage site / OT / mainly responsible for equipment operation monitoring and management) on the network topology.

[0034] The user-side management unit 421 is located close to the service customer (user / IT / mainly responsible for information service display, etc.) on the network topology.

[0035] As shown in Figure 2B, the server management unit 5221 has a first communication management module 52211, a data storage module 52212, a task execution module 52213, a device status acquisition module 52214, an exception handling module 52215, and an analysis and prediction module 52216.

[0036] The first communication management module 52211 is used to receive and / or upload data (as shown in Figure 2C, to receive data from the second communication management module 4211, or to upload data to the second communication management module 4211). The first communication management module 52211 can manage the priority order of uploading and receiving data according to a data channel priority setting value.

[0037] The data storage module 52212 is connected to the first communication management module 52211, the task execution module 52213, the device status acquisition module 52214, the exception handling module 52215 and the analysis and prediction module 52216, and is used to store the setting parameters and status data of different power terminal devices 51.

[0038] The task execution module 52213 is connected to the first communication management module 52211 and is used to receive task execution data through the first communication management module 52211. The content of the task execution data includes at least a device code and a setting parameter. The task execution module 52213 outputs the setting parameter to the power terminal device 51 corresponding to the device code based on the content of the task execution data (this invention can set codes for different power terminal devices to facilitate control), in order to set and adjust the relevant management and execution parameters of the power terminal device 51 and report the task execution results to the user terminal management unit 421.

[0039] The above-mentioned task execution results are stored in the task execution module 52213 with at least one task execution record file, and the task execution module 52213 is able to send the task execution record file back to the user terminal management unit 421.

[0040] The device status acquisition module 52214 is connected to the first communication management module 52211 to collect the status data of the power terminal device 51. The status data of different power terminal devices 51 are transmitted to the user terminal management unit 421 through the first communication management module 52211.

[0041] The anomaly handling module 52215 is connected to the first communication management module 52211 and the data storage module 52212. The anomaly handling module 52215 sends an alarm notification to the user management unit 421 through the first communication management module 52211 based on the status data of different power terminal devices 51 and an alarm judgment standard value. The user management unit 421 then sends back an emergency management command. The anomaly handling module 52215 processes the emergency management command (such as issuing an alarm, shutting down equipment, or performing fire-fighting actions).

[0042] The anomaly handling module 52215 can create an emergency contact file, which contains the communication information of a system administrator or an emergency contact person. The anomaly handling module 52215 can also send the warning notification information to the system administrator and / or the emergency contact person based on the content of the emergency contact file.

[0043] The analysis and prediction module 52216 is connected to the first communication management module 52211 and the data storage module 52212 to aggregate the status data of different power terminal devices 51 into an analysis data file. The analysis data file is divided into normal data and abnormal data, and the analysis data file is transmitted to the user terminal management unit 421 through the first communication management module 52211.

[0044] The analysis and prediction module 52216 is used to aggregate and analyze data and identify data that deviates from the norm. The purpose is to simplify the amount of regular data and mark and store data that deviates from the norm. Furthermore, it can be analyzed through machine learning to achieve the goal of automatically simplifying the amount of regular data uploaded to the cloud.

[0045] As shown in Figure 2C, the user terminal management unit 421 is electrically connected to the user terminal device 2 and at least one server terminal management unit 5221. The user terminal management unit 421 includes at least a second communication management module 4211, a third communication management module 4212, a data backup module 4213, a resource integration module 4214, a task assignment module 4215, a maintenance module 4216, and a transaction service analysis module 4217.

[0046] The second communication management module 4211 is connected to the first communication management module 52211. The second communication management module 4211 is used to receive and / or upload data (receive data from the first communication management module 52211, or upload data to the first communication management module 52211). The second communication management module 4211 can manage the priority order of uploading and receiving data according to the data channel priority setting value.

[0047] The priority setting of the data channel in this case is determined by the first communication management module 52211 and the second communication management module 4211. The first communication management module 52211 and the second communication management module 4211 have energy data channels with different priorities and different data transmission volumes per unit time.

[0048] The third communication management module 4212 is connected to the user terminal device 2 to receive transaction service work data assigned by the aggregator and to receive responses from users, aggregators, or power companies, wherein the transaction service work data contains at least one transaction request.

[0049] The third communication management module 4212 is also capable of connecting to external server devices (such as server devices in an AI training center).

