EMS-based band value regulation and control instruction issuing system and method

Abstract

The invention discloses a band value regulation and control instruction issuing system and method based on an EMS, and belongs to the technical field of energy management systems. Comprising an EMS server, an EMS workstation, a communication management machine and a coordination controller, wherein the EMS server is used for deploying an EMS program and is responsible for acquiring data of energy equipment in real time and monitoring a running state; the EMS workstation deploys an adjustment and optimization algorithm program, obtains EMS data in real time through an interface, generates an adjustment and optimization instruction in combination with the prediction model and returns the adjustment and optimization instruction to the EMS server; the EMS server analyzes the tuning instruction and then generates a band value regulation and control instruction, the band value regulation and control instruction is uploaded to the master station through a master station-substation communication architecture of the communication management machine, the communication management machine issues the instruction to the coordination controller through a data forwarding function, and accurate regulation and control of the energy equipment are achieved; through cooperative work of the tuning algorithm and the EMS, the problem that a traditional energy management system lacks dynamic optimization capability is solved, and equipment operation efficiency improvement and energy consumption optimization are realized.

Description

Technical Field

[0001] This invention relates to the field of energy management system (EMS) technology, and in particular to an EMS-based system and method for issuing control commands with specified values, used to realize real-time monitoring, prediction and optimized control of energy equipment. Background Technology

[0002] With the widespread application of energy management systems, how to achieve real-time monitoring, prediction, and optimized control of energy equipment has become an important issue. Existing EMS systems can usually only perform simple data acquisition and monitoring, lacking the ability to optimize and control equipment in real time. In traditional methods, the regulation of energy equipment usually relies on manual intervention or fixed rules, and cannot be dynamically adjusted according to real-time operating status, resulting in low energy utilization efficiency and high operating costs.

[0003] Furthermore, existing EMS systems often suffer from communication delays and inaccurate command parsing during command issuance, resulting in the inability to execute control commands in a timely and accurate manner. Therefore, how to achieve real-time issuance of value-based control commands through EMS systems to improve the operating efficiency of energy equipment and reduce energy consumption has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of this invention is to provide a value-based control command issuance system and method based on EMS, which achieves real-time monitoring, prediction and optimized control of energy equipment through the collaborative work of EMS server and optimization algorithm program, and solves the problems of communication delay, inaccurate command parsing and low control efficiency in the prior art.

[0005] To achieve the above objectives, the present invention provides a system and method for issuing value-based control commands based on EMS, specifically including the following: A value-based control instruction issuance system based on EMS includes: The EMS server is used to deploy EMS programs, collect real-time operating data of energy equipment, and monitor the equipment status. The EMS workstation is used to deploy optimization algorithm programs, obtain device data collected by the EMS server in real time through a database interface, generate optimization instructions based on a preset prediction model, and send the optimization instructions back to the EMS server. The communication management unit is used to realize the communication between the EMS server and the coordination controller, including the master station function and the data forwarding function. The master station function receives the value-based control instructions uploaded by the EMS server through the IEC104 protocol and the Modbus protocol, and the data forwarding function is used to send the value-based control instructions to the coordination controller. The coordination controller is used to receive and execute value-based control commands issued by the communication management unit to achieve real-time control of energy equipment.

[0006] In a preferred embodiment of the present invention, the EMS server is configured with a data acquisition module, a monitoring module, an instruction parsing module, a protocol forwarding module, and a data processing module; The data acquisition module is used to collect real-time operating data of energy equipment; The monitoring module is used to monitor the collected data in real time and trigger alarms in abnormal situations; The instruction parsing module is used to receive the tuning instructions generated by the tuning algorithm program and generate value-based control instructions; it can convert the value-based control instructions into corresponding protocol messages and complete data encoding according to the protocol standards supported by the target device; the value-based control instructions include device identifier, control parameter value, execution timestamp and verification information; The protocol forwarding module is used to receive control commands and encapsulate, forward, and adapt the control commands according to the protocol standards supported by the target device. The data processing module is the core module that supports instruction generation, verification, and execution feedback. It is responsible for processing and analyzing instruction data, device status data, and historical operation data throughout their entire lifecycle.

