WO2023004809A1 - Procédé et appareil de modélisation pour système de gestion d'énergie intelligent, et support de stockage - Google Patents

Procédé et appareil de modélisation pour système de gestion d'énergie intelligent, et support de stockage Download PDF

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WO2023004809A1
WO2023004809A1 PCT/CN2021/109882 CN2021109882W WO2023004809A1 WO 2023004809 A1 WO2023004809 A1 WO 2023004809A1 CN 2021109882 W CN2021109882 W CN 2021109882W WO 2023004809 A1 WO2023004809 A1 WO 2023004809A1
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management system
energy management
smart energy
model
data
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PCT/CN2021/109882
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Chinese (zh)
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王德慧
张拓
江宁
王刚
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西门子（中国）有限公司
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Priority to PCT/CN2021/109882 priority Critical patent/WO2023004809A1/fr
Priority to CN202180100710.XA priority patent/CN117769718A/zh
Publication of WO2023004809A1 publication Critical patent/WO2023004809A1/fr

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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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • 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

Definitions

  • the invention relates to the technical field of energy management, in particular to a modeling method, device and storage medium of a smart energy management system.
  • the traditional centralized energy supply system adopts large-capacity equipment and centralized production methods, and transmits various energies to many users in a large range through special transmission facilities (large power grid, large heating network, etc.).
  • special transmission facilities large power grid, large heating network, etc.
  • users have put forward further requirements for the economy, reliability, and flexibility of energy systems.
  • the smart energy management system takes the balance of cold and heat as the core, integrates various renewable energy sources such as geothermal energy, solar energy, air energy, water energy, natural gas, urban tap water, sewage, industrial waste heat, etc., and uses cold and heat recovery, energy storage, and heat balance , intelligent control and other technologies intelligently balance and control various energy flows to achieve cyclical utilization of energy, thereby integrating cooling and heating, hot water, refrigeration and freezing, drying and heating, breeding and planting, snow removal and ice, steam, power generation, etc. A variety of demand functions.
  • the embodiments of the present invention propose a modeling method, device and storage medium for a smart energy management system.
  • a data modeling method for a smart energy management system comprising:
  • the mechanism model of the smart energy management system includes the mechanism model of the subsystem and the connection relationship between the mechanism models of the subsystem;
  • a data model including the corresponding relationship between the target parameters and the influencing factors of the target parameters is generated. Since the simulation operation data embodies the full business logic of the entire system, this data model is a general data model that includes the full business logic. Moreover, based on the correspondence between the target parameters contained in the general data model and their influencing factors, complex problems can be decoupled, thereby promoting the operation optimization of the entire system.
  • the establishment of the mechanism model of the smart energy management system includes:
  • the mechanism model of the smart energy management system can be established quickly and intuitively through the topology modeling tool.
  • connection relationship includes at least one of the following:
  • the mechanism model of the smart energy management system can contain various forms of connection relationships.
  • the simulation operation of the mechanism model of the smart energy management system includes:
  • the input data of the mechanism model of the smart energy management system can be implemented in various ways, which enriches the data sources of the smart energy management system and improves the accuracy of the data model.
  • the generating the data model including the predetermined target parameter and the corresponding relationship between the influencing factors of the target parameter includes:
  • a separate data model is generated for each subsystem, wherein a part of the influencing factors is included in the mechanism model of the subsystem, and the rest of the influencing factors are included in the mechanism models of other subsystems.
  • the generating the data model including the predetermined target parameter and the corresponding relationship between the influencing factors of the target parameter includes:
  • a unified data model is generated for the smart energy management system, wherein all the influencing factors are included in the mechanism model of a single subsystem, or the influencing factors are dispersedly included in the mechanism models of at least two subsystems.
  • the retrieval result includes a correspondence between the query target parameter and the impact factor of the query target parameter retrieved from the multi-dimensional tensor table.
  • the embodiment of the present invention is based on the multidimensional tensor table storing the data model, which can conveniently provide the corresponding relationship between the target parameters and the influencing factors of the query target parameters.
  • a data modeling device for a smart energy management system comprising:
  • the first establishment module is used to establish the mechanism model of the smart energy management system, the mechanism model of the smart energy management system includes the mechanism model of the subsystem and the connection relationship between the mechanism models of the subsystem;
  • a simulation module used for simulating and running the mechanism model of the smart energy management system
  • the second building module is used to generate a data model including a predetermined target parameter and a corresponding relationship between an influencing factor of the target parameter based on the simulation operation data of the mechanism model of the smart energy management system, wherein the influencing factor Included in the mechanistic model of the subsystem.