[0050] The data backup module 4213 is connected to the second communication management module 4211 and the third communication management module 4212. It is used to receive status data of different power terminal devices through the second communication management module 4211 (where the regular data is simplified for backup purposes) and to receive the transaction service work data through the third communication management module 4212.

[0051] The data backup module 4213 can use distributed storage software frameworks for big data, such as Hadoop databases. With the addition of server-side management units, the number and architecture of databases can be flexibly adjusted to directly increase the scale of services. This avoids the problem of excessive initial investment in conventional technologies or the problem of data interoperability between two or more databases when the scale grows.

[0052] The resource integration module 4214 is connected to the data backup module 4213. The resource integration module 4214 determines whether the transaction requirements are met based on the status data of different power terminal devices and generates a service resource aggregation data.

[0053] The task assignment module 4215 is connected to the second communication management module 4211, the data backup module 4213 and the resource integration module 4214. It is used to analyze the status data of different power terminal devices and / or the service resource aggregation data to generate the task execution data. The task execution data is transmitted to the first communication management module 52211 through the second communication management module 4211.

[0054] In addition to the requirements of the transaction task, the task assignment module 4215 can also assign tasks based on the status data of the power terminal equipment to manage and control the daily operation status of the power terminal equipment.

[0055] The maintenance module 4216 is connected to the second communication management module 4211 and the data backup module 4213 to analyze an alarm notification to generate emergency maintenance task data and routine maintenance task data. The emergency maintenance task data or the routine maintenance task data is transmitted to the server management unit through the second communication management module 4211. The emergency maintenance task data includes at least an emergency management instruction, and the routine maintenance task data is a troubleshooting task scheduling instruction, an inspection task scheduling instruction, a maintenance task scheduling instruction, or a repair task scheduling instruction.

[0056] The scheduling instruction for this inspection task refers to the periodic checking of the condition and performance of equipment, systems, or facilities to ensure their proper functioning and to identify any potential problems or defects. Inspections are usually conducted regularly to identify problems early and take preventative measures to avoid possible failures or damage (regularly checking and confirming the condition of equipment and parts).

[0057] The maintenance task scheduling instruction refers to the process of repairing or adjusting problems or anomalies that have been identified. Maintenance may include replacing damaged parts, adjusting equipment or system parameters, cleaning or calibration, etc., to restore its normal operating condition (equipment malfunctions, problems are confirmed and repaired to normal functional state).

[0058] This maintenance task scheduling instruction defines preventive maintenance as a regular, preventative maintenance activity designed to reduce the chance of equipment, systems, or facilities failing. Maintenance typically includes cleaning, lubrication, calibration, inspection, and adjustment to ensure equipment is in good condition and extends its lifespan (regular parts maintenance, functional adjustments or replacements, ensuring equipment is in normal working order).

[0059] The maintenance task scheduling instruction defines maintenance as the repair work performed on actual faults or damage to equipment, systems, or facilities. Maintenance may require replacing damaged parts, rebuilding, or reinstalling to restore functionality (equipment has malfunctioned and is being repaired to function normally).

[0060] The maintenance module 4216 can perform machine learning (machine learning on non-routine data) based on the status data of different power terminal equipment to train an analysis and judgment training module. The analysis and judgment training module operates within the analysis and prediction module 52216. The analysis and judgment training module can monitor the normalized data and perform data reduction or data summarization on the normalized data to reduce storage and transmission costs, and schedule the upload to the user terminal management unit for backup.

[0061] The transaction service analysis module 4217 is connected to the data backup module 4213 and is used to perform transaction analysis processing on the transaction service work data. The analysis processing is a power service data analysis, a market strategy formulation analysis, a transaction execution processing analysis, or a transaction return processing analysis.

[0062] This electricity service data analysis involves in-depth analysis of relevant data, primarily to determine users' electricity demand and consumption patterns. This includes analyzing electricity consumption, load curves, peak and off-peak demand, and users' electricity usage habits. The aim is to optimize electricity supply, improve energy efficiency, and ensure stable electricity service.

[0063] This market strategy development analysis focuses on the dynamics and trends of the electricity market, and formulates corresponding market strategies. This includes analyzing market supply and demand, price fluctuations, competitor strategies, and policy changes. Through these analyses, optimal pricing strategies, resource allocation strategies, and market expansion plans can be developed to enhance market competitiveness and profitability.

