WO2021114661A1 - 一种基于边云协同的工厂电能管控系统及方法 - Google Patents

一种基于边云协同的工厂电能管控系统及方法 Download PDF

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WO2021114661A1
WO2021114661A1 PCT/CN2020/102200 CN2020102200W WO2021114661A1 WO 2021114661 A1 WO2021114661 A1 WO 2021114661A1 CN 2020102200 W CN2020102200 W CN 2020102200W WO 2021114661 A1 WO2021114661 A1 WO 2021114661A1
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power
time
edge
task
factory
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French (fr)
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王平
杨旭
魏旻
李彩芹
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重庆邮电大学
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Priority to US17/261,875 priority Critical patent/US11822417B2/en
Priority to KR1020217040190A priority patent/KR20220007644A/ko
Publication of WO2021114661A1 publication Critical patent/WO2021114661A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G06F1/329—Power saving characterised by the action undertaken by task scheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/28—Supervision thereof, e.g. detecting power-supply failure by out of limits supervision
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G06F1/3296—Power saving characterised by the action undertaken by lowering the supply or operating voltage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
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    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention belongs to the field of factory power management and control, and relates to a factory power management and control system and method based on edge-cloud collaboration.
  • Edge computing is a distributed and open platform that integrates core capabilities of network, computing, storage, and applications on the edge of the network close to the source of things or data. It provides edge intelligent services nearby to meet the needs of industry digitalization in agile connection, real-time business, data optimization, and application. Key requirements for intelligence, security, and privacy protection. It can serve as a bridge connecting the physical and digital worlds, enabling smart assets, smart gateways, smart systems, and smart services. Because the edge side needs to support multiple network interfaces, protocols and topologies, real-time business processing and deterministic delay, data processing and analysis, distributed intelligence and security and privacy protection. The cloud is difficult to meet the above requirements, and edge computing and cloud computing are required to collaborate in network, business, application, and intelligence.
  • Edge-cloud collaboration will magnify the value of edge computing and cloud computing.
  • Edge computing and cloud computing have their own strengths. Cloud computing is good at global, non-real-time, long-period big data processing and analysis, and can play its advantages in long-period maintenance, business decision support and other fields; edge computing is more suitable for locality and real-time , The processing and analysis of short-period data can better support the real-time intelligent decision-making and execution of local business.
  • edge computing and cloud computing are not a substitute relationship, but a complementary and synergistic relationship.
  • Edge computing and cloud computing need to be closely coordinated to better meet the matching of various demand scenarios, thereby magnifying the application value of edge computing and cloud computing.
  • Edge computing is not only close to the execution unit, but also a collection and preliminary processing unit of high-value data required by the cloud, which can better support cloud applications; conversely, cloud computing can issue business rules or models optimized for output through big data analysis to the edge
  • edge computing runs based on new business rules or models.
  • Edge collaboration involves resource collaboration, data collaboration, intelligent collaboration, application management collaboration, business management collaboration, service collaboration, etc.
  • the present invention is no longer limited to cloud computing, and further studies edge computing and integrates it into factory power management and control, and uses edge-cloud collaborative computing to improve the flexibility, safety, and real-time performance of the factory power management and control system. Reduce power costs and power supply costs to reduce factory power costs.
  • a factory power management and control system and method based on edge-cloud collaboration is provided.
  • the present invention provides the following technical solutions:
  • the present invention provides a factory power management and control system based on edge-cloud collaboration, which includes a cloud power management layer, an edge computing layer, a device layer, and a power supply side;
  • the cloud power management layer includes a cloud power management center and an industrial cloud server;
  • the cloud power management center is used to perform some calculation tasks and return the calculation results to the edge node, and is also used to store the STN task model of the production site and download Sent to the edge node;
  • the industrial cloud server is used to store the power supply and power consumption plan generated by the edge node, which will be used as a reference in the future generation of the plan;
  • the edge computing layer includes several edge nodes, and the edge nodes are used to: 1calculate the power demand value E d of the production task; 2according to the STN task model information issued by the cloud power management center and the real-time task information sent by the factory equipment layer, Calculate the required value of electricity to complete the established production task; 3Receive the power supply information data from the power supply side, including real-time electricity price model, power storage model and power generation model; 4Judging the algorithm complexity level O, judging the data real-time level TIME_priority, calculating Comprehensive level R; 5Used to send part of the calculation tasks to the cloud power management center according to the comprehensive level R, and accept the calculation results of this part, and perform the calculation of the remaining part of the tasks at the edge nodes; 6Comprehensively obtain the power plan Scheme based on the calculation results i and the power supply scheme Scheme p ; 7Send the power scheme p to the plant equipment layer to perform production tasks, and send the power supply scheme to the power supply side to perform power supply tasks;
  • the equipment layer includes field nodes, routing nodes, and production equipment; the field nodes form an industrial field network to collect production information of production equipment and send them to the edge nodes through the routing node; receive the power consumption plan from the edge nodes through the routing node, According to the plan, the equipment layer is controlled to execute production tasks; the production equipment is the equipment that executes the plan at the industrial site, and executes the production tasks in the power usage plan;
  • the power supply side includes a power supply agent, a power station outside the factory, a factory power station, and a factory storage power station; the power supply agent is used to store the power price model of the power station outside the factory, the power generation model of the factory power station, and the power storage model of the factory storage station.
  • the model is updated, it is sent to the edge node; the power station outside the factory is used to connect to the factory grid to supply power to the factory; the factory power station is a power station built by the factory to supply power to the factory;
  • the electricity price is low, it stores electricity, and when the electricity price is high, it supplies power to the factory.
  • the present invention provides a factory power management and control method based on edge-cloud collaboration, which includes the following steps:
  • S1 Store the STN task model M t of the production site at the cloud power management center, store the power generation model M g , electricity storage model M s , and electricity price model M p on the power supply side at the power supply agency, and specify the algorithm complexity level ;
  • S2 Field nodes, routing nodes and equipment performing production tasks in the equipment layer form an industrial field network
  • S3 The site node parses the production task information data I p and sends it to the edge node;
  • the cloud power management center delivers the STN model of the production site to the edge node;
  • the power supply agent on the power supply side sends the power generation model M g , the power storage model M s and the electricity price model M p to the edge node;
  • the edge node calculates the power E d required to complete these production tasks according to the production task information data I p and the STN task model M t ;
  • S8 The edge node judges the real-time data level according to Table 2 according to the value of TIME_priority in the data frame;
  • the edge node uses the value of the algorithm complexity level O and the value of the data real-time level TIME_priority to calculate the value of the comprehensive level R according to Table 3:
  • the edge node stores the calculations with the integrated level R value of 5, 6, 7, and 8 locally;
  • the edge node sends the calculation with the comprehensive level R value of 2, 3, 4 to the cloud power management center;
  • the edge node performs calculations with a comprehensive level R value of 5, 6, 7, and 8, and saves the calculation results locally;
  • the cloud power management center performs calculations with a comprehensive level value of 2, 3, and 4 sent by the edge node;
  • the edge node sends the power usage scheme Scheme i to the field node, and sends the power supply scheme Scheme p to the power supply agent;
  • S16 The site node sends Scheme i to the production equipment to perform production tasks; the power supply agent controls the factory power station, storage power station, and power supply station outside the factory to execute the corresponding plan;
  • the edge node stores the simultaneous plan in the cloud power management center for future operation plan reference.