  • a data model including the corresponding relationship between the target parameters and the influencing factors of the target parameters is generated. Since the simulation operation data embodies the full business logic of the entire system, this data model is a general data model that includes the full business logic. Moreover, based on the correspondence between the target parameters contained in the general data model and their influencing factors, complex problems can be decoupled, thereby promoting the operation optimization of the entire system.
  • the first building module is configured to use a topology modeling tool to select and move a mechanism model of a subsystem in a drag-and-drop manner; to establish a connection relationship between mechanism models of the subsystem.
  • the mechanism model of the smart energy management system can be established quickly and intuitively through the topology modeling tool.
  • connection relationship includes at least one of the following:
  • the mechanism model of the smart energy management system can contain various forms of connection relationships.
  • the simulation module is used to input real-time measurement data into the mechanism model of the smart energy management system to simulate the operation of the mechanism model of the smart energy management system; or, input historical measurement data into the smart energy management system
  • the mechanism model of the energy management system is used to simulate the operation of the mechanism model of the smart energy management system; or, input the combined data including real-time measurement data and historical measurement data into the mechanism model of the smart energy management system to simulate the operation of the smart energy Mechanistic model of the management system.
  • the input data of the mechanism model of the smart energy management system can be implemented in various ways, which enriches the data sources of the smart energy management system and improves the accuracy of the data model.
  • the second building module is configured to generate a respective data model for each subsystem, wherein all the influencing factors are included in the mechanism model of the subsystem; or, is configured to generate a data model for each subsystem Respective data models, wherein a part of the influencing factors is included in the mechanism model of the subsystem, and the rest of the influencing factors are included in the mechanism models of other subsystems.
  • the second building module is used to generate a unified data model for the smart energy management system, wherein all the influencing factors are included in the mechanism model of a single subsystem, or the influencing factors are dispersedly included In a mechanistic model of at least two subsystems.
  • it also includes: a storage module, configured to store the data model in a multidimensional tensor table; receive a retrieval request with the query target parameter as a retrieval item; send a retrieval result, wherein the retrieval result includes the data from the multidimensional The corresponding relationship between the query target parameter and the impact factor of the query target parameter retrieved from the tensor table.
  • a storage module configured to store the data model in a multidimensional tensor table; receive a retrieval request with the query target parameter as a retrieval item; send a retrieval result, wherein the retrieval result includes the data from the multidimensional The corresponding relationship between the query target parameter and the impact factor of the query target parameter retrieved from the tensor table.
  • the embodiment of the present invention is based on the multidimensional tensor table storing the data model, which can conveniently provide the corresponding relationship between the target parameters and the influencing factors of the query target parameters.
  • a data modeling device for a smart energy management system comprising a processor, a memory, and a computer program stored on the memory and operable on the processor, when the computer program is executed by the processor, the following A data modeling method for a smart energy management system described in any one of the preceding items.
  • the embodiment of the present invention also proposes a data modeling device for a smart energy management system with a processor-memory architecture, based on the simulation operation data of the mechanism model of the smart energy management system, generating a target parameter and a target parameter
  • the data model of the corresponding relationship between influencing factors can decouple complex problems and promote the operation optimization of the entire system.
  • a computer-readable storage medium where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the data modeling method for a smart energy management system as described in any one of the above items is implemented.
  • the embodiment of the present invention also proposes a computer-readable storage medium storing a computer program, based on the simulation operation data of the mechanism model of the smart energy management system, to generate the corresponding relationship between the target parameters and the influencing factors of the target parameters
  • the data model can decouple complex problems and promote the operation optimization of the entire system.
  • Fig. 1 is a flowchart of a modeling method of a smart energy management system according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of selecting a mechanism model of a subsystem in a drag-and-drop manner and establishing a connection relationship according to an embodiment of the present invention.
  • Fig. 3 is a first exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • Fig. 4 is a second exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • Fig. 5 is a third exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • Fig. 6 is a structural diagram of a modeling device of a smart energy management system according to an embodiment of the present invention.
  • FIG. 7 is an exemplary structural block diagram of a modeling device for a smart energy management system with a memory-processor architecture according to an embodiment of the present invention.