[0064] This analysis focuses on the entire electricity trading process, from initiation to execution. It covers aspects such as transaction matching, price determination, contract management, and risk control. The aim is to ensure efficient, secure, and compliant operation of the transactions, and to maximize success rates and economic benefits.

[0065] The transaction return processing analysis involves reviewing and analyzing completed electricity transactions. This includes verifying transaction results, organizing and archiving transaction data, reviewing transaction records, and conducting return analysis. Through these analyses, transaction performance can be evaluated, potential problems and areas for improvement can be identified, and data support can be provided for future transaction decisions.

[0066] Regarding the operation of the transaction service analysis module 4217, such as providing automatic frequency regulation control services, the transaction service analysis module 4217 collects the current electricity service market price, analyzes and formulates corresponding bidding strategies according to the user's relevant settings, and after confirming the winning bid through the bidding process, the transaction service analysis module 4217 must receive the dispatch instructions from the power company during the service period to provide corresponding real-time energy power services. When changes in the power grid load or renewable energy cause the grid frequency to deviate from the power company's dispatch target, the energy management system must supplement or draw power from the grid according to the rules to ensure that the grid frequency can be stabilized within the frequency range required by the power company, and report to the power company the relevant data on the execution of power services according to the winning bid requirements.

[0067] As shown in Figure 3A, the server management unit 5221 and the user management unit 421 can be respectively set in different server devices 4 and 52, or as shown in Figure 3B, the server management unit 621 and the user management unit 622 can be set in the same server device 6.

[0068] The connection methods between the aforementioned server management units 5221, 621 and the user management units 421, 622 may include, but are not limited to: (1) Internal communication within the same server or computer: Connection and communication are carried out using shared memory, inter-process communication (IPC) mechanisms such as pipes, message queues, signals, sockets, named pipes (FIFO), etc. (2) Physical network route: Connect different server devices through a wired network (such as an Ethernet cable). (3) Wireless Network (Wi-Fi): Connect different server devices through a wireless router, providing a flexible and convenient connection method. (4) Bluetooth: Suitable for short-range, low-power connections, often used for communication between small devices. (5) Cellular network (3G / 4G / 5G): Uses mobile communication network for connection, suitable for devices that require wide area network connection. (6) Zigbee or Z-Wave: These are low-power wireless communication protocols specifically designed for Internet of Things (IoT) devices, suitable for smart home or industrial applications. (7) Power line communication (PLC): It uses existing power lines for data transmission and is suitable for places where it is impossible to lay network lines. (8) Fiber optic communication: high-speed data transmission via fiber optics, suitable for applications requiring high bandwidth and low latency. (9) Satellite communication: Long-distance communication via satellite, suitable for remote areas or places where other means of connection are not possible.

[0069] The server devices 4, 52, and 6 include at least one processor 41, 521, and 61 and at least one computer-readable recording medium 42, 522, and 62. The computer-readable recording medium 42, 522, and 62 respectively have server management units 5221 and 621 and user management units 421 and 622. The computer-readable recording medium 42, 522, and 62 further stores computer-readable instructions. When the processors 41, 521, and 61 execute the computer-readable instructions, the server management units 5221 and 621 and the user management units 421 and 622 can operate.

[0070] The server devices 4, 52, and 6 are any of the following computing devices capable of computing and related applications: a desktop computer, a laptop computer, or a server device.

[0071] The processors 41, 521, and 61 are MCUs (Micro Controller Units). An MCU includes at least a CPU, memory (such as ROM and RAM), and peripheral devices (such as ADC, DAC, GPIO, and PWM). The actions, instructions, variables, and other data that can be programmed can be written into the memory, and then the CPU can execute these programmed actions in sequence.

[0072] As shown in Figure 4, this invention can expand the power terminal equipment 51, 71, 81 connecting multiple fields 5, 7, 8. Beyond expansion, this invention can also reduce the structural size, primarily due to the design structure used in the server management units 5221, 7221, 8221 and the user management unit 421. The design structure is explained below: (1) The server management unit 5221 can use virtual host or hyperconverged architecture, Docker and similar technologies to integrate multiple computers into one centralized management unit to reduce the problem of the inability to flexibly add or remove devices in conventional technologies, which causes the network and physical equipment management in the field to be too scattered (in this case, multiple data collection units can be integrated into one server management unit, that is, data collection is managed by the network topology corresponding to the virtual device). (2) The user management unit 421 is able to use a distributed storage software framework for big data to flexibly adjust the number and architecture of the server devices and directly increase the scale of the service devices. (3) The first communication management module 52211 and the second communication management module 4211 can manage the priority order of data channel scheduling according to the data channel priority setting value, and the data backup module 4213 can provide the data channel scheduling buffer function of the first communication management module. (4) The data channel between the first communication management module 52211 and the second communication management module 4211 can use a parallel communication architecture and a high availability seamless backup protocol in terms of communication architecture. (5) The data channel between the first communication management module 52211 and the second communication management module 4211 can use Kafka technology in the software framework to control the transmission order of different data through the priority of the channel.

