CN116581804A - Large-scale energy storage power station health management system and operation method - Google Patents
Large-scale energy storage power station health management system and operation method Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 227
- 230000036541 health Effects 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 15
- 238000005070 sampling Methods 0.000 claims abstract description 24
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 238000013210 evaluation model Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 7
- 238000004088 simulation Methods 0.000 claims description 7
- 230000006399 behavior Effects 0.000 claims description 6
- 238000007599 discharging Methods 0.000 claims description 5
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical group [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 229910001416 lithium ion Inorganic materials 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
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- 230000008569 process Effects 0.000 claims description 3
- 230000000007 visual effect Effects 0.000 claims description 3
- 210000000352 storage cell Anatomy 0.000 claims description 2
- 230000004044 response Effects 0.000 abstract description 6
- 238000004891 communication Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
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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
- H02J3/28—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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
- G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
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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
- 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]
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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
- 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
- H02J13/00002—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 monitoring
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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
- 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
- H02J13/00032—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for
- H02J13/00034—Systems characterised by the controlled or operated power network elements or equipment, the power network elements or equipment not otherwise provided for the elements or equipment being or involving an electric power substation
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention provides a health management system and an operation method of a large-scale energy storage power station, which belong to the technical field of power system control and comprise the following steps: the physical entity part of the large-scale energy storage power station comprises a plurality of energy storage subsystems, a sampling module and a core control module; the digital twin part of the large-scale energy storage power station is connected with the physical entity part of the large-scale energy storage power station, and is used for real-time monitoring of key parameters, and evaluating and predicting the running condition and internal parameters of the physical entity part of the large-scale energy storage power station by constructing a digital mirror image model of the physical entity. The key data of system operation can be obtained in real time, the charge and discharge states of all energy storage subsystems can be adjusted in time, and the safety and response speed of the large-scale energy storage power station are improved.
Description
Technical Field
The invention belongs to the technical field of power system control, and relates to a large-scale energy storage power station health management system and an operation method.
Background
The large-scale energy storage power station has higher response speed and adjustment precision, and is an important mode of inertia support and peak shaving of the power system. For the power system, the State of Charge (SOC) of each part of the energy storage system of the large-scale energy storage power station must have high consistency to ensure sufficient available capacity and safety. Along with the continuous huge large-scale energy storage power stations, great difficulty exists in real-time monitoring and evaluation of each energy storage subsystem and timely adjustment of the state of charge. Traditional energy storage power stations rely on high-bandwidth communication modes and control algorithms, so that the safety and response speed of the energy storage power stations are low. The large-scale energy storage power station has a plurality of energy storage sub-systems, the voltage, the current and other monitoring data of each system are various and huge in quantity, and a severe challenge is provided for the data transmission capacity and the transmission speed of the communication system.
The traditional large-scale energy storage power station mostly adopts a centralized control mode, and the monitoring and regulation of the control system on the energy storage subsystem extremely depend on a control algorithm, so that the requirements on the real-time performance and accuracy of data and the reliability of the control algorithm are too high. When any link of the communication line and the control system fails, the large-scale energy storage power station may face problems such as overcharge, overdischarge, and unbalanced SOC, so that the response speed of the system is reduced, and safety accidents may occur to the system when serious.
Disclosure of Invention
In order to achieve the above purpose, the invention provides a large-scale energy storage power station health management system and an operation method thereof, which solve the problem that the large-scale energy storage power station health management system has too high requirements on the real-time property and accuracy of data and the reliability of a control algorithm.
The technical scheme adopted by the invention is as follows:
a first aspect of an embodiment of the present invention provides a health management system for a large-scale energy storage power station, comprising: the physical entity part of the large-scale energy storage power station comprises a plurality of energy storage subsystems, a sampling module and a core control module; the energy storage subsystems are provided with #1 to #n energy storage subsystems, one energy storage subsystem corresponds to one sampling module and a plurality of energy storage batteries, and the number of the energy storage batteries corresponding to one energy storage subsystem is at least three; the core control module comprises a DSP+FPGA and is used for adjusting the output power instruction of each energy storage subsystem and controlling the charge and discharge behaviors of each energy storage subsystem; the digital twin part of the large-scale energy storage power station is connected with the physical entity part of the large-scale energy storage power station, and is used for real-time monitoring of key parameters, and evaluating and predicting the running condition and internal parameters of the physical entity part of the large-scale energy storage power station by constructing a digital mirror image model of the physical entity.