  • Modeling method of smart energy management system 101 ⁇ 103 step 200
  • Mechanism Model of Smart Energy Management System 201
  • the Mechanism Model of the First Subsystem 202
  • the Mechanism Model of the Second Subsystem 203
  • the Mechanism Model of the Fourth Subsystem 11 Real-time measurement data 12 historical measurement data twenty one
  • the data model of the first subsystem 31 Data model of the second subsystem 41
  • Data Model of Smart Energy Management System 600 Data modeling device for smart energy management system 601 first build module 602 simulation module 603
  • the second building block 604 storage module 700 Data modeling device for smart energy management system 701 processor 702 memory
  • the implementation mode of the present invention proposes a modeling method for the smart energy management system.
  • the general data model established by this method can describe the relationship between the target parameters and their influencing factors, and promote the integration of the system.
  • Fig. 1 is a flowchart of a modeling method of a smart energy management system according to an embodiment of the present invention.
  • the method includes:
  • Step 101 Establish a mechanism model of the smart energy management system, the mechanism model of the smart energy management system includes the mechanism model of the subsystems and the connection relationship between the mechanism models of the subsystems.
  • a smart energy management system usually includes: a perception layer, a network layer, a platform layer and an application layer.
  • the entire smart energy management system can adopt B/S architecture based on Internet technology software body, and each business function unit system adopts Java EE's lightweight SSH or a multi-layer structure of similar architecture.
  • the perception layer includes integrated data acquisition, measurement analysis and real-time control system, mainly composed of instrumentation equipment and on-site PLC controller;
  • the network layer can pass through various network systems (ADSL, GPRS, 3G, 4G, optical fiber, etc.) , to transmit real-time data to the dispatch management center, and the dispatch management center can also send control instructions to the on-site controller through the network system to execute the control and adjustment instructions;
  • the platform layer is responsible for receiving the data sent by the on-site monitoring equipment, and storing the real-time operation parameters In the data, it provides basic data for subsequent management, analysis, and control, and stores, analyzes, alarms, and prints reports for the data;
  • the application layer is the layer directly used by the operator, and continuously and dynamically analyzes the uploaded data in real time, and can Issue adjustment instructions based on the analysis results.
  • the smart energy management system can be divided based on the above-mentioned hierarchical manner, so as to determine the various subsystems that make up the smart energy management system.
  • the smart energy management system may also be divided in other ways, which is not limited in the embodiments of the present invention.
  • the process of establishing the mechanism model of the smart energy management system includes:
  • subsystems can include: energy supply network (such as power supply, gas supply, cooling/heating, etc.), energy exchange links (such as CCHP units, generator sets, boilers, air conditioners, heat pumps, etc.), energy storage links (electricity storage , gas storage, heat storage, cold storage, etc.), terminal comprehensive energy supply units (such as microgrids), and so on.
  • energy supply network such as power supply, gas supply, cooling/heating, etc.
  • energy exchange links such as CCHP units, generator sets, boilers, air conditioners, heat pumps, etc.
  • energy storage links electricality storage , gas storage, heat storage, cold storage, etc.
  • terminal comprehensive energy supply units such as microgrids
  • the mechanism model of the subsystem also known as the white box model of the subsystem, is an accurate mathematical model that describes the subsystem and is established based on the objects in the subsystem, the internal mechanism of the production process, or the transfer mechanism of the material flow. It can be a mathematical model of an object or process based on mass balance equations, energy balance equations, momentum balance equations, phase balance equations, some physical property equations, chemical reaction laws, etc.
  • the parameters of the mechanism model are easy to adjust, and the resulting model has strong adaptability.
  • connection relationship includes: electrical connection; energy transfer connection; liquid flow connection; gas flow connection; information transmission connection; value connection, etc.
  • FIG. 2 is a schematic diagram of selecting a mechanism model of a subsystem in a drag-and-drop manner and establishing a connection relationship according to an embodiment of the present invention.
  • the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem are selected by dragging and dropping in the interface provided by the visual modeling tool (such as Vertabelo or Apache Spark, etc.).
  • Subsystem 204 and establish the connection relationship between the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem 204 in the form of dragging and dropping, thereby forming the first subsystem 201, the second subsystem
  • the mechanism model 200 of the smart energy management system of the subsystem 202 , the third subsystem 203 and the fourth subsystem 204 are selected by dragging and dropping in the interface provided by the visual modeling tool (such as Vertabelo or Apache Spark, etc.).
