CN112332444A - Microgrid energy management system based on digital twins - Google Patents
Microgrid energy management system based on digital twins Download PDFInfo
- Publication number
- CN112332444A CN112332444A CN202010984190.0A CN202010984190A CN112332444A CN 112332444 A CN112332444 A CN 112332444A CN 202010984190 A CN202010984190 A CN 202010984190A CN 112332444 A CN112332444 A CN 112332444A
- Authority
- CN
- China
- Prior art keywords
- layer
- micro
- information
- energy management
- virtual
- 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
- 238000005457 optimization Methods 0.000 claims abstract description 50
- 230000003993 interaction Effects 0.000 claims abstract description 40
- 238000004891 communication Methods 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000005516 engineering process Methods 0.000 claims abstract description 14
- 230000007246 mechanism Effects 0.000 claims abstract description 14
- 230000005540 biological transmission Effects 0.000 claims abstract description 11
- 230000005611 electricity Effects 0.000 claims description 35
- 238000010248 power generation Methods 0.000 claims description 35
- 238000009826 distribution Methods 0.000 claims description 19
- 230000006870 function Effects 0.000 claims description 16
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 14
- 229910052799 carbon Inorganic materials 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 14
- 238000003860 storage Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 238000004146 energy storage Methods 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 10
- 239000013307 optical fiber Substances 0.000 claims description 7
- 230000007774 longterm Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 4
- 239000002245 particle Substances 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 claims description 3
- 238000013461 design Methods 0.000 claims description 3
- 239000002283 diesel fuel Substances 0.000 claims description 3
- 238000005286 illumination Methods 0.000 claims description 3
- 230000010365 information processing Effects 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 239000002994 raw material Substances 0.000 claims description 3
- 230000006403 short-term memory Effects 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 2
- 238000013079 data visualisation Methods 0.000 claims 1
- 239000002096 quantum dot Substances 0.000 claims 1
- 238000007726 management method Methods 0.000 description 82
- 238000010586 diagram Methods 0.000 description 4
- 230000036541 health Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00016—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
- H02J13/00017—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus using optical fiber
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00028—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment involving the use of Internet protocols
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/14—Energy storage units
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/12—Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
- Y04S40/124—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment using wired telecommunication networks or data transmission busses
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Power Engineering (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Computer Networks & Wireless Communication (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Human Computer Interaction (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A digital twin-based microgrid energy management system comprises an entity microgrid layer, a virtual space modeling layer, an energy management layer, an intelligent control layer and a man-machine interaction layer. The method comprises the steps of utilizing an industrial Internet of things technology based on an MQTT protocol to carry out communication between layers, establishing a virtual micro-grid system with the same life cycle as an entity in a data driving mode, making a preliminary scheme by an energy management layer through operation data of the entity system, adjusting the scheme by combining feedback information of the preliminary scheme executed by the virtual micro-grid system, applying the final scheme to the micro-grid system, and issuing a control instruction to the virtual micro-grid system and the micro-grid system by an operator through a human-computer interaction interface based on a publishing/subscribing mechanism. The invention perfects the function of the energy management system and makes a more accurate scheduling optimization scheme, thereby effectively improving the energy utilization rate, realizing more interaction between human-computer objects and improving the safety and reliability of information transmission between levels and systems.
Description
Technical Field
The invention relates to the field of microgrid energy management, in particular to an operation mechanism, a management method and inter-level communication of a microgrid energy management system based on digital twins.
Background
With the gradual depletion of fossil fuels and the continuous aggravation of problems such as environmental pollution, renewable energy distributed power generation technology is concerned by more and more countries with the characteristics of less pollution emission, resource conservation and the like. However, distributed power generation has the problems of randomness and intermittency, and the access of a large number of distributed power sources can affect the power quality of a power distribution network. The micro-grid is a small power generation and distribution system composed of a distributed power supply, an energy storage system and a load, has the distinct characteristic of strong flexibility, is used as an effective supplement for a large power grid, solves the influence on the power grid caused by large-scale distributed power supply access, and simultaneously can realize peak clipping, valley filling, peak load regulation and frequency modulation through reasonable control on the load and the energy storage system. The micro-grid energy management system has the effects of generating an effective energy scheduling strategy, improving the utilization rate of renewable energy sources through bidirectional interaction of a demand side and a power generation side, realizing optimal allocation of resources at different time intervals, and improving the economical efficiency and reliability of micro-grid operation while ensuring the quality of electric energy.
The existing microgrid energy management system has the following problems: firstly, the monitoring is focused, most of the information is operation information such as voltage, frequency and the like, the acquisition of equipment information is less, and the timely reaction to the health condition of the equipment cannot be realized; secondly, the whole simulation analysis of the operation of the micro-grid system is lacked; thirdly, the information transmission in the microgrid is closed, the connection among systems is not strong, and the transmission safety is poor; and fourthly, a man-machine interaction interface in the conventional energy management system usually only stays at a display level, the real-time control of an operator on the operation of the micro-grid system is not realized, the realization of man-machine interaction functions is less, and the optimization allocation of resources is not accurate enough.
Disclosure of Invention
The invention provides a digital twin-based micro-grid energy management system, which aims to solve the problems that the existing micro-grid system has less man-machine interaction, can not accurately analyze the health of equipment, has poor communication safety among levels, and has inaccurate scheduling and operation strategies.
The microgrid energy management system based on the digital twin is characterized by comprising an entity microgrid layer, a virtual space modeling layer, an energy management layer, an intelligent control layer and a man-machine interaction layer.
The entity microgrid layer comprises a microgrid system and a data acquisition and transmission unit, wherein the microgrid system comprises a distributed power supply, a rectifier, an inverter, a diesel generator, an energy storage system and a load. The load is divided into a key load and a controllable load, and the controllable load and the electric automobile form a virtual energy storage system together. The data acquisition and transmission unit comprises an intelligent sensor for data acquisition, a camera, an electronic tag in the equipment, an FPGA for data processing and transmission, an intelligent gateway, an optical fiber and the like.
Furthermore, the entity microgrid layer finishes the collection of various information such as running information and equipment information through an intelligent sensor, finishes the collection of audio and video information through a camera, finishes the collection of information such as the surrounding environment of a distributed power supply and the health condition of equipment through an electronic tag installed in the distributed power supply based on a radio frequency technology, finishes the collection of various information of the microgrid system based on the collection technology, mostly finishes the conversion from analog quantity to digital quantity by utilizing an A/D chip with the model of PCF8591 carried by an FPGA with the model of EP4CE10F17C8, stores the converted information into an FIFO module for temporary storage, then utilizes an RS232 serial port carried by the FPGA with the model of EP4CE10F17C8 to serially transmit data to an intelligent gateway based on an MQTT protocol based on a UART protocol, and finishes the conversion from analog quantity to digital quantity through a wired network such as optical fibers and a system modeling layer, And the energy management layer is connected to realize the transmission of the entity micro-grid layer information.
