CN117769718A - Modeling method and device of intelligent energy management system and storage medium - Google Patents
Modeling method and device of intelligent energy management system and storage medium Download PDFInfo
- Publication number
- CN117769718A CN117769718A CN202180100710.XA CN202180100710A CN117769718A CN 117769718 A CN117769718 A CN 117769718A CN 202180100710 A CN202180100710 A CN 202180100710A CN 117769718 A CN117769718 A CN 117769718A
- Authority
- CN
- China
- Prior art keywords
- management system
- energy management
- intelligent energy
- data
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000003860 storage Methods 0.000 title claims abstract description 21
- 230000007246 mechanism Effects 0.000 claims abstract description 186
- 238000007726 management method Methods 0.000 claims abstract description 165
- 238000013499 data model Methods 0.000 claims abstract description 77
- 238000004088 simulation Methods 0.000 claims abstract description 30
- 238000005259 measurement Methods 0.000 claims description 32
- 238000012546 transfer Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 8
- 239000007788 liquid Substances 0.000 claims description 7
- 238000005457 optimization Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 13
- 230000005611 electricity Effects 0.000 description 13
- 238000000611 regression analysis Methods 0.000 description 13
- 238000004422 calculation algorithm Methods 0.000 description 8
- 230000001419 dependent effect Effects 0.000 description 7
- 239000007789 gas Substances 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 4
- 239000002893 slag Substances 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 2
- 239000000498 cooling water Substances 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 238000007477 logistic regression Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 239000003345 natural gas Substances 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- 238000003786 synthesis reaction Methods 0.000 description 2
- 101150012579 ADSL gene Proteins 0.000 description 1
- 102100020775 Adenylosuccinate lyase Human genes 0.000 description 1
- 108700040193 Adenylosuccinate lyases Proteins 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 239000012065 filter cake Substances 0.000 description 1
- 238000007710 freezing Methods 0.000 description 1
- 230000008014 freezing Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000005338 heat storage Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000010842 industrial wastewater Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 239000008399 tap water Substances 0.000 description 1
- 235000020679 tap water Nutrition 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- 239000002918 waste heat Substances 0.000 description 1
Classifications
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A modeling method (100), apparatus (600) and storage medium for an intelligent energy management system are provided. The method (100) comprises: establishing a mechanism model (200) of the intelligent energy management system, wherein the mechanism model (200) of the intelligent energy management system comprises mechanism models (201-204) of the subsystems and connection relations (101) between the mechanism models (201-204) of the subsystems; a mechanism model (102) for simulating and running the intelligent energy management system; based on simulation operation data of a mechanism model (200) of the intelligent energy management system, a data model (41) containing predetermined target parameters and correspondence relations between influence factors of the target parameters is generated, wherein the influence factors are contained in mechanism models (201-204) of the subsystems (103). The modeling method (100) of the intelligent energy management system realizes a general data model of the intelligent energy management system, which comprises full-service logic, can describe the relation between target parameters and influence factors thereof, and promotes the operation optimization of the whole system by decoupling complex problems.
Description
The invention relates to the technical field of energy management, in particular to a modeling method, a modeling device and a storage medium of an intelligent energy management system.
Conventional centralized energy supply systems employ high-capacity equipment and centralized production, and deliver various energies to a large number of users over a wide range through specialized delivery facilities (large power grids, large heat grids, etc.). With the wide application of distributed power sources such as photovoltaic, wind power, natural gas cogeneration and the like, users have further requirements on economy, reliability, flexibility and the like of an energy system.
Currently, industry technology goes from electrification, automation, to digitization. The digitization of the energy field is an important part of industrial digitization. The intelligent energy management system takes cold and heat balance as a core, integrates various renewable energy sources such as geothermal energy, solar energy, air energy, water energy, natural gas, city tap water, sewage, industrial waste water and waste heat, and performs intelligent balance control on various energy flows by using technologies such as cold and heat recovery, energy storage, heat balance, intelligent control and the like so as to achieve cyclic and reciprocating utilization of the energy sources, thereby integrally meeting various demand functions such as refrigeration and heating, hot water, refrigeration and freezing, drying and heating, cultivation and planting, snow removal and deicing, steam, power generation and the like.
For intelligent digital energy management systems, it is highly desirable to provide a generic data model containing all-business logic for subsystems such as market trading, production security, financial analysis, and the like. However, there is still no discussion of this generic data model.
Disclosure of Invention
The embodiment of the invention provides a modeling method, a modeling device and a storage medium of an intelligent energy management system.
The technical scheme of the embodiment of the invention is as follows:
a data modeling method of an intelligent energy management system comprises the following steps:
establishing a mechanism model of an intelligent energy management system, wherein the mechanism model of the intelligent energy management system comprises a mechanism model of a subsystem and a connection relation between the mechanism models of the subsystem;
simulating and running a mechanism model of the intelligent energy management system;
based on the simulation operation data of the mechanism model of the intelligent energy management system, a data model containing a preset target parameter and a corresponding relation between influence factors of the target parameter is generated, wherein the influence factors are contained in the mechanism model of the subsystem.
Therefore, the embodiment of the invention generates the data model containing the target parameters and the corresponding relation between the influence factors of the target parameters based on the simulation operation data of the mechanism model of the intelligent energy management system. Because the full-service logic of the whole system is embodied in the simulation running data, the data model is a general data model containing the full-service logic. Moreover, based on the corresponding relation between the target parameters and the influence factors contained in the general data model, the complex problem can be decoupled, so that the operation optimization of the whole system is promoted.
