CN116345521B - Energy storage battery array cluster monitoring method, system and storage medium - Google Patents
Energy storage battery array cluster monitoring method, system and storage medium Download PDFInfo
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- 238000004146 energy storage Methods 0.000 title claims abstract description 103
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 36
- 239000000178 monomer Substances 0.000 claims description 34
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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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- 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
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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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- 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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- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to a method, a system and a storage medium for monitoring an energy storage battery array cluster, wherein the method comprises the following steps: real output parameters of the renewable energy system are collected in real time through EMS energy management service; predicting ultra-short term power according to the output parameters; if the fluctuation amount of the actual output parameters exceeds the limit range, decomposing the actual output power to obtain a stabilized expected value and an energy storage system power reference value; correcting the power reference value of the energy storage system through the SOC value and the charge-discharge power limit value of the battery system in the direct-current cabin; and the energy storage converter outputs according to the corrected power reference value. According to the invention, the distributed battery management and energy management integrated system nodes are used for carrying out data acquisition and subsequent control tasks of the respective direct current cabins, the real-time performance of data acquisition and calculation is high, and the control instructions can be issued at the time to carry out power balance on the renewable energy system, so that the grid-connected power of the renewable energy system is smooth, and the running stability of a power grid is ensured.
Description
Technical Field
The present invention relates to the field of energy storage control technologies, and in particular, to a method, a system, and a storage medium for monitoring an energy storage battery array cluster.
Background
The generated power of renewable energy sources is directly related to the meteorological factors, and the generated power has obvious random fluctuation and instability. Along with the gradual increase of the installed capacity of the renewable energy sources in the power grid, the random fluctuation of the generated power of the renewable energy sources can influence the real-time power balance of the power grid, so that the voltage and the frequency of the power grid are fluctuated, and the electric energy quality and the stability of the power grid are directly influenced. In addition, the random fluctuation brings a plurality of uncertainties to power grid dispatching, and increases the safety operation risk of the system.
With the continuous development of batteries and integrated technologies thereof, the application mode of large-scale distributed and centralized battery energy storage power stations is gradually becoming a preferred scheme. The existing BMS and EMS are supplied by different manufacturers, the BMS is deployed in each direct current battery compartment, the EMS is deployed in a monitoring compartment by adopting a single machine or a double-machine centralized redundant system, the data acquisition of the EMS is realized by adopting a polling mechanism, but the quantity of the acquired objects in a large energy storage system is huge, the data update period is long due to the fact that the polling mechanism is adopted by the EMS, corresponding data cannot be provided for some analysis of the system, and the problems of high storage resource cost and data acquisition lag are caused due to the fact that the capacity of the large energy storage system is increased to hundreds of megawatts, and the problem that obvious random fluctuation and instability of the generated power cannot be regulated in a low-delay mode is caused. And the EMS adopts redundant design, the same data needs to be backed up in two EMS, the user construction cost is high, and the data volume is larger.
The existing energy storage system monitoring scheme is mainly used for carrying out energy storage cooperative control by adding a cloud platform service unit to collect power grid parameters and adding parameters of power distribution nodes in a new controller collection area. Such as: the patent of China invention with publication number CN111725889A, energy storage cluster rapid control system and method based on 3 S+cloud architecture, which comprises more than one battery unit, more than one battery management optimizing unit, more than one power converting unit, 3S integrated controller, rapid control managing unit, cloud platform service unit, wherein the battery units are connected on a feeder, all battery units of the same distribution node on the feeder form a region, each region is provided with a battery management optimizing unit and a power converting unit, and the battery units in the region are connected with the battery management optimizing units in the region. However, since the architecture of the energy storage system is not changed in the above-mentioned invention patent, in which the battery management optimization system with the energy management function is still a traditional centralized EMS, the real-time performance of the system data is not fundamentally improved, and in addition, the deployment and construction of the battery management optimization system increase the construction cost of the user, for a large-scale energy storage system with a multi-level architecture, when the real-time adjustment of the power grid operation condition is required according to the collected data of the renewable energy system, the battery pack data, the battery cluster data and the overall data of the energy storage power station, the control task of the centralized control mode is heavy, which easily causes the problems of untimely issuing of the control command and the like, and the process of safely incorporating the renewable energy system into the power grid cannot be controlled in time. And when any one of the rapid control management unit, the battery management optimization system, the 3S integrated control system and the cloud platform service system fails, the overall operation of the system can be affected, and the availability of the system is low.