  • connection relationship between the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem 204 can be electrical connection, energy transfer connection, liquid flow connection, gas flow connection , information transfer connections or value connections (eg, cost relations), etc.
  • Step 102 Simulate and run the mechanism model of the smart energy management system.
  • input data is provided for the mechanism model of the smart energy management system established in step 101, and the mechanism model of the smart energy management system is simulated based on the input data.
  • input data can include:
  • the measuring points can be arranged in the subsystem, or on the connection line between the subsystems.
  • the combined data includes the real-time measurement data provided by each measuring point in the smart energy management system and the historical measurement data obtained from the database and provided by each measuring point.
  • step 102 the simulation operation data of the mechanism model of the smart energy management system can be obtained.
  • the simulation running data can reflect the running result of the whole business logic of the whole system.
  • Step 103 Based on the simulation operation data of the mechanism model of the smart energy management system, generate a data model including the corresponding relationship between the predetermined target parameters and the influencing factors of the target parameters, wherein the influencing factors are included in the subsystem in the mechanistic model.
  • target parameter is a predetermined target parameter (such as power consumption, power consumption cost), and so on.
  • target parameters can be internally defined parameters in any subsystem.
  • the target parameters may be determined by the user, or the target parameters may be automatically generated based on the system optimization target.
  • the corresponding relationship including the target parameters and the influencing factors of the target parameters can be determined.
  • the regression analysis algorithm is used to determine the influencing factors of the target parameters based on the simulation operation data, and the quantitative description of how the influencing factors affect the target parameters.
  • Regression analysis is a predictive modeling technique that studies the relationship between dependent variables and independent variables. This technique is commonly used in predictive analytics, time series modeling, and discovering causal relationships between variables.
  • variables are divided into two categories.
  • One type is the dependent variable, which is usually a type of indicator concerned in practical problems, usually represented by Y (that is, the target parameter); and the other type of variable that affects the value of the dependent variable is called the independent variable, represented by X (the impact factor).
  • the main problems of regression analysis research are: (1) determine the quantitative relationship expression between Y and X, this expression is called regression equation; (2) test the reliability of the obtained regression equation; (3) Judging whether the independent variable X has an influence on the dependent variable Y; (4) Use the obtained regression equation to predict and control.
  • multiple regression algorithms can be used, such as linear regression (Linear Regression) algorithm, logistic regression (Logistic Regression) algorithm, polynomial regression (Polynomial Regression) algorithm, stepwise regression (Stepwise Regression) algorithm, ridge regression ( Ridge Regression), Lasso Regression or ElasticNet Regression algorithms, etc.
  • a separate data model is generated for each subsystem, wherein all the influencing factors are included in the mechanism model of the subsystem.
  • a smart energy management system includes a gasifier as a subsystem.
  • Influencing factors include the operating efficiency of the gasifier.
  • the influencing factors affecting the working efficiency of the gasifier include the following parameters in the gasifier model: temperature of syngas; composition of syngas; temperature difference of burner cooling water ; burner support absorbs heat; water wall absorbs heat; oxygen-coal steam ratio; slag port absorbs heat; slag particle size distribution; slag and filter cake ratio; Regression analysis to determine how these parameters as influencing factors affect the quantitative description of the target parameters (eg, generate X as independent variables (including syngas temperature, syngas composition, burner cooling water temperature difference, burner support heat absorption , water-cooled wall heat absorption, oxygen-coal steam ratio, and slag mouth heat absorption) and the relationship between Y (the working efficiency of the gasifier) as the dependent variable).
  • the data model generated in step 103 is stored in a multidimensional
  • a separate data model is generated for each subsystem, wherein a part of the influencing factors is included in the mechanism model of the subsystem, and the rest of the influencing factors are included in the mechanism models of other subsystems middle.
  • a smart energy management system includes boilers and steam turbines as subsystems.
  • Influencing factors include the electricity consumption of the boiler.
  • the influencing factors affecting the power consumption of the boiler include the following parameters in the boiler model: steam quantity; steam pressure; steam temperature and the following parameters in the steam turbine model: main Steam temperature in front of steam valve; steam pressure in front of main steam valve.
  • X including steam quantity, steam pressure, steam temperature
  • Y power consumption of the boiler
  • a unified data model is generated for the smart energy management system, wherein all influencing factors are included in the mechanism model of a single subsystem, or the influencing factors are dispersedly included in the mechanism models of at least two subsystems.