In one embodiment, the building a mechanism model of the intelligent energy management system includes:
selecting and moving a mechanism model of the subsystem in a dragging mode by utilizing a topology modeling tool;
and establishing a connection relation between mechanism models of the subsystems.
Therefore, a mechanism model of the intelligent energy management system can be quickly and intuitively established through a topology modeling tool.
In one embodiment, the connection relationship includes at least one of the following:
electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value links.
Therefore, the mechanism model of the intelligent energy management system can comprise various connection relations.
In one embodiment, the simulation runs a mechanism model of the intelligent energy management system comprising:
inputting real-time measurement data to a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting historical measurement data into a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting combined data including real-time measurement data and historical measurement data to the mechanism model of the intelligent energy management system to simulate the mechanism model of operating the intelligent energy management system.
Therefore, the input data of the mechanism model of the intelligent energy management system can have various embodiments, enriches the data sources of the intelligent energy management system and improves the accuracy of the data model.
In one embodiment, the generating the data model including the correspondence between the predetermined target parameter and the influence factor of the target parameter includes:
generating a respective data model for each subsystem, wherein the impact factors are all contained in a mechanism model of the subsystem; or (b)
A respective data model is generated for each subsystem, wherein a portion of the influencing factors are included in the mechanism model of that subsystem and the remaining portion of the influencing factors are included in the mechanism models of the other subsystems.
Thus, a separate data model may be generated for each subsystem, and the impact factors have a variety of implementations.
In one embodiment, the generating the data model including the correspondence between the predetermined target parameter and the influence factor of the target parameter includes:
a unified data model is generated for the intelligent energy management system, wherein the influencing factors are all contained in the mechanism model of a single subsystem or the influencing factors are contained in the mechanism models of at least two subsystems in a scattered manner.
It can be seen that a data model can be generated for the entire intelligent energy management system, and that the impact factors have a variety of embodiments.
In one embodiment, the method further comprises:
storing the data model in a multi-dimension Zhang Liangbiao;
receiving a search request taking a query target parameter as a search term;
and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
Therefore, the embodiment of the invention can conveniently provide the corresponding relation between the target parameter and the influence factor of the query target parameter based on the multidimensional Zhang Liangbiao of the stored data model.
A data modeling apparatus of an intelligent energy management system, comprising:
the system comprises a first establishing module, a second establishing module and a third establishing module, wherein the first establishing module is used for establishing a mechanism model of an intelligent energy management system, and the mechanism model of the intelligent energy management system comprises a mechanism model of a subsystem and a connection relation between the mechanism models of the subsystem;
the simulation module is used for simulating and running the mechanism model of the intelligent energy management system;
and the second building module is used for generating a data model containing a preset target parameter and a corresponding relation between influence factors of the target parameter based on simulation operation data of the mechanism model of the intelligent energy management system, wherein the influence factors are contained in the mechanism model of the subsystem.
Therefore, the embodiment of the invention generates the data model containing the target parameters and the corresponding relation between the influence factors of the target parameters based on the simulation operation data of the mechanism model of the intelligent energy management system. Because the full-service logic of the whole system is embodied in the simulation running data, the data model is a general data model containing the full-service logic. Moreover, based on the corresponding relation between the target parameters and the influence factors contained in the general data model, the complex problem can be decoupled, so that the operation optimization of the whole system is promoted.
In one embodiment, the first building module is configured to select and move a mechanism model of the subsystem in a dragging manner using a topology modeling tool; and establishing a connection relation between mechanism models of the subsystems.
The mechanism model of the intelligent energy management system can be quickly and intuitively established through a topology modeling tool.
In one embodiment, the connection relationship includes at least one of the following:
electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value links.
Therefore, the mechanism model of the intelligent energy management system can comprise various connection relations.
In one embodiment, the simulation module is configured to input real-time measurement data to the mechanism model of the smart energy management system to simulate the mechanism model of operating the smart energy management system; or, inputting historical measurement data into a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting combined data including real-time measurement data and historical measurement data to the mechanism model of the intelligent energy management system to simulate the mechanism model of operating the intelligent energy management system.
Therefore, the input data of the mechanism model of the intelligent energy management system can have various embodiments, enriches the data sources of the intelligent energy management system and improves the accuracy of the data model.
In one embodiment, the second building module is configured to generate a respective data model for each subsystem, where the impact factors are all included in a mechanism model of the subsystem; or, for generating a respective data model for each subsystem, wherein a part of the influencing factors are included in the mechanism model of that subsystem and the remaining part of the influencing factors are included in the mechanism models of the other subsystems.
Thus, a separate data model may be generated for each subsystem, and the impact factors have a variety of implementations.
In one embodiment, the second building module is configured to generate a unified data model for the intelligent energy management system, wherein the impact factors are all contained in a mechanism model of a single subsystem or the impact factors are dispersed in a mechanism model of at least two subsystems.
It can be seen that a data model can be generated for the entire intelligent energy management system, and that the impact factors have a variety of embodiments.
In one embodiment, the method further comprises: a storage module for storing the data model in a plurality of dimensions Zhang Liangbiao; receiving a search request taking a query target parameter as a search term; and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
Therefore, the embodiment of the invention can conveniently provide the corresponding relation between the target parameter and the influence factor of the query target parameter based on the multidimensional Zhang Liangbiao of the stored data model.
A data modeling apparatus of a smart energy management system comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing a data modeling method of a smart energy management system as claimed in any one of the preceding claims.