Disclosure of Invention
To achieve the above and other advantages and in accordance with the purpose of the present invention, a first object of the present invention is to provide a method for monitoring an energy storage battery array cluster, comprising the steps of:
the method comprises the steps that actual output parameters of a renewable energy system are collected in real time through EMS energy management service in a DC cabin battery management and energy management integrated system node;
acquiring the last actual output parameter of the renewable energy system in the corresponding distributed database node through EMS energy management service;
predicting ultra-short-term power of the renewable energy system according to the last actual output parameter and the current actual output parameter of the renewable energy system by using an EMS energy management service;
judging whether the fluctuation amount of the actual output parameters of the renewable energy system in the preset time is within the grid-connected power limit range of the renewable energy system or not through EMS energy management service;
otherwise, decomposing the actual output power of the renewable energy system through EMS energy management service to obtain a stabilization expected value of the renewable energy system and a power reference value of the energy storage system;
calculating the SOC value of the battery system in the direct-current cabin in real time through BMS energy management service in the node of the battery management and energy management integrated system in the direct-current cabin;
Judging whether the calculated SOC value of the battery system in the direct-current cabin reaches an SOC limit value or not through EMS energy management service, or whether the actual output power of the battery system reaches a charge-discharge power limit value or not;
respectively correcting the power reference values of the energy storage system in the charge and discharge modes through EMS energy management service to obtain corrected power reference values of the energy storage system;
transmitting the corrected energy storage system power reference value to an energy storage converter corresponding to a battery management and energy management integrated system node in the direct-current cabin through EMS energy management service;
and outputting according to the corrected power reference value of the energy storage system through the energy storage converter to obtain the actual output power of the battery system in the direct-current cabin.
Further, the method comprises the steps that the ultra-short-term power of the renewable energy system is predicted according to the last actual output parameter and the current actual output parameter of the renewable energy system through the EMS energy management service, and the ultra-short-term power of the renewable energy system is predicted according to the actual output power and the last actual output power of the renewable energy system through a renewable energy system power prediction model in the EMS energy management service; and the renewable energy system power prediction model is a BP neural network prediction model.
Further, the determining, by the EMS energy management service, whether the fluctuation amount of the ultra-short-term power of the renewable energy system within the preset time is within the grid-connected power limit range of the renewable energy system includes the following steps:
calculating the fluctuation amount of the actual output power of the renewable energy system within 1min and 10 min;
judging whether the fluctuation amount of the actual output power of the renewable energy system within 1min exceeds 1/10 of the rated power or not;
if yes, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to the step of decomposing the actual output power of the renewable energy system through the EMS energy management service;
judging whether the fluctuation amount of the actual output power of the renewable energy system within 10min exceeds 1/3 of the rated power or not;
and if so, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to the step of decomposing the actual output power of the renewable energy system through the EMS energy management service.
Further, the decomposing the actual output power of the renewable energy system by the EMS energy management service includes the steps of:
Carrying out a plurality of layers of wavelet packet decomposition on the actual output power of the renewable energy system by adopting the wavelet packet to obtain a low-frequency component and a high-frequency component;
judging whether the frequency corresponding to the obtained low-frequency component is within the grid-connected power limit range of the renewable energy system or not;
and if so, determining the current decomposition layer number, taking the frequency corresponding to the low-frequency component corresponding to the current decomposition layer number as a stabilizing expected value of the renewable energy system, and taking the frequency corresponding to the high-frequency component corresponding to the current decomposition layer number as a power reference value of the energy storage system.