[0073] The data communication channel between the first communication management module 52211 and the second communication management module 4211 consists of at least two physical network lines connected together (the wiring method can be PRP or HSR structure) to enable the execution of backup protocols. The data communication channel between the two can schedule the streaming transmission of data between the cloud and the ground. Important data will be sent and received in real time through a high-priority channel.

[0074] The first communication management module 52211 and the second communication management module 4211 agree on the routing and priority of data transmission through a framework protocol at both ends. This architecture is called a data communication channel, just like traffic flow is divided into fast and slow lanes. Examples of different data channel priorities and data types are as follows: (1) First priority, data categories are fire alarm, gas and other detector alarm, energy device alarm, battery alarm, power control alarm, fire / exhaust control, circuit breaker status / control, switch status / control. (2) Second priority, data categories are: energy module alarm, battery module alarm, power control module alarm, detector data, energy module data (alarm status), battery module data (alarm status), power control module data (alarm status), equipment abnormal status data, real-time service indicator data / control, real-time service key data (related equipment, power service reference meter, etc.), equipment setting data / control, key equipment status data, and inferred abnormal trends (machine learning inference). (3) Third priority, data categories are energy module data (general state), battery module data (general state), power control module data (general state), meter data, detector setting data, fire protection setting data, non-critical equipment status data, monitor real-time images, and system real-time settings. (4) Fourth priority, data categories are integrated statistical data, test status data / settings, maintenance settings, monitor image archive data, system update data, learning model update (machine training), and system scheduling update settings.

[0075] The examples of priorities and data categories mentioned above are only one implementation model, but users can still change the priorities according to their actual needs.

[0076] The following is a comparative explanation of the differences between conventional EMS and the EMS used in this case: (1) Commonly used EMS (a) When the battery module malfunctions, or even when the fire sensor has detected smoke, the data transmission unit (DTU) of the battery module and the sensor module will capture the abnormal alarm data and upload it to the EMS. (b) According to the traditional EMS architecture, when alarm data is being uploaded, it will cause a delay in receiving abnormal alarm data because normal data from other modules or data from different modules in different fields are being received. (c) In the above situations, when the EMS and the module that is malfunctioning are not in the same field, and the distance between the EMS and the DTU or RTU in the physical data channel or virtual network topology is even greater, and when the data channel needs to receive data from the battery, power control, electricity meter, various sensors and monitor modules, congestion can easily occur when receiving various types of data. At this time, the EMS will have problems with the timeliness of receiving abnormal alarm data, and the EMS will not be able to quickly analyze and deal with the problem that has occurred (leading to a delay in abnormal alarm). (2) This case system (a) This case divides EMS into a server management unit and a user management unit. The server management unit is close to the modules in the EMS management field in terms of network topology design, namely, power storage sensing and other equipment, while the user management unit is close to the power energy service end. (b) The two management units (server management unit and user management unit) have their own communication units. The first communication management module and the second communication management module will classify the types of data to be transmitted and exchange data through different priority and network transmission volume channels. (c) The server management unit is equipped with a data storage module, which stores the data collected by the device status acquisition module, and then transmits it to the second communication management module of the user management unit through the data channel of the first communication management module, and stores it in the data backup module of the user management unit. (d) When the battery module malfunctions or the fire sensor detects smoke, the server management unit will transmit alarm-related data through the priority data channel after receiving data from the battery module or fire sensor. Other module data channels will have lower priority than alarm-related emergency data. (e) When providing real-time power service, when the user management unit receives a power command in the third communication management module, it will use the data channel management between the second and first communication management modules to receive key data of the site through the priority channel. (f) When the resource integration module of the user terminal management unit performs calculations and comparisons, and splits and updates the task schedule through the task assignment module, it is then sent to the server management unit through the priority channel of the second communication management module. (g) When the first communication management module receives a new task schedule, it will hand it over to the task execution module for scheduled execution. (h) This approach reduces the urgency of conventional technologies requiring constant adjustments to the settings of power-end devices via commands when providing real-time services. (i) In addition to its normal data storage function, the data storage module of the server management unit in this case provides a buffer function for the low-priority data channel from the first communication management module to the second communication management module in the event of data channel congestion. (j) In addition to realizing the function of distributed data backup and backup in different locations, the data backup module of the user-end management unit in this case provides a buffer function for the low-priority data channel from the second communication management module to the first communication management module in the event of data channel congestion.