Therefore, the embodiment of the invention also provides a data modeling device of the intelligent energy management system with a processor-memory architecture, which generates a data model containing the target parameters and the corresponding relation between the influence factors of the target parameters based on the simulation operation data of the mechanism model of the intelligent energy management system, can decouple the complex problem and promote the operation optimization of the whole system.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the data modeling method of the intelligent energy management system of any of the above.
Therefore, the embodiment of the invention also provides a computer readable storage medium storing a computer program, which generates a data model containing the corresponding relation between the target parameters and the influence factors of the target parameters based on the simulation operation data of the mechanism model of the intelligent energy management system, can decouple the complex problem and promote the operation optimization of the whole system.
Fig. 1 is a flowchart of a modeling method of an intelligent energy management system according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of a mechanism model and connection establishment of a subsystem in a drag mode 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 block diagram of a modeling apparatus of an intelligent energy management system according to an embodiment of the present invention.
FIG. 7 is an exemplary block diagram of a modeling apparatus of an intelligent energy management system with a memory-processor architecture according to an embodiment of the present invention.
Wherein, the reference numerals are as follows:
| reference numerals | Meaning of |
| 100 | Modeling method of intelligent energy management system |
| 101～103 | Step (a) |
| 200 | Mechanism model of intelligent energy management system |
| 201 | Mechanism model of first subsystem |
| 202 | Mechanism model of second subsystem |
| 203 | Mechanism model of third subsystem |
| 204 | Mechanism model of fourth subsystem |
| 11 | Real-time measurement data |
| 12 | Historical measurement data |
| 21 | Data model of first subsystem |
| 31 | Data model of second subsystem |
| 41 | Data model of intelligent energy management system |
| 600 | Data modeling device of intelligent energy management system |
| 601 | First building module |
| 602 | Simulation module |
| 603 | Second building module |
| 604 | Memory module |
| 700 | Data modeling device of intelligent energy management system |
| 701 | Processor and method for controlling the same |
| 702 | Memory device |
In order to make the technical scheme and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description is intended to illustrate the invention and is not intended to limit the scope of the invention.
For simplicity and clarity of description, the following description sets forth aspects of the invention by describing several exemplary embodiments. Numerous details in the embodiments are provided solely to aid in the understanding of the invention. It will be apparent, however, that the embodiments of the invention may be practiced without limitation to these specific details. Some embodiments are not described in detail in order to avoid unnecessarily obscuring aspects of the present invention, but rather only to present a framework. Hereinafter, "comprising" means "including but not limited to", "according to … …" means "according to at least … …, but not limited to only … …". The term "a" or "an" is used herein to refer to a number of components, either one or more, or at least one, unless otherwise specified.
In view of the current lack of a generic data model that includes all-business logic of an intelligent energy management system, embodiments of the present invention provide a method for modeling an intelligent energy management system. The general data model established by the method can describe the relation between the target parameters and the influence factors thereof, and promote the fusion of the system.
Fig. 1 is a flowchart of a modeling method of an intelligent energy management system according to an embodiment of the present invention.
As shown in fig. 1, the method includes:
step 101: and establishing a mechanism model of the intelligent energy management system, wherein the mechanism model of the intelligent energy management system comprises a mechanism model of a subsystem and a connection relation between the mechanism models of the subsystem.
Intelligent energy management systems generally include: a perception layer, a network layer, a platform layer and an application layer. For example, the whole intelligent energy management system can adopt a B/S architecture based on an internet technology software main body, and each service functional unit system adopts a lightweight SSH of Java EE or a multi-layer structure of a similar architecture. Wherein: the sensing layer comprises an integrated data acquisition, metering analysis and real-time control system, and mainly comprises instrument and meter equipment and a field PLC controller; the network layer can transmit real-time data to a dispatching management center through various network systems (ADSL, GPRS, 3G, 4G, optical fibers and the like), and the dispatching management center can also transmit control instructions to a field controller through the network systems so as to execute control and regulation instructions; the platform layer is responsible for receiving data sent by each field monitoring device, storing real-time operation parameters in the data, providing basic data for subsequent management, analysis and control, and storing, analyzing, alarming and printing reports on the data; the application layer is a direct use layer of operators, continuously and dynamically analyzes the uploaded data in real time, and can issue an adjusting instruction according to an analysis result.
The intelligent energy management system may be partitioned based on the above-described hierarchical manner to determine the various subsystems that make up the intelligent energy management system. The intelligent energy management system may be divided in other ways, and the embodiment of the present invention is not limited thereto.
The process of establishing the mechanism model of the intelligent energy management system comprises the following steps:
(1) And acquiring a mechanism model of each subsystem included in the intelligent energy management system. Wherein a mechanism model for each subsystem may be pre-generated based on a variety of modeling approaches. For example, the subsystem may include: the system comprises an energy supply network (such as a power supply, air supply, cold/heat supply network and the like), an energy exchange link (such as a CCHP unit, a generator set, a boiler, an air conditioner, a heat pump and the like), an energy storage link (such as electricity storage, air storage, heat storage, cold storage and the like), a terminal comprehensive energy supply unit (such as a micro-grid) and the like.
The mechanism model of the subsystem, also called a white-box model of the subsystem, is an accurate mathematical model describing the subsystem, which is established according to the objects in the subsystem, the internal mechanisms of the production process or the transmission mechanism of the material flow. It may be a mathematical model of an object or process based on mass balance equations, energy balance equations, momentum balance equations, phase balance equations, and certain physical equations, chemical reaction laws, and the like. The mechanism model parameters are easy to adjust, and the obtained model has strong adaptability.