  • step S1 specifically includes the following content:
  • Algorithm complexity calculation The power system manager of the factory tests all the algorithms that need to be executed on the edge node. Considering the space complexity and time complexity, the algorithm complexity attribute is analyzed as ⁇ complexity Very high, high complexity, medium complexity, low complexity ⁇ . And the algorithm is classified according to the algorithm complexity attribute, denoted by O. The algorithm complexity level corresponds to Table 1, O ⁇ [1-4]. And mark the value of O at the head of the algorithm.
  • the algorithm complexity is divided into 4 levels, and the algorithm complexity decreases successively.
  • Level 1 indicates that the algorithm complexity is extremely high, and level 4 indicates that the algorithm complexity is low.
  • the complexity of the algorithm is different, the calculation time is different, and the storage space occupied when the calculation is executed is also different.
  • the corresponding complexity attributes of the complexity level are shown in Table 1.
  • step S2 specifically includes: the data frame information table collected by the site node from the production equipment is ⁇ Task_number, T i , protocol, ID, data_source, Task_type ⁇ , which respectively represent the task number, the time required for task execution, industrial protocol, Network ID, data source address, task type.
  • the field node in the MAC layer marks the data frame with different real-time levels according to the task type Task_type in the data frame information table, and the marked frame information table becomes ⁇ Task_number,T i ,protocol,ID,data_source,Task_type,TIME_priority ⁇ , Where TIME_priority ⁇ [1-4]; TIME_priority represents the real-time level of data, and the data attribute represented by its value can be seen in Table 2.
  • the value of TIME_priority corresponds to 4 real-time grades, which are 1 to 4, and the priority grades increase in order. 1 means not high real-time requirements, 4 means extremely high real-time requirements; real-time grades correspond to real-time attributes as shown in Table 2. Shown.
  • the input of the demand response algorithm in step S14 is the electricity demand value E d , the power generation model M g , the electricity storage model M s , and the electricity price model M p ;
  • the output is the electricity consumption scheme Scheme i and the power supply scheme Scheme p ;
  • x g represents the number of generators that need to be operated by the power station in the factory
  • T g represents the generation time of each generator
  • T s represents the time that the storage motor is in the charging/discharging state
  • P represents the current electricity price
  • T p_ S p represents the power supply time under the state S p
  • T p_ 01 represents only to equipment supplying time
  • T p_ 10 indicates that only power plant to storage time
  • E d E sp *T s +T p_ 01*E p +E g *T g
  • Edge-cloud collaborative computing improves real-time performance:
  • the edge node localizes the production task data from the equipment layer, forms a real-time feedback control loop with field equipment, effectively improves management and control efficiency, shortens the command transmission time, and generates real-time power supply and electricity plans. It is also provided to production equipment and power stations on the power supply side for execution.
  • This method can store the power data of the power plant and the generated plan in the cloud, and can shorten the generation time of the future plan.
  • This method uses the advantages of edge computing and cloud computing to classify algorithm complexity and real-time data, and obtain a comprehensive level. Upload calculations with comprehensive levels 2, 3, and 4 to the cloud for execution, and perform calculations with comprehensive levels 5, 6, 7, and 8 at the edge.
  • the plan is updated in real time, which shortens the plan generation and instruction transmission time, thereby reducing the cost of electricity.
  • real-time performance will greatly improve the performance of the power management and control system.
  • Supply-electricity joint dispatching improves flexibility: In this scheme, the joint dispatching of the power-consuming side and the power-supply side will not only "cut peaks and fill valleys", that is, when the electricity price is low, not only the high-power equipment is turned on at the equipment level, but the power supply side is also activated.
  • the charging function of the power storage system When the electricity price is high, the equipment layer turns on low-power equipment, and the power supply side system starts the discharge function of the power storage system.
  • the cost reduction on the power consumption side is mainly achieved by adding production tasks when the electricity price is low.
  • the cost reduction on the power supply side is the charging of the storage station when the electricity price is low, and the discharge of the storage station when the electricity price is high. Compared with the centralized dispatch mechanism, the simultaneous dispatch of power and consumption can significantly improve the performance of the electric energy management and control system.
  • Edge cloud collaborative distributed computing to improve security In this scheme, the main computing tasks are completed by edge nodes, and the edge nodes are distributed relatively scattered. Even if the security of several edge nodes is threatened, other edge nodes will continue to execute The task has little impact on the entire power supply control system. Compared with centralized dispatch, if the centralized dispatch center goes down, it will have a fatal blow to the entire production system. Therefore, the edge-cloud collaboration technology can improve the security of the power management and control system.
  • Fig. 1 is a schematic diagram of the structure of the factory power management and control system based on edge-cloud collaboration according to the present invention
  • FIG. 2 is a flow chart of the method for power management and control of a factory based on edge-cloud collaboration according to the present invention
  • Fig. 3 is a schematic flow diagram of the demand response algorithm of the present invention.
  • the present invention provides a factory power management and control system based on edge-cloud collaboration, as shown in Figure 1, including cloud power management, edge computing, equipment, and power supply side;
  • the cloud power management layer includes a cloud power management center and an industrial cloud server;
  • the cloud power management center is used to perform some calculation tasks and return the calculation results to the edge node, and is also used to store the STN task model of the production site and download Sent to the edge node;
  • the industrial cloud server is used to store the power supply and power consumption plan generated by the edge node, which will be used as a reference in the future generation of the plan;
  • the edge computing layer includes several edge nodes, and the edge nodes are used to: 1calculate the power demand value E d of the production task; 2according to the STN task model information issued by the cloud power management center and the real-time task information sent by the factory equipment layer, Calculate the required value of electricity to complete the established production task; 3Receive the power supply information data from the power supply side, including real-time electricity price model, power storage model and power generation model; 4Judging the algorithm complexity level O, judging the data real-time level TIME_priority, calculating Comprehensive level R; 5Used to send part of the calculation tasks to the cloud power management center according to the comprehensive level R, and accept the calculation results of this part, and perform the calculation of the remaining part of the tasks at the edge nodes; 6Comprehensively obtain the power plan Scheme based on the calculation results i and the power supply scheme Scheme p ; 7Send the power scheme p to the plant equipment layer to perform production tasks, and send the power supply scheme to the power supply side to perform power supply tasks;
  • the equipment layer includes field nodes, routing nodes, and production equipment; the field nodes form an industrial field network to collect production information of production equipment and send them to the edge nodes through the routing node; receive the power consumption plan from the edge nodes through the routing node, According to the plan, the equipment layer is controlled to execute production tasks; the production equipment is the equipment that executes the plan at the industrial site, and executes the production tasks in the power usage plan;
  • the power supply side includes a power supply agent, a power station outside the factory, a factory power station, and a factory storage power station; the power supply agent is used to store the power price model of the power station outside the factory, the power generation model of the factory power station, and the power storage model of the factory storage station.
