CN112542837A - Plant-level load optimization distribution method based on big data analysis and multi-objective optimization - Google Patents
Plant-level load optimization distribution method based on big data analysis and multi-objective optimization Download PDFInfo
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- CN112542837A CN112542837A CN202011239093.5A CN202011239093A CN112542837A CN 112542837 A CN112542837 A CN 112542837A CN 202011239093 A CN202011239093 A CN 202011239093A CN 112542837 A CN112542837 A CN 112542837A
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- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- 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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to a plant-level load optimal distribution method based on big data analysis and multi-objective optimization, which is characterized in that a control node is added in a power plant monitoring system network, a plant-level AGC system which is communicated with a network control system is arranged to receive a whole plant load instruction sent in real time by scheduling, all unit production operation data are collected on line at the same time, an intelligent optimization algorithm is adopted for optimization, an optimal load distribution strategy is obtained, a given target value optimal distribution is set according to a remote regulation instruction and an operation attendant, the output of a unit is automatically adjusted, and the distribution strategy meets the requirements of energy-saving power generation scheduling. The invention can realize the purposes of energy conservation and consumption reduction of the generator set.
Description
Technical Field
The invention belongs to the technical field of thermal power generation, and particularly relates to a plant-level load optimization distribution method based on big data analysis and multi-objective optimization.
Background
At present, when a power plant carries out the hierarchy of 'electricity replacement and renewable energy source unit priority' in the aspect of energy-saving economic dispatching, in the aspect of dispatching of coal-fired units, two aspects of unit reliability and electric energy quality guarantee are mainly considered by a power grid company. AGC is one of paid auxiliary services provided by a grid-connected power plant, a generator set tracks an instruction issued by a main dispatching center of a power grid within a specified output adjustment range, and the generated output is adjusted in real time according to a certain adjustment rate so as to meet the control requirements of the frequency of a power system and the power of a tie line. Therefore, a powerful measure for saving energy and reducing consumption of the generator set is urgently needed to be solved.
Disclosure of Invention
The invention aims to provide a plant-level load optimization distribution method based on big data analysis and multi-objective optimization so as to achieve the purposes of energy conservation and consumption reduction of a generator set.
The invention provides a plant-level load optimal distribution method based on big data analysis and multi-objective optimization, which is characterized in that a control node is added in a power plant monitoring system network, a plant-level AGC system which is communicated with a network control system is arranged to receive a whole plant load instruction sent in real time by scheduling, production operation data of all units are collected on line at the same time, optimization is carried out by adopting an intelligent optimization algorithm, an optimal load distribution strategy is obtained, a given target value optimal distribution is set according to a remote regulation instruction and an operation attendant, the output of the units is automatically adjusted, and the distribution strategy meets the requirements of energy-saving power generation scheduling.
Furthermore, the plant-level AGC intelligent control unit receives the unit operation data and booster station electrical data transmitted by the network control system in real time and receives the load instruction forwarded by the network control system;
the plant-level AGC operator station is communicated with the plant-level AGC intelligent control unit to realize information exchange and data sharing so as to finish automatic monitoring and control of the operation of a plant-level AGC system;
and performing AGC offline guidance through an AGC intelligent guidance system, and acquiring data from a vibration interface server by connecting an industrial personal computer to a vibration management expert system network through required DCS data.
And further, an AGC intelligent control unit is arranged in a safety I area, required network control system and unit DCS related data are transmitted to an AGC performance server in a safety II area through a forward network safety isolation device, and the data communication is one-way transmission.
Furthermore, a reverse network safety isolation device is arranged between the industrial personal computer and the AGC intelligent control unit, and the industrial personal computer sends the received DCS related data to the plant-level AGC intelligent control unit in a file mode.
Further, receiving a dispatching single machine AGC and a plant-level AGC through a plant-level AGC system, and directly sending a single machine AGC command to each unit set DCS when in a dispatching single machine mode; and issuing the plant instructions to each unit DCS according to the distribution strategy when the plant mode or the local power plant mode is dispatched.
By means of the scheme, the purposes of energy conservation and consumption reduction of the generator set can be achieved through the plant-level load optimization distribution method based on big data analysis and multi-objective optimization.
The foregoing is a summary of the present invention, and in order to provide a clear understanding of the technical means of the present invention and to be implemented in accordance with the present specification, the following is a detailed description of the preferred embodiments of the present invention.