(2) After the mechanism models of the subsystems are obtained, the mechanism models of the subsystems can be selected in a dragging mode by utilizing a topology modeling tool, and connection relations among the mechanism models of the subsystems are established. Wherein, the connection relation includes: electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value connections, and so forth.
FIG. 2 is a schematic diagram of a mechanism model and connection establishment of a subsystem in a drag mode according to an embodiment of the present invention.
As can be seen from fig. 2, the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem 204 are selected in a drag form in an interface provided by a visual modeling tool (such as a veriabelo or Apache Spark, etc.), and a connection relationship among the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem 204 is established in a drag form, thereby forming a mechanism model 200 of the smart energy management system including the first subsystem 201, the second subsystem 202, the third subsystem 203 and the fourth subsystem 204. It should be noted that: the connection relationship between the first subsystem 201, the second subsystem 202, the third subsystem 203, and the fourth subsystem 204 may be an electrical connection, an energy transfer connection, a liquid flow connection, a gas flow connection, an information transfer connection or a value connection (e.g., a cost relationship), and so forth.
While the foregoing describes typical examples of modeling the mechanisms of the intelligent energy management system, those skilled in the art will recognize that this description is exemplary only and is not intended to limit the scope of embodiments of the present invention.
Step 102: and (5) simulating and running a mechanism model of the intelligent energy management system.
Here, input data is provided for the mechanism model of the intelligent energy management system established in step 101, and the mechanism model of the intelligent energy management system is simulated to be operated based on the input data. Wherein: the input data may include:
(1) Real-time measurement data provided by each measuring point in the intelligent energy management system. Wherein the stations may be arranged in the subsystems or on connection lines between the subsystems.
(2) Historical measurement data provided by each measurement point obtained from a database.
(3) And the combined data comprises real-time measurement data provided by each measuring point in the intelligent energy management system and historical measurement data provided by each measuring point and obtained from a database.
After step 102 is completed, simulation operational data of the mechanism model of the intelligent energy management system may be obtained. The simulation operation data can embody the operation result of the full-service logic of the whole system.
Step 103: based on simulation operation data of a mechanism model of the intelligent energy management system, a data model containing a preset target parameter and a corresponding relation between influence factors of the target parameter is generated, wherein the influence factors are contained in the mechanism model of the subsystem.
Here, the target parameter is a predetermined, target parameter (such as electricity consumption amount, electricity use fee), and the like. For example, the target parameter may be an internally defined parameter in any subsystem.
In one embodiment, the target parameters may be determined by the user at his own time, or may be automatically generated based on the system optimization objectives. The correspondence between the target parameters and the influence factors including the target parameters may be determined based on the simulation operation data obtained in step 102. For example, a regression analysis algorithm is used to determine the impact factors of the target parameters based on the simulated operating data, and how the impact factors affect the quantitative description of the target parameters. Wherein: regression analysis is a predictive modeling technique that studies the relationship between dependent and independent variables. This technique is commonly used for predictive analysis, time series models, and finding causal relationships between variables.
In regression analysis, variables are divided into two categories. One is a dependent variable, which is typically an indicator of the type of interest in the actual problem, generally denoted by Y (i.e., the target parameter); and another type of variable that affects the value of the dependent variable is called an independent variable, denoted by X (i.e., an impact factor). The main problems of regression analysis studies are: (1) Determining a quantitative relation expression between Y and X, wherein the expression is called a regression equation; (2) checking the reliability of the obtained regression equation; (3) judging whether the independent variable X has influence on the dependent variable Y or not; (4) And predicting and controlling by using the obtained regression equation.
In embodiments of the present invention, a variety of Regression algorithms may be employed, such as a linear Regression (Linear Regression) algorithm, a logistic Regression (Logistic Regression) algorithm, a polynomial Regression (Polynomial Regression) algorithm, a stepwise Regression (Stepwise Regression) algorithm, a Ridge Regression (Ridge Regression), a Lasso Regression (Lasso Regression) or an elastic network (ElasticNet Regression) algorithm, and so forth.
The foregoing exemplary description describes typical examples of regression algorithms, and those skilled in the art will recognize that such descriptions are merely exemplary and are not intended to limit the scope of embodiments of the present invention.
In one embodiment, a respective data model is generated for each subsystem, wherein the impact factors are all contained in the mechanism model of that subsystem.
For example, assume that the intelligent energy management system includes a gasifier as a subsystem. The influencing factors include the operating efficiency of the gasifier. Based on the regression analysis for the simulated operation data obtained in step 102, it is determined that the impact factors affecting the operating efficiency of the gasifier include the following parameters in the gasifier model: the synthesis gas temperature; synthesis gas component; temperature difference of cooling water of the burner; the burner supports absorb heat; the water-cooled wall absorbs heat; oxygen-coal steam ratio; the slag hole absorbs heat; particle size distribution of the slag; slag to filter cake ratio; the thermocouple temperature distribution, and based on the regression analysis of the simulated operation data obtained in step 102, determines how these parameters as influencing factors influence the quantitative description of the target parameters (e.g., generating a relationship of X (including syngas temperature, syngas composition, burner cooling water temperature difference, burner support absorption heat, water wall absorption heat, oxygen-to-coal-steam ratio, and slag hole absorption heat) as independent variables and Y (operating efficiency of gasifier) as dependent variables). The data model generated in step 103 is preferably stored in a multi-dimension Zhang Liangbiao.
In one embodiment, a respective data model is generated for each subsystem, wherein a portion of the influencing factors are included in the mechanism model of that subsystem and the remaining portion of the influencing factors are included in the mechanism models of the other subsystems.