Further, the digital twin part of the large-scale energy storage power station comprises a full life cycle model and a digital model operation module; the full life cycle model is arranged in a digital system and used as an independent model and a real-time online model for carrying out simulation experiments and data analysis;
the full life cycle model comprises an SOC evaluation model, and the SOC evaluation model is calculated by adopting the following formula:
（1）
in the formula ,representing energy storage battery->At->Time->Status (S)>Representing energy storage battery->At->Time->Status (S)>Representing energy storage battery->At->Output current at time, ">Indicating the charge-discharge efficiency of the battery, +.>Representation->Evaluating the residual capacity of the energy storage battery in the model environment; wherein,is obtained by sampling a system;
the full life cycle model also comprises a charge and discharge loss model, and the charge and discharge loss power is calculated by adopting the following formula:
（2）
in the formula ,representing charge/discharge loss power of the energy storage battery, < >>Representing the charge-discharge current of the energy storage battery, +.>Representing the equivalent output resistance of the energy storage battery;
the full life cycle model also comprises a life evaluation model, and is calculated by the following formula:
（3）
in the formula ,representation->Number of remaining cycles of the number-storing battery, +.>Representation->Rated total cycle number of number-storing battery, +.>Representation->Number of times the number of energy storage battery has been cycled, +.>Representing the life loss coefficient of the energy storage battery, selecting the energy storage battery to be between 0.1 and 0.3 according to the departure setting of the energy storage battery, and adding the energy storage battery to the energy storage battery>Representing the total energy conversion quantity of the energy storage battery;
and the digital model operation part is used for collecting key operation data of the actual system of the large-scale energy storage power station and inputting the key operation data into the digital model operation server for solving and operation.
Further, the digital model operation part adopts an X86 server architecture to operate for multithreading data processing.
Further, the energy storage subsystem is formed by connecting a plurality of groups of energy storage batteries in parallel and is used for increasing the output current and redundancy capacity of the energy storage system.
Further, the energy storage battery is a lithium ion energy storage battery.
Further, the sampling module samples the total output voltage and the total output current of each energy storage subsystem and outputs the total output voltage and the total output current to the system of the digital twin part.
Further, the core control module adjusts output power instructions of all the energy storage subsystems according to the charge and discharge adjustment instructions of the energy storage subsystems generated by the digital twin model, and simultaneously controls charge and discharge behaviors of all the energy storage subsystems.
In this embodiment, the operation method based on the large-scale energy storage power station health management system includes:
step 1, acquiring real-time operation key data of a large-scale energy storage power station through a physical entity part of the large-scale energy storage power station, wherein after a period of time passes through a charging and discharging process of the large-scale energy storage power station, the SOC of each energy storage subsystem is changed in a differentiated mode, and a sampling module acquires real-time output voltage and current key data of each energy storage subsystem to provide data support for analysis and decision making of a full life cycle model;
step 2, through a full life cycle model, the method is used for carrying out simulation experiments and data analysis, and carrying out multi-time scale high-resolution reduction on a large-scale energy storage power station entity;
and 3, acquiring key operation data of the actual system of the large-scale energy storage power station through a digital model operation module, inputting the key operation data into a digital model operation server to solve and operate, reproducing and analyzing the real-time operation condition of the large-scale energy storage power station, generating corresponding charts and data, and performing visual display.
The beneficial effects of the invention are as follows: the health management system of the large-scale energy storage power station comprises: the physical entity part of the large-scale energy storage power station comprises a plurality of energy storage subsystems, a sampling module and a core control module; the digital twin part of the large-scale energy storage power station is connected with the physical entity part of the large-scale energy storage power station, and is used for real-time monitoring of key parameters, and evaluating and predicting the running condition and internal parameters of the physical entity part of the large-scale energy storage power station by constructing a digital mirror image model of the physical entity. The method realizes the real-time collection of the operation data and the operation in the digital model, can evaluate and predict the charge and discharge power and the SOC of each energy storage subsystem in real time, further generates the charge and discharge adjustment instruction of the energy storage subsystem, and improves the safety, the service life and the response speed of the large-scale energy storage power station.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a system architecture for health management of a large-scale energy storage power station based on digital twinning technology according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of a model operation flow of a full life cycle model according to an embodiment of the present invention;
FIG. 3 is a flowchart of a system for health management of a large-scale energy storage power station based on digital twinning technology according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a health management system of a large-scale energy storage power station based on a digital twin technology according to an embodiment of the present invention.
A first aspect of an embodiment of the present invention provides a health management system for a large-scale energy storage power station, comprising: the physical entity part of the large-scale energy storage power station comprises a plurality of energy storage subsystems, a sampling module and a core control module; the digital twin part of the large-scale energy storage power station is connected with the physical entity part of the large-scale energy storage power station, and is used for real-time monitoring of key parameters, and evaluating and predicting the running condition and internal parameters of the physical entity part of the large-scale energy storage power station by constructing a digital mirror image model of the physical entity.