Drawings
FIG. 1 is a flow chart of a single AGC control of a power plant;
FIG. 2 is a flow chart of the factory level AGC control of the present invention;
FIG. 3 is a diagram of the RTU and AGC intelligent control unit communication architecture of the present invention;
FIG. 4 is a diagram of the AGC performance server communication architecture of the present invention;
FIG. 5 is a diagram of a vibration interface server communication architecture in accordance with the present invention;
fig. 6 is a network topology diagram of the plant level AGC system of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
The embodiment provides a plant-level load optimal distribution method based on big data analysis and multi-objective optimization, wherein a control node is added in a power plant monitoring system network, a plant-level AGC system which is communicated with a network control system is arranged to receive a whole plant load instruction sent in real time by scheduling, all unit production operation data are collected on line at the same time, an intelligent optimization algorithm is adopted for optimization, an optimal load distribution strategy is obtained, given target value optimal distribution is set according to a remote regulation instruction and an operation attendant, the output of a unit is automatically adjusted, and the distribution strategy meets the requirements of energy-saving power generation scheduling.
The specific scheme is as follows:
1. plant-level AGC intelligent control unit
The appearance of the plant-level AGC intelligent control unit is of a standard 19-inch structure, the height of the plant-level AGC intelligent control unit is 2U, a high-reliability platform is adopted on hardware, no hard disk or fan is adopted, an operating system adopts Linux, communication software adopts modular design and multitask operation, and the operation is reliable and stable.
The hardware integrates 6 Ethernet interfaces and 10 serial interfaces, and is provided with a plurality of PCBs and PCEExpress expansion interfaces. According to actual application requirements, a plurality of communication and control interfaces such as switching value input, relay output, IRIG-B codes, IEEE-1588, optical fibers, CAN, GPRS, wireless and the like CAN be expanded.
The method has the characteristics of high reliability, high integration degree, long life cycle, flexible expansibility and the like, supports the working temperature range of-40 ℃ to +70 ℃ at most and works stably for a long time.
The front panel is provided with a power supply, a hard disk, six Ethernet, ten serial ports, eight self-defining and two groups of expansion interface indicating lamps, wherein the Ethernet indicating lamps respectively correspond to connection indication and data receiving and transmitting indication; the serial port indicator lamp corresponds to a data receiving indicator and a data sending indicator of the serial port respectively. The back panel is provided with interfaces for installing equipment such as a power supply, a serial port, a network port, a time setting device, a display, a keyboard, a mouse and the like.
2. Plant-level AGC workstation
The plant-level AGC workstation is based on a Client/Server system, an operating system is Linux, the Linux operating system is communicated with the plant-level AGC intelligent control unit to realize information exchange and data sharing, and automatic monitoring and control of the operation of the plant-level AGC optimization control system are completed. And providing a user interface, displaying the running state of the system in various visual forms (such as characters, tables, graphs and the like) and finishing various plant-level AGC applications.
3. Industrial control machine
A rack-mounted industrial personal computer is adopted and is provided with an Intel Pentium DualCoreG2010 processor, so that the CPU24 hours can be guaranteed to run at full speed under the industrial condition that the ambient temperature is up to 40 ℃. 2 Ethernet ports (100M/1000M self-adaptation) and two serial ports COM1 and COM2 are configured, wherein the COM2 can support three modes (selected through a BIOS menu) of RS232/RS485/RS422, and two video output interfaces DVI-D and VGA can output simultaneously.
4. Forward network safety isolation device
Is a safety guard device between the dispatch data network and the public information network for unidirectional data transfer from zone I to zone II/III. The system can identify illegal requests and prevent data access and operation exceeding the authority, thereby effectively resisting malicious damage and attack activities of viruses, hackers and the like to the power network system initiated in various forms, protecting the safety of the real-time closed-loop monitoring system and the scheduling data network, simultaneously realizing information and resource sharing of the two networks by adopting a non-network transmission mode, and ensuring the safe and stable operation of the power system.
5. Reverse network safety isolation device
For one-way text data transfer from zone III/II to zone I. The network safety isolation device is a safety isolation system formed by two high-performance embedded microcomputers and an auxiliary device, the embedded microprocessor adopts a RISC system structure, the probability of attack is reduced, the non-network data exchange between two safety areas is realized, a safety algorithm is adopted to ensure that the internal and external processing systems of the safety isolation device are not communicated simultaneously, and the high-speed data exchange is realized on the premise of ensuring the safety isolation.
6. Factory-level AGC performance server
And the factory-level AGC performance server is provided with an AGC offline guidance system, and the operating system is windows.
At present, a power plant adopts a single-machine AGC operation mode, a power plant RTU receives load instructions of each unit sent by a network dispatching unit in real time, forwards the load instructions to an AGC interface module after protocol conversion, outputs 4-20mA current to a Distributed Control System (DCS) of the corresponding unit, and completes load regulation of the unit by a Coordinated Control System (CCS), and a control flow chart of the system is shown in fig. 1, taking two units as an example.