For example, assume that an intelligent energy management system includes a boiler and a steam turbine as subsystems. The influencing factors include the power consumption of the boiler. Based on the regression analysis for the simulated operating data obtained in step 102, it is determined that the impact factors affecting the power consumption of the boiler include the following parameters in the boiler model: the amount of steam; steam pressure; steam temperature and the following parameters in the turbine model: the main valve front steam temperature; front main valve vapor pressure. Further, based on the regression analysis of the simulated operation data obtained in step 102, it is determined how these parameters as influence factors influence the quantitative description of the target parameters (e.g., a relational expression of X (including the steam quantity, steam pressure, steam temperature, main pre-steam temperature, and main pre-steam pressure) as independent variables and Y (electricity consumption of the boiler) as dependent variables is generated.
In one embodiment, a unified data model is generated for the intelligent energy management system, wherein the impact factors are all contained in the mechanism model of a single subsystem, or the impact factors are contained in the mechanism models of at least two subsystems in a decentralized manner.
For example, assume that an intelligent energy management system includes a boiler and a steam turbine as subsystems. The impact factors include electricity prices of the intelligent energy management system. Based on the regression analysis for the simulated operation data obtained in step 102, it is determined that the impact factors affecting the electricity price of the intelligent energy management system include the following parameters in the boiler model: the amount of steam; steam pressure; steam temperature and the following parameters in the turbine model: the main valve front steam temperature; front main valve steam pressure; rated steam intake. Further, based on the regression analysis of the simulated operation data obtained in step 102, it is determined how these parameters as influence factors influence the quantitative description of the target parameters (e.g., generating a relational expression of X (including the steam quantity, the steam pressure, the steam temperature, the main pre-valve steam pressure, and the rated intake steam quantity) as independent variables and Y (electricity price of the intelligent energy management system) as dependent variables).
The foregoing exemplary description describes typical examples of generating a data model based on simulated operational data of a mechanism model of an intelligent energy management system, and those skilled in the art will recognize that this description is merely exemplary and is not intended to limit the scope of embodiments of the present invention.
In one embodiment, the method 100 further comprises: storing the data model in a multi-dimension Zhang Liangbiao; receiving a search request taking a query target parameter as a search term; and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
For example, in the data model a stored in the multidimensional Zhang Liangbiao, a correspondence (e.g., b=b1× (b2+b3)) of the target parameter B and its influence factor (e.g., B1, B2, B3) and a correspondence (e.g., c=c1/C2) of the target parameter C and its influence factor (e.g., C1 and C2) are stored. When a search request containing a query target parameter (specifically, C) as a search term is received, a corresponding relationship c=c1/C2 is searched out, and a search result containing the corresponding relationship (c=c1/C2) is returned.
Therefore, the embodiment of the invention can conveniently provide the corresponding relation between the target parameter and the influence factor of the query target parameter based on the multidimensional Zhang Liangbiao of the stored data model.
In summary, in the embodiment of the invention, the data model is combined with the real-time data and the historical data, the actual situation of the equipment is reflected in real time, the advanced intelligent application which needs the performance of the components in the whole system or the subsystem is supported, the method has the advantages of real-time, quick and accurate calculation, and the method has the capability of coordinating and communicating the relation among the advanced applications, can decouple the complex problem, and enables the operation optimization of the whole system to be decomposed into a plurality of operation optimization problems of each sub-optimization.
FIG. 3 is a first exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
In fig. 3, the mechanism model 200 of the smart energy management system comprises 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 intelligent 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 operation intelligent energy management system. Setting a certain internal defined parameter A in the first subsystem as a target parameter, and carrying out regression analysis on the simulation operation data to obtain an influence factor (the influence factor can be all in a mechanism model 201 of the first subsystem) of the internal defined parameter A in the first subsystem, or a part of the influence factor is in the mechanism model 201 of the first subsystem, and another part of the influence factor is in a mechanism model of other systems) and a corresponding relation (such as a regressed relation) between the internal defined parameter A and the influence factor. The data model 21 of the first subsystem may be obtained by storing (e.g. by means of a tensor table) the correspondence.
FIG. 4 is a second exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
In fig. 4, 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 and the history measurement data 12 as input data are provided to the mechanism model 200 of the intelligent 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 operation intelligent energy management system. Setting a certain internal defined parameter A in the first subsystem as a target parameter, setting a certain internal defined parameter B in the second subsystem as a target parameter, and carrying out regression analysis on simulation operation data to obtain an influence factor of the target parameter A in the first subsystem and a first corresponding relation between the target parameter A and an influence factor thereof (the influence factor can be all in a mechanism model 201 of the first subsystem, or a part of the influence factor is in the mechanism model 201 of the first subsystem, another part of the influence factor is in a mechanism model of other systems), and a second corresponding relation between the influence factor of the internal defined parameter B and the target parameter B and an influence factor thereof (the influence factor can be all in a mechanism model 202 of the second subsystem, or a part of the influence factor is in the mechanism model 202 of the second subsystem, and another part of the influence factor is in the mechanism model of other systems). Storing (e.g., via a tensor table) the first correspondence to obtain a data model 21 of the first subsystem; the second correspondence is stored (e.g., via a tensor table) to obtain the data model 31 of the second subsystem.
FIG. 5 is a third exemplary schematic diagram of generating a data model according to an embodiment of the present invention.