The virtual space modeling layer comprises a cloud server and a client, the cloud server can meet the access of a large amount of sensor data, and the functions of the cloud server include information processing and modeling. The information processing is realized by receiving and analyzing various information transmitted by the entity micro-grid layer, the communication with the energy management layer is realized based on the cloud server, the energy management layer can transmit various historical operation information to the virtual space modeling layer based on a TCP/IP protocol, and the virtual space modeling layer can transmit the virtual system operation information. The modeling means that a cloud server establishes a virtual microgrid system with a ratio of 1: 1 with the microgrid system by means of massive historical data and real-time data based on a data driving mode through cloud computing, machine learning and the like, and evolves in real time by receiving data from an entity object, so that the virtual microgrid system is consistent with an entity in a full life cycle, the mapping of the microgrid system to a virtual space is realized, the 1: 1 modeling is completed, the life cycle identical to that of the entity is realized, and the real mapping of virtual is really realized.
The energy management layer comprises a cloud server which is communicated with the cloud server of the virtual space modeling layer based on TCP/IP, and the client side are communicated by adopting a publishing/subscribing mechanism based on MQTT. The energy management layer and the virtual space modeling layer both comprise cloud servers, and the two servers respectively perform their own functions, so that the computing capacity is enhanced, the speed is increased, and the twin micro-grid system can be better established. The cloud server comprises a real-time information database, a historical fault information database, a virtual system operation information database, a monitoring and early warning module, a prediction module, a scheduling module, an optimization module and a service release module.
The real-time information database stores various information received and analyzed by the cloud server during the operation of the micro-grid system. And storing the information in the real-time database into a historical database through a set time interval. The historical database comprises data transmitted by the real-time database and historical data of previous operation of the micro-grid system, the range of various types of operation data and parameters of normal operation of the micro-grid system can be obtained based on information in the historical database, and meanwhile, the cloud server interacts issued historical database information with the virtual space modeling layer. The historical fault information database stores various data changes based on fault reasons after accidents of the micro-grid system occur. The virtual system operation information database comprises various types of information of virtual micro-grid system operation transmitted by a virtual micro-grid layer based on TCP/IP. The monitoring module compares the information in the real-time database with the range of each data and parameter of the microgrid based on the historical database during normal operation, compares whether the real-time data are all operated within normal values, and can classify the fault or potential safety hazard based on the fault information in the historical fault information base and methods such as a K-nearest neighbor method and a neural network and the like if any data exceed the normal range, so as to realize the diagnosis and early warning of the fault. The energy management layer prediction module comprises load prediction, electricity price prediction and power generation prediction.
The load prediction can realize long-term and short-term prediction of the load by utilizing the load information in a historical database and a real-time database, firstly, data characteristics are extracted, different characteristics are subjected to normalization processing, the processed load information is subjected to clustering processing by utilizing a k-mean clustering method due to the regional characteristics of the load, a time sequence model is constructed based on historical sequence data and real-time sequence data of each type of load according to the clustered result, a neural network is trained, the short-term prediction of the load can be realized based on the neural network, the prediction can be corrected by combining with expert experience when the load fluctuation is large, the prediction accuracy is enhanced, and the medium-term and long-term load can be predicted by utilizing a support vector machine in the same method.
The power rate prediction is to predict the short-term power rate, which is related to not only the historical power rate and the load value, but also external factors such as weather. Therefore, the forecasting is carried out by combining the electricity price information and the forecasted load information in the historical database and the meteorological information transmitted by the meteorological station in a communication mode, wherein a forecasting model of the short-term electricity price is E (t) ═ O (t) + B (t) + W (t) + S (t) + R (t), E (t) represents the electricity price at the time t, O (t) represents raw material use, B (t) represents a basic load component, W (t) represents a weather change condition, and S (t) represents a special event. And (4) realizing the prediction of the real-time electricity price by using an LSTM (long short term memory network) based on a model.
The power generation prediction comprises power generation prediction of each distributed power supply, wherein the power generation prediction of each distributed power supply is combined with weather information such as mechanical performance, illumination intensity, temperature and wind speed to predict power generation of each distributed power supply, a neural network is trained by using the weather information and the power generation information, the prediction of the power generation of each distributed power supply is realized based on the neural network, and the overall power generation prediction is the sum of the power generation prediction of each distributed power supply.
The dispatching module of the energy management layer comprehensively considers the relationship between the output of the distributed power supply and the load, combines the result obtained by the prediction module, and controls the controllable load, the diesel engine and the energy storage system to ensure the reliability of power supply. Under the grid-connected operation, when the output of the distributed power supply is greater than the electric quantity required by the load, the redundant electric quantity can charge the storage battery, the upper limit and the service life of the storage battery are considered, the virtual energy storage system consisting of controllable loads can absorb the redundant electric energy, meanwhile, the electric energy can be sold to a power grid during the peak electricity price period, when the output of the distributed power supply is not enough to provide the electric energy for the load, the storage battery can be discharged when the electric quantity of the storage battery is not lower than the lower limit, meanwhile, the virtual energy storage system can reduce the electric energy consumption under the condition that the normal use is not influenced, the power supply of a key load is ensured, and the power. When the power distribution network breaks down, the connection between the micro-grid and the power distribution network is disconnected, so that the micro-grid operates in an island operation mode, and the mode is different from the grid connection mode in that the interaction between the micro-grid and the power distribution network is cut off, and is a self-sufficient mode. Thereby determining a preliminary scheduling scheme. And correcting the preliminary scheduling scheme by combining the feedback information of the preliminary scheduling scheme executed by the virtual microgrid, and determining the scheduling scheme again.
The optimization module of the energy management layer optimizes an operation scheme on the basis of the scheduling module, the micro-grid operation is optimized and planned with the purposes of economy, minimum electricity purchasing quantity from a power distribution network and minimum carbon emission quantity on the premise of ensuring the power supply reliability by combining the result predicted by the prediction module, and the optimization target is determined to meet the multi-objective functions of economy, minimum peak electricity purchasing quantity, minimum carbon emission quantity and the like by combining constraint conditions such as generator constraint, power balance constraint, battery constraint and the like on the basis of a twin micro-grid system model established by the virtual space modeling layer. And optimizing by using an intelligent algorithm, wherein the optimization is carried out by using an improved particle swarm algorithm, so that a preliminary optimization operation scheme is determined.
Wherein the economic objective function is
The total cost of the operation of the micro-grid is N, the total number of dispatching sections, c, the price of diesel oil, f, the oil consumption of a diesel generator, g, the electricity purchase price from the micro-grid to a power distribution network and PgFor purchasing electric power, s is the price of selling electric power to the microgrid, PSFor the amount of electricity sold. Wherein gPg，sPSOnly in grid-connected operation conditions and zero in island operation conditions. M is the number of distributed energy sources, a is the cost of power generation of the distributed power supply, wherein renewable energy sources such as light energy, wind energy and the like are removed, PaIs the power generated;
wherein the carbon emission objective function is
X is the carbon emission coefficient of the diesel generator for the total carbon emission. The virtual microgrid layer subscribes a primary operation scheme and is applied to the virtual microgrid system, the virtual microgrid layer issues various kinds of information of operation of the virtual microgrid system after the optimization scheme is executed, the energy management layer receives feedback information of the microgrid execution operation scheme based on a TCP/IP protocol, the information fed back by the virtual microgrid system execution primary optimization scheme in the virtual microgrid database is combined, the primary optimization operation scheme is adjusted, and the adjusted operation scheme is a final optimization scheme.