  • the model is updated, it is sent to the edge node; the power station outside the factory is used to connect to the factory grid to supply power to the factory; the factory power station is a power station built by the factory to supply power to the factory;
  • the electricity price is low, it stores electricity, and when the electricity price is high, it supplies power to the factory.
  • the present invention provides a method for power management and control of a factory based on edge-cloud collaboration.
  • the design of a method for power management and control of a factory based on edge-cloud collaboration is described below with reference to examples.
  • the present invention is aimed at the situation where it is necessary to quickly generate a power supply scheme and a power consumption scheme at an edge node in an industrial field, and provides a specific implementation manner of the patent.
  • Algorithm complexity calculation The power system manager of the factory tests all the algorithms that need to be executed on the edge node, and marks the value of the algorithm complexity O at the head of the algorithm.
  • Step 1 The data frame information table collected by the site node from the production equipment is ⁇ Task_number,T i ,protocol,ID,data_source,Task_type,TIME_priority ⁇ , which respectively represent the task number, time required for task execution, industrial protocol, and network ID , Data source address, task type, real-time level.
  • the field node obtains the following task information after analyzing the collected data frame.
  • Step 2 The site node parses the production task information data I p and sends it to the edge node.
  • Step 4 The power supply agent on the power supply side will send the power generation model M g , the electricity storage model M s , and the electricity price model M p to the edge node.
  • Step 5 The edge node calculates the power required to complete these production tasks according to the production task information data I p and the STN task model M t
  • Step 6 The head of the edge node search algorithm gets the value of O.
  • Step 8 The edge node calculates the value of the comprehensive level R according to Table 3 through the value of the algorithm complexity level O and the value of the data real-time level TIME_priority.
  • Step 9 The edge node stores the calculations with the comprehensive grade R value of 5, 6, 7, and 8 locally.
  • Step 10 The edge node sends the calculation with the comprehensive grade R value of 2, 3, and 4 to the cloud power management center.
  • Step 11 The edge node performs calculations with a comprehensive R value of 5, 6, 7, and 8, and saves the calculation results locally.
  • the cloud power management center performs calculations with a comprehensive level value of 2, 3, and 4 sent by the edge node.
  • Step 12 The cloud power management center returns the calculation result to the edge node.
  • Step 13 Execute the demand response algorithm at the edge node.
  • the demand response (DR) algorithm inputs are the electricity demand value E d , the power generation model M g , the electricity storage model M s , and the electricity price model M p .
  • the output is the power scheme Scheme i and the power supply scheme Scheme p .
  • the STN task model M t can calculate the power demand value E d .
  • the power generation model can be calculated from the price of raw materials consumed by the generator per unit time. Assume that the power generation cost per unit time of a generator is P g . Suppose the number of generators that the factory needs to run is x g . This section ignores the cost of starting and shutting down the engine. E g represents the power supply per unit time of the power station in the factory, and T g represents the power generation time of each power generation price. Therefore, the power generation model is
  • M g ⁇ P g , x g , E g , T g ⁇
  • the charging power of a storage motor is E s
  • the discharge power E sp is the current equipment power, and it is only charged when the power supply station outside the factory supplies power.
  • T s represents the time that the electric storage motor is in the charging/discharging state
  • P s represents the electricity price when the electric storage motor is charging. Therefore, the power storage model is
  • M s ⁇ E s , E sp , S s , T s , P s ⁇
  • the electricity price model is determined according to the situation of the external power supply unit. Generally, there are a day-ahead electricity price model, a real-time electricity price model, and a segmented electricity price model.
  • P represents the price of power supply to the factory. Indicates the power supply per unit time of the power station outside the factory. Therefore, the electricity price model is
  • Scheme p ⁇ x g ,T g ,S s ,T s ,P,S p ,T p_01 ,T p_10 ⁇ .
  • x g represents the number of generators that need to be operated by the power station in the factory
  • T g represents the generation time of each generator
  • T s represents the time that the storage motor is in the charging/discharging state
  • P represents the current electricity price
  • T p_ S p represents the power supply time under the state S p, T p_ 01 represents only to equipment supplying time, T p_ 10 indicates that only power plant to storage time.
  • E d E sp *T s +T p_ 01*E p +E g *T g
  • E d E sp *T s +T p_ 01*E p +E g *T g
  • a power scheme Scheme i and a power supply scheme Scheme p can be generated.
  • This plan is a task execution plan and a power supply plan for completing 6 production processes within 24 hours a day.
  • the 6 production processes means that task 1 is executed for 6 hours, task 2 is executed for 12 hours, task 3 is executed for a total of 18 hours, and task 4 is executed for 24 hours without interruption.
  • Step 14 The edge node sends the power usage scheme Scheme i to the field node, and the edge node sends the power supply scheme Scheme p to the power supply agent.
  • Step 15 The site node further sends Scheme i to the production equipment to perform production tasks.
  • the power supply agent controls the factory power station, storage power station, and power supply station outside the factory to implement the corresponding plan.
  • Step 16 The edge node stores the plan in the cloud power management center at the same time for reference for future operation plans.