In fig. 5, 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 and the history measurement data 12 as input data are provided to the mechanism model 200 of the intelligent 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 operation intelligent energy management system. A certain global quantity K of the intelligent energy management system (for example, the electricity consumption amount or the electricity consumption price of the whole intelligent energy management system) is set as a target parameter, and an expression between the global quantity K and internally defined parameters in each subsystem (for example, the electricity consumption amount or the electricity consumption price K1 in the first subsystem and the electricity consumption amount or the electricity consumption price K2 in the second subsystem) is set. By performing regression analysis on the simulation operation data, a first correspondence between the defined parameter K1 and its influencing factors (e.g., T1, T2, and T3) in the first subsystem, and a second correspondence between K2 and its influencing factors (e.g., T4 and T5) in the second subsystem may be obtained. Based on the first correspondence, the second correspondence and the expression between K and the internal defined parameters in the respective subsystems, i.e. K1 and K2, a third correspondence between the global quantity K and the influencing factors (T1, T2 and T3) of the defined parameter K1 and the influencing factors (T4 and T5) of the defined parameter K2 can be obtained. The third correspondence is stored (e.g., via a tensor table) to obtain the data model 41 of the intelligent energy management system.
The foregoing exemplary description describes typical examples of generating a data model, and those skilled in the art will recognize that such descriptions are merely exemplary and are not intended to limit the scope of embodiments of the present invention.
Fig. 6 is a block diagram of a modeling apparatus of an intelligent energy management system according to an embodiment of the present invention.
As shown in fig. 6, the data modeling apparatus 600 of the intelligent energy management system includes:
a first establishing module 601, configured to establish a mechanism model of an intelligent energy management system, where the mechanism model of the intelligent energy management system includes a mechanism model of a subsystem and a connection relationship between the mechanism models of the subsystem;
the simulation module 602 is used for simulating and running a mechanism model of the intelligent energy management system;
a second establishing module 603, configured to generate a data model including a predetermined target parameter and a correspondence between influencing factors of the target parameter, where the influencing factors are included in the mechanism model of the subsystem, based on simulation operation data of the mechanism model of the intelligent energy management system.
In one embodiment, a first building module 601 is configured to select and move a mechanism model of a subsystem in a drag manner using a topology modeling tool; and establishing a connection relation between mechanism models of the subsystems.
In one embodiment, the connection relationship includes at least one of the following: electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value links.
In one embodiment, a simulation module 602 for inputting real-time measurement data into the mechanism model of the smart energy management system to simulate the mechanism model of operating the smart energy management system; or, inputting historical measurement data into a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting combined data including real-time measurement data and historical measurement data to the mechanism model of the intelligent energy management system to simulate the mechanism model of operating the intelligent energy management system.
In one embodiment, the second building block 603 is configured to generate a respective data model for each subsystem, where the impact factors are all included in the mechanism model of the subsystem; or, for generating a respective data model for each subsystem, wherein a part of the influencing factors are included in the mechanism model of that subsystem and the remaining part of the influencing factors are included in the mechanism models of the other subsystems.
In one embodiment, the second building module 603 is configured to generate a unified data model for the intelligent energy management system, where the impact factors are all included in the mechanism model of a single subsystem, or the impact factors are included in the mechanism models of at least two subsystems in a scattered manner.
In one embodiment, the method further comprises: a storage module 604 for storing the data model in a plurality of dimensions Zhang Liangbiao; receiving a search request taking a query target parameter as a search term; and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
Based on the above description, the embodiment of the invention also provides a data modeling device of an intelligent energy management system with a memory-processor architecture.
FIG. 7 is an exemplary block diagram of a data modeling apparatus for an intelligent energy management system with a memory-processor architecture according to an embodiment of the present invention.
As shown in fig. 7, the data modeling apparatus 700 of the intelligent energy management system includes a processor 701, a memory 702, and a computer program stored on the memory 702 and executable on the processor 701, which when executed by the processor 701 implements the data modeling method of the intelligent energy management system as described above. The memory 702 may be implemented as a variety of storage media such as an electrically erasable programmable read-only memory (EEPROM), a Flash memory (Flash memory), a programmable read-only memory (PROM), and the like. The processor 701 may be implemented to include one or more central processors or one or more field programmable gate arrays, where the field programmable gate arrays integrate one or more central processor cores. In particular, the central processor or central processor core may be implemented as a CPU or MCU or DSP, etc.
It should be noted that not all the steps and modules in the above processes and the structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution sequence of the steps is not fixed and can be adjusted as required. The division of the modules is merely for convenience of description and the division of functions adopted in the embodiments, and in actual implementation, one module may be implemented by a plurality of modules, and functions of a plurality of modules may be implemented by the same module, and the modules may be located in the same device or different devices. The hardware modules in the various embodiments may be implemented mechanically or electronically. For example, a hardware module may include specially designed permanent circuits or logic devices (e.g., special purpose processors such as FPGAs or ASICs) for performing certain operations. A hardware module may also include programmable logic devices or circuits (e.g., including a general purpose processor or other programmable processor) temporarily configured by software for performing particular operations. As regards implementation of the hardware modules in a mechanical manner, either by dedicated permanent circuits or by circuits that are temporarily configured (e.g. by software), this may be determined by cost and time considerations.
The present invention also provides a machine-readable storage medium storing instructions for causing a machine to perform a method as described herein. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium. Further, some or all of the actual operations may be performed by an operating system or the like operating on a computer based on instructions of the program code. The program code read out from the storage medium may also be written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion unit connected to the computer, and then, based on instructions of the program code, a CPU or the like mounted on the expansion board or the expansion unit may be caused to perform part or all of actual operations, thereby realizing the functions of any of the above embodiments. Storage medium implementations for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or cloud by a communications network.
