Energy management EMS system supporting multiple scheduling strategies

Abstract

The invention discloses an energy management EMS (energy management system) supporting multiple scheduling strategies, which relates to the technical field of energy management and comprises S1, a state monitoring module; s2, data encryption transmission; s3, a local logic module; s4, optimizing a control algorithm module; and S5, a database module and a remote control module. According to the energy management EMS system supporting the multiple scheduling strategies, the EMS host adopts a high-performance low-power-consumption embedded industrial grade 32-bit communication processor, the system is low in occupied resource and low in cost, an algorithm model is optimized in combination with collected equipment data and in combination with local scheduling strategies and environment states, and the energy management efficiency is improved. Therefore, the local logic module presets a plurality of operation scheduling strategies for each device based on the device data, the local time, the local environment state and the scheduling strategies, so that the device operation cost is reduced by switching the scheduling strategies while the normal operation of the device in each time is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy management EMS system supporting multiple scheduling strategies. Background Art

[0002] In recent years, research on energy storage and energy management has achieved certain results. A common solution on the market is to use an energy storage and energy management system (EMS) to store excess energy in the power system during low load periods, and release the stored electricity to supply the system load during peak load periods, thereby shaving peaks and filling valleys, effectively eliminating the peak-valley difference, ensuring power supply reliability and operational stability, and bringing significant benefits to industrial production and people's lives.

[0003] However, such common solutions also have some shortcomings. For example, the hardware often uses X86 architecture computer hosts, which are large in size, high in cost, and high in power consumption. The software functions are localized and run independently, and there are data islands that cannot be linked and controlled. The data is not actively reported, and cloud-based remote monitoring and control cannot be achieved. The scheduling strategy is single and only implements peak shaving and valley filling strategies, which cannot meet the rapidly developing intelligent requirements of the power system.

[0004] Therefore, in view of this, the existing structure and deficiencies are studied and improved, and an energy management EMS system that supports multiple scheduling strategies is proposed. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides an energy management EMS system that supports multiple scheduling strategies, solving the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention is implemented by the following technical solutions: an energy management EMS system supporting multiple scheduling strategies, the energy management EMS system supporting multiple scheduling strategies comprises the following steps:

[0007] S1. Status monitoring module:

[0008] The status monitoring module is used to monitor the data information of the collection equipment in real time, and compare the collected real-time data with the preset data of each device. For inconsistent data or data exceeding the threshold, attention is paid and warnings are given. The status monitoring module also has a sensor self-check function in conjunction with the equipment. Specifically:

[0009] The preset data generated regularly is preset inside the equipment. The preset data will be collected and fed back by the sensor regularly. If the collected data is consistent with the preset data, it means that the sensor is collecting accurately. If it is inconsistent, it means that the sensor is collecting inaccurately and needs to be repaired and calibrated.

[0010] S2. Data encryption transmission:

[0011] Since the data of each device often needs to be kept confidential, in order to prevent data leakage during transmission after collection, the sensor self-check function is changed from periodic self-check to real-time self-check. The device data collected by the sensor will include preset data and device real-time data. Furthermore, the preset data and device real-time data can be cross-arranged. The status monitoring module will send the arrangement order to the receiving end separately, and the receiving end will re-arrange the preset data and device real-time data based on the arrangement order.

[0012] S3, local logic module:

[0013] Adjust the status of each device based on the collected data to ensure the equipment is in the best operating state while reducing costs;

[0014] S4, optimization control algorithm module:

[0015] Optimize the algorithm model based on the collected equipment data and combined with local scheduling strategies and environmental conditions;

[0016] S5, database module and remote control module:

[0017] The contents in the above steps are all recorded and stored in the database module, and the EMS system can be remotely controlled and managed through the remote control module.

[0018] Furthermore, in step S1, the status monitoring module adopts Internet of Things technology and sensor technology, based on Modbus TCP and Modbus RTU acquisition protocols, to collect data information of high-voltage boxes, inverters, electric energy meters, air conditioners, sensors and other equipment in the energy storage system in real time to build an online status monitoring system, to monitor and collect data on the operating status of the energy storage equipment in real time, to detect abnormal situations in time and to carry out early warning processing.

[0019] Furthermore, in step S3, the local logic module performs such logical protection based on the collected data in combination with the local environmental status, scheduling strategy, and operating system, such as automatically managing charging and discharging according to peak and valley times, controlling the air conditioner to adjust the temperature based on the battery cell and ambient temperature, and stopping the system according to local emergencies. Therefore, the local logic module presets several operating scheduling strategies for each device based on device data, local time, local environmental status, scheduling strategy, and operating system, so as to keep the device running normally at each time while reducing the device operating cost by switching scheduling strategies.