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Abstract

一种基于边云协同的工厂电能管控系统，包括云电能管理层、边缘计算层、设备层、供电侧；一种基于边云协同的工厂电能管控方法，包括：1)设备层中的现场节点，路由节点以及执行生产任务的设备组成工业现场网络；2)解析生产任务信息数据并发送；3)将生产现场的STN模型下发到边缘节点；4)供电代理将发电模型、储电模型、电价模型发给边缘节点；5)边缘节点计算完成生产任务所需电量；6)计算综合等级的值；7)等级高的由边缘节点执行，等级低的由云电能管理中心执行；8)边缘节点执行需求响应算法；9)生产设备根据计算结果执行生产任务；工厂发电站、储电站、工厂外供电站根据结果执行相应方案。

Description

一种基于边云协同的工厂电能管控系统及方法 技术领域
本发明属于工厂电能管控领域，涉及一种基于边云协同的工厂电能管控系统及方法。
背景技术
边缘计算是在靠近物或数据源头的网络边缘侧，融合网络、计算、存储、应用核心能力的分布式开放平台，就近提供边缘智能服务，满足行业数字化在敏捷联接、实时业务、数据优化、应用智能、安全与隐私保护等方面的关键需求。它可以作为联接物理和数字世界的桥梁，使能智能资产、智能网关、智能系统和智能服务。由于边缘侧需要支持多种网络接口、协议与拓扑，业务实时处理与确定性时延，数据处理与分析，分布式智能和安全与隐私保护。云端难以满足上述要求，需要边缘计算与云计算在网络、业务、应用和智能方面进行协同。
边云协同会放大边缘计算与云计算价值。边缘计算与云计算各有所长，云计算擅长全局性、非实时、长周期的大数据处理与分析，能够在长周期维护、业务决策支撑等领域发挥优势；边缘计算更适用局部性、实时、短周期数据的处理与分析，能更好地支撑本地业务的实时智能化决策与执行。
因此，边缘计算与云计算之间不是替代关系，而是互补协同关系。边缘计算与云计算需要通过紧密协同才能更好的满足各种需求场景的匹配，从而放大边缘计算和云计算的应用价值。边缘计算既靠近执行单元，更是云端所需高价值数据的采集和初步处理单元，可以更好地支撑云端应用；反之，云计算通过大数据分析优化输出的业务规则或模型可以下发到边缘侧，边缘计算基于新的业务规则或模型运行。边缘协同涉及到资源协同、数据协同、智能协同、应用管理协同、业务管理协同、服务协同等方面。
在工厂电能管控方面，国内外的研究主要关注集中管控工厂电能、解决传统电能管控方式受限、工厂用电成本高的问题。集中管控方法有几大局限：
①实时性不够：工厂现场电能数据信息巨大，将电能数据全部移动到云中集中计算，会造成控制指令传输延迟，导致用电成本增加。
②供电-用电不能联合管控：工厂用电层与工厂供电层不能实现联合管控，造成供电不足或供电过剩，导致用电成本增加。
③安全性不够：工厂云作为集中式计算中心，对整个工厂电能管控系统起到集中调度的作用，一旦面临安全威胁，后果极为严重。
发明内容
有鉴于此，本发明不再拘泥于云计算，进一步研究边缘计算并将其也融入工厂电能管控中，利用边云协同计算来提升工厂电能管控系统的灵活性、安全性、实时性，通过同时降低用电成本及供电成本两方面来降低工厂用电成本。提供一种基于边云协同的工厂电能管控系统及方法。
为达到上述目的，本发明提供如下技术方案：
一方面，本发明提供一种基于边云协同的工厂电能管控系统，包括云电能管理层、边缘计算层、设备层、供电侧；
所述云电能管理层包括云电能管理中心、工业云服务器；所述云电能管理中心用于执行部分计算任务，并将计算结果返回到边缘节点，还用于存储生产现场的STN任务模型并下发到边缘节点；所述工业云服务器用于存储边缘节点已生成的供电用电方案，在将来的生成方案过程中作为参考；
所述边缘计算层包括若干边缘节点，所述边缘节点用于：①计算生产任务电量需求值E d；②根据云电能管理中心下发的STN任务模型信息和工厂设备层发送的实时任务信息，计算出完成既定生产任务电量的需求值；③接收来自供电侧的供电信息数据，包括实时电价模型，储电模型及发电模型；④判断算法复杂度等级O、判断数据实时性等级TIME_priority、计算出综合等级R；⑤用于根据综合等级R将部分计算任务发送到云电能管理中心，并接受该部分计算结果，在边缘节点执行剩余部分任务的计算；⑥根据计算结果综合得出用电方案Scheme i及供电方案Scheme p；⑦将用电方案Scheme p发送给工厂设备层执行生产任务，将供电方案发送给供电侧执行供电任务；
所述设备层包括现场节点、路由节点、生产设备；所述现场节点组成工业现场网络采集生产设备的生产信息，并通过路由节点发送给边缘节点；通过路由节点接收来自边缘节点的用电方案，依据该方案控制设备层执行生产任务；所述生产设备即工业现场执行计划的设备，执行用电方案中的生产任务；
所述供电侧包括供电代理、工厂外电站、工厂发电站、工厂储电站；所述供电代理用于存储工厂外电站电价模型、工厂发电站的发电模型及工厂储电站的储电模型，当有模型更新时，发送给边缘节点；所述工厂外电站用于连接工厂电网，对工厂进行供电；所述工厂发电站是工厂自建的发电站，对工厂进行供电；所述工厂储电站在电价较低时进行储电，在电价较高时进行给工厂供电。
另一方面，本发明提供一种基于边云协同的工厂电能管控方法，包括以下步骤：
S1：将生产现场的STN任务模型M t存储在云电能管理中心处，将供电侧的发电模型M g、储电模型M s、电价模型M p存储在供电代理处，并规定算法复杂度等级；
S2：设备层中的现场节点，路由节点以及执行生产任务的设备组成工业现场网络；
S3：现场节点解析生产任务信息数据I p并发送给边缘节点；
S4：云电能管理中心将生产现场的STN模型下发到边缘节点；
S5：供电侧的供电代理将发电模型M g、储电模型M s及电价模型M p发给边缘节点；
S6：边缘节点根据生产任务信息数据I p，STN任务模型M t计算完成这些生产任务所需电量E d；
S7：边缘节点依据算法头部O的值和表1判断算法复杂度等级；
S8：边缘节点通过数据帧中TIME_priority的值依据表2判断数据实时性等级；
S9：边缘节点通过算法复杂度等级O的值及数据实时性等级TIME_priority的值依据表3计算综合等级R的值：
表3边云协同计算
序号 计算类别 实时性等级 算法复杂度 综合等级 边云协同
1 实时性低，复杂度极高 1 1 2 云计算
2 实时性中，复杂度极高 2 1 3 云计算
3 实时性低，复杂度高 1 2 3 云计算
4 实时性中，复杂度高 2 2 4 云计算
5 实时性低，复杂度中 1 3 4 云计算
6 实时性低，复杂度极高 3 1 4 云计算
7 实时性低，复杂度低 1 4 5 边缘计算
8 实时性低，复杂度极高 4 1 5 边缘计算
9 实时性中，复杂度中 2 3 5 边缘计算
10 实时性低，复杂度高 3 2 5 边缘计算
11 实时性中，复杂度低 2 4 6 边缘计算
12 实时性极高，复杂度高 4 2 6 边缘计算
13 实时性低，复杂度中 3 3 6 边缘计算
14 实时性高，复杂度低 3 4 7 边缘计算
15 实时性极高，复杂度中 4 3 7 边缘计算
16 实时性极高，复杂度低 4 4 8 边缘计算
S10：边缘节点将综合等级R值为5,6,7,8的计算存储到本地；
S11：边缘节点将综合等级R值为2,3,4的计算发送到云电能管理中心；