In this embodiment, the energy storage subsystem is formed by connecting several groups of energy storage batteries in parallel, so as to increase the output current and redundancy capability of the energy storage system. The energy storage battery module mainly adopts a lithium ion energy storage battery and has the advantages of high power density, strong charging and discharging capability, long service life and the like.
It should be noted that, the plurality of energy storage subsystems are provided with #1 to #n energy storage subsystems, wherein n is a corresponding value according to the experimental requirement. One sub-energy storage subsystem corresponds to one sampling module and a plurality of energy storage batteries, and the number of the energy storage batteries corresponding to one sub-energy storage subsystem is at least three.
In this embodiment, the sampling module may include a signal acquisition circuit, a signal sampling device, and the like, where the sampling module samples the total output voltage and the total output current of each energy storage subsystem, and then outputs the sampled total output voltage and the total output current to the digital twin system.
In this embodiment, the core control module includes a dsp+fpga, and adjusts output power instructions of each energy storage subsystem according to charge and discharge adjustment instructions of the energy storage subsystem generated by the digital twin model, and controls charge and discharge behaviors of each energy storage subsystem.
In this embodiment, the full life cycle model is a digital model of a large-scale energy storage power station built in a digital system, and the full life cycle model can be used as an independent model for performing simulation experiments or as a real-time online model for performing data analysis.
Furthermore, the digital model operation part is used for collecting key operation data of the actual system of the large-scale energy storage power station in real time, and mainly comprises total output voltage and total output current of each energy storage subsystem, and then the data are input into the digital model operation server and are solved and operated.
The model operation adopts an X86 server architecture for operation, has the advantages of high execution efficiency, strong multithreading processing capacity and the like, and can provide great convenience for visualization. The actual system 1:1 is reflected in the digital system by establishing a digital twin model of the large-scale energy storage power station.
Further, when the SOC of a certain part of the actual system is changed, the SOC is directly reflected on the output voltage and current, at the moment, the digital twin system collects the operation data of the actual system, the charge and discharge power and the SOC of each energy storage subsystem are estimated and predicted in real time, then the charge and discharge increment instruction of the energy storage subsystem is determined through a real-time intelligent algorithm, and the charge and discharge increment instruction is returned to the actual system through a serial port communication mode.
Further, the digital twin part of the large-scale energy storage power station comprises a full life cycle model and a digital model operation module;
referring to fig. 2, fig. 2 is a schematic diagram of a model operation flow of a full life cycle model according to an embodiment of the invention.
The full life cycle model is arranged in a digital system and used as an independent model and a real-time online model for carrying out simulation experiments and data analysis; and the digital model operation part is used for collecting key operation data of the actual system of the large-scale energy storage power station and inputting the key operation data into the digital model operation server for solving and operation.
Further, the digital model operation part adopts an X86 server architecture to operate for multithreading data processing.
It should be noted that The X86 architecture (The X86 architecture) is a set of computer language instructions executed by a microprocessor.
Furthermore, the energy storage subsystem is formed by connecting a plurality of groups of energy storage batteries in parallel, and is used for increasing the output current and redundancy capacity of the energy storage system, wherein the output current capacity depends on the parallel connection quantity of the energy storage batteries, when the quantity of the parallel connection batteries is increased, when one of the energy storage batteries fails, the output power of the energy storage battery sub-module can be reduced, but the normal operation of other residual energy storage batteries can not be influenced.
Further, the energy storage battery is a lithium ion energy storage battery, and the model is CATL-280Ah.
Further, the sampling module samples the total output voltage and the total output current of each energy storage subsystem and outputs the total output voltage and the total output current to the digital twin system, the sampling chip is an ADS8556 high-precision analog-to-digital conversion sampling chip, and the transmission cable adopts RVSP4 core twisted pair shielding wires.
Further, the core control module adjusts output power instructions of all the energy storage subsystems according to charge and discharge adjustment instructions of the energy storage subsystems generated by the digital twin model, meanwhile controls charge and discharge behaviors of all the energy storage subsystems, the model of the DSP is TMS320F28335, and the model of the FPGA is ALTERA EP4CE6E22C8N.
Referring to fig. 3, fig. 3 is a flowchart illustrating an operation of a large-scale energy storage power station health management system based on a digital twin technology according to an embodiment of the present invention.