Further, the calculating, in real time, the SOC value of the battery system in the dc compartment through the BMS energy management service in the node of the integrated battery management and energy management system in the dc compartment includes the following steps:
in the running process of the battery system of the direct current cabin, estimating the SOC of the corresponding battery cluster through the battery cluster controller;
and acquiring the estimated battery cluster SOC value of the battery cluster controller through EMS energy management service, and averaging all the acquired battery cluster SOC value estimation values to obtain the SOC value of the battery system in the direct-current cabin.
Further, the estimating of the SOC of the corresponding battery cluster by the battery cluster controller includes the steps of:
Establishing an equivalent circuit model of the battery cluster;
identifying model parameters by a forgetting factor least square method based on HPPC test data at-20 ℃ to 25 ℃; the HPPC test data based on the temperature of-20 ℃ to 25 ℃ is a data set of HPPC test of the battery cluster at the temperature of-20 ℃ to 25 ℃, wherein the data set comprises temperature, voltage and current;
establishing a cell database of the environmental temperature through the model parameter identification result;
and estimating the SOC of the battery cluster data acquired by the battery cluster controller through a battery core database.
Further, the determining, by the EMS energy management service, whether the calculated SOC value of the battery system in the dc compartment reaches the SOC limit value includes the steps of:
if the power reference value of the energy storage system is negative, judging that the working state of the energy storage system is a discharging state, and judging whether the calculated SOC value of the battery system in the direct current cabin reaches a preset value at the end of discharging;
if yes, jumping to the step of respectively correcting the power reference values of the energy storage system in the charge-discharge mode through the EMS energy management service;
if the power reference value of the energy storage system is a positive number, judging that the working state of the energy storage system is a charging state, and judging whether the calculated SOC value of the battery system in the direct current cabin reaches a preset value at the end of charging;
And if yes, jumping to the step of respectively correcting the power reference values of the energy storage system in the charge and discharge modes through the EMS energy management service.
Further, the correcting the power reference values of the energy storage system in the charge-discharge mode through the EMS energy management service includes the following steps:
calculating SOC percentages respectively through a preset value at the end of charge and a preset value at the end of discharge;
correcting the power reference value of the energy storage system in the charge-discharge mode through the SOC percentage and the correction coefficient corresponding to the SOC;
obtaining a monomer highest voltage value, a monomer lowest voltage value and a monomer highest temperature value through EMS energy management service;
if the highest voltage value of the monomer in the battery system is larger than the monomer charge cut-off voltage and/or the lowest voltage value of the monomer is smaller than the monomer discharge cut-off voltage and/or the highest temperature value of the monomer is larger than the charge and discharge cut-off temperature, PID adjustment is carried out on the power reference value of the energy storage system in the charge and discharge mode through the current PID adjustment value and the highest voltage value, the lowest voltage value and the highest temperature value of the monomer in the battery system;
the method also comprises the following steps:
if the Kubernetes cluster service in the direct current cabin finds out that the software service in the main controller fails, starting a new service and closing an abnormal service;
If the Kubernetes cluster service in the direct current cabin finds out the hardware service fault in the main controller, the new starting service in other main controllers is allocated, and the subordinate system of the fault main controller is taken over.
A second object of the present invention is to provide a computer readable storage medium having stored thereon program instructions which, when executed, implement the above-described method.