S12：边缘节点执行综合等级R值为5,6,7,8的计算，并且将计算结果保存到本地；云电能管理中心执行边缘节点发送的综合等级值为2,3,4的计算；
S13：云电能管理中心将计算结果返回给边缘节点；
S14：在边缘节点执行需求响应算法；
S15：边缘节点将用电方案Scheme i发送给现场节点，将供电方案Scheme p发送给供电代 理；
S16：现场节点将Scheme i发送给生产设备执行生产任务；供电代理控制工厂发电站、储电站、工厂外供电站执行相应方案；
S17：边缘节点将同时方案存储在云电能管理中心，以供未来的操作方案参考。
进一步，步骤S1具体包括以下内容：
(1)工厂电能系统管理人员需要事先把生产现场的STN任务模型M t存储在云电能管理中心处，M t＝{Task_number＝i,Electricity i＝E i，}，E i为任务i在单位时间内运行所需耗电量。
(2)需要事先把供电侧的发电模型M g，储电模型M s，电价模型M p存储在供电代理处。
(3)算法复杂度计算：工厂电能系统管理人员在边缘节点将所有需要执行的算法上机测试，在综合考虑空间复杂度和时间复杂度的情况下，分析出算法复杂度属性为{复杂度极高，复杂度高，复杂度中，复杂度低}。并且将算法按照算法复杂度属性分级，用O表示。算法复杂度等级对应表1，O∈[1-4]。并且将O的值标记在算法的头部。
算法复杂度分为4个等级，算法复杂度依次递减，1级表示算法复杂度极高，4级表示算法复杂度低。算法复杂度不同，其计算时间不同，执行计算时所占用的存储空间也不一样，复杂度等级对应复杂度属性如表1所示。
表1算法复杂度等级
算法复杂度等级 算法复杂度属性
O＝1 复杂度极高
O＝2 复杂度高
O＝3 复杂度中
O＝4 复杂度低
进一步，步骤S2具体包括：现场节点从生产设备处采集到的数据帧信息表为{Task_number,T i，protocol,ID,data_source,Task_type}，分别表示任务编号、任务执行所需时间，工业协议、网络ID、数据源地址、任务类型。现场节点在MAC层根据数据帧信息表中的任务类型Task_type对数据帧进行不同实时性等级的标记，标记后的帧信息表变为{Task_number,T i，protocol,ID,data_source,Task_type,TIME_priority}，其中TIME_priority∈[1-4]；TIME_priority表示数据实时性等级，其值表征的数据属性查表2可知。TIME_priority的值对应为4个实时性等级，依次为1～4，优先等级依次递增，1为对实时性要求不高，4表示对实时性要求极高；实时性等级对应实时性属性如表2所示。
表2数据实时性等级
实时性等级 实时性属性
TIME_priority＝1 低
TIME_priority＝2 中
TIME_priority＝3 高
TIME_priority＝4 极高
进一步，步骤S3中，I p可从帧信息表中解析出来，I p＝{Task_number＝i,i∈N*，T i，T i∈R+},i表示任务编号，T i表示任务执行所需时间。
进一步，步骤S14中所述需求响应算法的输入为电量需求值E d，发电模型M g，储电模型M s，电价模型M p；输出为用电方案Scheme i，供电方案Scheme p；
1)结合生产任务信息数据I p，STN任务模型M t计算电量需求值E d，
Figure PCTCN2020102200-appb-000001
2)发电模型为M g＝{P g，x g，E g，T g}，其中P g为一台发电机单位时间内发电成本，x g为工厂需要运行的发电机数，E g表示工厂内发电站单位时间供电量，T g表示每台发电价发电时间；
3)储电模型为M s＝{E s，E sp，S s，T s，P s}，其中，假设一台储电机充电功率为E s，放电功率E sp为当前设备功率，且只在工厂外供电站供电的情况下充电，S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态，T s表示储电机处于充/放电状态的时间，P s表示储电机充电时的电价；
4)电价模型为M p＝{P，E p}，P表示给工厂供电的价格，E p表示工厂外电站单位时间供电量；
5)在计算出所有结果之后，边缘节点生成用电方案Scheme i，Scheme i＝{Task_number＝i,i∈N*；T_exe；Period；T_ exe,Period∈R+}，其中Task_number为任务编号，T_exe为任务执行时刻，Period为任务执行持续时间；
6)在计算出所有结果之后，边缘节点生成供电方案Scheme p，Scheme p＝{x g,T g,S s,T s,P,S p,T p_01，T p_10}，其中
x g表示需要工厂内发电站运行的发电机的数量；
T g表示每台发电机发电时间；
S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态；
T s表示储电机处于充/放电状态的时间；
P表示当前电价；
S p表示工厂外电站的供电状态，当S p＝00时表示休眠，S p＝01时表示只给生产设备供电，当S p＝10时表示只给储电站供电，S p＝11表示同时给设备层和储电站供电；
T p_S p表示在S p状态下的供电时间，T p_01表示只给生产设备供电时间，T p_10表示只给储电站供电时间；
7)计算如下公式
E d＝E sp*T s+T p_01*E p+E g*T g
C＝T p_10*P s+T p_01*P+x g*P g*T g
在满足用电侧电量E d需求的情况下，算出在满足上述两式的情况下，选取C较小的参数组合作为最终方案；
8)生成方案：经过以上计算，生成用电方案Scheme i，供电方案Scheme p。
本发明的有益效果在于：
边云协同计算提高实时性：本方案中边缘节点本地化处理来自设备层的生产任务数据，与现场设备形成实时反馈控制回路，有效提高管控效率，缩短指令传输时间，实时生成供电用电方案，并提供给生产设备及供电侧各电站执行。该方法能够云端存储电厂电能数据及已生成的方案，能够缩短以后方案生成时间。本方法利用边缘计算及云计算的优点，将算法复杂度及数据实时性进行分级，并得出一个综合等级。将综合等级为2,3,4的计算上传到云端执行，综合等级为5,6,7,8的计算放在边缘本地化执行。在提高整个电能管控系统实时性的前提下，实时地更新方案，缩短了方案生成及指令传输时间，从而降低了用电成本。相比于集中调度机制来说，实时性将极大提升电能管控系统性能。
供-用电联合调度提高灵活性：本方案中对用电侧及供电侧进行联合调度，不仅“削峰填谷”，即在低电价时不仅在设备层开启大功率设备，供电侧也启动储电系统的充电功能。高电价时设备层开启小功率设备，供电侧系统启动储电系统的放电功能。用电侧成本的降低主要是从在低电价时增加生产任务来实现，供电侧成本的降低则是在低电价时储电站进行充电，在高电价的时候储电站进行放电。相对于集中调度机制来说，对供电用电同时调度能够明显提升电能管控系统的性能。
边云协同分布式计算提高安全性：在这个方案中主要的计算任务由边缘节点完成，边缘节点分布得比较分散，即便是其中几个边缘节点的安全受到威胁，也会由其他边缘节点继续执行任务，对整个供电调控系统的影响也不大。相对于集中调度来说，如果集中调度中心瘫痪，则对整个生产系统有致命的打击。因此边云协同技术能够提升电能管控系统的安全性。