The service publishing module of the energy management layer is based on an MQTT subscription/publishing mechanism, the energy management layer is based on a cloud server to perform operation processing and analysis on transmitted data, and can publish various services to a client through the cloud server, wherein the client of the virtual microgrid layer can subscribe a preliminary scheduling scheme and a preliminary optimization operation scheme, the virtual microgrid system is controlled according to the scheme, the client of the intelligent control layer subscribes a final optimization operation scheme to control the microgrid system, and the client of the human-computer interaction layer can subscribe various information to check and know the operation state of the microgrid.
The man-machine interaction layer comprises client equipment, an operator can know various parameters of micro-grid operation by subscribing various information of the energy management layer, visual processing of data can be achieved based on application software, the operator can issue control instructions to the entity and the virtual micro-grid system based on a publishing/subscribing mechanism according to self experience, and man-machine interaction is achieved better.
The intelligent control layer comprises a controller and a grid-connected switch, the micro-grid system is controlled by subscribing a scheduling scheme and an operation optimization scheme of the energy management layer, the control mode comprises the feedback of the operation information of the virtual micro-grid system, and the virtual control and real closed-loop feedback is realized. Control including each dc-to-ac converter and the converter of control distributed generator, can accomplish distributed generator, diesel generator opens and stops, adjusts output, operations such as energy storage system charge-discharge, realizes simultaneously through control leaving the switch that is incorporated into the power networks that operating personnel assigns the operation mode instruction to, adopts PQ control under the operation mode of being incorporated into the power networks through the control to the dc-to-ac converter, satisfies the requirement of frequency quality, under the island operation mode, adopts droop control, guarantees the unity of little electric wire netting frequency and voltage.
A micro-grid energy management system operation mechanism based on digital twinning is characterized by comprising the following steps;
step 1: various information of the micro-grid system is acquired in real time by an intelligent sensor, a camera and a radio frequency technology based on a label;
step 2: the method comprises the steps that various collected information is subjected to analog quantity conversion through an A/D conversion chip with the model of PCF8591, which is onboard an FPGA with the model of EP4CE10F17C8, converted digital quantity is temporarily stored in an FIFO module of the FPGA, the converted digital quantity information is sequentially and serially transmitted to an intelligent gateway through an onboard RS232 serial port based on a UART protocol, and the information is transmitted to cloud servers of an energy management layer and a virtual modeling layer through an optical fiber wired network technology based on an MQTT protocol after the gateway;
and step 3: the cloud server of the energy management layer analyzes and stores the acquired information, transmits various kinds of historical information of the microgrid to the virtual space modeling layer based on a TCP/IP protocol, monitors various kinds of data in real time based on the variation range of the various kinds of data in normal operation in a historical database, compares the parameters with the information of the historical fault database when the parameters exceed the range, and can determine the type of the fault based on a K-nearest neighbor method;
and 4, step 4: the cloud server of the virtual space modeling layer analyzes information transmitted from the entity micro-shop network layer, and completes modeling of the virtual micro-grid system by data driving based on real-time and historical data information in combination with various historical data information transmitted by the energy management layer, wherein the built virtual micro-grid system keeps a 1: 1 ratio with the original system and has the same life cycle as the micro-grid system;
and 5: the energy management layer realizes load prediction, electricity price prediction and load prediction based on the cloud server;
step 6: the energy management layer determines an initial scheduling scheme based on the power supply reliability based on the cloud server and provides a theme service of the initial scheduling scheme;
and 7: a client of the virtual space modeling layer controls the virtual micro-grid system to operate according to an initial scheduling scheme by subscribing the initial scheduling scheme;
and 8: the method comprises the steps that various kinds of running information after a virtual micro-grid system executes a scheduling scheme is transmitted to a cloud server of a virtual space modeling layer through a gateway, and the cloud server of the virtual space modeling layer transmits the running information after preliminary scheduling is executed to a cloud server of an energy management layer on the basis of a TCP/IP protocol;
and step 9: the cloud server of the energy management layer adjusts the primary scheduling scheme based on information fed back after the virtual micro-grid system executes the primary scheduling scheme;
step 10: the client side of the virtual space modeling layer modifies the scheduling instruction according to the topic service of the subscription scheduling scheme;
step 11: the cloud server of the energy management layer determines a primary operation optimization scheme on the basis of a scheduling scheme and issues a primary operation optimization scheme subject service;
step 12: a client of the virtual space modeling layer controls the virtual micro-grid system to adjust according to an initial optimization scheme by subscribing the initial operation optimization scheme;
step 13: the method comprises the steps that various kinds of running information after a virtual micro-grid system executes a running optimization scheme is transmitted to a cloud server of a virtual space modeling layer through a gateway, and the cloud server of the virtual space modeling layer transmits the running information after preliminary scheduling is executed to a cloud server of an energy management layer on the basis of a TCP/IP protocol;
step 14: the cloud server of the energy management layer adjusts the primary operation optimization scheme based on information fed back after the virtual micro-grid system executes the primary operation optimization scheme;
step 15: the client side of the intelligent control layer controls the controller by subscribing the operation optimization scheme, so that the micro-grid system is controlled to operate according to the release scheme;
step 16: the man-machine interaction layer can master various kinds of operation information of the micro-grid by subscribing various services issued by the energy management layer, and design and quote software to complete visual processing of data by means of programming and the like;
and step 17: at the client of the human-computer interaction layer, an operator can issue a scheduling instruction to the server of the energy management layer according to various observed running information and self experiences, and the clients of the virtual space modeling layer and the intelligent control layer realize the control of the entity and the virtual microgrid system by subscribing the control instruction issued by the client of the human-computer interaction layer.
Compared with the prior art, the invention has the following advantages:
(1) by combining the feedback of the operation information of the virtual micro-grid system, the scheduling and operation optimization scheme can be more accurate.
(2) The publish/subscribe mechanism based on the MQTT protocol can make information transmission more secure.
(3) By means of implanting 'labels' into the distributed power supply and adopting a radio frequency technology to acquire information, equipment information and surrounding information can be acquired better.
(4) The human-computer interaction not only stays on the display layer, but also can realize good human-computer interaction.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a plan view of a digital twin based microgrid energy management system;
FIG. 2 is a diagram of a digital twin-based inter-tier communication structure of a microgrid energy management system;
fig. 3 is a flow chart of an operation mechanism of a digital twin-based microgrid energy management system.