The third object of the present invention is to provide an energy storage battery array cluster monitoring system for implementing the above method, which includes a battery management and energy management integrated system node disposed in each dc cabin, the battery management and energy management integrated system node in each dc cabin is in communication connection with each other, the battery management and energy management integrated system node in each dc cabin is in communication connection with a corresponding energy storage converter and a coordination controller, the battery management and energy management integrated system node includes a cluster service management, a database middleware and a distributed database node, the cluster service management, the database middleware and the distributed database node are disposed in a master controller, the master controller is in communication connection with a plurality of battery cluster controllers, the battery cluster controllers are in communication connection with a plurality of battery pack controllers, the master controller is used for management control of the dc cabin, the battery cluster controllers are used for collection and control of corresponding battery cluster data, the battery pack controllers are used for data collection of the battery cores, all the battery management and energy management integrated system nodes include a kuberer, a kuberer manager and a BMS monitor server, and a BMS monitor server are distributed to a plurality of battery clusters through a network manager, and a BMS monitor server are distributed to a management server, and a BMS monitor server is distributed to a data manager, and a management server is distributed to a management server, and a management server is used for collecting data of a management system;
The Kubernetes cluster service is used for responding to a user request, and checking and controlling operation parameters, alarm states and operation modes of all node data and equipment in the management system; when the software service fault in the main controller is found, starting a new service and closing an abnormal service; when the hardware service fault in the main controller is found, the new starting service in other main controllers is allocated, and the subordinate system of the fault main controller is taken over;
the database middleware is used for setting and managing the redundant backup proportion of each distributed database node, allocating the newly started service in other main controllers and taking over the subordinate system of the fault main controller.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a monitoring method and a system for an energy storage battery array cluster, which support distributed data acquisition monitoring management and data storage, distribute data acquisition pressure to each sub-node service, greatly improve the real-time performance and reliability of system data, and simultaneously adopt a BMS and EMS integrated comprehensive management mode to reduce the construction cost of a user system.
According to the invention, the distributed battery management and energy management integrated system nodes are used for carrying out data acquisition and subsequent control tasks of the respective direct current cabins, the real-time performance of data acquisition and calculation is high, and the control instructions can be issued at the time to carry out power balance on the renewable energy system, so that the grid-connected power of the renewable energy system is smooth, and the running stability of a power grid is ensured.
According to the invention, the influence of working conditions and states on the battery system in the direct-current cabin is considered, the performance is reduced, the maximum charge and discharge power and the residual capacity of the battery system can be changed, the SOC and the charge and discharge power of the battery system in each direct-current cabin are adjusted through the distributed battery management and energy management integrated system nodes, and then the power reference value of each direct-current cabin battery system is corrected through the SOC and the charge and discharge power of the battery system in the direct-current cabin, so that the damage of deep charge and discharge to the battery system in the direct-current cabin can be avoided, the battery system works in the optimal power range, and the service life of the system is prolonged.
According to the invention, each battery management and energy management integrated system node is deployed in each direct current cabin, all monitoring management services are uniformly managed and monitored by the Kubernetes cluster service, when service abnormality is found, new services are started in time, abnormal services are closed, and new starting services in other controllers are allocated, the subordinate system of the fault main controller is taken over, and the high availability of the services is ensured.
The monitoring management service stores the acquired data into the distributed database through the database middleware, and the redundant backup proportion of each distributed database node can be managed through the middleware setting, so that high-speed data reading and writing and node hot standby are realized.
Because the system adopts distributed services and nodes, multi-pair multi-data acquisition and storage can be realized, and data interaction can be more efficiently carried out than a centralized EMS system, and the real-time performance and high availability of the system are ensured.
The foregoing description is only an overview of the present invention, and is intended to provide a better understanding of the present invention, as it is embodied in the following description, with reference to the preferred embodiments of the present invention and the accompanying drawings. Specific embodiments of the present invention are given in detail by the following examples and the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method for monitoring an energy storage battery array cluster according to embodiment 1;
fig. 2 is a schematic diagram of an energy storage battery array cluster monitoring system architecture according to embodiment 2;
fig. 3 is a physical deployment schematic diagram of an energy storage battery array cluster monitoring system in embodiment 2;
FIG. 4 is a schematic diagram of the internal topology of a system node according to embodiment 2;
FIG. 5 is a second schematic diagram of the internal topology of the system node according to embodiment 2;
Fig. 6 is a schematic diagram of a storage medium of embodiment 3.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and detailed description, wherein it is to be understood that, on the premise of no conflict, the following embodiments or technical features may be arbitrarily combined to form new embodiments.
In the following description, suffixes such as "module", "component", or "unit" for representing elements are used only for facilitating the description of the present invention, and have no specific meaning per se. Thus, "module," "component," or "unit" may be used in combination.