[0020] Furthermore, in step S4, the optimization control algorithm module optimizes the charging and discharging process of the energy storage device based on the optimization algorithm to reduce energy consumption and increase the service life of the device.

[0021] Furthermore, in step S5, the database module uses the sqlite3 database to record local key data, alarm data, log and other information to provide a basis for subsequent queries.

[0022] Furthermore, in step S5, the remote control module reports to the cloud platform via the 4G network and the MQTT protocol, providing a basis for statistical analysis of cloud platform data and enabling local EMS control and management through the cloud.

[0023] Furthermore, the status monitoring module and the local logic module are arranged in the EMS host, and the EMS host is connected to the devices in the energy storage system such as high-voltage boxes, inverters, air conditioners, and electric meters through Ethernet and RS485 interfaces to obtain status data in real time.

[0024] Furthermore, the optimization control algorithm module and the database module are arranged in the cloud.

[0025] Furthermore, the remote control module is arranged inside the local screen, and the local screen is interconnected with the EMS host via Ethernet, so as to obtain and display various data of the EMS system in real time and to configure parameters of the EMS remotely.

[0026] The present invention provides an energy management EMS system that supports multiple scheduling strategies and has the following beneficial effects:

[0027] 1. The energy management EMS system supports multiple scheduling strategies. The EMS host adopts a high-performance, low-power embedded industrial-grade 32-bit communication processor. The system occupies low resources and has low cost. It combines the collected device data with the local scheduling strategy and environmental status to optimize the algorithm model. Therefore, the local logic module presets several operation scheduling strategies for each device based on device data, local time, local environmental status, scheduling strategy, and operating system to keep the device running normally at each time while reducing the device operating cost by switching the scheduling strategy.

[0028] 2. The energy management EMS system that supports multiple scheduling strategies presets data in the device. When the real-time data of the device is collected by the sensor, the accuracy of the sensor can be judged based on whether the preset data is consistent after the regularly generated preset data is collected by the sensor, thereby ensuring that the actual data of the device transmitted is accurate to prevent errors. At the same time, the preset data and the real-time data of the device can be mixed and arranged, and the arrangement order can be sent to the receiving end separately, so that the receiving end can restore the real-time data of the device through the arrangement order, thereby preventing the leakage of the device data during transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a schematic diagram of the operation flow of an energy management EMS system that supports multiple scheduling strategies. DETAILED DESCRIPTION

[0030] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0031] like Figure 1 As shown, the present invention provides a technical solution: an energy management EMS system supporting multiple scheduling strategies, the energy management EMS system supporting multiple scheduling strategies includes the following steps:

[0032] S1. Status monitoring module:

[0033] The status monitoring module is used to monitor the data information of the collection equipment in real time, and compare the collected real-time data with the preset data of each device. For inconsistent data or data exceeding the threshold, attention is paid and warnings are given. The status monitoring module also has a sensor self-check function in conjunction with the equipment. Specifically:

[0034] Preset data generated regularly are preset inside the equipment. The preset data will be collected and fed back by the sensor regularly. If the collected data is consistent with the preset data, it means that the sensor is collecting accurately. If it is inconsistent, it means that the sensor is collecting inaccurately and needs to be repaired and corrected. The preset data is the operating parameters of the equipment when it is running. It is generated and output by the simulation program to be collected by the sensor;

[0035] The status monitoring module uses IoT technology and sensor technology, based on Modbus TCP and Modbus RTU acquisition protocols, to collect data information from high-voltage boxes, inverters, energy meters, air conditioners, sensors and other equipment in the energy storage system in real time to build an online status monitoring system, monitor the operating status of the energy storage equipment in real time and collect data, detect abnormal situations in time and conduct early warning processing;

[0036] S2. Data encryption transmission:

[0037] Since the data of each device often needs to be kept confidential, in order to prevent data leakage during transmission after collection, the sensor self-check function is changed from periodic self-check to real-time self-check. The device data collected by the sensor will include preset data and device real-time data. Furthermore, the preset data and device real-time data can be cross-arranged. The status monitoring module will send the arrangement order to the receiving end separately, and the receiving end will re-arrange the preset data and device real-time data based on the arrangement order.

[0038] S3, local logic module:

[0039] Based on the collected data, the status of each device is adjusted to ensure that the device is in the best operating state while reducing costs. The local logic module is based on the collected data, combined with the local environmental status, scheduling strategy, and operating system. For example, it automatically manages charging and discharging according to peak and valley times, controls the air conditioner to adjust the temperature based on the battery cell and ambient temperature, and stops the system according to local emergencies. This type of logical protection, so the local logic module presets several operation scheduling strategies for each device based on device data, local time, local environmental status, scheduling strategy, and operating system to keep the device running normally at each time while reducing the device operating cost by switching scheduling strategies;

[0040] S4, optimization control algorithm module:

[0041] Based on the collected equipment data and combined with the local dispatch strategy and environmental conditions to optimize the algorithm model, in short, the optimization control algorithm module uses the optimization algorithm to optimize the charging and discharging process of the energy storage equipment to reduce energy consumption and increase the service life of the equipment;

[0042] S5, database module and remote control module:

[0043] The contents of the above steps are all recorded and stored in the database module, and the EMS system can be remotely controlled and managed through the remote control module. The database module uses the sqlite3 database to record local key data, alarm data, log and other information to provide a basis for subsequent queries. The remote control module reports to the cloud platform through the 4G network and the MQTT protocol, providing a basis for cloud platform data statistical analysis, and can perform local EMS control and management through the cloud.

[0044] The above-mentioned status monitoring module and local logic module are set in the EMS host. The EMS host is connected to the devices in the energy storage system such as high-voltage boxes, inverters, air conditioners, and electric meters through Ethernet and RS485 interfaces to obtain status data in real time, while the optimization control algorithm module and database module are set in the cloud, and the remote control module is set inside the local screen. The local screen is interconnected with the EMS host through Ethernet to obtain and display various data of the EMS system in real time and can remotely configure the parameters of the EMS.

[0045] The EMS host uses a high-performance, low-power embedded industrial-grade 32-bit communication processor. The system occupies low resources and has low cost. It uses software multi-level detection and hardware multiple protection mechanisms to improve equipment stability. The mean time between failures (MTBF) is not less than 100,000 hours. It uses a metal shell with a protection level of IP30. The shell and system are safely isolated. The operating temperature range is -40 to +75°C, which is particularly suitable for industrial control field applications. The above system uses 4G network communication and is interconnected with the cloud platform data to facilitate data supervision and remote scheduling.

[0046] And according to needs, the EMS host can be integrated with the local screen hardware and software, combining the two into one, that is, to realize an EMS host with screen display function, further saving costs.

[0047] Based on the above description, the present invention presets data in the device, and when the real-time data of the device is collected by the sensor, the accuracy of the sensor can be judged based on whether the preset data is consistent after the regularly generated preset data is collected by the sensor, thereby ensuring that the actual data of the device transmitted is accurate to prevent errors. At the same time, the preset data and the real-time data of the device can be mixed and arranged, and the arrangement order can be sent to the receiving end separately, so that the receiving end can restore the real-time data of the device through the arrangement order, thereby preventing the leakage of the device data when it is transmitted.

[0048] In summary, when using the energy management EMS system that supports multiple scheduling strategies, the status monitoring module is first used to monitor the data information of the acquisition equipment in real time, and the real-time data collected is compared with the preset data of each device. For inconsistent data or data exceeding the threshold, attention is paid and warnings are given. The status monitoring module also has a sensor self-check function in conjunction with the equipment, specifically:

[0049] Preset data generated regularly are preset inside the equipment. The preset data will be collected and fed back by the sensor regularly. If the collected data is consistent with the preset data, it means that the sensor is collecting accurately. If it is inconsistent, it means that the sensor is collecting inaccurately and needs to be repaired and corrected. The preset data is the operating parameters of the equipment when it is running. It is generated and output by the simulation program to be collected by the sensor;

[0050] Since the data of each device often needs to be kept confidential, in order to prevent data leakage during transmission after collection, the sensor self-check function is changed from periodic self-check to real-time self-check. The device data collected by the sensor will include preset data and device real-time data. Furthermore, the preset data and device real-time data can be cross-arranged. The status monitoring module will send the arrangement order to the receiving end separately, and the receiving end will re-arrange the preset data and device real-time data based on the arrangement order.

[0051] Based on the collected data, the status of each device is adjusted to ensure that the device is in the best operating state while reducing costs. The local logic module is based on the collected data, combined with the local environmental status, scheduling strategy, and operating system. For example, it automatically manages charging and discharging according to peak and valley times, controls the air conditioner to adjust the temperature based on the battery cell and ambient temperature, and stops the system according to local emergencies. This type of logical protection, so the local logic module presets several operation scheduling strategies for each device based on device data, local time, local environmental status, scheduling strategy, and operating system to keep the device running normally at each time while reducing the device operating cost by switching scheduling strategies;

[0052] Based on the collected equipment data and combined with the local dispatch strategy and environmental conditions to optimize the algorithm model, in short, the optimization control algorithm module uses the optimization algorithm to optimize the charging and discharging process of the energy storage equipment to reduce energy consumption and increase the service life of the equipment;

[0053] The contents of the above steps are all recorded and stored in the database module, and the EMS system can be remotely controlled and managed through the remote control module. The database module uses the sqlite3 database to record local key data, alarm data, log and other information to provide a basis for subsequent queries. The remote control module reports to the cloud platform through the 4G network and the MQTT protocol, providing a basis for cloud platform data statistical analysis, and can perform local EMS control and management through the cloud.

[0054] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. An energy management EMS system supporting multiple scheduling strategies, characterized by: The energy management EMS system supporting multiple scheduling strategies includes the following steps: S1. Status monitoring module: The status monitoring module is used to monitor the data information of the collection equipment in real time, and compare the collected real-time data with the preset data of each device. For inconsistent data or data exceeding the threshold, attention is paid and warnings are given. The status monitoring module also has a sensor self-check function in conjunction with the equipment. Specifically: The preset data generated regularly is preset inside the equipment. The preset data will be collected and fed back by the sensor regularly. If the collected data is consistent with the preset data, it means that the sensor is collecting accurately. If it is inconsistent, it means that the sensor is collecting inaccurately and needs to be repaired and calibrated. S2. Data encryption transmission: Since the data of each device often needs to be kept confidential, in order to prevent data leakage during transmission after collection, the sensor self-check function is changed from periodic self-check to real-time self-check. The device data collected by the sensor will include preset data and device real-time data. Furthermore, the preset data and device real-time data can be cross-arranged. The status monitoring module will send the arrangement order to the receiving end separately, and the receiving end will re-arrange the preset data and device real-time data based on the arrangement order. S3, local logic module: Adjust the status of each device based on the collected data to ensure the equipment is in the best operating state while reducing costs; S4, optimization control algorithm module: Optimize the algorithm model based on the collected equipment data and combined with local scheduling strategies and environmental conditions; S5, database module and remote control module: The contents in the above steps are all recorded and stored in the database module, and the EMS system can be remotely controlled and managed through the remote control module.

2. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: In step S1, the status monitoring module adopts the Internet of Things technology and sensor technology, based on the Modbus TCP and Modbus RTU acquisition protocols, to collect data information of the high-voltage box, inverter, electric energy meter, air conditioner, sensor and other equipment in the energy storage system in real time to build an online status monitoring system, to monitor and collect data of the operating status of the energy storage equipment in real time, to detect abnormal situations in time and to carry out early warning processing.

3. The energy management EMS system supporting multiple scheduling strategies according to claim 1 is characterized in that: In step S3, the local logic module performs such logical protection based on the collected data in combination with the local environmental status, scheduling strategy, and operating system, such as automatically managing charging and discharging according to peak and valley times, controlling the air conditioner to adjust the temperature based on the battery cell and ambient temperature, and stopping the system according to local emergencies. Therefore, the local logic module presets several operating scheduling strategies for each device based on device data, local time, local environmental status, scheduling strategy, and operating system, so as to keep the device running normally at each time while reducing the device operating cost by switching scheduling strategies.

4. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: In step S4, the optimization control algorithm module optimizes the charging and discharging process of the energy storage device based on the optimization algorithm to reduce energy consumption and increase the service life of the device.

5. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: In step S5, the database module uses the sqlite3 database to record local key data, alarm data, log and other information to provide a basis for subsequent queries.

6. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: In step S5, the remote control module reports to the cloud platform via the 4G network and the MQTT protocol, providing a basis for statistical analysis of cloud platform data and enabling local EMS control and management through the cloud.

7. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: The state monitoring module and the local logic module are arranged in the EMS host, and the EMS host is connected to the devices in the energy storage system such as high-voltage boxes, inverters, air conditioners, and electric meters through Ethernet and RS485 interfaces to obtain state data in real time.

8. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: The optimization control algorithm module and the database module are arranged in the cloud.

9. The energy management EMS system supporting multiple scheduling strategies according to claim 1, characterized in that: The remote control module is arranged inside the local screen, and the local screen is interconnected with the EMS host via Ethernet, so as to obtain and display various data of the EMS system in real time and configure the parameters of the EMS remotely.

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