Fig. 1 is a plan structure diagram of a digital twin-based microgrid energy management system provided by the invention, and fig. 1 comprises a microgrid system, a virtual microgrid system, a power distribution network, an energy management system and a central controller, wherein the microgrid system specifically comprises a photovoltaic module, a solar panel, a D/A (digital/analog) converter and a switch, a fan module, an energy storage system, a diesel generator, a switch, a load and a switch, the photovoltaic module is connected to the microgrid main network through the A/D, D/A converter and the switch, the energy storage system is connected to the microgrid main network through the D/A converter, the diesel generator is connected to the microgrid main network through the switch, the load is connected to the microgrid main network through the switch, and the microgrid main network is connected to the 0.4kv/10kv power distribution network through. In fig. 1, the composition of the virtual microgrid system is the same as that of the microgrid system, an energy management layer is connected with a central controller, an entity microgrid layer and controllers of the virtual microgrid layer, and the controllers are connected with a D/A inverter and a switch.
Fig. 2 is a diagram of inter-level communication of a microgrid energy management system based on a digital twin, where fig. 2 includes an entity microgrid layer, a virtual space modeling layer, an energy management layer, a human-computer interaction layer, an intelligent control layer, and communication among the layers, where the communication among the layers includes communication between the entity microgrid layer and the energy management layer as well as the virtual space modeling layer, communication between the energy management layer and the human-computer interaction layer, communication between the intelligent control layer and the energy management layer, communication between a client of the human-computer interaction layer and a client of the intelligent control layer, and communication between clients of the virtual space modeling layer.
The communication between the entity microgrid layer, the energy management layer and the virtual space modeling layer is realized, analog information acquired based on a radio frequency technology is converted into analog quantity through an A/D conversion chip with the model of PCF8591 and loaded on an FPGA lock board with the model of EP4CE10F17C8 in the microgrid system, the converted digital quantity is temporarily stored in an FIFO module of the FPGA, the converted digital quantity information is sequentially and serially transmitted to an intelligent gateway through an RS232 serial port on board and based on a UART protocol, the information is transmitted to cloud servers of the energy management layer and the virtual modeling layer through an optical fiber wired network technology based on the MQTT protocol after the gateway, and the cloud servers of the energy management layer and the virtual space modeling layer can analyze the transmitted information and perform operation processing and the like on the information.
The energy management layer and the virtual space modeling layer comprise communication between a cloud server of the energy management layer and a cloud server between the virtual space modeling layers, and communication between the cloud server of the energy management layer and a client of the virtual space modeling layers. The communication between the energy management layer and the cloud end server of the virtual space modeling layer is realized based on a TCP/IP protocol, so that the historical database information in the energy management layer is transmitted to the virtual space modeling layer to be used for modeling of the virtual microgrid system, and the virtual space modeling layer can also feed back the virtual microgrid operation information after executing the primary operation scheme to the energy management layer based on the TCP/IP protocol to help generate a final scheduling and operation scheme. The cloud server of the energy management layer is communicated with the client of the virtual space modeling layer, the cloud server of the energy management layer can generate services such as preliminary scheduling and optimization schemes through operation, and the virtual space modeling layer controls the virtual micro-grid system to operate according to the operation schemes by subscribing the preliminary operation schemes.
The virtual space modeling layer and the energy management layer both comprise cloud servers, but in fact, the functions can be realized only by reserving the cloud servers of the energy management layer, but due to the fact that the calculated amount is large, the required time is long, the servers are additionally arranged on the virtual space modeling layer, so that the servers can help to share important modeling tasks, the two servers respectively perform their own functions, the operation speed is increased, and the life cycle identical to that of a micro-grid system can be realized.
The energy management layer is communicated with the man-machine interaction layer, the cloud server of the energy management layer can provide various services, and the client of the man-machine interaction layer can subscribe various services such as operation parameters, equipment conditions, scheduling schemes, optimized operation schemes and the like. The man-machine interaction layer can perform visualization processing on data based on an application program.
Interaction between the energy management layer and the intelligent control layer is based on a subscription/release mechanism, a server of the energy management layer can release a final scheduling scheme and an optimization scheme for service, and the intelligent control layer can subscribe the final scheduling scheme and the optimization scheme to guide operation of the micro-grid system.
The communication between the man-machine interaction layer and the virtual microgrid layer as well as between the intelligent control layers is realized based on a publishing/subscribing mechanism, the man-machine interaction layer can realize comprehensive understanding of the microgrid system by subscribing the service of the energy management layer, the man-machine interaction layer can issue a control command to a cloud server of the energy management layer, clients of the virtual space modeling layer and the intelligent control layer subscribe information issued by the man-machine interaction layer, the cloud server based on the energy management layer can realize indirect communication between the man-machine interaction layer and the virtual microgrid layer as well as the intelligent control layer, and can realize control of an operator on the virtual space modeling layer and the intelligent control layer,
the communication service quality of the energy management layer and each layer of client side adopts QOS2 service quality, so that the accuracy of information transfer can be ensured.
Fig. 3 is a flow chart of an operation mechanism of a microgrid energy management system based on a digital twin, and the operation mechanism of the microgrid energy management system of fig. 3 is described with reference to a planar structure diagram of fig. 1 and communication between layers of fig. 2, and specific steps include:
step 1: various information of the micro-grid system is acquired in real time by an intelligent sensor, a camera and a radio frequency technology based on a label;
step 2: the method comprises the steps that various collected information is subjected to analog quantity conversion through an A/D conversion chip with the model of PCF8591, which is onboard an FPGA with the model of EP4CE10F17C8, converted digital quantity is temporarily stored in an FIFO module of the FPGA, the converted digital quantity information is sequentially and serially transmitted to an intelligent gateway through an onboard RS232 serial port based on a UART protocol, and the information is transmitted to cloud servers of an energy management layer and a virtual modeling layer through wired network technologies such as optical fibers based on an MQTT protocol after the gateway;
and step 3: the cloud server of the energy management layer analyzes and stores the acquired information, transmits various historical information of the microgrid to the virtual space modeling layer based on a TCP/IP protocol, and monitors various data in real time based on the variation range of the various data in normal operation in the historical database. When the parameter exceeds the range and is compared with the historical fault database information, the type of the fault can be determined based on a K-nearest neighbor method;