Because renewable energy systems (such as wind and light resource systems) have large fluctuation, the fluctuation of output can influence the stable operation of the power grid system. The method comprises the steps of collecting current operation information of a renewable energy system in real time through distributed battery management and energy management integrated system nodes, predicting power fluctuation trend of the renewable energy system in real time based on the collected operation information of the renewable energy system, and evaluating charge and discharge capacity of an energy storage battery system in real time. And by means of the rapid adjustment of the energy storage battery system, a stabilizing control strategy is formulated in real time, and the control strategy is rapidly issued, so that the power fluctuation of the renewable energy system is stabilized, namely the ultra-short-term power stabilization of the renewable energy system is realized.
Example 1
An energy storage battery array cluster monitoring method, as shown in fig. 1, comprises the following steps:
the method comprises the steps that actual output parameters of a renewable energy system are collected in real time through EMS energy management service in a DC cabin battery management and energy management integrated system node; as shown in fig. 3, fig. 4 and fig. 5, the EMS energy management service is deployed in a Kubernetes cluster service, the Kubernetes cluster service is deployed in a main controller of the dc cabin, the main controller performs data interaction with a plurality of battery cluster controllers, and the battery cluster controllers performs data interaction with a plurality of battery pack controllers; the actual output parameters of the renewable energy system include actual output power;
acquiring the last actual output parameter of the renewable energy system in the corresponding distributed database node through EMS energy management service; the Kubernetes cluster service stores the acquired data into distributed database nodes through database middleware, and the redundant backup proportion of each distributed database node is managed through database middleware setting;
predicting ultra-short-term power of the renewable energy system according to the last actual output parameter and the current actual output parameter of the renewable energy system by using an EMS energy management service; specifically, a trained renewable energy system power prediction model is arranged in the EMS energy management service, and the renewable energy system power prediction model in the EMS energy management service is used for predicting the ultra-short-term power of the renewable energy system according to the actual output power of the renewable energy system and the last actual output power; the renewable energy system power prediction model is a BP neural network prediction model. The nonlinear relation can be fitted well through the renewable energy system power prediction model, the used data has strong fault tolerance, and the relation between the input data and the output data can be established quickly.
Judging whether the fluctuation amount of the actual output parameters of the renewable energy system in the preset time is within the grid-connected power limit range of the renewable energy system or not through EMS energy management service;
otherwise, decomposing the actual output power of the renewable energy system through EMS energy management service to obtain a stabilization expected value of the renewable energy system and a power reference value of the energy storage system;
when the renewable energy system is safely incorporated into the grid, the maximum limit of active power variation in a short time scale is mainly exceeded. The method specifically comprises the following steps:
calculating the fluctuation amount of the actual output power of the renewable energy system within 1min and 10 min;
judging whether the fluctuation amount of the actual output power of the renewable energy system within 1min exceeds 1/10 of the rated power or not; the power fluctuation range of the renewable energy system is not limited to the above-described setting value, and may be set according to the installed capacity of the renewable energy system.
If yes, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to a step of decomposing the actual output power of the renewable energy system through EMS energy management service;
Judging whether the fluctuation amount of the actual output power of the renewable energy system within 10min exceeds 1/3 of the rated power or not;
and if so, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to a step of decomposing the actual output power of the renewable energy system through EMS energy management service.
In this embodiment, decomposing the actual output power of the renewable energy system by the EMS energy management service includes the steps of:
carrying out a plurality of layers of wavelet packet decomposition on the actual output power of the renewable energy system by adopting the wavelet packet to obtain a low-frequency component and a high-frequency component; the energy of the output power of the renewable energy system is mainly concentrated in a low frequency band, and the high frequency component is a fluctuation component of the renewable energy system and needs to be stabilized by an energy storage system.
Judging whether the frequency corresponding to the obtained low-frequency component is within the grid-connected power limit range of the renewable energy system or not; for specific judgment, reference is made to the above steps.
And if so, determining the current decomposition layer number, taking the frequency corresponding to the low-frequency component corresponding to the current decomposition layer number as a stabilizing expected value of the renewable energy system, and taking the frequency corresponding to the high-frequency component corresponding to the current decomposition layer number as a power reference value of the energy storage system.