In this document, "schematic" means "serving as an example, instance, or illustration," and any illustrations, embodiments described herein as "schematic" should not be construed as a more preferred or advantageous solution. For simplicity of the drawing, the parts relevant to the present invention are shown only schematically in the drawings, and do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. In this document, "a" does not mean to limit the number of relevant portions of the present invention to "only one thereof", and "an" does not mean to exclude the case where the number of relevant portions of the present invention is "more than one". In this document, "upper", "lower", "front", "rear", "left", "right", "inner", "outer", and the like are used merely to indicate relative positional relationships between the relevant portions, and do not limit the absolute positions of the relevant portions.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (16)
- A data modeling method (100) of an intelligent energy management system, comprising:establishing a mechanism model of the intelligent energy management system, wherein the mechanism model of the intelligent energy management system comprises a mechanism model of a subsystem and a connection relation (101) between the mechanism models of the subsystem;-simulating a mechanism model (102) running the intelligent energy management system;based on the simulation operation data of the mechanism model of the intelligent energy management system, a data model is generated, which comprises a predetermined target parameter and a corresponding relation between influencing factors of the target parameter, wherein the influencing factors are contained in the mechanism model of the subsystem (103).
- The method (100) of modeling data of an intelligent energy management system according to claim 1, wherein said building a mechanism model (101) of the intelligent energy management system comprises:selecting and moving a mechanism model of the subsystem in a dragging mode by utilizing a topology modeling tool;and establishing a connection relation between mechanism models of the subsystems.
- The method (100) of data modeling of an intelligent energy management system according to claim 2, wherein the connection relationship comprises at least one of:Electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value links.
- The method (100) of modeling data of a smart energy management system of claim 1, wherein said simulating a mechanism model (102) running said smart energy management system comprises:inputting real-time measurement data to a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or (b)Inputting historical measurement data into a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or (b)Inputting combined data comprising real-time measurement data and historical measurement data into a mechanism model of the smart energy management system to simulate a mechanism model running the smart energy management system.
- The method (100) of data modeling of an intelligent energy management system according to claim 1, wherein the generating a data model (103) comprising a predetermined target parameter and a correspondence between influencing factors of the target parameter comprises:generating a respective data model for each subsystem, wherein the impact factors are all contained in a mechanism model of the subsystem; or (b)A respective data model is generated for each subsystem, wherein a portion of the influencing factors are included in the mechanism model of that subsystem and the remaining portion of the influencing factors are included in the mechanism models of the other subsystems.
- The method (100) of data modeling of an intelligent energy management system according to claim 1, wherein the generating a data model (103) comprising a predetermined target parameter and a correspondence between influencing factors of the target parameter comprises:a unified data model is generated for the intelligent energy management system, wherein the influencing factors are all contained in the mechanism model of a single subsystem or the influencing factors are contained in the mechanism models of at least two subsystems in a scattered manner.
- The data modeling method (100) of an intelligent energy management system of any of claims 1-6, further comprising:storing the data model in a multi-dimension Zhang Liangbiao;receiving a search request taking a query target parameter as a search term;and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
- A data modeling apparatus (600) of an intelligent energy management system, comprising:a first establishing module (601) for establishing a mechanism model of an intelligent energy management system, wherein the mechanism model of the intelligent energy management system comprises a mechanism model of a subsystem and a connection relation between the mechanism models of the subsystem;a simulation module (602) for simulating a mechanism model running the intelligent energy management system;and a second building module (603) for generating a data model comprising a predetermined target parameter and a correspondence between influencing factors of the target parameter based on simulation operation data of a mechanism model of the intelligent energy management system, wherein the influencing factors are included in the mechanism model of the subsystem.
- The data modeling apparatus (600) of an intelligent energy management system according to claim 8, wherein,the first building module (601) is used for selecting and moving a mechanism model of the subsystem in a dragging mode by utilizing a topology modeling tool; and establishing a connection relation between mechanism models of the subsystems.
- The data modeling apparatus (600) of an intelligent energy management system of claim 9, wherein the connection relationship comprises at least one of:Electrically connecting; an energy transfer connection; a liquid flow connection; a gas flow connection; an information transfer connection; value links.
- The data modeling apparatus (600) of an intelligent energy management system according to claim 8, wherein,the simulation module (602) is used for inputting real-time measurement data into the mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting historical measurement data into a mechanism model of the intelligent energy management system to simulate and run the mechanism model of the intelligent energy management system; or, inputting combined data including real-time measurement data and historical measurement data to the mechanism model of the intelligent energy management system to simulate the mechanism model of operating the intelligent energy management system.
- The data modeling apparatus (600) of an intelligent energy management system according to claim 8, wherein,the second building block (603) is configured to generate a respective data model for each subsystem, wherein the influence factors are all included in a mechanism model of the subsystem; or, for generating a respective data model for each subsystem, wherein a part of the influencing factors are included in the mechanism model of that subsystem and the remaining part of the influencing factors are included in the mechanism models of the other subsystems.
- The data modeling apparatus (600) of an intelligent energy management system according to claim 8, wherein,the second building module (603) is configured to generate a unified data model for the intelligent energy management system, wherein the influencing factors are all contained in the mechanism model of a single subsystem or the influencing factors are dispersed in the mechanism models of at least two subsystems.