[0077] The differences between the wiring used in conventional EMS and that in this case are explained below: (1) The conventional EMS technology is only the network connection between EMS and RTU / DTU, which follows the general network communication protocol and does not make proper arrangements for the data channel. (2) The distributed energy management system in this case is divided into user-end management units and server-end management units. The communication departments of multiple management units are configured with data channels. Under the architecture of at least two physical networks connected between the communication departments, the channels can implement high-reliability network communication protocol architectures such as parallel communication architecture (PRP) or high availability seamless backup protocol (HSR). (3) The architecture of this case can be changed or combined by switching the communication architecture to cope with the network quality status and increase the reliability of the overall network architecture.

[0078] The following is a comparison of the scalability of conventional EMS and this project: (1) When using EMS technology, when the field area increases to a certain extent, it is necessary to expand the database or increase the number of databases to provide sufficient data transmission service capacity. (2) The distributed energy management system in this case distinguishes between the user-end management unit and the server-end management unit. There are data channels provided by different communication management modules between the user-end management unit and the server-end management unit. Therefore, a distributed big data storage architecture such as Hadoop database can be used. When the field is expanded or reduced, the user-end management unit and the server-end management unit can dynamically expand or reduce the storage unit in accordance with the adjustment of the field. (3) This case can improve the rigidity of known technology in database selection and adjustment.

[0079] The following is an explanation of how a machine learning-based analysis and judgment training module was developed for this case: (1) The architecture of this case can separate the training and inference functions of artificial intelligence machine learning. The training function can be executed by the user-end management unit (or by sending data to another AI training center through a third communication management module), and the inference function can be executed by the server-end management unit. (2) The analysis and prediction module of the server management unit can perform machine learning inference functions. After performing integrated inference on the stored data, it can distinguish between normal and abnormal data. Normal data in the normal data can be statistically processed to reduce the transmission of a large amount of identical and repetitive data, that is, reduce a large amount of network carbon emissions. (3) The above-mentioned normal data, such as the voltage and current data of the battery and power control module with very small change rate during standby, or the normal temperature data received from the temperature detector and the screen data of the monitoring screen that does not change, can be transmitted through a data channel with lower priority after statistical reduction by inference. (4) Identifiable abnormal data in normal data can also be classified, and different data channels can be used to transmit the abnormal data according to the severity of the abnormal data, or the data trend can be statistically controlled. (5) In the above example, machine learning infers that the power output temperature of the power control module is abnormally higher than the normal temperature. Different priority data channels can be assigned to the abnormal trend. If the change is too fast, it will be assigned to a high priority channel. The abnormal data that changes slowly can be statistically summarized into trend changes and assigned to the second highest priority channel for transmission to the user management unit. (6) After the non-normal data is uploaded to the user-end management unit, it can be trained by machine in the maintenance module to complete the training model, and then handed over to the analysis and prediction module of the server-end management unit to perform inference (or handed over to the cloud AI training center to perform). (7) If the relationship between the battery voltage and the power control and the high temperature data cannot be deduced in the above situation, the data can be uploaded to the user terminal management unit according to the priority of the channel of each type of data, and then handed over to artificial intelligence for machine training to confirm the correct inference model.

[0080] The distributed energy management system provided by this invention has the following advantages compared with other conventional technologies: (1) This proposal proposes a distributed structure to solve the problem of not being able to dynamically and flexibly adjust the scale of users. In addition, this proposal uses priority data channel transmission control to solve the problem caused by data congestion. (2) The architecture of this project can be changed or combined by switching the communication architecture to cope with the network quality status and increase the reliability of the overall network architecture. Therefore, this project can also dynamically adjust the scale of users according to the increase of the server and use big data database for effective management. (3) This case can improve the rigidity of known technology in database selection and adjustment. (4) The energy service architecture in this case is that multiple servers collect data and report back to a single user, rather than one server sending data to multiple users. (5) The data collected for energy management in this case is mainly "routine" data. Therefore, this case uses machine learning system architecture and methods to reduce the problem of needing to upload a large amount of routine data in real time. (6) This case separates the information technology (IT) and operation technology (OT) of the energy management system into two ends: the cloud (user-end management unit) and the ground (server-end management unit) to design the network topology, so that information services are closer to the customer user end and operation management is closer to the equipment server end. (7) Through the design of the architecture, this case integrates the originally scattered ground data terminal units (DTUs) or remote terminal units (RTUs) into a server management unit, and uses virtualization (VM) or containerization (such as Docker) technology to facilitate centralized management. (8) The data collected by the server management unit in this case is mostly in a normal state. The data transmission volume to the cloud (user management unit) can be reduced through the machine learning (artificial intelligence) architecture, thereby greatly reducing the energy consumption of network streaming transmission. (9) The user-end management unit in this case may have more resources to interface and interact with other energy aggregators (such as aggregators) or electricity trading terminals (such as user-end equipment), collect transaction-related data, and provide more diverse transaction services. (10) The data channel between the user management unit and the server management unit in this case uses at least two physical networks to implement a backup agreement, thereby increasing the reliability of network communication.

[0081] The present invention has been disclosed above through the embodiments described above, but it is not intended to limit the present invention. Any person skilled in the art can make some modifications and refinements after understanding the foregoing technical features and embodiments of the present invention, without departing from the spirit and scope of the present invention. Therefore, the scope of patent protection of the present invention shall be determined by the claims attached to this specification.

[0082] 1: Energy Management System 2: End-user equipment 31: Field 311: Data Transmission Unit 312: Power terminal equipment 32: Field 321: Data Transmission Unit 322: Power supply equipment 33: Field 331: Data Transmission Unit 332: Power supply equipment 4: Server equipment 41: Processor 42: Computer-readable recording media 421: User-side management unit 4211: Second Communication Management Module 4212: Third Communication Management Module 4213: Data Backup Module 4214: Resource Integration Module 4215: Task Assignment Module 4216: Maintenance and Repair Module 4217: Transaction Service Analysis Module 5: Field 51: Power terminal equipment 52: Server devices 521: Processor 522: Computer-readable recording media 5221: Server Management Unit 52211: First Communication Management Module 52212: Data Storage Module 52213: Task Execution Module 52214: Device Status Acquisition Module 52215: Exception Handling Module 52216: Analysis and Prediction Module 6: Server devices 61: Processor 62: Computer-readable recording media 621: Server Management Unit 622: User-side management unit 7: Field 71: Power terminal equipment 7221: Server Management Unit 8: Field 81: Power terminal equipment 8221: Server Management Unit

Claims

1. A distributed energy management system electrically connected to a user terminal device and at least one power terminal device in a field, the distributed energy management system comprising: at least one server management unit electrically connected to one or more power terminal devices, the server management unit comprising at least: a first communication management module for receiving and / or uploading data, the first communication management module being able to manage the priority order of uploading and receiving data according to a data channel priority setting value; a task execution module connected to the first communication management module for receiving task execution data through the first communication management module, the content of the task execution data having at least a device code and a setting parameter, and the task execution module outputting the setting parameter to the power terminal device corresponding to the device code according to the content of the task execution data; and a device status acquisition module connected to the first communication management module for collecting status data of the power terminal devices, and the status data of different power terminal devices being transmitted through the first communication management module; A data storage module, connected to the first communication management module, the task execution module, and the device status acquisition module, is used to store the setting parameters and status data of different power-end devices; a user-end management unit, electrically connected to the user-end device and at least one server-end management unit, the user-end management unit comprising at least: a second communication management module, connected to the first communication management module, the second communication management module being used to receive and / or upload data, the second communication management module being able to manage the priority order of uploading and receiving data according to the data channel priority setting value; and a data backup module, connected to the second communication management module, being used to receive status data of different power-end devices through the second communication management module; wherein... The first communication management module and the second communication management module can manage the priority order of data channel scheduling according to the data channel priority setting value. The data backup module can also provide the data channel scheduling buffer function of the first communication management module, and the data storage module can also provide the data channel scheduling buffer function of the second communication management module.