In order to avoid the phenomenon of overcharge and overdischarge of the energy storage system, the safe operation, the service performance and the service life of the energy storage system are ensured. In the embodiment, the SOC value and the charge-discharge limiting power of the energy storage system are introduced to correct the power reference value of the energy storage system, and the corrected power reference value of the energy storage system is utilized to perform power balance.
Calculating the SOC value of the battery system in the direct-current cabin in real time through BMS energy management service in the node of the battery management and energy management integrated system in the direct-current cabin; the BMS energy management service is deployed in the Kubernetes cluster service; the method specifically comprises the following steps:
in the running process of the battery system of the direct current cabin, estimating the SOC of the corresponding battery cluster through the battery cluster controller; the method comprises the following specific steps:
establishing an equivalent circuit model of the battery cluster;
identifying model parameters by a forgetting factor least square method based on HPPC test data at-20 ℃ to 25 ℃; based on HPPC test data at-20 ℃ to 25 ℃, the battery cluster is a data set of HPPC test at-20 ℃ to 25 ℃, and the data set comprises temperature, voltage and current;
establishing a cell database of the environmental temperature through the model parameter identification result;
and carrying out SOC estimation on the battery cluster data acquired by the battery cluster controller through the battery core database.
And acquiring the estimated battery cluster SOC value of the battery cluster controller through EMS energy management service, and averaging all the acquired battery cluster SOC value estimation values to obtain the SOC value of the battery system in the direct-current cabin.
Judging whether the calculated SOC value of the battery system in the direct-current cabin reaches an SOC limit value or not through EMS energy management service, or whether the actual output power of the battery system reaches a charge-discharge power limit value or not;
respectively correcting the power reference values of the energy storage system in the charge and discharge modes through EMS energy management service to obtain corrected power reference values of the energy storage system;
the method for judging whether the calculated SOC value of the battery system in the direct-current cabin reaches the SOC limit value or whether the actual output power of the battery system reaches the charge-discharge power limit value through EMS energy management service specifically comprises the following steps:
if the power reference value of the energy storage system is negative, the working state of the energy storage system is judged to be a discharging state, whether the calculated SOC value of the battery system in the direct-current cabin reaches a preset value at the end of discharging is judged, and the preset value at the end of discharging can be set to be equal to 10% in SOC, namely, discharging is stopped when the SOC is equal to 10%.
If yes, jumping to respectively correcting the power reference values of the energy storage system in the charge-discharge mode through EMS energy management service;
If the power reference value of the energy storage system is positive, the working state of the energy storage system is judged to be the charging state, whether the calculated SOC value of the battery system in the direct-current cabin reaches the preset value at the end of charging is judged, the preset value at the end of charging can be set to be equal to 90% in SOC, namely, charging is stopped when the SOC is equal to 90%.
And if yes, jumping to the step of respectively correcting the power reference values of the energy storage system in the charge and discharge modes through the EMS energy management service.
In this embodiment, the correction of the power reference values of the energy storage system in the charge and discharge modes by the EMS energy management service includes the following steps:
calculating SOC percentages respectively through a preset value at the end of charge and a preset value at the end of discharge; when the energy storage system works in a discharging state, taking the ratio of the difference value between the preset value at the discharging end and the calculated SOC value of the battery system in the direct current cabin to the preset value at the discharging end as the SOC percentage in the discharging state; when the energy storage system works in a charging state, taking the ratio of the calculated difference value between the SOC value of the battery system in the direct current cabin and the preset value at the end of charging as the SOC percentage in the charging state;
correcting the power reference value of the energy storage system in the charge-discharge mode through the SOC percentage and the correction coefficient corresponding to the SOC; the correction coefficient corresponding to the SOC can be summarized according to experimental data.
Obtaining a monomer highest voltage value cv_max, a monomer lowest voltage value cv_min and a monomer highest temperature value ct_max through EMS energy management service; the data of the single battery cell is collected by the battery pack controller.