and 4, step 4: the cloud server of the virtual space modeling layer analyzes information transmitted by the entity microgrid layer, and completes modeling of the virtual microgrid system by data driving based on real-time and historical data information in combination with various historical data information transmitted by the energy management layer, wherein the built virtual microgrid system keeps a ratio of 1: 1 to the original system and has the same life cycle as the microgrid system;
and 5: the energy management layer realizes load prediction, electricity price prediction and load prediction based on the cloud server;
the load prediction can realize long-term and short-term prediction of loads by utilizing load information in a historical database and a real-time database, firstly, data characteristics are extracted, different characteristics are subjected to normalization processing, the processed load information is subjected to clustering processing by utilizing a k-means clustering method due to the regional characteristics of the loads, historical sequence data and real-time sequence data of each type of loads are constructed by utilizing data according to the clustered results, a time sequence model is trained on a neural network, the short-term prediction of the loads can be realized on the basis of the neural network, the prediction can be corrected by combining with expert experience when the load fluctuation is large, the prediction accuracy is enhanced, and the prediction of medium-term and long-term loads can be realized by utilizing a support vector machine in the same method;
the power rate prediction is to predict the short-term power rate, which is related to not only the historical power rate and the load value, but also external factors such as weather. Therefore, the forecasting is carried out by combining the electricity price information and the forecasted load information in the historical database and the meteorological information transmitted by the meteorological station in a communication mode, wherein a forecasting model of the short-term electricity price is E (t) ═ O (t) + B (t) + W (t) + S (t) + R (t), E (t) represents the electricity price at the time t, O (t) represents raw material use, B (t) represents a basic load component, W (t) represents a weather change condition, and S (t) represents a special event. Predicting the real-time electricity price by using an LSTM (long short term memory network) based on a model;
the power generation prediction comprises power generation prediction of each distributed power supply, wherein the power generation prediction of each distributed power supply is combined with weather information such as mechanical performance, illumination intensity, temperature and wind speed to predict the power generation of each distributed power supply, a neural network is trained by using the weather information and the power generation information, the prediction of the power generation of each distributed power supply is realized based on the neural network, and the whole power generation prediction is the sum of the power generation prediction of each distributed power supply;
step 6: the energy management layer determines an initial scheduling scheme based on the cloud server and issues related theme services of the initial scheduling scheme;
the initial scheduling scheme considers the relationship between the output of the distributed power supply and the load, ensures the reliability of power supply by controlling the controllable load, the diesel engine and the energy storage system, the realization is that when the output of the distributed power supply is larger than the electric quantity required by the load under the grid-connected operation by combining the result of the prediction module, the redundant electric quantity can charge the storage battery, the virtual energy storage system consisting of the controllable load can absorb the redundant electric energy by considering the upper limit and the service life of the storage battery, meanwhile, the electric energy can be sold to the power grid during the peak electricity price period, when the output of the distributed power supply is not enough to provide the load electricity, the storage battery can be discharged when the electric quantity of the storage battery is not lower than the lower limit, meanwhile, the virtual energy storage system can reduce the power consumption under the condition of not influencing the normal use, so that the power supply of a key load is ensured, and the power generation of the diesel generator can be controlled to ensure the reliable supply of the power. When the power distribution network has a fault, the connection between the micro-grid and the power distribution network is disconnected, so that the micro-grid operates in an island operation mode, and the mode is different from the grid connection mode in that the interaction between the micro-grid and the power distribution network is cut off, and is a self-sufficient mode;
and 7: a client of the virtual space modeling layer controls the virtual micro-grid system to operate according to an initial scheduling scheme by subscribing the initial scheduling scheme;
and 8: and the cloud server of the virtual space modeling layer transmits the operation information after the preliminary scheduling is executed to the cloud server of the energy management layer based on a TCP/IP protocol.
And step 9: the cloud server of the energy management layer adjusts the primary scheduling scheme based on information fed back after the virtual micro-grid system executes the primary scheduling scheme;
step 10: the client side of the virtual space modeling layer modifies the scheduling instruction according to the topic service of the subscription scheduling scheme;
step 11: the cloud server of the energy management layer determines a primary operation optimization scheme on the basis of a scheduling scheme and issues a primary operation optimization scheme subject service;
the preliminary economic optimization operation scheme is that an operation scheme is optimized on the basis of a scheduling scheme, the operation of the micro-grid is optimized and planned with the purposes of economy, minimum electricity purchasing from the power distribution network and minimum carbon emission by combining a result predicted by a prediction module on the premise of ensuring the reliability of power supply, and an optimization target is determined to meet the multi-objective functions of economy, minimum peak electricity purchasing, minimum carbon emission and the like by combining constraint conditions such as generator constraint, power balance constraint, battery constraint and the like on the basis of a twin micro-grid system model established by a virtual space modeling layer. Optimizing by using an intelligent algorithm, wherein an improved particle swarm algorithm is adopted for optimizing, so that a preliminary optimization operation scheme is determined;
wherein the economic objective function is
The total cost of the operation of the micro-grid is N, the total number of dispatching sections, c, the price of diesel oil, f, the oil consumption of a diesel generator, g, the electricity purchase price from the micro-grid to a power distribution network and PgFor purchasing electric power, s is the price of selling electric power to the microgrid, PSFor the amount of electricity sold. Wherein gPg，sPSOnly in grid-connected operation conditions and zero in island operation conditions. M is the number of distributed energy sources, a is the cost of power generation of the distributed power supply, wherein renewable energy sources such as light energy, wind energy and the like are removed, PaIs the power generated;
wherein the carbon emission objective function is
X is the carbon emission coefficient of the diesel generator;
step 12: a client of the virtual space modeling layer controls the virtual micro-grid system to adjust according to an initial optimization scheme by subscribing the initial operation optimization scheme;
step 13: the method comprises the steps that various kinds of running information after a virtual micro-grid system executes a running optimization scheme is transmitted to a cloud server of a virtual space modeling layer through a gateway, and the cloud server of the virtual space modeling layer transmits the running information after preliminary scheduling is executed to a cloud server of an energy management layer on the basis of a TCP/IP protocol;
step 14: and the cloud server of the energy management layer adjusts the primary operation optimization scheme based on information fed back after the virtual micro-grid system executes the primary operation optimization scheme.