  • a smart energy management system includes boilers and steam turbines as subsystems.
  • Influencing factors include electricity prices for smart energy management systems.
  • the influencing factors affecting the electricity price of the smart energy management system include the following parameters in the boiler model: steam quantity; steam pressure; steam temperature and the following parameters in the steam turbine model Parameters: steam temperature in front of the main steam valve; steam pressure in front of the main steam valve; rated steam intake.
  • X including steam quantity, steam pressure, steam temperature
  • Y the electricity price of the smart energy management system
  • the method 100 further includes: storing the data model in a multidimensional tensor table; receiving a retrieval request with the query target parameter as a retrieval item; sending a retrieval result, wherein the retrieval result includes data from the multidimensional tensor table The retrieved correspondence between the query target parameters and the impact factors of the query target parameters.
  • the embodiment of the present invention is based on the multidimensional tensor table storing the data model, which can conveniently provide the corresponding relationship between the target parameters and the influencing factors of the query target parameters.
  • the data model is combined with real-time data and historical data, which reflects the actual situation of the equipment in real time, supports advanced intelligent applications that require the performance of components in the overall system or subsystem, and has real-time calculation,
  • the advantages of speed and accuracy, and the ability to coordinate and communicate the relationship between various advanced applications can decouple complex problems, so that the operation optimization of the entire system can be decomposed into multiple operation optimization problems for each sub-optimization.
  • Fig. 3 is a first exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • the mechanism model 200 of the smart energy management system includes a mechanism model 201 of the first subsystem, a mechanism model 202 of the second subsystem, a mechanism model 203 of the third subsystem and a mechanism model 204 of the fourth subsystem.
  • the real-time measurement data 11 as input data is provided to the mechanism model 200 of the smart energy management system. Based on the input data, a large amount of simulation operation data can be obtained by simulating the mechanism model 200 of the smart energy management system.
  • an internally defined parameter A in the first subsystem as the target parameter, and the influence factor of the internally defined parameter A in the first subsystem can be obtained by performing regression analysis on the simulation data (the influence factor can be All of them are in the mechanism model 201 of the first subsystem; or, some of the influencing factors are in the mechanism model 201 of the first subsystem, and the other part of the influencing factors are in the mechanism models of other systems) and between the internally defined parameters A and the influencing factors
  • the corresponding relationship between for example, the relational expression from the regression).
  • the data model 21 of the first subsystem can be obtained by storing (for example, through a tensor table) the corresponding relationship.
  • Fig. 4 is a second exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • the mechanism model 200 of the smart energy management system includes a mechanism model 201 of the first subsystem, a mechanism model 202 of the second subsystem, a mechanism model 203 of the third subsystem and a mechanism model 204 of the fourth subsystem.
  • Real-time measurement data 11 and historical measurement data 12 as input data are provided to the mechanism model 200 of the smart energy management system.
  • a large amount of simulation operation data can be obtained by simulating the mechanism model 200 of the smart energy management system.
  • the first subsystem By performing regression analysis on the simulation data, the first subsystem can be obtained The influence factor of target parameter A in the system and target parameter A and its influence factor (this influence factor can all be in the mechanism model 201 of the first subsystem; Or, a part of influence factor is in the mechanism model 201 of the first subsystem, another Part of the influence factors in the mechanism model of other systems), and the influence factor of the internally defined parameter B in the second subsystem, and the target parameter B and its influence factor (the influence factors can all be in In the mechanism model 202 of the second subsystem; or, the second corresponding relationship between a part of the influencing factors in the mechanism model 202 of the second subsystem and another part of the influencing factors in the mechanism models of other systems).
  • the data model 21 of the first subsystem can be obtained by storing (for example, through a tensor table) the first correspondence relationship; the data model 31 of the second subsystem can be obtained by storing (for example, through a tensor table) the second correspondence relationship.
  • Fig. 5 is a third exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
  • the mechanism model 200 of the smart energy management system includes a mechanism model 201 of the first subsystem, a mechanism model 202 of the second subsystem, a mechanism model 203 of the third subsystem and a mechanism model 204 of the fourth subsystem.
  • Real-time measurement data 11 and historical measurement data 12 as input data are provided to the mechanism model 200 of the smart energy management system. Based on the input data, a large amount of simulation operation data can be obtained by simulating the mechanism model 200 of the smart energy management system.