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某 种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。
附图说明
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作优选的详细描述，其中：
图1为本发明所述基于边云协同的工厂电能管控系统结构示意图；
图2为本发明所述基于边云协同的工厂电能管控方法流程图；
图3为本发明所述需求响应算法流程示意图。
具体实施方式
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
其中，附图仅用于示例性说明，表示的仅是示意图，而非实物图，不能理解为对本发明的限制；为了更好地说明本发明的实施例，附图某些部件会有省略、放大或缩小，并不代表实际产品的尺寸；对本领域技术人员来说，附图中某些公知结构及其说明可能省略是可以理解的。
本发明实施例的附图中相同或相似的标号对应相同或相似的部件；在本发明的描述中，需要理解的是，若有术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此附图中描述位置关系的用语仅用于示例性说明，不能理解为对本发明的限制，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。
一方面，本发明提供一种基于边云协同的工厂电能管控系统，如图1所示，包括云电能管理层、边缘计算层、设备层、供电侧；
所述云电能管理层包括云电能管理中心、工业云服务器；所述云电能管理中心用于执行部分计算任务，并将计算结果返回到边缘节点，还用于存储生产现场的STN任务模型并下发到边缘节点；所述工业云服务器用于存储边缘节点已生成的供电用电方案，在将来的生成 方案过程中作为参考；
所述边缘计算层包括若干边缘节点，所述边缘节点用于：①计算生产任务电量需求值E d；②根据云电能管理中心下发的STN任务模型信息和工厂设备层发送的实时任务信息，计算出完成既定生产任务电量的需求值；③接收来自供电侧的供电信息数据，包括实时电价模型，储电模型及发电模型；④判断算法复杂度等级O、判断数据实时性等级TIME_priority、计算出综合等级R；⑤用于根据综合等级R将部分计算任务发送到云电能管理中心，并接受该部分计算结果，在边缘节点执行剩余部分任务的计算；⑥根据计算结果综合得出用电方案Scheme i及供电方案Scheme p；⑦将用电方案Scheme p发送给工厂设备层执行生产任务，将供电方案发送给供电侧执行供电任务；
所述设备层包括现场节点、路由节点、生产设备；所述现场节点组成工业现场网络采集生产设备的生产信息，并通过路由节点发送给边缘节点；通过路由节点接收来自边缘节点的用电方案，依据该方案控制设备层执行生产任务；所述生产设备即工业现场执行计划的设备，执行用电方案中的生产任务；
所述供电侧包括供电代理、工厂外电站、工厂发电站、工厂储电站；所述供电代理用于存储工厂外电站电价模型、工厂发电站的发电模型及工厂储电站的储电模型，当有模型更新时，发送给边缘节点；所述工厂外电站用于连接工厂电网，对工厂进行供电；所述工厂发电站是工厂自建的发电站，对工厂进行供电；所述工厂储电站在电价较低时进行储电，在电价较高时进行给工厂供电。
另一方面，如图2所示，本发明提供一种基于边云协同的工厂电能管控方法，下面结合实例对一种基于边云协同的工厂电能管控方法的设计做一个全面的描述。本发明针对的是在工业现场需要通过在边缘节点快速产生供电方案及用电方案的情况，给出本专利的具体实施方式。
首先进行准备工作：
(1)工厂电能系统管理人员需要事先把生产现场的STN任务模型M t存储在云电能管理中心处，M t＝{Task_number＝i,Electricity i＝E i，}，i为任务编号，每小时耗电量可以用千瓦时来计算，则可以直接用功率表示各任务耗电大小。该生产流程需要完成四个任务Task_number＝{1,2,3,4}，其中任务4需要一直运行。设E 1＝5KW,E 2＝10KW,E 3＝30KW,E 4＝50KW。
(2)需要事先把供电侧的发电模型＝{P g＝1.0元/KWH,x g,E g,T g}；储电模型M s＝{E s＝6.25KW,E sp,S s，T s，P s}，充电功率为6.25KW，且储电站最大容量为50KWH，即8小时可以将储电站充满电；放电概率E sp为当前用电设备功率，电价模型M p(如表4)存储在 供电代理处。
表4电价模型
Figure PCTCN2020102200-appb-000002
(3)算法复杂度计算：工厂电能系统管理人员在边缘节点将所有需要执行的算法上机测试，并且将算法复杂度O的值标记在算法的头部。
步骤1：现场节点从生产设备处采集到的数据帧信息表为{Task_number,T i，protocol,ID,data_source,Task_type,TIME_priority}，分别表示任务编号、任务执行所需时间，工业协议、网络ID、数据源地址、任务类型、实时性等级。现场节点解析采集到的数据帧之后得到如下任务信息。
表5任务模型
Figure PCTCN2020102200-appb-000003
步骤2：现场节点解析生产任务信息数据I p并发送给边缘节点。I p可从帧信息表中解析出来，I p＝{(Task_number＝1，T i＝1)；(Task_number＝2，T i＝2)；(Task_number＝3，T i＝3)；(Task_number＝4，T i＝4)},i表示任务编号，T i表示任务执行所需时间。该生产流程需要完成四个任务Task_number＝{1,2,3,4}，其中任务4需要一直运行。
步骤3：云电能管理中心将生产现场的STN模型M t＝{(1,5KW)；(2,10KW)；(3,30KW)；(1,50KW)；}下发到边缘节点。
步骤4：供电侧的供电代理会将发电模型M g，储电模型M s,电价模型M p发给边缘节点。
步骤5：边缘节点根据生产任务信息数据I p，STN任务模型M t计算完成这些生产任务所需电量
E d＝1*5KW+2*10KW+3*30KW+4*50KW＝315KWH
步骤6：边缘节点查找算法的头部得到O的值。
步骤7：边缘节点通过数据帧中TIME_priority的值依据表1判断数据实时性等级得 TIME_priority(1)＝1；TIME_priority(2)＝2；TIME_priority(3)＝3；TIME_priority(4)＝4；。
步骤8：边缘节点通过算法复杂度等级O的值及数据实时性等级TIME_priority的值依据表3计算出综合等级R的值。
步骤9：边缘节点将综合等级R值为5,6,7,8的计算存储到本地。
步骤10：边缘节点将综合等级R值为2,3,4的计算发送到云电能管理中心。
步骤11：边缘节点执行综合等级R值为5,6,7,8的计算，并且将计算结果保存到本地。云电能管理中心执行边缘节点发送的综合等级值为2,3,4的计算。
步骤12：云电能管理中心将计算结果返回给边缘节点。
步骤13：在边缘节点执行需求响应算法。
如图3所示，该需求响应(Demand Response，简称DR)算法输入为电量需求值E d，发电模型M g，储电模型M s，电价模型M p。输出为用电方案Scheme i，供电方案Scheme p。
(1)电量需求值E d
结合生产任务信息数据I p，STN任务模型M t可以算出电量需求值E d。I p可从帧信息表中解析出来，I p＝{Task_number＝i,i∈N*，T i，T i∈R+},i表示任务编号，T i表示任务执行所需时间；M t＝{Task_number＝i,Electricity i＝E i，}，E i为任务i在单位时间内运行所需耗电量。
电量需求值
Figure PCTCN2020102200-appb-000004
(2)发电模型M g
在这里假设发电系统由柴油和天然气发电，发电模型可从发电机单位时间内消耗发电原料的价格入手计算，假设一台发电机单位时间内发电成本为P g。假设工厂需要运行的发电机数为x g。本部分忽略发动机启动和关闭产生的成本，E g表示工厂内发电站单位时间供电量,T g表示每台发电价发电时间。因此发电模型为
M g＝{P g，x g，E g，T g}
(3)储电模型M s
假设一台储电机充电功率为E s，放电功率E sp为当前设备功率，且只在工厂外供电站供电的情况下充电。S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态。T s表示储电机处于充/放电状态的时间，P s表示储电机充电时的电价。因此储电模型为
M s＝{E s，E sp，S s，T s，P s}
(4)电价模型M p
该电价模型依据外部供电单位的情况而定，一般有Day-ahead电价模型，实时电价模型，分段式电价模型。P表示给工厂供电的价格。表示工厂外电站单位时间供电量。因此电价模 型为
M p＝{P，E p}
(5)用电方案Scheme i
在计算出所有结果之后，边缘节点会生成Scheme i。Scheme i＝{Task_number＝i,i∈N*；T_exe；Period；T_ exe,Period∈R+},Task_number为任务编号，T_exe为任务执行时刻，Period为任务执行持续时间。
(6)供电方案Scheme p
在计算出所有结果之后，边缘节点会生成Scheme p。Scheme p＝{x g,T g,S s,T s,P,S p,T p_01，T p_10}。
x g表示需要工厂内发电站运行的发电机的数量；
T g表示每台发电机发电时间；
S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态；
T s表示储电机处于充/放电状态的时间；
P表示当前电价；
S p表示工厂外电站的供电状态，当S p＝00时表示休眠，S p＝01时表示只给生产设备供电，当S p＝10时表示只给储电站供电，S p＝11表示同时给设备层和储电站供电。T p_S p表示在S p状态下的供电时间，T p_01表示只给生产设备供电时间，T p_10表示只给储电站供电时间。
(7)计算如下公式
E d＝E sp*T s+T p_01*E p+E g*T g
C＝T p_10*P s+T p_01*P+x g*P g*T g
在满足用电侧电量E d需求的情况下，算出在满足上述两式的情况下，选取C较小的参数组合作为最终方案。
(8)生成方案
经过以上的计算，可以生成用电方案Scheme i，供电方案Scheme p。
在本实施例中：
1)电量需求值E d＝315KWH
2)发电模型M g＝{P g＝1.0元/KWH,x g,E g,T g}
3)储电模型M s＝{E s＝6.25KW,E sp,S s，T s，P s}
4)电价模型M p＝{P，E p}
根据公式
E d＝E sp*T s+T p_01*E p+E g*T g
C＝T p_10*P s+T p_01*P+x g*P g T g
在满足用电侧电量需求的情况下，算出在满足上述两式的情况下，选取C较小的参数组合作为最终方案。
5)生成方案
经过以上的计算，可以生成用电方案Scheme i，供电方案Scheme p。具体方案有很多种，表6列出来其中之一。该方案即为在一天24H小时内，完成6次生产流程的任务执行方案及供电方案。6次生产流程即任务1共执行6小时，任务2执行12小时，任务3共执行18小时，任务4不间断执行24小时。
表6用电方案与供电方案
Figure PCTCN2020102200-appb-000005
注1：当且仅当储电站充满电时可给任务4供电1小时
步骤14：边缘节点将用电方案Scheme i发送给现场节点，边缘节点将供电方案Scheme p发送给供电代理。
步骤15：现场节点再进一步将Scheme i发送给生产设备执行生产任务。供电代理控制工厂发电站、储电站、工厂外供电站执行相应方案。
步骤16：边缘节点同时将方案存储在云电能管理中心，以供未来的操作方案参考。
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。

Claims (6)

  1. 一种基于边云协同的工厂电能管控系统，其特征在于：包括云电能管理层、边缘计算层、设备层、供电侧；
    所述云电能管理层包括云电能管理中心、工业云服务器；所述云电能管理中心用于执行部分计算任务，并将计算结果返回到边缘节点，还用于存储生产现场的STN任务模型并下发到边缘节点；所述工业云服务器用于存储边缘节点已生成的供电用电方案，在将来的生成方案过程中作为参考；
    所述边缘计算层包括若干边缘节点，所述边缘节点用于：①计算生产任务电量需求值E d；②根据云电能管理中心下发的STN任务模型信息和工厂设备层发送的实时任务信息，计算出完成既定生产任务电量的需求值；③接收来自供电侧的供电信息数据，包括实时电价模型，储电模型及发电模型；④判断算法复杂度等级O、判断数据实时性等级TIME_priority、计算出综合等级R；⑤用于根据综合等级R将部分计算任务发送到云电能管理中心，并接受该部分计算结果，在边缘节点执行剩余部分任务的计算；⑥根据计算结果综合得出用电方案Scheme i及供电方案Scheme p；⑦将用电方案Scheme p发送给工厂设备层执行生产任务，将供电方案发送给供电侧执行供电任务；
    所述设备层包括现场节点、路由节点、生产设备；所述现场节点组成工业现场网络采集生产设备的生产信息，并通过路由节点发送给边缘节点；通过路由节点接收来自边缘节点的用电方案，依据该方案控制设备层执行生产任务；所述生产设备即工业现场执行计划的设备，执行用电方案中的生产任务；
    所述供电侧包括供电代理、工厂外电站、工厂发电站、工厂储电站；所述供电代理用于存储工厂外电站电价模型、工厂发电站的发电模型及工厂储电站的储电模型，当有模型更新时，发送给边缘节点；所述工厂外电站用于连接工厂电网，对工厂进行供电；所述工厂发电站是工厂自建的发电站，对工厂进行供电；所述工厂储电站在电价较低时进行储电，在电价较高时进行给工厂供电。
  2. 一种基于边云协同的工厂电能管控方法，其特征在于：包括以下步骤：
    S1：将生产现场的STN任务模型M t存储在云电能管理中心处，将供电侧的发电模型M g、储电模型M s、电价模型M p存储在供电代理处，并规定算法复杂度等级；
    S2：设备层中的现场节点，路由节点以及执行生产任务的设备组成工业现场网络；
    S3：现场节点解析生产任务信息数据I p并发送给边缘节点；
    S4：云电能管理中心将生产现场的STN模型下发到边缘节点；
    S5：供电侧的供电代理将发电模型M g、储电模型M s及电价模型M p发给边缘节点；
    S6：边缘节点根据生产任务信息数据I p，STN任务模型M t计算完成这些生产任务所需电量E d；
    S7：边缘节点依据算法头部O的值判断算法复杂度等级；
    S8：边缘节点通过数据帧的属性值判断数据实时性等级；
    S9：边缘节点通过算法复杂度等级O的值及数据实时性等级TIME_priority的值计算综合等级R的值：
    序号 计算类别 实时性等级 算法复杂度 综合等级 边云协同 1 实时性低，复杂度极高 1 1 2 云计算 2 实时性中，复杂度极高 2 1 3 云计算 3 实时性低，复杂度高 1 2 3 云计算 4 实时性中，复杂度高 2 2 4 云计算 5 实时性低，复杂度中 1 3 4 云计算 6 实时性低，复杂度极高 3 1 4 云计算 7 实时性低，复杂度低 1 4 5 边缘计算 8 实时性低，复杂度极高 4 1 5 边缘计算 9 实时性中，复杂度中 2 3 5 边缘计算 10 实时性低，复杂度高 3 2 5 边缘计算 11 实时性中，复杂度低 2 4 6 边缘计算 12 实时性极高，复杂度高 4 2 6 边缘计算 13 实时性低，复杂度中 3 3 6 边缘计算 14 实时性高，复杂度低 3 4 7 边缘计算 15 实时性极高，复杂度中 4 3 7 边缘计算 16 实时性极高，复杂度低 4 4 8 边缘计算
    S10：边缘节点将综合等级R值为5,6,7,8的计算存储到本地；
    S11：边缘节点将综合等级R值为2,3,4的计算发送到云电能管理中心；
    S12：边缘节点执行综合等级R值为5,6,7,8的计算，并且将计算结果保存到本地；云电能管理中心执行边缘节点发送的综合等级值为2,3,4的计算；
    S13：云电能管理中心将计算结果返回给边缘节点；
    S14：在边缘节点执行需求响应算法；
    S15：边缘节点将用电方案Scheme i发送给现场节点，将供电方案Scheme p发送给供电代理；
    S16：现场节点将Scheme i发送给生产设备执行生产任务；供电代理控制工厂发电站、储电站、工厂外供电站执行相应方案；
    S17：边缘节点同时将方案存储在云电能管理中心，以供未来的操作方案参考。
  3. 根据权利要求2所述的基于边云协同的工厂电能管控方法，其特征在于：步骤S1具体包括以下内容：
    将生产现场的STN任务模型M t存储在云电能管理中心处，M t＝{Task_number＝i, Electricity i＝E i，}，E i为任务i在单位时间内运行所需耗电量；
    将供电侧的发电模型M g、储电模型M s、电价模型M p存储在供电代理处；
    算法复杂度计算：在边缘节点将所有需要执行的算法上机测试，在综合考虑空间复杂度和时间复杂度的情况下，分析出算法复杂度属性为{复杂度极高，复杂度高，复杂度中，复杂度低}，并且将算法按照算法复杂度属性分级，用O表示，算法复杂度等级对应表1，O∈[1-4]，并且将O的值标记在算法的头部；算法复杂度分为4个等级，算法复杂度依次递减，1级表示算法复杂度极高，4级表示算法复杂度低。
  4. 根据权利要求3所述的基于边云协同的工厂电能管控方法，其特征在于：步骤S2具体包括：现场节点从生产设备处采集到的数据帧信息表为{Task_number,T i，protocol,ID,data_source,Task_type}，分别表示任务编号、任务执行所需时间、工业协议、网络ID、数据源地址、任务类型；
    现场节点在MAC层根据数据帧信息表中的任务类型Task_type对数据帧进行不同实时性等级的标记，标记后的帧信息表变为{Task_number,T i，protocol,ID,data_source,Task_type,TIME_priority}，其中TIME_priority∈[1-4]；
    TIME_priority表示数据实时性等级，其值对应为4个实时性等级，依次为1～4，优先等级依次递增，1为对实时性要求不高，4表示对实时性要求极高。
  5. 根据权利要求4所述的基于边云协同的工厂电能管控方法，其特征在于：步骤S3中，I p从帧信息表中解析而出，I p＝{Task_number＝i,i∈N*，T i，T i∈R+}，i表示任务编号，T i表示任务执行所需时间。
  6. 根据权利要求5所述的基于边云协同的工厂电能管控方法，其特征在于：步骤S14中所述需求响应算法的输入为电量需求值E d，发电模型M g，储电模型M s，电价模型M p；输出为用电方案Scheme i，供电方案Scheme p；
    1)结合生产任务信息数据I p，STN任务模型M t计算电量需求值E d，
    Figure PCTCN2020102200-appb-100001
    2)发电模型为M g＝{P g，x g，E g，T g}，其中P g为一台发电机单位时间内发电成本，x g为工厂需要运行的发电机数，E g表示工厂内发电站单位时间供电量，T g表示每台发电价发电时间；
    3)储电模型为M s＝{E s，E sp，S s，T s，P s}，其中，假设一台储电机充电功率为E s，放电功率E sp为当前设备功率，且只在工厂外供电站供电的情况下充电，S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态，T s表示储电机处于充/放电状态的时间，P s表示储电机充电时的电价；
    4)电价模型为M p＝{P，E p}，P表示给工厂供电的价格，E p表示工厂外电站单位时间供电量；
    5)在计算出所有结果之后，边缘节点生成用电方案Scheme i，Scheme i＝{Task_number＝i,i∈N*；T_exe；Period；T_ exe,Period∈R+}，其中Task_number为任务编号，T_exe为任务执行时刻，Period为任务执行持续时间；
    6)在计算出所有结果之后，边缘节点生成供电方案Scheme p，Scheme p＝{x g,T g,S s,T s,P,S p,T p_01，T p_10}，其中
    x g表示需要工厂内发电站运行的发电机的数量；
    T g表示每台发电机发电时间；
    S s表示储电机的状态，S s＝0时表示处于放电状态，S s＝1时表示处于充电状态；
    T s表示储电机处于充/放电状态的时间；
    P表示当前电价；
    S p表示工厂外电站的供电状态，当S p＝00时表示休眠，S p＝01时表示只给生产设备供电，当S p＝10时表示只给储电站供电，S p＝11表示同时给设备层和储电站供电；
    T p_S p表示在S p状态下的供电时间，T p_01表示只给生产设备供电时间，T p_10表示只给储电站供电时间；
    7)计算如下公式
    E d＝E sp*T s+T p_01*E p+E g*T g
    C＝T p_10*P s+T p_01*P+x g*P g*T g
    在满足用电侧电量E d需求的情况下，算出在满足上述两式的情况下，选取C较小的参数组合作为最终方案；
    8)生成方案：经过以上计算，生成用电方案Scheme i，供电方案Scheme p。
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