Step 15: the client side of the intelligent control layer controls the controller by subscribing the operation optimization scheme, so that the micro-grid system is controlled to operate according to the release scheme;
step 16: the man-machine interaction layer can master various kinds of operation information of the micro-grid by subscribing various services issued by the energy management layer, and design and quote software to complete visual processing of data by means of programming and the like;
and step 17: at the client of the human-computer interaction layer, an operator can issue a scheduling instruction to the server of the energy management layer according to various observed running information and self experiences, and the clients of the virtual space modeling layer and the intelligent control layer realize the control of the entity and the virtual microgrid system by subscribing the control instruction issued by the client of the human-computer interaction layer.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A micro-grid energy management system based on digital twinning is characterized in that,
the microgrid energy management system comprises an entity microgrid layer, an energy management layer, a virtual space modeling layer, an intelligent control layer and a man-machine interaction layer;
the entity micro-grid layer comprises a micro-grid system and a data acquisition and transmission unit;
the micro-grid system comprises a distributed power supply, a rectifier, an inverter, a diesel generator, an energy storage system and a load;
the data acquisition and transmission unit comprises an intelligent sensor for data acquisition, a camera, an electronic tag in equipment, an FPGA (field programmable gate array), a gateway, optical fibers and other equipment for information processing and transmission, and acquired information is transmitted through a wired network technology based on an MQTT (multiple quantum dots) protocol;
the virtual space modeling layer includes: the cloud server is used for analyzing data, completing modeling of the virtual micro-grid system based on data drive by combining historical data and real-time data, and meanwhile, transmitting the operation information of the virtual micro-grid system to the server through the gateway; the client is used for subscribing the primary scheduling and operation optimization information issued by the energy management layer and realizing the control of the virtual micro-grid system;
the energy management layer comprises a cloud server, and the cloud server comprises:
the information storage module comprises a real-time information database, a historical information database, a fault information database and a virtual system operation information database;
the monitoring and early warning module is used for determining the range of each data in normal work based on the historical database microgrid, comparing whether the real-time data all operate in normal values, and further determining the fault type based on the fault information database by adopting a k-nearest neighbor method if the parameter deviates from the range;
the prediction module comprises load prediction, power generation prediction and electricity price prediction;
the dispatching module reasonably dispatches the energy storage battery, the diesel generator, the controllable load on the demand side and a virtual energy storage system consisting of the electric automobile so as to ensure the reliability of power supply;
the operation optimization module is used for optimizing based on a particle swarm algorithm on the basis of the scheduling module by taking economy, minimum peak power purchase from a power grid and minimum carbon emission as objective functions;
the service and publishing module is used for publishing the theme service of each module;
the intelligent control layer comprises a client and a controller, and controls the micro-grid system according to a scheme by subscribing scheduling and running scheme subject services of the energy management layer;
the man-machine interaction layer comprises a client, comprehensive control over the micro-grid system can be achieved by subscribing various services of the energy management layer, data visualization can be achieved based on an application program, an operator can issue a control command to the cloud server according to experience, the client of the virtual space modeling layer and the client of the intelligent control layer subscribe issued theme services, and control over an entity and the virtual micro-grid system by the man-machine interaction layer is achieved.
2. The microgrid energy management system of claim 1, wherein the cloud server and the cloud server communicate based on a TCP/IP protocol, and the cloud server and the client communicate with a mass quality of service (QOS) 2 based on a publish-subscribe mechanism of an MQTT protocol.
3. The microgrid energy management system of claim 1, wherein the microgrid energy management system comprises two cloud servers, one cloud server can satisfy the functions, but considering that the time required for the operation amount is large and the virtual microgrid system needs to keep the same life cycle as the microgrid system, the cloud servers are added into the virtual space modeling layer, and the two cloud servers respectively play their roles to increase the operation speed so that the virtual microgrid system has the same life cycle as the microgrid system.
4. A micro-grid energy management system operation mechanism based on digital twinning is characterized by comprising the following steps:
step 1: various information of the micro-grid system is acquired in real time by an intelligent sensor, a camera and a radio frequency technology based on a label;
step 2: the method comprises the steps that various collected information is subjected to analog quantity conversion through an A/D conversion chip with the model of PCF8591, which is onboard an FPGA with the model of EP4CE10F17C8, converted digital quantity is temporarily stored in an FIFO module of the FPGA, the converted digital quantity information is sequentially and serially transmitted to an intelligent gateway through an onboard RS232 serial port based on a UART protocol, and the information is transmitted to cloud servers of an energy management layer and a virtual modeling layer through an optical fiber wired network technology based on an MQTT protocol after the gateway;
and step 3: the cloud server of the energy management layer analyzes and stores the acquired information, transmits various kinds of historical information of the microgrid to the virtual space modeling layer based on a TCP/IP protocol, monitors various kinds of data in real time based on the variation range of the various kinds of data in normal operation in a historical database, compares the parameters with the information of the historical fault database when the parameters exceed the range, and can determine the type of the fault based on a K-nearest neighbor method;
and 4, step 4: the cloud server of the virtual space modeling layer analyzes information transmitted from the entity micro-shop network layer, and completes modeling of the virtual micro-grid system by data driving based on real-time and historical data information in combination with various historical data information transmitted by the energy management layer, wherein the built virtual micro-grid system keeps a 1: 1 ratio with the original system and has the same life cycle as the micro-grid system;
and 5: the energy management layer realizes load prediction, electricity price prediction and load prediction based on the cloud server;
the load prediction can realize long-term and short-term prediction of loads by utilizing load information in a historical database and a real-time database, firstly, data characteristics are extracted, different characteristics are subjected to normalization processing, the processed load information is subjected to clustering processing by utilizing a k-means clustering method due to the regional characteristics of the loads, historical sequence data and real-time sequence data of each type of loads are constructed by utilizing data according to the clustered results, a time sequence model is trained on a neural network, the short-term prediction of the loads can be realized on the basis of the neural network, the prediction can be corrected by combining with expert experience when the load fluctuation is large, the prediction accuracy is enhanced, and the prediction of medium-term and long-term loads can be realized by utilizing a support vector machine in the same method;