[0007] In a preferred embodiment of the present invention, the EMS workstation includes: The data preprocessing module is used to clean and normalize the real-time data obtained from the EMS server. The prediction model module uses deep learning algorithms to train on historical and real-time data to generate prediction results of the equipment's operating status. The optimization calculation module generates tuning instructions based on the prediction results using a multi-objective optimization algorithm. The tuning instructions include the adjustment values ​​and adjustment times of the equipment operating parameters. The instruction issuance module is used to send tuning instructions back to the EMS server via an interface; The historical data analysis module is a key module supporting system optimization, fault diagnosis, and decision improvement. It is responsible for integrating instruction issuance records, device execution feedback data, environmental parameters, and system logs from the protocol forwarding module; and generating core indicators to achieve closed-loop verification by comparing instruction settings with actual execution values.

[0008] In a preferred embodiment of the present invention, the EMS server and the EMS workstation are connected via a high-speed communication line, and the communication protocol includes, but is not limited to, Modbus, IEC 104 or OPC UA; the interface between the EMS server and the tuning algorithm program uses a standardized data format for data interaction, including a real-time data acquisition interface and a tuning command issuance interface.

[0009] In a preferred embodiment of the present invention, the communication management unit includes a master station function module and a data forwarding module; The main station functional module supports multiple communication protocols, including IEC 60870-5-104, DNP3, or MQTT, for data interaction with the EMS server; The data forwarding module is used to send value-based control instructions to the coordination controller via wired or wireless networks.

[0010] In a preferred embodiment of the present invention, the coordination controller includes an instruction receiving module, an instruction verification module, and an execution module; The instruction receiving module is used to receive value-based control instructions issued by the communication management unit; The instruction verification module is used to verify the integrity and legality of value-based control instructions; The execution module is used to perform real-time control of energy equipment according to the verified value control instructions.

[0011] A method for issuing value-based control commands based on EMS includes the following steps: Step 1: Deploy EMS server and EMS workstation: Deploy the EMS program on the EMS server, which is responsible for data collection, monitoring and command issuance; deploy the optimization algorithm program on the EMS workstation, which is responsible for real-time data analysis, prediction and generation of optimization commands. Step 2: Configure communication wiring: Configure the communication wiring between the EMS server and the EMS workstation to ensure the real-time performance and reliability of data transmission; Step 3: Develop the interface: Develop the interface between the EMS server and the tuning algorithm program. The tuning algorithm program obtains device data from the EMS server in real time through the interface and generates tuning instructions to send back to the EMS server. Step 4: Generate Value-Based Control Instructions: The EMS server parses the tuning instructions, generates value-based control instructions, and uploads them to the communication management machine through the main station function of the communication management machine. Step 5: Issuing the value-based control command: The communication management unit issues the value-based control command to the coordination controller through the data forwarding function; Step 6: Execute control instructions: The coordinating controller receives and executes control instructions with specified values ​​to achieve real-time control of energy equipment.

[0012] Compared with existing technologies, this invention achieves real-time monitoring, prediction, and optimized control of energy equipment through the collaborative work of an EMS server and an optimization algorithm program, and has the following advantages: 1. Real-time performance: Through high-speed communication lines and standardized interfaces, the real-time performance of data acquisition, command generation and issuance is ensured.

[0013] 2. Accuracy: The optimization algorithm program generates accurate optimization instructions based on the prediction model, ensuring the scientific nature and effectiveness of the control instructions.

[0014] 3. High efficiency: Through the master-slave architecture and data forwarding function of the communication management unit, the efficient issuance and execution of instructions are achieved.

[0015] 4. Economic efficiency: By optimizing the operating parameters of energy equipment, energy consumption is reduced and energy utilization efficiency is improved, resulting in significant economic benefits. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of a value-based control command issuance system architecture based on EMS in this embodiment.

[0017] Figure 2 This is a schematic diagram of the communication architecture and control logic of a value-based control command issuance system based on EMS in this embodiment. Detailed Implementation

[0018] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings, so that those skilled in the art can better understand and implement the present invention.