The plant-level AGC receives a plant load instruction sent by scheduling in real time, collects production operation data of all units on line, meets the requirement of load response rapidity, realizes optimal distribution of loads among the units according to the principles of economy, adjustment frequency and the like, and sends an optimal distribution result to the CCS system, so that automatic increase and decrease of the loads of the units are realized, and a control flow chart is shown in fig. 2.
The plant-level AGC implementation scheme does not change the original NCS network system, and control nodes are directly added in a power plant monitoring system network and comprise a plant-level AGC intelligent control unit, a plant-level AGC operator station and an AGC intelligent guidance system. The hierarchical architecture between the NCS system RTU and the AGC intelligent control unit in the original plant is shown in fig. 3. The power plant telecontrol RTU keeps the original communication mode unchanged with the dispatching, the telecontrol RTU is additionally provided with a channel to communicate with the plant-level AGC intelligent control unit, the unit operation data and the booster station electrical data are sent to the plant-level AGC intelligent control unit in real time, and when the telecontrol RTU receives a load instruction issued by the dispatching, the load instruction is transferred to the plant-level AGC intelligent control unit.
The AGC intelligent control unit is positioned in a safety I area, required network control system and unit DCS related data are transmitted to an AGC performance server positioned in a safety II area through a forward network safety isolation device, the data communication is unidirectional transmission, and the architecture diagram is shown in figure 4.
DCS data required by the intelligent algorithm of the AGC performance server acquires data from the vibration interface server by means of a vibration management expert system network communication framework, and a corresponding communication framework diagram 5 shows. The method includes the steps that corresponding software, namely factory-level AGC pre-interface conversion software, is deployed on an industrial personal computer, and the industrial personal computer is connected to a vibration management expert system network to obtain DCS data. Because the factory-level AGC pre-interface conversion software needs to transmit related data from the security II to the security I area, and a reverse isolation gateway needs to be added in the middle, the reverse isolation gateway can only transmit files, and therefore the factory-level AGC pre-interface conversion software needs to transmit the received DCS related data to two factory-level AGC intelligent control units in a file mode.
A complete network topology diagram of a plant-level AGC system is shown in fig. 6, and related devices are respectively disposed between a # 1 network control building, a long-distance room and a # 6 machine electronics.
The plant-level AGC system of the embodiment includes the following specific contents:
the factory-level AGC has the functions of receiving and dispatching the single AGC and the factory-level AGC, seamless switching can be realized, and when the single AGC is dispatched, the factory-level AGC can directly send a single AGC command to each unit set DCS; when the whole plant mode or the local power plant mode is dispatched, the plant-level AGC can issue the whole plant instruction to each unit DCS according to the distribution strategy.
The plant-level AGC system has a whole plant control mode and a single machine control mode, and can set the whole plant control/single machine control mode according to requirements. When a factory control mode is adopted, the single machine control function is automatically locked; when a single machine control mode is adopted, the whole plant control function is automatically locked. The plant-level AGC system has the following functions:
communication: the system can communicate with a network control system RTU and a scheduling master station system, and supports various communication modes including a private line communication mode and a network communication mode.
The remote control (remote regulation)/local automatic control (automatic control)/manual operation control (manual)/open-loop simulation control (guidance) can be set and can be switched between adjacent control modes.
Data acquisition and processing: the method has the advantages that the operation information of each generator set in the power plant and the on-off state quantity of other equipment are collected in real time, the operation condition of the whole thermal power plant is known, and data support is provided for the economic optimal distribution of the load of each generator set.
And (3) coal consumption curve fitting: a reasonable mathematical model can be constructed, and a power supply coal consumption characteristic curve of the unit can be fitted along with the change of the operation condition of the unit.
And (3) load optimization distribution: and optimizing by adopting an intelligent optimization algorithm to obtain an optimal load distribution strategy.
And (3) according to a remote regulation instruction, operating a watchman to set a given target value for optimal distribution, automatically regulating the output of the unit, and meeting the requirement of energy-saving power generation dispatching by a distribution strategy.
And operation monitoring, namely acquiring the operation information of the plant-level AGC system and the switching state quantity of other equipment in real time, conveniently monitoring the operation condition, the switching state, the equipment operation state and the communication state with other equipment of the system, and automatically monitoring the operation state and the control performance of the unit.
And calculating statistics, namely performing statistics such as average value, maximum value and occurrence time, minimum value and occurrence time and the like on the specified acquisition quantity, and performing statistics on the availability, investment rate, qualification rate and adjustment precision of the AGC function.