  • the first corresponding relationship between the defined parameter K1 and its influencing factors (for example, T1, T2 and T3) in the first subsystem can be obtained by performing regression analysis on the simulation running data, as well as K2 and The second correspondence between its impact factors (eg, T4 and T5). Based on the first correspondence, the second correspondence and the expressions between K and the internal defined parameters (i.e.
  • the influence factors (T1, T2) of the global quantity K and the defined parameters K1 can be obtained and T3) and the third correspondence between the influence factors (T4 and T5) of the defined parameter K2.
  • the data model 41 of the smart energy management system can be obtained by storing (for example, through a tensor table) the third correspondence.
  • Fig. 6 is a structural diagram of a modeling device of a smart energy management system according to an embodiment of the present invention.
  • the data modeling device 600 of the smart energy management system includes:
  • the first establishment module 601 is used to establish the mechanism model of the smart energy management system, the mechanism model of the smart energy management system includes the mechanism model of the subsystem and the connection relationship between the mechanism models of the subsystem;
  • the simulation module 602 is used to simulate and run the mechanism model of the smart energy management system
  • the second building module 603 is used to generate a data model including the corresponding relationship between predetermined target parameters and influencing factors of the target parameters based on the simulation operation data of the mechanism model of the smart energy management system, wherein the influencing factors Factors are included in the mechanistic model of the subsystem.
  • the first establishing module 601 is configured to use a topology modeling tool to select and move a mechanism model of a subsystem in a dragging manner; to establish a connection relationship between mechanism models of the subsystem.
  • connection relationship includes at least one of the following: electrical connection; energy transfer connection; liquid flow connection; gas flow connection; information transfer connection; value connection.
  • the simulation module 602 is used to input real-time measurement data into the mechanism model of the smart energy management system to simulate the operation of the mechanism model of the smart energy management system; or, input historical measurement data into the smart energy
  • the mechanism model of the management system is used to simulate the operation of the mechanism model of the smart energy management system; or, input the combined data including real-time measurement data and historical measurement data into the mechanism model of the smart energy management system to simulate the operation of the smart energy management system Mechanistic model of the system.
  • the second building module 603 is used to generate a respective data model for each subsystem, wherein all the influencing factors are included in the mechanism model of the subsystem; or, is used to generate a separate data model for each subsystem
  • the data model of the above-mentioned influence factors is included in the mechanism model of the subsystem, and the rest of the influence factors are included in the mechanism models of other subsystems.
  • the second building module 603 is used to generate a unified data model for the smart energy management system, wherein all the influencing factors are contained in the mechanism model of a single subsystem, or the influencing factors are dispersedly contained in In a mechanistic model of at least two subsystems.
  • a storage module 604 configured to store the data model in a multidimensional tensor table; receive a retrieval request with the query target parameter as a retrieval item; send a retrieval result, wherein the retrieval result includes the The corresponding relationship between the query target parameter and the impact factor of the query target parameter retrieved from the multidimensional tensor table.
  • the embodiment of the present invention also proposes a data modeling device for a smart energy management system with a memory-processor architecture.
  • FIG. 7 is an exemplary structural block diagram of a data modeling device of a smart energy management system with a memory-processor architecture according to an embodiment of the present invention.
  • the data modeling device 700 of the smart energy management system includes a processor 701, a memory 702, and a computer program stored in the memory 702 and operable on the processor 701.
  • the computer program is programmed by the processor When executing 701, implement the data modeling method of the smart energy management system described in any one of the above items.
  • the memory 702 can be specifically implemented as various storage media such as electrically erasable programmable read-only memory (EEPROM), flash memory (Flash memory), and programmable program read-only memory (PROM).
  • the processor 701 may be implemented to include one or more central processing units or one or more field programmable gate arrays, wherein the field programmable gate arrays integrate one or more central processing unit cores.
  • the central processing unit or the central processing unit core may be implemented as a CPU, MCU, or DSP, and so on.
  • a hardware module may include specially designed permanent circuits or logic devices (such as special-purpose processors, such as FPGAs or ASICs) to perform specific operations.
  • Hardware modules may also include programmable logic devices or circuits (eg, including general-purpose processors or other programmable processors) temporarily configured by software to perform particular operations.
  • programmable logic devices or circuits eg, including general-purpose processors or other programmable processors
  • temporarily configured by software it can be decided according to cost and time considerations.