[0019] like Figure 1 As shown, a schematic diagram of a value-based control command issuance system architecture based on EMS is presented, including: The EMS server is used to deploy the EMS main program, collect real-time operating data of energy equipment, such as line voltage, current, active power, reactive power, and power factor, and store the collected data in the database for the algorithm program to call. The EMS workstation is used to deploy tuning algorithm programs, obtain device data collected by the EMS server through a database interface, generate tuning instructions based on a preset prediction model, and send the tuning instructions to the EMS server. The communication management unit is used to realize the communication between the EMS server and the coordination controller. Specifically, it has the functions of master station and data forwarding. The master station function receives the value-based control instructions uploaded by the EMS server through the IEC104 protocol and the Modbus protocol. The data forwarding function is used to send the value-based control instructions to the coordination controller. The coordination controller is used to receive and execute value-based control commands issued by the communication management unit to achieve real-time control of energy equipment.

[0020] Preferably, the EMS server is configured with a data acquisition module, a data monitoring module, an instruction parsing module, a protocol forwarding module, and a data processing module; The data acquisition module acquires real-time operating parameters of energy equipment (such as transformers, generators, energy storage equipment, etc.) (such as total active power of photovoltaic systems, energy storage SOC, etc.). The data monitoring module is used to monitor the collected data in real time and trigger alarms in abnormal situations, such as triggering a level one alarm when the DC bus voltage exceeds 110% of the rated value (refer to GB / T 34133-2017). The instruction parsing module is used to receive the tuning instructions generated by the tuning algorithm program and generate value-bearing control instructions (including device identifier, control parameter value, execution timestamp and verification information); then, according to the protocol standards supported by the target device (such as relay protection device, inverter, SCADA system), the instructions are converted into corresponding protocol messages and data encoding is completed, including the encoding method of processing values ​​(such as floating point to fixed point, big endian / little endian byte order), check code generation (CRC, parity check) and message assembly.

[0021] The protocol forwarding module is used to encapsulate, forward, and adapt control commands (which typically include specific numerical parameters, such as power setpoints and voltage regulation amounts) according to the communication protocols supported by the target device or subsystem (such as IEC 60870-5-104, DNP3, Modbus TCP / RTU, etc.).

[0022] The data processing module is the core module supporting instruction generation, verification, and execution feedback. It is responsible for processing and analyzing instruction data, equipment status data, and historical operating data throughout their entire lifecycle. It also receives control instructions (such as power adjustment amounts and voltage setpoints) from scheduling algorithms, manual input, or other systems, and performs format verification, range checks (such as whether power values ​​exceed limits), and logical rationality verification (such as instruction conflict detection).

[0023] Preferably, the data preprocessing module is used to appropriately filter and organize the real-time data obtained from the EMS server, such as removing obviously abnormal data from the dataset. The prediction model module is based on the time-series characteristics of historical power data. Based on historical photovoltaic data, the input dimension is determined to be 8 (irradiance, ambient temperature, module temperature, wind speed, relative humidity, historical power, cloud cover, inverter efficiency) through feature filtering. The hidden layer dimension is determined to be 64 nodes through grid search. The photovoltaic output is predicted at a 15-minute granularity. The model is trained on historical and real-time data to generate prediction results of equipment operating status. Through training, the prediction model can provide status information such as power demand and temperature changes of the equipment in the future, such as short-term prediction of the power of the load acquisition system and the photovoltaic system. The optimization calculation module sets a power deviation threshold of ±3% (refer to Q / GDW1867-2019) based on the prediction results, and generates instructions through the configured rules (e.g., limiting the charging current when the energy storage SOC>80%). The optimization instructions consist of the adjustment values ​​and adjustment time of the device operating parameters. The instruction issuance module is used to send tuning instructions back to the EMS server via an interface; The historical data analysis module integrates command issuance records from the protocol forwarding module, device execution feedback data (such as actual power values ​​and response times), environmental parameters (temperature and light intensity), and system logs (error codes and alarm events) to generate core indicators such as command success rate, average response time, and device availability. By comparing command settings with actual execution values ​​(such as power adjustment deviation analysis), closed-loop verification is achieved.