- The data modeling apparatus (600) of an intelligent energy management system of any of claims 8-13, further comprising:a storage module (604) for storing the data model in a plurality of dimensions Zhang Liangbiao; receiving a search request taking a query target parameter as a search term; and sending a search result, wherein the search result comprises the corresponding relation between the query target parameter and the influence factor of the query target parameter, which are searched from the multidimensional Zhang Liangbiao.
- A data modeling apparatus (700) of a smart energy management system, comprising a processor (701), a memory (702) and a computer program stored on the memory (702) and executable on the processor (701), which computer program, when executed by the processor, implements the data modeling method (100) of a smart energy management system as claimed in any one of claims 1 to 7.
- A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements a data modeling method (100) of an intelligent energy management system according to any of the claims 1 to 7.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2021/109882 WO2023004809A1 (en) | 2021-07-30 | 2021-07-30 | Modeling method and apparatus for smart energy management system, and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117769718A true CN117769718A (en) | 2024-03-26 |
Family
ID=85087402
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202180100710.XA Pending CN117769718A (en) | 2021-07-30 | 2021-07-30 | Modeling method and device of intelligent energy management system and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN117769718A (en) |
| WO (1) | WO2023004809A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117272598B (en) * | 2023-08-25 | 2024-09-03 | 华能苏州热电有限责任公司 | Intelligent heating system based on simulation |
| CN118095897A (en) * | 2024-03-26 | 2024-05-28 | 山东泽悦信息技术有限公司 | Intelligent energy station energy efficiency analysis system and method |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9915965B2 (en) * | 2015-03-16 | 2018-03-13 | The Florida International University Board Of Trustees | Flexible, secure energy management system |
| KR101645689B1 (en) * | 2015-06-26 | 2016-08-05 | (주)네모파트너즈엔이씨 | The apparatus and method of cloud application moudule in public tender with smartphone |
| CN106295900A (en) * | 2016-08-19 | 2017-01-04 | 中节能（常州）城市节能研究院有限公司 | A kind of city intelligent energy management system |
| CN108494021B (en) * | 2018-04-20 | 2021-06-01 | 东北大学 | Stability evaluation and static control method of electricity-heat-gas comprehensive energy system |
| CN114424217A (en) * | 2019-09-30 | 2022-04-29 | 西门子股份公司 | Optimization method and device of integrated energy system and computer readable storage medium |
| CN111626587B (en) * | 2020-05-21 | 2022-09-06 | 浙江大学 | Comprehensive energy system topology optimization method considering energy flow delay characteristics |
| CN112926899B (en) * | 2021-04-13 | 2022-01-28 | 山东国研自动化有限公司 | Model construction method, system and equipment of energy management system |
-
2021
- 2021-07-30 CN CN202180100710.XA patent/CN117769718A/en active Pending
- 2021-07-30 WO PCT/CN2021/109882 patent/WO2023004809A1/en active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| WO2023004809A1 (en) | 2023-02-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101619850B (en) | Dispatching method and dispatching system based on load online forecasting of thermoelectric power system | |
| WO2021062748A1 (en) | Optimization method and apparatus for integrated energy system and computer readable storage medium | |
| WO2021062753A1 (en) | Integrated energy system simulation method, apparatus and computer-readable storage medium | |
| WO2024040608A1 (en) | Model training method for energy management system, apparatus, and storage medium | |
| CN117769718A (en) | Modeling method and device of intelligent energy management system and storage medium | |
| CN110287509A (en) | Flexibility analysis and the fault of construction diagnosis of municipal heating systems and localization method and system | |
| WO2021062749A1 (en) | Method and apparatus for controlling integrated energy system, and computer-readable storage medium | |
| CN113239645B (en) | Dynamic tracking and real-time computing system and method for natural gas components in natural gas pipe network | |
| Saletti et al. | A control-oriented scalable model for demand side management in district heating aggregated communities | |
| Li et al. | Study on operation strategy and load forecasting for distributed energy system based on Chinese supply-side power grid reform | |
| Zheng et al. | Digital twin modeling for district heating network based on hydraulic resistance identification and heat load prediction | |
| Qin et al. | Increasing flexibility of combined heat and power systems through optimal dispatch with variable mass flow | |
| Chen et al. | Application of deep learning modelling of the optimal operation conditions of auxiliary equipment of combined cycle gas turbine power station | |
| CN114266471A (en) | Energy efficiency evaluation method for multi-energy flow coupling comprehensive energy of cold, heat and electricity | |
| Parfenenko et al. | Information system for monitoring and forecast of building heat consumption | |
| Usharani et al. | Smart energy management techniques in industries 5.0 | |
| CN112670997A (en) | Electric heating energy source system time sequence probability load flow calculation method considering photovoltaic uncertainty | |
| Ma et al. | Fast decomposed method for dynamic energy flow calculation in integrated electricity and heat system | |
| Zlatkovikj et al. | Influence of the transient operation of a large-scale thermal energy storage system on the flexibility provided by CHP plants | |
| Gambarotta et al. | Demonstrating a smart controller in a hospital integrated energy system | |
| CN115619334A (en) | Dynamic monitoring device and method for energy management system, electronic device and medium | |
| CN118034181B (en) | Monitoring system for heating | |
| Zhou | Numerical Analysis of Digital Twin System Modeling Methods Aided by Graph‐Theoretic Combinatorial Optimization | |
| Müller‐Kirchenbauer et al. | A Network Modeling Systematics for Transition Paths Toward Climate Neutral Gas Networks—NeMoSys ['nεmɒsis]– | |
| He et al. | Optimization of Multiobjective Configuration of Multienergy Microgrid Based on HMM and Typical Scenarios: A Case Study of Smart Park |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |