CN116560838B - Edge computing terminal equipment, comprehensive energy station, management platform and control method thereof - Google Patents
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Abstract
The disclosure provides an edge computing terminal device, a comprehensive energy station, a management platform and a control method thereof, wherein the edge computing terminal device is arranged in the comprehensive energy station, and the comprehensive energy station comprises a plurality of energy devices; the edge computing terminal equipment comprises an edge main controller and a plurality of computing units; an energy data prediction model is arranged in each calculation unit; the computing unit is used for acquiring energy information of the corresponding energy equipment, inputting the energy information into the energy data prediction model to output energy prediction data, and sending the energy prediction data to the edge main controller; the edge main controller is provided with an energy scheduling strategy prediction model, and the energy scheduling strategy prediction model is input with energy prediction data to output an energy scheduling strategy. The cloud scheduling method and the cloud scheduling system reduce the pressure of the cloud and shorten the response time to the cloud scheduling; according to the type, scale, energy demand and supply of different comprehensive energy station equipment, the computing units can be integrated timely, and universality and flexible usability of the edge computing terminal equipment are improved.
Description
Technical Field
The disclosure relates to the technical field of energy management, in particular to an edge computing terminal device, a comprehensive energy station, a management platform and a control method thereof.
Background
The economic growth is in direct proportion to the energy consumption, the energy consumption increases day by day with the time, and the number and real-time requirements of the energy transaction are continuously improved. In order to respond to the development targets of double carbon and the popularization and application of new energy, the requirements of multi-energy complementation and the improvement of the comprehensive utilization rate of various energy sources are continuously put forward. At this time, the integrated energy stations and the multi-energy transactions and the demand responses are generated.
In a single comprehensive energy station, when the phenomena of energy variety change, energy power generation equipment update, energy power generation equipment capacity increase and the like occur or the energy demand and supply quantity increase, as for a conventional edge computing terminal, the algorithm and the algorithm are fixed, the algorithm can not be timely improved, the computing capacity of the edge computing terminal can not meet the requirement, the shutdown maintenance is needed, the whole equipment in the comprehensive energy station is replaced, and the universality and the flexibility of the edge computing terminal are low.
When multi-energy transaction and complementary interaction are carried out among a plurality of comprehensive energy stations, the requirements of cloud demand response and scheduling control cannot be met rapidly compared with a conventional edge computing terminal.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to overcome the defect that an edge computing terminal in a comprehensive energy station cannot timely perform calculation force improvement and cannot rapidly meet requirements of cloud demand response and scheduling control in the prior art, and provide an edge computing terminal device, a comprehensive energy station, a management platform and a control method thereof.
The technical problems are solved by the following technical scheme:
in a first aspect, an edge computing terminal device is provided, where the edge computing terminal device is disposed in a comprehensive energy station, and the comprehensive energy station includes a plurality of energy devices;
the edge computing terminal equipment comprises an edge main controller and a plurality of computing units;
the edge master controller comprises a plurality of communication interfaces, and each computing unit is in communication connection with one communication interface;
an energy data prediction model is arranged in each computing unit;
wherein different computing units correspond to different or the same energy data prediction models;
the computing unit is used for acquiring energy information of the corresponding energy equipment, inputting the energy information into the energy data prediction model to output energy prediction data, and sending the energy prediction data to the edge main controller;
an energy scheduling strategy prediction model is arranged in the edge master controller;
the edge master controller is used for receiving the energy prediction data and the energy demand instructions sent by different computing units, and inputting the energy prediction data into the energy scheduling policy prediction model to output an energy scheduling policy.
Preferably, the energy devices with different device parameters correspond to different energy data prediction models.
Preferably, the computing unit is further configured to collect device operation information of the energy device, and send the device operation information to the edge master controller;
the edge master controller is used for generating equipment diagnosis results based on the equipment operation information.
Preferably, when the device diagnosis result indicates that the actual running state of the energy device does not meet the preset normal running state, the edge master controller is further configured to generate alarm information based on the device diagnosis result.
Preferably, the edge master controller is further configured to update the energy scheduling policy based on the received new energy demand instruction.
Preferably, the edge main controller is configured to monitor an actual computing force of each computing unit, obtain a computing unit with the actual computing force smaller than a set computing force threshold, serve as a unit to be processed, and generate computing force reminding information;
the computing power reminding information is used for reminding and calling the rest computing units which are in an idle state in the edge computing terminal equipment and have the same energy data prediction model as the units to be processed;
or reminding to integrate a new computing unit which has the same energy data prediction model as the unit to be processed in the edge computing terminal equipment.
Preferably, the edge master controller is further configured to detect an actual operation state of the energy device, and generate device operation information;
the device operation information is used for representing whether the actual operation state accords with a preset operation state or not.
Preferably, the type of the computing unit includes at least one of ARM (Advanced RISC Machine, a microprocessor), FPGA (Field Programmable Gate Array ), DPU (Data Processing Unit, data processor) and CPU (Central Processing Unit );
and/or the number of the groups of groups,
the energy source equipment comprises at least one of wind power equipment, photovoltaic equipment, energy storage equipment, power grid equipment, cogeneration equipment, ground source heat pump equipment, heat storage equipment, electric nitrogen production equipment and a gas turbine.
In a second aspect, there is also provided an integrated energy station comprising a plurality of different types of energy devices, and a plurality of edge computing terminal devices as described above in communication with the energy devices.
In a third aspect, an energy management platform is further provided, including a cloud end, a local control center, and a plurality of integrated energy stations;
the edge computing terminal in the comprehensive energy station is respectively in communication connection with the cloud end and the local control center;
the local control center is used for summarizing energy information corresponding to each energy device in each comprehensive energy station;
the cloud end is used for issuing an energy demand instruction to the corresponding comprehensive energy station;
the edge master controller in the comprehensive energy station is used for generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, issuing the energy scheduling strategy to the local control center and sending the energy scheduling strategy to the cloud;
the local control center is also used for generating a corresponding control instruction based on the energy scheduling strategy so as to control the operation of the corresponding energy equipment.
In a fourth aspect, a control method of an energy management platform is further provided, where the control method is implemented based on the energy management platform;
the control method comprises the following steps:
acquiring energy information of energy equipment, and generating energy prediction data based on the energy information;
generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, and uploading the energy scheduling strategy to a cloud;
and controlling the operation of the corresponding energy equipment in the comprehensive energy station based on the energy scheduling strategy.
On the basis of conforming to the common knowledge in the art, the preferred conditions can be arbitrarily combined to obtain the preferred examples of the disclosure.
The positive progress effect of the present disclosure is:
the edge computing terminal equipment, the comprehensive energy station, the management platform and the control method thereof are disclosed, wherein the edge computing terminal equipment is arranged in the comprehensive energy station, the comprehensive energy station comprises a plurality of energy equipment, and the edge computing terminal equipment comprises an edge main controller and a plurality of computing units; by setting the energy data prediction model in each calculation unit, the calculation units can directly process the acquired energy information of the energy equipment and output the energy prediction data to the edge main controller, so that the practicability of the calculation units is improved, and the operation cost is reduced; the energy scheduling strategy prediction model is arranged in the edge main controller by the edge main controller, so that the edge main controller can directly output the energy scheduling strategy, the pressure of the cloud is reduced, and the response time to the cloud scheduling is shortened; because the computing unit integrates algorithms or acceleration algorithms integrating different functions, the computing unit can be integrated timely according to different comprehensive energy station equipment types, scales, energy demands and supply amounts, and the computing unit with different computing power and algorithm functions is configured for the edge main controller, so that the plug-and-play edge computing terminal equipment is formed, the universality and flexible usability of the plug-and-play edge computing terminal equipment are improved, the computing power is improved timely, and the equipment replacement cost and the downtime are reduced.
Drawings
Fig. 1 is a first schematic structural diagram of an edge computing terminal device provided in embodiment 1 of the present disclosure;
fig. 2 is a second schematic structural diagram of an edge computing terminal device provided in embodiment 1 of the present disclosure;
FIG. 3 is a schematic diagram of the integrated energy station according to embodiment 2 of the present disclosure;
fig. 4 is a first schematic structural diagram of an energy management platform provided in embodiment 3 of the present disclosure;
FIG. 5 is a second schematic structural diagram of the energy management platform according to embodiment 3 of the present disclosure;
fig. 6 is a flowchart illustrating a control method of the energy management platform according to embodiment 4 of the present disclosure.
Detailed Description
The present disclosure is further illustrated by way of examples below, but is not thereby limited to the scope of the examples described.
Example 1
The embodiment provides an edge computing terminal device, wherein the edge computing terminal device is arranged in a comprehensive energy station, and the comprehensive energy station comprises a plurality of energy devices; as shown in fig. 1 and 2, the edge computing terminal device 1 includes an edge main controller 11 and a plurality of computing units 12;
the edge master controller 11 comprises a plurality of communication interfaces 111, and each computing unit 12 is in communication connection with one communication interface 111;
an energy data prediction model is provided in each computing unit 12;
wherein different computing units 12 correspond to different or the same energy data prediction models;
the computing unit 12 is configured to obtain energy information of a corresponding energy device, input the energy information to the energy data prediction model to output energy prediction data, and send the energy prediction data to the edge main controller 11;
an energy scheduling strategy prediction model is arranged in the edge main controller 11;
the edge main controller 11 is configured to receive the energy prediction data and the energy demand instruction sent by the different computing units 12, and input the energy prediction data to the energy scheduling policy prediction model to output an energy scheduling policy.
The ellipses in fig. 1 indicate that the edge master controller 11 includes a plurality of communication interfaces, and the edge computing terminal device 1 includes a plurality of computing units.
The comprehensive energy station comprises a plurality of energy devices, each energy device is provided with corresponding energy information, for example, the energy information corresponding to the wind power device is wind power type data, the energy information corresponding to the heat storage device is heat storage type data, and the corresponding energy data prediction models are required to be used for different types of data, so that energy prediction data are obtained.
Each computing unit in the embodiment is provided with an energy data prediction model, and the energy data prediction model can be used for directly predicting energy information, so that energy prediction data are output, the energy information is not required to be predicted by means of a prediction model stored in a cloud or other databases, and the computing efficiency of the computing unit is improved; different computing units correspond to different or same energy data prediction models, and in the processing process of the energy information, the corresponding energy data prediction models can be used according to the type and the computing requirement of the energy information, so that computing resources are effectively utilized.
The edge main controller in the embodiment is provided with the energy scheduling policy prediction model, when an energy demand instruction is received, for example, the energy demand instruction issued by the cloud is sent out, and the energy scheduling policy prediction model can be used for directly making an energy scheduling policy.
The edge master controller of the embodiment comprises a plurality of communication interfaces, each computing unit is in communication connection with one communication interface to form plug-and-play edge computing terminal equipment, and plug-and-play is realized in a hardware layer by reserving a uniform hardware communication interface which is easy to secondarily expand; plug and play is implemented at the software layer by formulating a specific interface communication protocol. Algorithms with different functions or acceleration algorithms (namely, energy data prediction models) are integrated in each computing unit, so that popularization and use of the edge computing terminal equipment in different comprehensive energy stations are realized, and the demand of computational enhancement caused by scale expansion of the comprehensive energy stations can be met.
According to the edge computing terminal equipment, the energy data prediction model is arranged in each computing unit, so that the computing units can directly process the acquired energy information of the energy equipment, output the energy prediction data to the edge main controller, the practicability of the computing units is improved, and the operation cost is reduced; the energy scheduling strategy prediction model is arranged in the edge main controller by the edge main controller, so that the edge main controller can directly output the energy scheduling strategy, the pressure of the cloud is reduced, and the response time to the cloud scheduling is shortened; because the computing unit integrates algorithms or acceleration algorithms integrating different functions, the computing unit can be integrated timely according to different comprehensive energy station equipment types, scales, energy demands and supply amounts, and the computing unit with different computing power and algorithm functions is configured for the edge main controller, so that the plug-and-play edge computing terminal equipment is formed, the universality and flexible usability of the plug-and-play edge computing terminal equipment are improved, the computing power is improved timely, and the equipment replacement cost and the downtime are reduced.
In an alternative embodiment, the energy devices of different device parameters correspond to different energy data prediction models.
Specifically, the device parameters include a device type, a device scale, a device application scene and the like, and a corresponding energy data prediction model is configured for the computing unit according to the device parameters.
The energy data prediction model is built in advance, model parameters are used for training, energy equipment with different equipment parameters corresponds to different energy data prediction models, and when the equipment parameters of the energy equipment change, the model parameters need to be adjusted or optimized.
For example, a certain integrated energy station does not have photovoltaic equipment, and an energy prediction model capable of predicting photovoltaic energy data of the photovoltaic equipment does not exist in a configured computing unit, and at this time, an energy prediction model capable of processing the photovoltaic energy data needs to be constructed or integrated so as to ensure universality and use flexibility of the edge computing terminal equipment. According to the type and scale of energy sources in the comprehensive energy station (i.e. the equipment parameters of the energy source equipment), the effect of 'instant use' is achieved through 'instant plug' of the corresponding function and calculation units, and the equipment configuration time is shortened.
According to the edge computing terminal device, different energy devices correspond to different energy data prediction models, and universality and use flexibility of the edge computing terminal device are guaranteed by configuring the corresponding energy data prediction models for the energy devices with different device parameters.
In an alternative embodiment, the computing unit is further configured to collect device operation information of the energy device, and send the device operation information to the edge master controller; the edge master controller is used for generating equipment diagnosis results based on the equipment operation information.
The equipment diagnosis result can comprise normal operation and equipment failure, and the actual operation state of the equipment is monitored through the equipment diagnosis result.
The device diagnosis model can be constructed based on the historical operation data of the energy device, and the device operation information of the energy device is processed through the device diagnosis model so as to output a device diagnosis result corresponding to the device operation information.
The method can also extract state information representing normal operation or abnormal operation of the equipment based on historical operation data of the energy equipment, and establish a preset state corresponding to the energy equipment, wherein the preset state can comprise a preset normal state and a preset abnormal state representing normal operation and equipment failure respectively.
According to the edge computing terminal device, the edge main controller generates the device diagnosis result based on the device operation information, so that the monitoring of the operation state of the energy device can be realized, and the operation safety of the energy device is improved.
In an alternative embodiment, the edge master controller is further configured to generate the alarm information based on the device diagnostic result when the device diagnostic result indicates that the actual operating state of the energy device does not satisfy the preset normal operating state.
If the device diagnosis result indicates that the actual running state of the energy device does not meet the preset normal running state, the energy device is in an abnormal running state at the moment, and alarm information is generated at the moment so as to warn a user to check and overhaul the energy device and ensure the running safety of the energy device.
Specifically, the warning information can be prompted by voice, text or other modes.
According to the edge computing terminal device, the edge main controller generates alarm information based on the device diagnosis result, so that the alarm of the abnormal operation state of the energy device can be realized, and the operation safety of the energy device is improved.
In an alternative embodiment, the edge master controller is further configured to update the energy scheduling policy based on the received new energy demand command.
When the edge main controller receives a new energy demand instruction, for example, the energy demand instruction issued by the cloud end, the energy scheduling strategy can be timely adjusted and updated based on the current energy prediction data, so that the real-time response to the energy demand instruction is realized, the pressure of the cloud end is reduced, and the response time to the cloud end scheduling is shortened.
In an optional embodiment, the edge master controller is configured to monitor an actual computing force of each computing unit, obtain computing units with actual computing forces smaller than a set computing force threshold, as a unit to be processed, and generate computing force reminding information;
the computing power reminding information is used for reminding a residual computing unit which is in an idle state in the calling edge computing terminal equipment and has the same energy data prediction model as the unit to be processed;
or reminding to integrate a new computing unit with the same energy data prediction model as the unit to be processed in the edge computing terminal equipment.
The energy data prediction model is arranged in the computing unit, the energy data prediction model is an energy data prediction algorithm, the energy data prediction model corresponds to a certain computing power, the data volume of the energy data is greatly increased along with the increase of energy demand and supply, the actual computing power of the original energy data prediction model is reduced, the situation of insufficient computing power occurs, the computing response time of the computing unit is greatly increased, and the computing speed is greatly reduced, so that the actual computing power of each computing unit needs to be monitored.
For example, a certain edge computing terminal includes 6 computing units, where the computing units a and B have the same energy data prediction model, and can both process photovoltaic energy data, if the edge main controller monitors that the actual computing power of the computing unit a is smaller than the set computing power threshold, the computing unit a is used as a unit to be processed, and generates computing power reminding information, if the computing unit B is in an idle state at this time, the computing unit B is the remaining computing unit, and the computing unit B is called to perform computation, so as to reduce the computing power of the computing unit a, share the computing power of the computing unit a, and further promote the computing power of the computing unit a.
Wherein the actual computing power of the computing unit is inversely related to the computing response time, and the higher the computing power is, the shorter the computing response time is. When the actual calculation force of a certain calculation unit is smaller than the set calculation force threshold, the calculation speed is too slow, the calculation response time is too long, the actual calculation force of the calculation unit can not meet the current calculation requirement, and the calculation force is required to be lifted in time. In the actual application process, the calculation threshold value can be set according to the actual requirement.
If the existing computing units in the edge computing terminal cannot meet the current computing power demand, a new computing unit with the same energy data prediction model as the unit to be processed needs to be reminded to be integrated in the edge computing terminal equipment, for example, the new computing unit is accessed through an idle communication interface, the computing amount of the original computing unit is shared through the new computing unit, and the computing power of the original computing unit is further improved. That is, when the energy amount of the comprehensive energy station is large, the calculation power of a single calculation unit is insufficient to meet the calculation, and at the moment, a new calculation unit performs the acceleration calculation in a parallel calculation mode.
According to the edge computing terminal device, the edge main controller is used for monitoring the actual computing force of each computing unit, and when the actual computing force is insufficient, the edge computing terminal device can respond in time, so that the computing force is improved in time, and the replacement cost of energy equipment and the equipment downtime are reduced.
In an alternative embodiment, the types of computing units include, but are not limited to, ARM, FPGA, DPU and CPU.
The ARM has small calculation power and low acquisition cost; the FPGA has large calculation power and high development cost, but has high acquisition cost; the DPU has large calculation power and confidentiality function, but has high acquisition cost; the CPU has high calculation power, but the purchase cost is high, and reasonable selection can be performed according to the calculation power required by the calculation unit.
In an alternative embodiment, the energy devices include, but are not limited to, wind power devices, photovoltaic devices, energy storage devices, grid devices, CCHP devices (Combined Cooling Heating and Power, combined cooling and heating), ground source heat pump devices, heat storage devices, electrical nitrogen generation devices, and gas turbines.
The computing unit acquires the energy information of each energy device, outputs energy prediction data to the edge main controller, acquires the device operation information of each energy device, sends the device operation information to the edge main controller, and makes an energy scheduling strategy and a unit treatment plan through the edge main controller to monitor the actual operation state of each energy device.
Example 2
The present embodiment provides an integrated energy station, as shown in fig. 3, the integrated energy station 2 includes a plurality of different types of energy devices 21, and a plurality of edge computing terminal devices 1 as in embodiment 1 communicatively connected to the energy devices 21.
The energy devices shown in fig. 3 may be of different types or the same type. The ellipses in fig. 3 represent that a number of edge computing terminal devices may be included in the integrated energy station, each edge computing terminal device being communicatively coupled to a number of energy devices.
The comprehensive energy station of the embodiment comprises the edge computing terminal equipment in the embodiment 1, and the edge computing terminal equipment is used for processing the energy information and the equipment operation information of the energy equipment to generate an energy scheduling strategy and equipment aiming at the diagnosis result so as to provide guarantee for safe operation and orderly operation of the comprehensive energy station.
The working principle of the edge computing terminal device in this embodiment is already discussed in detail in embodiment 1, and will not be described here again.
According to the comprehensive energy station, the effect of ' instant use ' is achieved through the ' instant plug ' corresponding function and the computing unit of the computing power ' according to the type and scale of energy in the comprehensive energy station (i.e. the equipment parameters of the energy equipment), and the equipment configuration time is shortened. Because the computing unit integrates algorithms or acceleration algorithms integrating different functions, the computing unit can be integrated timely according to different comprehensive energy station equipment types, scales, energy demands and supply amounts, and the computing unit with different computing power and algorithm functions is configured for the edge main controller, so that the plug-and-play edge computing terminal equipment is formed, the universality and flexible usability of the plug-and-play edge computing terminal equipment are improved, the computing power is improved timely, and the equipment replacement cost and the downtime are reduced.
Example 3
The present embodiment provides an energy management platform, as shown in fig. 4 and 5, where the energy management platform 3 includes a cloud end 31 and a local control center 32, and a plurality of integrated energy stations 2 as in embodiment 2; the edge computing terminals in the comprehensive energy station 2 are respectively in communication connection with the cloud 31 and the local control center 32; the local control center 32 is configured to aggregate energy information corresponding to each energy device in each integrated energy station; the cloud end 31 is used for issuing an energy demand instruction to the corresponding comprehensive energy station 2; the edge master controller in the comprehensive energy station 2 is used for generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, issuing the energy scheduling strategy to the local control center 32, and sending the energy scheduling strategy to the cloud 31; the local control center 32 is further configured to generate corresponding control instructions based on the energy scheduling policy to control operation of the corresponding energy devices.
Specifically, a computing unit in the edge computing terminal collects energy information of corresponding energy equipment from a local control center, generates energy prediction data and sends the energy prediction data to an edge main controller. And a control instruction generated based on an energy scheduling strategy, namely the output plan of each unit.
The computing unit in the edge computing terminal collects equipment operation information of the corresponding energy equipment from the local control center and sends the equipment operation information to the main edge main controller, the edge main controller is used for generating equipment diagnosis results based on the equipment operation information and sending the equipment diagnosis results to the local control center, and the local control center generates corresponding operation monitoring instructions based on the equipment diagnosis results so as to monitor the operation of the corresponding energy equipment.
The operation architecture of the integrated energy station and the working principle of the edge computing terminal device in this embodiment have been discussed in embodiment 1 and embodiment 2 respectively, and will not be described here again.
The ellipses in fig. 4 and 5 indicate that the energy management platform may include several integrated energy stations.
The local control center can be independently arranged outside the comprehensive energy station or inside the comprehensive energy station to be used as a part of the comprehensive energy station.
The energy management platform of the embodiment comprises the comprehensive energy station in the embodiment 2, and the scheduling strategy is formulated from the cloud execution to the edge computing terminal execution through the data interaction between the comprehensive energy station and the cloud and the local control center, so that the computing pressure of the cloud is reduced, the response speed of the edge side to the cloud instruction is improved, and the response time of the edge side to the cloud scheduling is shortened.
Example 4
The embodiment provides a control method of an energy management platform, which is realized based on the energy management platform in embodiment 3; as shown in fig. 6, the control method includes:
s101, acquiring energy information of energy equipment, and generating energy prediction data based on the energy information.
S102, generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, and uploading the energy scheduling strategy to the cloud.
S103, controlling the operation of the corresponding energy equipment in the comprehensive energy station based on the energy scheduling strategy.
For example, step S101 may be implemented by a computing unit in the edge computing terminal device, step S102 may be implemented by an edge master controller in the edge computing terminal device, and step S103 may be implemented by a local control center.
In an alternative embodiment, before step S101, the method further includes:
s1001, monitoring actual computing power of each computing unit, acquiring computing units with the actual computing power smaller than a set computing power threshold as to-be-processed units, and generating computing power reminding information.
S1002, reminding and calling a residual computing unit which is in an idle state in the edge computing terminal equipment and has the same energy data prediction model as a unit to be processed based on calculation force reminding information; or reminding to integrate a new computing unit with the same energy data prediction model as the unit to be processed in the edge computing terminal equipment.
For example, steps S1001 and S1002 may be implemented by an edge master controller in an edge computing terminal device.
In an alternative embodiment, the control method further includes:
s104, detecting whether the device parameters of the energy device change.
If yes, step S105 is performed.
S105, reminding to update the energy data prediction model of the computing unit corresponding to the energy equipment.
For example, steps S104 and 105 may be implemented by an edge master controller in the edge computing terminal device.
In an alternative embodiment, the control method further includes:
s106, collecting equipment operation information of the energy equipment;
s107, generating a device diagnosis result based on the device operation information.
For example, step S106 may be implemented by a computing unit in the edge computing terminal device, and step S107 may be implemented by an edge master controller in the edge computing terminal device.
In an alternative embodiment, the control method further includes:
s108, when the equipment diagnosis result represents that the actual running state of the energy equipment does not meet the preset normal running state, generating alarm information based on the equipment diagnosis result.
For example, step S108 may be implemented by an edge master controller in the edge computing terminal device.
In an alternative embodiment, the control method further includes:
s109, receiving a new energy demand instruction and updating an energy scheduling strategy.
For example, step S109 may be implemented by an edge master controller in the edge computing terminal device.
The control method of the energy management platform is realized based on the energy management platform in the embodiment 3, and depends on the cloud end, the local control center and the comprehensive energy station in the energy management platform, and realizes the data interaction between the comprehensive energy station and the cloud end and the local control center through mutual cooperation.
By setting the energy data prediction model in each calculation unit, the calculation units can directly process the acquired energy information of the energy equipment and output the energy prediction data to the edge main controller, so that the practicability of the calculation units is improved, and the operation cost is reduced; the energy scheduling strategy prediction model is arranged in the edge main controller by the edge main controller, so that the edge main controller can directly output the energy scheduling strategy, the pressure of the cloud is reduced, and the response time to the cloud scheduling is shortened; because the computing unit integrates algorithms or acceleration algorithms integrating different functions, the computing unit can be integrated timely according to different comprehensive energy station equipment types, scales, energy demands and supply amounts, and the computing unit with different computing power and algorithm functions is configured for the edge main controller, so that the plug-and-play edge computing terminal equipment is formed, the universality and flexible usability of the plug-and-play edge computing terminal equipment are improved, the computing power is improved timely, and the equipment replacement cost and the downtime are reduced.
While specific embodiments of the present disclosure have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the disclosure is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the disclosure, but such changes and modifications fall within the scope of the disclosure.
Claims (10)
1. The edge computing terminal equipment is characterized by being arranged in an integrated energy station, wherein the integrated energy station comprises a plurality of energy equipment;
the edge computing terminal equipment comprises an edge main controller and a plurality of computing units;
the edge master controller comprises a plurality of communication interfaces, and each computing unit is in communication connection with one communication interface;
an energy data prediction model is arranged in each computing unit;
wherein different computing units correspond to different or the same energy data prediction models;
the computing unit is used for acquiring energy information of the corresponding energy equipment, inputting the energy information into the energy data prediction model to output energy prediction data, and sending the energy prediction data to the edge main controller;
an energy scheduling strategy prediction model is arranged in the edge master controller;
the edge master controller is used for receiving the energy prediction data and the energy demand instructions sent by different computing units, and inputting the energy prediction data into the energy scheduling policy prediction model to output an energy scheduling policy.
2. The edge computing terminal device of claim 1, wherein the energy devices of different device parameters correspond to different energy data prediction models.
3. The edge computing terminal device according to claim 1, wherein the computing unit is further configured to collect device operation information of the energy device, and send the device operation information to the edge master controller;
the edge master controller is used for generating equipment diagnosis results based on the equipment operation information.
4. The edge computing terminal device of claim 3, wherein the edge master controller is further configured to generate alert information based on the device diagnostic result when the device diagnostic result characterizes that the actual operating state of the energy device does not satisfy a preset normal operating state.
5. The edge computing terminal device of claim 1, wherein the edge master controller is further configured to update the energy scheduling policy based on the received new energy demand instruction.
6. The edge computing terminal device according to claim 1, wherein the edge main controller is configured to monitor an actual computing force of each computing unit, obtain a computing unit with the actual computing force smaller than a set computing force threshold value as a unit to be processed, and generate computing force reminding information;
the computing power reminding information is used for reminding and calling the rest computing units which are in an idle state in the edge computing terminal equipment and have the same energy data prediction model as the units to be processed;
or reminding to integrate a new computing unit which has the same energy data prediction model as the unit to be processed in the edge computing terminal equipment.
7. The edge computing terminal device of any of claims 1-6, wherein the type of computing unit comprises at least one of ARM, FPGA, DPU and CPU;
and/or the number of the groups of groups,
the energy source equipment comprises at least one of wind power equipment, photovoltaic equipment, energy storage equipment, power grid equipment, cogeneration equipment, ground source heat pump equipment, heat storage equipment, electric nitrogen production equipment and a gas turbine.
8. An integrated energy station comprising a number of different types of energy devices and a number of edge computing terminal devices according to any of claims 1-7 in communicative connection with the energy devices.
9. An energy management platform, comprising a cloud end, a local control center and a plurality of comprehensive energy stations according to claim 8;
the edge computing terminal in the comprehensive energy station is respectively in communication connection with the cloud end and the local control center;
the local control center is used for summarizing energy information corresponding to each energy device in each comprehensive energy station;
the cloud end is used for issuing an energy demand instruction to the corresponding comprehensive energy station;
the edge master controller in the comprehensive energy station is used for generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, issuing the energy scheduling strategy to the local control center and sending the energy scheduling strategy to the cloud;
the local control center is also used for generating a corresponding control instruction based on the energy scheduling strategy so as to control the operation of the corresponding energy equipment.
10. A control method of an energy management platform, characterized in that the control method is implemented based on the energy management platform according to claim 9;
the control method comprises the following steps:
acquiring energy information of energy equipment, and generating energy prediction data based on the energy information;
generating an energy scheduling strategy based on the energy demand instruction and the energy prediction data, and uploading the energy scheduling strategy to a cloud;
and controlling the operation of the corresponding energy equipment in the comprehensive energy station based on the energy scheduling strategy.
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