2. The distributed energy management system as described in claim 1, wherein the power terminal equipment is one or more of a distributed power generation module, a battery module, a power control module, a meter module, a variety of sensor modules, a monitor module, and a field control device.

3. The distributed energy management system as described in claim 1, wherein the task execution module is further capable of storing at least one task execution record file, and the task execution module is capable of sending the task execution record file back to the user-end management unit.

4. The distributed energy management system as described in claim 1, wherein the server management unit further includes an anomaly handling module, which is connected to the first communication management module and the data storage module. The anomaly handling module sends an alarm notification to the user management unit through the first communication management module based on the status data of different power terminal devices and an alarm judgment standard value. The user management unit further sends back an emergency management command, and the anomaly handling module processes the data according to the emergency management command.

5. The distributed energy management system as described in claim 4, wherein the anomaly handling module is capable of establishing an emergency contact file, which contains the communication information of a system administrator or an emergency contact person, and the anomaly handling module is capable of sending the warning notification information to the system administrator and / or the emergency contact person through the content of the emergency contact file.

6. The distributed energy management system as described in claim 1, wherein the server management unit further includes an analysis and prediction module, which is connected to the first communication management module and the data storage module, for compiling status data of different power terminal devices into an analysis data file, which is divided into normal data and abnormal data, and the analysis data file is transmitted to the user management unit through the first communication management module.

7. The distributed energy management system as described in claim 6, wherein the user-end management unit is further capable of performing machine learning based on the status data of different power-end devices to train an analysis and judgment training module, the analysis and judgment training module operating within the analysis and prediction module, and the analysis and judgment training module being able to monitor the normalized data, perform data summarization or data abstraction processing on the normalized data, and schedule the upload to the user-end management unit for backup.

8. The distributed energy management system as described in claim 1, wherein the user-end management unit further includes a maintenance module, which is connected to the second communication management module and the data backup module to analyze an alarm notification to generate emergency maintenance task data and routine maintenance task data. The emergency maintenance task data or the routine maintenance task data is transmitted to the server-end management unit through the second communication management module. The emergency maintenance task data includes at least an emergency management instruction, and the routine maintenance task data is a troubleshooting task scheduling instruction, an inspection task scheduling instruction, a maintenance task scheduling instruction, or a repair task scheduling instruction.

9. The distributed energy management system as described in claim 1, wherein the user-end management unit further includes a third communication management module, the third communication management module being connected to the user-end device for receiving transaction service work data, the transaction service work data having at least one transaction demand content, and the data backup module being connected to the third communication management module, wherein the data backup module is used to receive the transaction service work data through the third communication management module.

10. The distributed energy management system as described in claim 9, wherein the user-end management unit further includes a resource integration module connected to the data backup module, the resource integration module determining whether the transaction requirements are met based on the status data of different power-end devices, and generating service resource aggregation data.

11. The distributed energy management system as described in claim 10, wherein the user-end management unit further includes a task dispatch module connected to the second communication management module, the data backup module and the resource integration module, for analyzing the status data of different power-end devices and / or the service resource aggregation data to generate the task execution data, and the task execution data is transmitted through the second communication management module.

12. The distributed energy management system as described in claim 9, wherein the user-end management unit further includes a transaction service analysis module connected to the data backup module, the transaction service analysis module being used to perform transaction analysis processing on the transaction service work data, the analysis processing being a power service data analysis, a market strategy formulation analysis, a transaction execution processing analysis, or a transaction return processing analysis.

13. The distributed energy management system as described in claim 1, wherein the server management unit and the user management unit can be located in the same server device or in different server devices.

14. The distributed energy management system as described in claim 13, wherein the server management unit is capable of using virtual hosting, hyperconverged infrastructure, and Docker technologies to virtually integrate and centrally manage multiple server devices.

15. The distributed energy management system as described in claim 13, wherein the user-end management unit is a software framework capable of using distributed storage of big data to flexibly adjust the number and architecture of the server devices, directly increasing the scale of the service devices.

16. The distributed energy management system as described in claim 1, wherein the data channel between the first communication management module and the second communication management module can use a parallel communication architecture and a high-availability seamless backup protocol in terms of communication architecture.

17. The distributed energy management system as described in claim 1, wherein the data channel between the first communication management module and the second communication management module can use Kafka technology in the software framework to control the transmission order of different data through channel priority.

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