  • the present invention also provides a machine-readable storage medium storing instructions for making a machine execute the method described in the present application.
  • a system or device equipped with a storage medium may be provided, on which the software program codes for realizing the functions of any one of the above-mentioned embodiments are stored, and the computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.
  • an operating system or the like operated on a computer may also complete part or all of the actual operations through instructions based on program codes.
  • Embodiments of storage media for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), Tape, non-volatile memory card, and ROM.
  • the program code can be downloaded from a server computer or cloud by a communication network.

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Abstract

L'invention concerne un procédé (100) et un appareil (600) de modélisation pour un système de gestion d'énergie intelligent, et un support de stockage. Le procédé (100) comprend : l'établissement d'un modèle de mécanisme (200) du système de gestion d'énergie intelligent, le modèle de mécanisme (200) du système de gestion d'énergie intelligent comprenant des modèles de mécanisme (201-204) de sous-systèmes, et la relation de connexion entre les modèles de mécanisme (201-204) des sous-systèmes (101) ; la simulation pour exécuter le modèle de mécanisme du système de gestion d'énergie intelligent (102) ; et sur la base de données de fonctionnement de simulation du modèle de mécanisme (200) du système de gestion d'énergie intelligent, la génération d'un modèle de données (41) comprenant la correspondance entre un paramètre cible prédéterminé et un facteur d'influence du paramètre cible, le facteur d'influence étant compris dans les modèles de mécanisme (201-204) des sous-systèmes (103). Le procédé de modélisation (100) pour le système de gestion d'énergie intelligent met en œuvre un modèle de données général du système de gestion d'énergie intelligent qui comprend une logique de service complet et peut décrire la relation entre le paramètre cible et le facteur d'influence de celui-ci, et favorise l'optimisation du fonctionnement de l'ensemble du système par découplage du problème complexe.
PCT/CN2021/109882 2021-07-30 2021-07-30 Procédé et appareil de modélisation pour système de gestion d'énergie intelligent, et support de stockage WO2023004809A1 (fr)

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CN202180100710.XA CN117769718A (zh) 2021-07-30 2021-07-30 一种智慧能源管理系统的建模方法、装置和存储介质

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160274608A1 (en) * 2015-03-16 2016-09-22 The Florida International University Board Of Trustees Flexible, secure energy management system
US20160378894A1 (en) * 2015-06-26 2016-12-29 Nemo Partners Nec Method and apparatus for analyzing economics of power demand management business project using smart power demand resources modeling data simulation module
CN106295900A (zh) * 2016-08-19 2017-01-04 中节能（常州）城市节能研究院有限公司 一种城市智慧能源管理系统
CN108494021A (zh) * 2018-04-20 2018-09-04 东北大学 电-热-气综合能源系统的稳定评估与静态控制方法
CN111626587A (zh) * 2020-05-21 2020-09-04 浙江大学 一种计及能流延迟特性的综合能源系统拓扑优化方法
WO2021062748A1 (fr) * 2019-09-30 2021-04-08 西门子股份公司 Procédé et appareil d'optimisation pour système d'énergie intégré et support d'informations lisible par ordinateur
CN112926899A (zh) * 2021-04-13 2021-06-08 山东国研自动化有限公司 能源管理系统的模型构建方法、系统及设备

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160274608A1 (en) * 2015-03-16 2016-09-22 The Florida International University Board Of Trustees Flexible, secure energy management system
US20160378894A1 (en) * 2015-06-26 2016-12-29 Nemo Partners Nec Method and apparatus for analyzing economics of power demand management business project using smart power demand resources modeling data simulation module
CN106295900A (zh) * 2016-08-19 2017-01-04 中节能（常州）城市节能研究院有限公司 一种城市智慧能源管理系统
CN108494021A (zh) * 2018-04-20 2018-09-04 东北大学 电-热-气综合能源系统的稳定评估与静态控制方法
WO2021062748A1 (fr) * 2019-09-30 2021-04-08 西门子股份公司 Procédé et appareil d'optimisation pour système d'énergie intégré et support d'informations lisible par ordinateur
CN111626587A (zh) * 2020-05-21 2020-09-04 浙江大学 一种计及能流延迟特性的综合能源系统拓扑优化方法
CN112926899A (zh) * 2021-04-13 2021-06-08 山东国研自动化有限公司 能源管理系统的模型构建方法、系统及设备

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