the power rate prediction is to predict the short-term power rate, which is related to not only the historical power rate and the load value, but also external factors such as weather. Therefore, the forecasting is carried out by combining the electricity price information and the forecasted load information in the historical database and the meteorological information transmitted by the meteorological station in a communication mode, wherein a forecasting model of the short-term electricity price is E (t) ═ O (t) + B (t) + W (t) + S (t) + R (t), E (t) represents the electricity price at the time t, O (t) represents raw material use, B (t) represents a basic load component, and W (t) represents a weather change condition S (t) represents a special event. Predicting the real-time electricity price by using an LSTM (long short term memory network) based on a model;
the power generation prediction comprises power generation prediction of each distributed power supply, wherein the power generation prediction of each distributed power supply is combined with weather information such as mechanical performance, illumination intensity, temperature and wind speed to predict the power generation of each distributed power supply, a neural network is trained by using the weather information and the power generation information, the prediction of the power generation of each distributed power supply is realized based on the neural network, and the whole power generation prediction is the sum of the power generation prediction of each distributed power supply;
step 6: the energy management layer determines an initial scheduling scheme based on the cloud server and provides a theme service of the initial scheduling scheme;
the initial scheduling scheme considers the relationship between the output of the distributed power supply and the load, ensures the reliability of power supply by controlling the controllable load, the diesel engine and the energy storage system, the realization is that when the output of the distributed power supply is larger than the electric quantity required by the load under the grid-connected operation by combining the result of the prediction module, the redundant electric quantity can charge the storage battery, the virtual energy storage system consisting of the controllable load can absorb the redundant electric energy by considering the upper limit and the service life of the storage battery, meanwhile, the electric energy can be sold to the power grid during the peak electricity price period, when the output of the distributed power supply is not enough to provide the load electricity, the storage battery can be discharged when the electric quantity of the storage battery is not lower than the lower limit, meanwhile, the virtual energy storage system can reduce the power consumption under the condition of not influencing the normal use, so that the power supply of a key load is ensured, and the power generation of the diesel generator can be controlled to ensure the reliable supply of the power. When the power distribution network has a fault, the connection between the micro-grid and the power distribution network is disconnected, so that the micro-grid operates in an island operation mode, and the mode is different from the grid connection mode in that the interaction between the micro-grid and the power distribution network is cut off, and is a self-sufficient mode;
and 7: a client of the virtual space modeling layer controls the virtual micro-grid system to operate according to an initial scheduling scheme by subscribing the initial scheduling scheme;
and 8: the method comprises the steps that various kinds of running information after a virtual micro-grid system executes a scheduling scheme is transmitted to a cloud server of a virtual space modeling layer through a gateway, and the cloud server of the virtual space modeling layer transmits the running information after preliminary scheduling is executed to a cloud server of an energy management layer on the basis of a TCP/IP protocol;
and step 9: the cloud server of the energy management layer adjusts the primary scheduling scheme based on information fed back after the virtual micro-grid system executes the primary scheduling scheme;
step 10: the client side of the virtual space modeling layer modifies the scheduling instruction according to the topic service of the subscription scheduling scheme;
step 11: the cloud server of the energy management layer determines a primary operation optimization scheme on the basis of a scheduling scheme and issues a primary operation optimization scheme subject service;
the preliminary economic optimization operation scheme is that an operation scheme is optimized on the basis of a scheduling scheme, the operation of the micro-grid is optimized and planned by combining a result predicted by a prediction module, namely on the premise of ensuring the reliability of power supply, by taking the economy, the minimum electricity purchasing and the minimum carbon emission of the power distribution network as targets, and the optimization target is determined to meet the multi-objective functions of economy, the minimum peak electricity purchasing, the minimum carbon emission and the like by combining constraint conditions such as generator constraint, power balance constraint, battery constraint and the like on the basis of a twin micro-grid system model established by a virtual space modeling layer. Optimizing by using an intelligent algorithm, wherein an improved particle swarm algorithm is adopted for optimizing, so that a preliminary optimization operation scheme is determined;
wherein the economic objective function is
The total cost of the operation of the micro-grid is N, the total number of dispatching sections, c, the price of diesel oil, f, the oil consumption of a diesel generator, g, the electricity purchase price from the micro-grid to a power distribution network and PgFor purchasing electric power, s isPrice, P, of electricity sold to a microgridSFor the amount of electricity sold. Wherein gPg，sPSOnly in grid-connected operation conditions and zero in island operation conditions. M is the number of distributed energy sources, a is the cost of power generation of the distributed power supply, wherein renewable energy sources such as light energy, wind energy and the like are removed, PaIs the power generated;
wherein the carbon emission objective function is
X is the carbon emission coefficient of the diesel generator;
step 12: a client of the virtual space modeling layer controls the virtual micro-grid system to adjust according to an initial optimization scheme by subscribing the initial operation optimization scheme;
step 13: the method comprises the steps that various kinds of running information after a virtual micro-grid system executes a running optimization scheme is transmitted to a cloud server of a virtual space modeling layer through a gateway, and the cloud server of the virtual space modeling layer transmits the running information after preliminary scheduling is executed to a cloud server of an energy management layer on the basis of a TCP/IP protocol;
step 14: the cloud server of the energy management layer adjusts the primary operation optimization scheme based on information fed back after the virtual micro-grid system executes the primary operation optimization scheme;
step 15: the client side of the intelligent control layer controls the controller by subscribing the operation optimization scheme, so that the micro-grid system is controlled to operate according to the release scheme;
step 16: the man-machine interaction layer can master various kinds of operation information of the micro-grid by subscribing various services issued by the energy management layer, and design and quote software to complete visual processing of data by means of programming and the like;
and step 17: at the client of the human-computer interaction layer, an operator can issue a scheduling instruction to the server of the energy management layer according to various observed running information and self experiences, and the clients of the virtual space modeling layer and the intelligent control layer realize the control of the entity and the virtual microgrid system by subscribing the control instruction issued by the client of the human-computer interaction layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010984190.0A CN112332444A (en) | 2020-09-14 | 2020-09-14 | Microgrid energy management system based on digital twins |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010984190.0A CN112332444A (en) | 2020-09-14 | 2020-09-14 | Microgrid energy management system based on digital twins |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112332444A true CN112332444A (en) | 2021-02-05 |
Family
ID=74304529
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010984190.0A Pending CN112332444A (en) | 2020-09-14 | 2020-09-14 | Microgrid energy management system based on digital twins |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112332444A (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113356916A (en) * | 2021-07-08 | 2021-09-07 | 长安大学 | Mine air flow regulation and control virtual system based on digital twin technology and intelligent regulation and control method |
| CN113435054A (en) * | 2021-07-06 | 2021-09-24 | 天津水泥工业设计研究院有限公司 | Carbon emission assessment method and system based on digital twin model |
| CN113675863A (en) * | 2021-08-23 | 2021-11-19 | 南京理工大学 | Digital twin-based micro-grid frequency secondary cooperative control method |
| CN113922506A (en) * | 2021-10-18 | 2022-01-11 | 普世通（北京）电气有限公司 | Method for realizing multiple physical models of dynamic voltage recovery device |
| CN114154429A (en) * | 2022-02-10 | 2022-03-08 | 中国长江三峡集团有限公司 | Digital twin body construction method and device of energy storage system and storage medium |
| CN114352947A (en) * | 2021-12-08 | 2022-04-15 | 天翼物联科技有限公司 | Gas pipeline leakage detection method, system and device and storage medium |
| CN114374233A (en) * | 2022-03-22 | 2022-04-19 | 长沙电机厂集团长瑞有限公司 | Method and system for adjusting micro-grid power output based on virtual generator |
| CN114674114A (en) * | 2022-03-23 | 2022-06-28 | 中海石油气电集团有限责任公司 | Intelligent monitoring and operation optimization method and system for LNG liquefaction process |
| CN114899853A (en) * | 2022-05-06 | 2022-08-12 | 怀化学院 | Novel energy power generation and energy storage intelligent management equipment and system |
| CN115098472A (en) * | 2022-06-30 | 2022-09-23 | 北京亚控科技发展有限公司 | Digital twinning method, device and equipment for full life cycle of physical entity |
| CN115545450A (en) * | 2022-09-27 | 2022-12-30 | 广东师大维智信息科技有限公司 | Carbon emission collaborative prediction method based on digital twinning |
| CN115859700A (en) * | 2023-03-02 | 2023-03-28 | 国网湖北省电力有限公司电力科学研究院 | Power grid modeling method based on digital twinning technology |
| TWI805470B (en) * | 2022-08-15 | 2023-06-11 | 中原大學 | Three-phase inverter device with reactive power compensation |
| CN116923168A (en) * | 2023-06-21 | 2023-10-24 | 上海旋荣科技股份有限公司 | Charging pile electric energy dispatching system and dispatching method based on transformer substation networking |
| CN118040911A (en) * | 2024-04-15 | 2024-05-14 | 国网山西省电力公司晋城供电公司 | Energy storage micro-grid monitoring system and monitoring method, storage medium and computer equipment |
| CN118508431A (en) * | 2024-07-12 | 2024-08-16 | 中南大学 | Energy optimization management method applied to intelligent micro-grid |
| CN118739608A (en) * | 2024-08-28 | 2024-10-01 | 浙江碳银数智绿能科技有限公司 | Micro-grid monitoring system based on source network charge storage |
| CN118508431B (en) * | 2024-07-12 | 2024-11-05 | 中南大学 | Energy optimization management method applied to intelligent micro-grid |
-
2020
- 2020-09-14 CN CN202010984190.0A patent/CN112332444A/en active Pending
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113435054A (en) * | 2021-07-06 | 2021-09-24 | 天津水泥工业设计研究院有限公司 | Carbon emission assessment method and system based on digital twin model |
| CN113356916A (en) * | 2021-07-08 | 2021-09-07 | 长安大学 | Mine air flow regulation and control virtual system based on digital twin technology and intelligent regulation and control method |
| CN113675863A (en) * | 2021-08-23 | 2021-11-19 | 南京理工大学 | Digital twin-based micro-grid frequency secondary cooperative control method |
| CN113675863B (en) * | 2021-08-23 | 2024-02-06 | 南京理工大学 | Digital twin-based micro-grid frequency secondary cooperative control method |
| CN113922506A (en) * | 2021-10-18 | 2022-01-11 | 普世通（北京）电气有限公司 | Method for realizing multiple physical models of dynamic voltage recovery device |
| CN114352947A (en) * | 2021-12-08 | 2022-04-15 | 天翼物联科技有限公司 | Gas pipeline leakage detection method, system and device and storage medium |
| CN114352947B (en) * | 2021-12-08 | 2024-03-12 | 天翼物联科技有限公司 | Gas pipeline leakage detection method, system, device and storage medium |
| CN114154429A (en) * | 2022-02-10 | 2022-03-08 | 中国长江三峡集团有限公司 | Digital twin body construction method and device of energy storage system and storage medium |
| CN114374233A (en) * | 2022-03-22 | 2022-04-19 | 长沙电机厂集团长瑞有限公司 | Method and system for adjusting micro-grid power output based on virtual generator |
| CN114674114B (en) * | 2022-03-23 | 2023-12-05 | 中海石油气电集团有限责任公司 | Intelligent monitoring and operation optimizing method and system for LNG liquefaction process |
| CN114674114A (en) * | 2022-03-23 | 2022-06-28 | 中海石油气电集团有限责任公司 | Intelligent monitoring and operation optimization method and system for LNG liquefaction process |
| CN114899853B (en) * | 2022-05-06 | 2024-04-19 | 怀化学院 | Novel intelligent energy power generation and energy storage management equipment and system |
| CN114899853A (en) * | 2022-05-06 | 2022-08-12 | 怀化学院 | Novel energy power generation and energy storage intelligent management equipment and system |
| CN115098472A (en) * | 2022-06-30 | 2022-09-23 | 北京亚控科技发展有限公司 | Digital twinning method, device and equipment for full life cycle of physical entity |
| TWI805470B (en) * | 2022-08-15 | 2023-06-11 | 中原大學 | Three-phase inverter device with reactive power compensation |
| CN115545450B (en) * | 2022-09-27 | 2023-06-06 | 广东师大维智信息科技有限公司 | Carbon emission collaborative prediction method based on digital twin |
| CN115545450A (en) * | 2022-09-27 | 2022-12-30 | 广东师大维智信息科技有限公司 | Carbon emission collaborative prediction method based on digital twinning |
| CN115859700A (en) * | 2023-03-02 | 2023-03-28 | 国网湖北省电力有限公司电力科学研究院 | Power grid modeling method based on digital twinning technology |
| CN116923168A (en) * | 2023-06-21 | 2023-10-24 | 上海旋荣科技股份有限公司 | Charging pile electric energy dispatching system and dispatching method based on transformer substation networking |
| CN116923168B (en) * | 2023-06-21 | 2024-04-26 | 上海旋荣科技股份有限公司 | Charging pile electric energy dispatching system and dispatching method based on transformer substation networking |
| CN118040911A (en) * | 2024-04-15 | 2024-05-14 | 国网山西省电力公司晋城供电公司 | Energy storage micro-grid monitoring system and monitoring method, storage medium and computer equipment |
| CN118040911B (en) * | 2024-04-15 | 2024-06-18 | 国网山西省电力公司晋城供电公司 | Energy storage micro-grid monitoring system and monitoring method, storage medium and computer equipment |
| CN118508431A (en) * | 2024-07-12 | 2024-08-16 | 中南大学 | Energy optimization management method applied to intelligent micro-grid |
| CN118508431B (en) * | 2024-07-12 | 2024-11-05 | 中南大学 | Energy optimization management method applied to intelligent micro-grid |
| CN118739608A (en) * | 2024-08-28 | 2024-10-01 | 浙江碳银数智绿能科技有限公司 | Micro-grid monitoring system based on source network charge storage |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112332444A (en) | Microgrid energy management system based on digital twins | |
| CN110416998B (en) | Regional complex distribution network scheduling control management system based on virtual power plant | |
| CN105048457B (en) | A kind of intelligent micro-grid electric energy management system | |
| CN103001225B (en) | MAS-based (multi-agent system) multi-microgrid energy management system simulation method | |
| CN113364062A (en) | Micro-grid site predictive control for multi-power resource management with machine learning | |
| CN106786547A (en) | A kind of new micro-grid system and the networking scheduling method based on the system | |
| KR101927759B1 (en) | optimum control system for photovoltaic energy generation system | |
| CN109510247A (en) | A kind of controllable burden cooperates with optimal control method with clean energy resource generation technology | |
| CN112332401B (en) | Green energy supply charging station system based on block chain, and management equipment and method | |
| CN112950001B (en) | Intelligent energy management and control system and method based on cloud edge closed-loop architecture | |
| CN117411039A (en) | Intelligent energy storage charging system | |
| CN110855006B (en) | Distributed optical storage and charging regulation and control system based on edge internet of things agent | |
| CN114567072B (en) | Control system of photovoltaic power station | |
| CN113595235B (en) | Energy storage management system based on ubiquitous power internet of things | |
| CN108346009B (en) | Power production configuration method and device based on user model self-learning | |
| CN117526432A (en) | Distribution network regulation and control system and method for source-load interaction | |
| CN115514097A (en) | Village domain CPS regulation and control system and method based on distributed photovoltaic supply and demand interactive coordination control | |
| CN117249537B (en) | Virtual power plant scheduling and control system and method based on central air conditioner | |
| CN118214006A (en) | AC/DC micro-grid optimization operation method considering demand response and cooperative game | |
| CN109726416B (en) | Scheduling decision method based on new energy cluster prediction and load flow calculation | |
| CN113765099A (en) | Intelligent scheduling system based on Internet of things and load scheduling method thereof | |
| CN117670299A (en) | Digital operation and maintenance supervision method and system for building park | |
| KR101775269B1 (en) | Management and exchange system for distributed generations and power amount allocation method thereof | |
| CN116961133A (en) | Unified control system of inverter cluster based on electricity consumption information acquisition | |
| CN116885840A (en) | Distributed new energy online monitoring method and system based on real-time data |
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 |