[0024] Preferably, the EMS server and the EMS workstation are connected via a gigabit network, and the communication protocol is OPC UA (port 4840) or IEC 104 (port 2404); the interface between the EMS server and the tuning algorithm program uses a standardized data format such as JSON for data interaction, including a real-time data acquisition interface and a tuning instruction issuance interface; Preferably, the communication management unit hardware is a NARI WTS-661 network communication server, which has a master station function module and a data forwarding module; The protocol stack of the main station functional module can be dynamically adapted (default IEC104, switch to ModbusTCP when connecting to energy storage PCS) to realize data interaction with the EMS server. The data forwarding module is used to send value-based control instructions to the coordination controller via wired or wireless networks.

[0025] Preferably, the communication management unit supports multiple communication protocols (such as IEC 60870-5-104, DNP3, MQTT, etc.) to adapt to the communication needs of different devices.

[0026] Preferably, the communication management unit adds a protocol conversion module to uniformly convert instructions from different protocols into a standard format, ensuring instruction compatibility.

[0027] Preferably, the communication management unit supports distributed deployment and can communicate with multiple EMS servers and coordination controllers simultaneously.

[0028] Preferably, the communication management unit ensures the efficiency and reliability of command issuance by adding a load balancing module.

[0029] Preferably, the communication management unit supports instruction priority management, and can dynamically adjust the order in which instructions are issued according to their importance and urgency. For example, emergency control instructions are issued first, while ordinary control instructions are issued in sequence.

[0030] Preferably, the coordination controller comprises an instruction receiving module, an instruction verification module, and an execution module; The instruction receiving module is used to receive value-based control instructions issued by the communication management unit; The instruction verification module uses CRC-32 verification (polynomial 0x04C11DB7) combined with timestamp window verification (±5s). The execution module is used to perform real-time control of energy equipment according to the verified value control instructions.

[0031] like Figure 2 The diagram shows a communication architecture and control logic of an EMS-based value-based control command issuance system, including the following steps: Step 1: Deploy the EMS program on the EMS server and the optimization algorithm program on the EMS workstation.

[0032] 1. EMS server deployment: Install the EMS program on the EMS server. The server is a HPE DL380 from H3C, and the operating system is Ubuntu 20.04. Configure the data acquisition module, monitoring module, command parsing module, protocol forwarding module, and data processing module.

[0033] 2. EMS workstation deployment: Install the optimization algorithm program on the EMS workstation, and configure the data preprocessing module, prediction model module, optimization calculation module, and command issuance module.

[0034] Step 2: Configure the high-speed communication line and communication protocol between the EMS server and the EMS workstation.

[0035] Use high-speed communication lines (such as fiber optic or gigabit Ethernet) to connect the EMS server and EMS workstation to ensure real-time and reliable data transmission.

[0036] Configure communication protocols (such as Modbus, IEC 104, or OPC UA) to ensure smooth data exchange between the EMS server and the EMS workstation.

[0037] like Figure 2 As shown, the system mainly consists of a communication management unit, a switch, a coordination controller, a data acquisition module, and a remote control module with on-line control. It achieves global time alignment through a dual clock synchronization mechanism. The communication management unit is responsible for establishing communication links with external devices and other subsystems, ensuring reliable transmission of commands and data. The switch, acting as a network hub, connects the data acquisition module and the coordination controller, aggregating power grid operating parameters in real time. After obtaining status information from the underlying devices, the data acquisition module transmits the time-synchronized and calibrated data to the coordination controller, providing accurate input for command generation.

[0038] The value-based remote control module supports binding commands with preset thresholds or target values, ensuring the accuracy and safety of the control process. The system employs a dual clock synchronization mechanism, operating at the communication management layer and the data application layer respectively. The former ensures the timing consistency of command transmission across devices, while the latter ensures strict synchronization between data acquisition and command execution, improving the EMS system's response efficiency and stability under complex operating conditions.

[0039] Step 3: Develop an interface between the EMS server and the tuning algorithm program. The tuning algorithm program obtains device data from the EMS server in real time through the interface and generates tuning instructions to be sent back to the EMS server.

[0040] 1. Develop the interface between the EMS server and the tuning algorithm program, including a real-time data acquisition interface and a tuning command issuance interface.

[0041] Real-time data acquisition interface: The optimization algorithm program obtains real-time operating data of energy equipment from the EMS server through this interface.