Data storage: the collected data points can be stored and a historical database can be formed for drawing a trend curve and forming reports, and the supported reports comprise AGC system operation statistical reports, generator operation statistical reports and the like.
Controlling and regulating: and multiple working modes are provided, the real-time load of each unit can be directly scheduled, and the distribution mode can be selected from manual distribution, proportion distribution, system optimization distribution and the like.
And (3) alarm processing: when the system is abnormal or fails, the system can automatically alarm, stop the output of the distribution result and form an event record.
Event recording: event records can be formed for plant-level AGC system alarms, locking reasons, personnel operation and the like, all limiting conditions are automatically checked, the conditions cannot be automatically locked, and the conditions are restored to be automatically unlocked.
And (3) authority management: the system has the authority management function, supports user-defined users and operation authorities, prohibits unauthorized operation, and has operation logs for future reference in all manual operations.
Data acquisition analysis
The plant-level AGC load optimization scheduling project of the power plant can be divided into the following six parts in total through data acquisition and analysis:
(1) and collecting and analyzing DCS data. Data acquisition (all consisting of field points) is performed from a plurality of machine sets, and parameters required by calculation of coal consumption curves and online distribution are mainly used.
(2) The plant-level AGC intelligent control unit inputs data analysis to an online load distribution program, the source is divided into three parts, namely DCS, RTU and operator station, and the data are all required by the online distribution program.
(3) The plant-level AGC intelligent control unit outputs data to an online load distribution program, and the data are divided into two parts, namely an RTU and an operator station. The RTU part issues and executes the allocation command, and the operator station part is used for display.
(4) The operator station inputs data, the source is divided into three parts, DCS, RTU and, for the picture display data source.
(5) The operator station outputs data, and the destination is a plant-level AGC intelligent control list, and provides data for online distribution.
(6) The data collected by the AGC performance server of the second area come from the RTU and the DCS, and the related data are sent to the second area through the AGC intelligent control unit for off-line distribution and use.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (5)
1. A plant-level load optimal distribution method based on big data analysis and multi-objective optimization is characterized in that a control node is added in a power plant monitoring system network, a plant-level AGC system which is communicated with a network control system is arranged to receive a whole plant load instruction sent in real time by scheduling, all unit production operation data are collected on line at the same time, an intelligent optimization algorithm is adopted for optimization, an optimal load distribution strategy is obtained, given target value optimal distribution is set according to a remote regulation instruction and an operation attendant, the output of a unit is automatically adjusted, and the distribution strategy meets the requirements of energy-saving power generation scheduling.
2. The plant-level load optimization distribution method based on big data analysis and multi-objective optimization according to claim 1, characterized in that the plant-level AGC intelligent control unit receives the unit operation data and booster station electrical data transmitted by the network control system in real time and receives the load instruction forwarded by the network control system;
the plant-level AGC operator station is communicated with the plant-level AGC intelligent control unit to realize information exchange and data sharing so as to finish automatic monitoring and control of the operation of a plant-level AGC system;
and performing AGC offline guidance through an AGC intelligent guidance system, and acquiring data from a vibration interface server by connecting an industrial personal computer to a vibration management expert system network through required DCS data.
3. The plant-level load optimization distribution method based on big data analysis and multi-objective optimization according to claim 2, wherein the AGC intelligent control unit is arranged in a safety I area, required network control system and unit DCS related data are transmitted to an AGC performance server in a safety II area through a forward network safety isolation device, and the data communication is one-way transmission.
4. The plant-level load optimization distribution method based on big data analysis and multi-objective optimization according to claim 3, characterized in that a reverse network security isolation device is arranged between the industrial personal computer and the AGC intelligent control unit, and the industrial personal computer sends the received DCS related data to the plant-level AGC intelligent control unit in a file manner.
5. The plant-level load optimization distribution method based on big data analysis and multi-objective optimization according to claim 1, characterized in that a plant-level AGC system receives a dispatching single-machine AGC and a plant-level AGC, and directly sends a single-machine AGC command to each unit set DCS when dispatching a single-machine mode; and issuing the plant instructions to each unit DCS according to the distribution strategy when the plant mode or the local power plant mode is dispatched.
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| CN113300410A (en) * | 2021-04-14 | 2021-08-24 | 华能国际电力股份有限公司大连电厂 | Whole-plant load optimization control system and method for cogeneration unit |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113300410A (en) * | 2021-04-14 | 2021-08-24 | 华能国际电力股份有限公司大连电厂 | Whole-plant load optimization control system and method for cogeneration unit |
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