A second aspect of an embodiment of the present invention provides a method for operating a health management system of a large-scale energy storage power station, including: step 1, acquiring real-time operation key data of a large-scale energy storage power station through a physical entity part of the large-scale energy storage power station, wherein after a period of time passes through a charging and discharging process of the large-scale energy storage power station, the SOC of each energy storage subsystem is changed in a differentiated mode, and a sampling module acquires real-time output voltage and current key data of each energy storage subsystem to provide data support for analysis and decision making of a full life cycle model; step 2, through a full life cycle model, the method is used for carrying out simulation experiments and data analysis, and carrying out multi-time scale high-resolution reduction on a large-scale energy storage power station entity; and 3, acquiring key operation data of the actual system of the large-scale energy storage power station through a digital model operation module, inputting the key operation data into a digital model operation server to solve and operate, reproducing and analyzing the real-time operation condition of the large-scale energy storage power station, generating corresponding charts and data, and performing visual display.
In this embodiment, real-time operation key data of the large-scale energy storage power station is collected, including that after a period of time of charging and discharging of the large-scale energy storage power station, SOC of each energy storage subsystem is changed differently. At this time, the sampling module needs to collect real-time output voltage and current key data of each energy storage subsystem, and data support is provided for analysis and decision of the full life cycle model.
Further, a full life cycle model of the large-scale energy storage power station is established, wherein the full life cycle model comprises a circuit model, an SOC evaluation model, a charge and discharge loss model, a life evaluation model, a communication interaction model and the like of each energy storage subsystem.
It should be noted that, the function of the SOC estimation model is to infer an estimation model of the remaining capacity of the energy storage battery according to the collected voltage and current key data of the energy storage subsystem. The SOC estimation model is calculated using the following equation:
(1)
in the formula ,representing energy storage battery->At->State of charge (SOC) at time->Representing an energy storage batteryAt->State of charge (SOC) at time->Representing energy storage battery->At->Output current at time, ">Indicating the charge-discharge efficiency of the battery, +.>And representing the residual capacity of the energy storage battery in the SOC estimation model environment. Wherein (1)>Is obtained by sampling the system.
It should be noted that, the charge-discharge loss model is used to calculate the charge-discharge loss power of the energy storage subsystem. The charge-discharge loss power is calculated by adopting the following formula:
(2)
in the formula ,representing charge/discharge loss power of the energy storage battery, < >>Representing the charge-discharge current of the energy storage battery, +.>Representing the equivalent output resistance of the energy storage cell.
It should be noted that the life assessment model is used to assess the remaining life of the battery of the energy storage subsystem, i.e. the number of remaining cycles. The remaining number of cycles is affected by the depth of each discharge and the total number of cycles, calculated by the following equation:
(3)
in the formula ,representation->Number of remaining cycles of the number-storing battery, +.>Representation->Rated total cycle number of number-storing battery, +.>Representation->Number of times the number of energy storage battery has been cycled, +.>Representing the life loss coefficient of the energy storage battery, selecting the energy storage battery to be between 0.1 and 0.3 according to the departure setting of the energy storage battery, and adding the energy storage battery to the energy storage battery>Representing the total energy conversion of the energy storage battery.
In this embodiment, the communication interaction model adopts an RS485 communication mode.
In this embodiment, by establishing a full life cycle model, multi-time scale high resolution reduction can be performed on large scale energy storage power station entities. The circuit model is exemplary of the physical characteristics of millisecond response of the energy storage battery, and the life assessment model can be exemplary of the physical characteristics of long-time scale performance and life decay of the large-scale energy storage power station.
Further, by combining the established full life cycle model and the acquired real-time operation key data, the real-time operation condition of the large-scale energy storage power station is reproduced and analyzed in the digital model operation server, and charts and data such as a real-time SOC state distribution diagram, a voltage current curve, a charge-discharge loss ratio, circulation times, expected residual life, fault monitoring and alarming, a time prediction window and the like are generated and are visually displayed. The operation staff can master the whole condition and the local detail of the physical entity according to the generated real-time operation condition, predict and pre-judge the future operation condition in advance according to the time prediction result of the digital twin system, and further generate the charge and discharge adjustment instruction of the energy storage subsystem of the large-scale energy storage power station.
In this embodiment, the steps described above with reference to fig. 3 further include the steps of detecting the SOC of the large-scale energy storage power station, collecting real-time operation key data of the large-scale energy storage power station, inputting the real-time operation key data into the full-life-cycle model, and simultaneously establishing the full-life-cycle model of the large-scale energy storage power station.
Further, model operation is performed, graphs and data such as a real-time SOC state distribution diagram, expected residual life, a time prediction window and the like are produced, running conditions are prejudged in advance, and a charge and discharge adjustment instruction of an energy storage subsystem of the large-scale energy storage power station is produced.
Claims (7)
1. A large scale energy storage power plant health management system, comprising:
the physical entity part of the large-scale energy storage power station comprises a plurality of energy storage subsystems, a sampling module and a core control module;
the system comprises a plurality of energy storage subsystems, a sampling module, a plurality of energy storage batteries and at least three energy storage subsystems, wherein #1 to #n energy storage subsystems are arranged in the plurality of energy storage subsystems, one energy storage subsystem corresponds to the sampling module and the plurality of energy storage batteries, and the number of the energy storage batteries corresponding to the one energy storage subsystem is at least three;
the core control module comprises a DSP+FPGA and is used for adjusting the output power instruction of each energy storage subsystem and controlling the charge and discharge behaviors of each energy storage subsystem;
the digital twin part of the large-scale energy storage power station is connected with the physical entity part of the large-scale energy storage power station, and is used for real-time monitoring of key parameters, and evaluating and predicting the running condition and internal parameters of the physical entity part of the large-scale energy storage power station by constructing a digital mirror image model of the physical entity;
the digital twin part of the large-scale energy storage power station comprises a full life cycle model and a digital model operation module;
the full life cycle model is arranged in a digital system and used as an independent model and a real-time online model for carrying out simulation experiments and data analysis;
the full life cycle model comprises an SOC evaluation model, and the SOC evaluation model is calculated by adopting the following formula:
（1）
in the formula ,representing energy storage battery->At->Time->Status (S)>Representing energy storage battery->At->Time->Status (S)>Representing energy storage battery->At->Output current at time, ">Indicating the charge-discharge efficiency of the battery, +.>Representation->Evaluating the residual capacity of the energy storage battery in the model environment; wherein (1)>Is obtained by sampling a system;
the full life cycle model also comprises a charge and discharge loss model, and the charge and discharge loss power is calculated by adopting the following formula:
（2）
in the formula ,representing charge/discharge loss power of the energy storage battery, < >>Representing the charge-discharge current of the energy storage battery, +.>Representing the equivalent output resistance of the energy storage battery;
the full life cycle model also comprises a life evaluation model, and is calculated by the following formula:
（3）
in the formula ,representation->Number of remaining cycles of the number-storing battery, +.>Representation->Rated total cycle number of number-storing battery, +.>Representation->Number of times the number of energy storage battery has been cycled, +.>Representing the life loss coefficient of the energy storage battery, selecting the energy storage battery to be between 0.1 and 0.3 according to the departure setting of the energy storage battery, and adding the energy storage battery to the energy storage battery>Representing the total energy conversion quantity of the energy storage battery;
and the digital model operation part is used for collecting key operation data of the actual system of the large-scale energy storage power station and inputting the key operation data into the digital model operation server to solve and operate.
2. The system of claim 1, wherein the digital model operation portion operates using an X86 server architecture for multithreading data processing.
3. The large scale energy storage power station health management system of claim 1, wherein said energy storage subsystem is formed of a plurality of sets of energy storage cells connected in parallel for increasing the output current and redundancy capabilities of the energy storage system.
4. The large scale energy storage power station health management system of claim 1, wherein said energy storage battery is a lithium ion energy storage battery.
5. The large scale energy storage power station health management system of claim 1, wherein said sampling module samples the total output voltage and total output current of each energy storage subsystem before outputting to said digital twin section system.
6. The large scale energy storage power station health management system of claim 1, wherein said core control module adjusts output power instructions of each of said energy storage subsystems according to energy storage subsystem charge-discharge adjustment instructions generated by a digital twin model while controlling charge-discharge behavior of each of said energy storage subsystems.
7. A method of operation based on the large scale energy storage power station health management system of claim 1, comprising:
step 1, acquiring real-time operation key data of a large-scale energy storage power station through a physical entity part of the large-scale energy storage power station, wherein after a period of time passes through a charging and discharging process of the large-scale energy storage power station, the SOC of each energy storage subsystem is changed in a differentiated mode, and a sampling module acquires real-time output voltage and current key data of each energy storage subsystem to provide data support for analysis and decision making of a full life cycle model;
step 2, through a full life cycle model, the method is used for carrying out simulation experiments and data analysis, and carrying out multi-time scale high-resolution reduction on a large-scale energy storage power station entity;
and 3, acquiring key operation data of an actual system of the large-scale energy storage power station through a digital model operation module, inputting the key operation data into a digital model operation server to solve and operate, reproducing and analyzing the real-time operation condition of the large-scale energy storage power station, generating corresponding charts and data, and performing visual display.
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