[0042] Tuning instruction issuance interface: The tuning algorithm program uses this interface to send the generated tuning instructions back to the EMS server.

[0043] 2. The interface uses a standardized data format (such as JSON or XML) to ensure the compatibility and scalability of data interaction.

[0044] Step 4: The EMS server parses the tuning instructions, generates value-based control instructions, and uploads them to the communication management machine through the main station function of the communication management machine.

[0045] After receiving the tuning instruction, the EMS server generates a value-based tuning instruction through the instruction parsing module.

[0046] Value-based control instructions include the following information: Equipment identification: Indicates the energy equipment that needs to be regulated.

[0047] Control parameter values: These specify the adjustment values ​​for the equipment's operating parameters (such as voltage setpoints, power output values, etc.).

[0048] Execution timestamp: Indicates the time when the instruction was executed.

[0049] Verification information: Used to ensure the integrity and validity of instructions.

[0050] Step 5: The communication management unit sends the value-based control command to the coordination controller through the data forwarding function.

[0051] 1. The communication management unit has an open master station function and supports multiple communication protocols (such as IEC 60870-5-104, MODBUS, TCP / RTU, or MQTT).

[0052] 2. The EMS server transmits the value-bearing control instructions to the communication management machine through the substation upload function.

[0053] 3. The communication management unit uses the data forwarding function to send value-based control commands to the coordination controller.

[0054] Step 6: The coordinating controller receives and executes value-based control commands to achieve real-time control of energy equipment.

[0055] 1. The coordinating controller receives control commands with values ​​and verifies the integrity and legality of the commands through the command verification module.

[0056] 2. After the verification is passed, the execution module of the coordinating controller will adjust the energy equipment in real time according to the instructions.

[0057] 3. After the control is completed, the coordination controller will feed back the execution results to the communication management unit, which will then upload the results to the EMS server, forming a closed-loop control.

[0058] Based on Example 1, the prediction model and optimization algorithm of the tuning algorithm program can be further optimized to improve the accuracy and scientific nature of the control instructions.

[0059] For example: optimizing prediction models 1. Use deep learning algorithms (such as LSTM neural networks) to train on historical and real-time data to generate high-precision prediction models.

[0060] 2. Predictive models can predict the operating status of equipment over a period of time (such as power demand, temperature changes, etc.), providing data support for optimization calculations.

[0061] For example: Optimize the expansion of the computing module 1. Introduce a multi-objective optimization algorithm (such as NSGA-II) into the optimization calculation module, which simultaneously considers multiple optimization objectives such as minimum energy consumption, highest operating efficiency, and longest equipment lifespan.

[0062] 2. Based on the prediction results and optimization objectives, generate the optimal tuning instructions.

[0063] For example: dynamically adjusting control strategies 1. Dynamically adjust the parameters and strategies of the optimization algorithm program based on real-time operating data and changes in the external environment (such as weather, electricity prices, etc.).

[0064] 2. For example, during peak electricity price periods, prioritize reducing energy consumption; when equipment temperature is too high, prioritize adjusting cooling system parameters.

[0065] The preferred embodiments of the present invention disclosed above are only for the purpose of illustrating the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation described herein. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention.

Claims

1. A value-based control command issuance system based on EMS, characterized in that, include: The EMS server is used to deploy EMS programs, collect real-time operating data of energy equipment, and monitor the equipment status. The EMS workstation is used to deploy optimization algorithm programs, obtain device data collected by the EMS server in real time through a database interface, generate optimization instructions based on a preset prediction model, and send the optimization instructions back to the EMS server. The communication management unit is used to realize the communication between the EMS server and the coordination controller, including the master station function and the data forwarding function. The master station function receives the value-based control instructions uploaded by the EMS server through the IEC104 protocol and the Modbus protocol, and the data forwarding function is used to send the value-based control instructions to the coordination controller. The coordination controller is used to receive and execute value-based control commands issued by the communication management unit to achieve real-time control of energy equipment.

2. The EMS-based value-based control command issuance system according to claim 1, characterized in that: The EMS server is equipped with a data acquisition module, a monitoring module, an instruction parsing module, a protocol forwarding module, and a data processing module; The data acquisition module is used to collect real-time operating data of energy equipment; The monitoring module is used to monitor the collected data in real time and trigger alarms in abnormal situations; The instruction parsing module is used to receive tuning instructions generated by the tuning algorithm program and generate value-based control instructions; it can convert value-based control instructions into corresponding protocol messages and complete data encoding according to the protocol standards supported by the target device. The value-based control command includes the device identifier, control parameter value, execution timestamp, and verification information; The protocol forwarding module is used to receive control commands and encapsulate, forward, and adapt the control commands according to the protocol standards supported by the target device. The data processing module is the core module that supports instruction generation, verification, and execution feedback. It is responsible for processing and analyzing instruction data, device status data, and historical operation data throughout their entire lifecycle.

3. The EMS-based value-based control command issuance system according to claim 1, characterized in that, The EMS workstation includes: The data preprocessing module is used to clean and normalize the real-time data obtained from the EMS server. The prediction model module uses deep learning algorithms to train on historical and real-time data to generate prediction results of the equipment's operating status. The optimization calculation module generates tuning instructions based on the prediction results using a multi-objective optimization algorithm. The tuning instructions include the adjustment values ​​and adjustment times for the equipment operating parameters. The instruction issuance module is used to send tuning instructions back to the EMS server via an interface; The historical data analysis module is a key module supporting system optimization, fault diagnosis, and decision improvement. It is responsible for integrating instruction issuance records, device execution feedback data, environmental parameters, and system logs from the protocol forwarding module; and generating core indicators to achieve closed-loop verification by comparing instruction settings with actual execution values.

4. The EMS-based value-based control command issuance system according to claim 1, characterized in that: The EMS server and the EMS workstation are connected via a high-speed communication line, and the communication protocols include, but are not limited to, Modbus, IEC 104 or OPCUA; the interface between the EMS server and the tuning algorithm program uses a standardized data format for data interaction, including a real-time data acquisition interface and a tuning command issuance interface.

5. The EMS-based value-based control command issuance system according to claim 1, characterized in that: The communication management unit includes a master station function module and a data forwarding module; The main station functional module supports multiple communication protocols, including IEC 60870-5-104, DNP3, or MQTT, for data interaction with the EMS server; The data forwarding module is used to send value-based control instructions to the coordination controller via wired or wireless networks.

6. The EMS-based value-based control command issuance system according to claim 1, characterized in that, The coordination controller includes an instruction receiving module, an instruction verification module, and an execution module; The instruction receiving module is used to receive value-based control instructions issued by the communication management unit; The instruction verification module is used to verify the integrity and legality of value-based control instructions; The execution module is used to perform real-time control of energy equipment according to the verified value control instructions.

7. A method for issuing value-based control commands based on EMS, characterized in that, Includes the following steps: Step 1: Deploy the EMS program on the EMS server and the optimization algorithm program on the EMS workstation; Step 2: Configure the high-speed communication line and communication protocol between the EMS server and the EMS workstation; Step 3: Develop an interface between the EMS server and the tuning algorithm program. The tuning algorithm program obtains device data from the EMS server in real time through the interface and generates tuning instructions to be sent back to the EMS server. Step 4: The EMS server parses the tuning instructions, generates value-based control instructions, and uploads them to the communication management machine through the main station function of the communication management machine; Step 5: The communication management unit sends the value-based control command to the coordination controller through the data forwarding function; Step 6: The coordinating controller receives and executes the value-based control instructions to achieve real-time control of energy equipment.

8. The method for issuing value-based control commands based on EMS according to claim 7, characterized in that, In step 3, the process of generating tuning instructions by the tuning algorithm program includes: Preprocess real-time data, including data cleaning and normalization; Deep learning algorithms are used to train on historical and real-time data to generate prediction results of equipment operating status; Based on the prediction results, a tuning instruction is generated using a multi-objective optimization algorithm. The tuning instruction includes the adjustment value and adjustment time of the equipment operating parameters.

9. The method for issuing value-based control commands based on EMS according to claim 7, characterized in that, In step 5, after receiving the value-based control instruction, the communication management unit performs format conversion and protocol adaptation of the instruction to ensure that the instruction can be sent to the coordination controller through the target communication protocol.

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