And if the highest voltage value of the monomer in the battery system is larger than the monomer charge cut-off voltage and/or the lowest voltage value of the monomer is smaller than the monomer discharge cut-off voltage and/or the highest temperature value of the monomer is larger than the charge and discharge cut-off temperature, PID adjustment is carried out on the power reference value of the energy storage system in the charge and discharge mode through the current PID adjustment value, the highest voltage value of the monomer, the lowest voltage value of the monomer and the highest temperature value of the monomer in the battery system, P (t+1) =PID (P (t), cv_max, cv_min and ct_max), so that cv_max does not exceed the monomer charge cut-off voltage, cv_min does not exceed the monomer discharge cut-off voltage, and ct_max does not exceed the discharge cut-off temperature.
The calculation process of the initial value P0 of the SOP of the energy storage system comprises the following steps:
and the main controller acquires the SOP value reported by the battery cluster controller through communication, and further calculates SOP at the system level.
If the energy storage system normally operates, namely in a charging or discharging state, acquiring the number N of the battery clusters which effectively operate in the energy storage system through a main controller; the battery clusters which effectively run are battery clusters which are connected in at high voltage and participate in charge and discharge operations;
Acquiring a charge-discharge SOP predicted value reported by a battery cluster controller corresponding to a battery cluster which effectively operates through a main controller, and acquiring a minimum SOP predicted value Pmin of the battery cluster;
and calculating the product of the number of the battery clusters which effectively run in the energy storage system and the minimum SOP predicted value of the battery clusters by the main controller to obtain an SOP initial value P0 of the energy storage system, namely P0=Pmin.
The process for acquiring the predicted value of the SOP of the battery cluster comprises the following steps:
the battery cluster controller obtains an initial SOP value of the battery cluster by inquiring a power-electric quantity-thermometer P (SOC, T) preset when the energy storage system leaves a factory;
if the current working state of the battery cluster is a charging state, acquiring a single highest voltage value cv_max and a single highest temperature value ct_max in the battery cluster;
if the current working state of the battery cluster is a discharging state, acquiring a single minimum voltage value cv_min and a single maximum temperature value ct_max in the battery cluster;
if the current working state of the battery cluster is a charging state, judging whether the highest voltage value of the single cells in the battery cluster is larger than the single cell charging cut-off voltage;
if yes, jumping to a step of PID regulation of power corresponding to the current working state through the current PID regulation value and the battery cluster SOP regulation information;
Otherwise, judging whether the highest temperature value of the monomer in the battery cluster is larger than the charge cut-off temperature value;
if yes, jumping to a step of PID regulation of power corresponding to the current working state through the current PID regulation value and the battery cluster SOP regulation information;
otherwise, jumping to the step of outputting the SOP predicted value of the battery cluster in the current working state;
if the current working state of the battery cluster is a discharging state, judging whether the lowest voltage value of the single body in the battery cluster is smaller than the single body discharging cut-off voltage;
if yes, jumping to a step of PID regulation of power corresponding to the current working state through the current PID regulation value and the battery cluster SOP regulation information;
otherwise, judging whether the highest temperature value of the monomer in the battery cluster is larger than the discharge cut-off temperature value;
if yes, jumping to a step of PID regulation of the power corresponding to the current working state through the current PID regulation value and the battery cluster SOP regulation information.
If yes, PID adjustment is carried out on the power corresponding to the current working state through the current PID adjustment value and the battery cluster SOP adjustment information, so that the battery cluster SOP adjustment information does not exceed the monomer cut-off condition;
otherwise, outputting an SOP predicted value of the battery cluster in the current working state, and jumping to a step of calculating an SOP initial value of the battery cluster;
If the current working state of the battery cluster is a charging state, PID adjustment is carried out on the charging power through the current PID adjustment value, the highest voltage value of the single body in the battery cluster and the highest temperature value of the single body, and P (t+1) =PID (P (t), cv_max and ct_max) so that the cv_max does not exceed the single body charging cut-off voltage and the ct_max does not exceed the charging cut-off temperature;
if the current working state of the battery cluster is a discharging state, PID adjustment is carried out on the discharging power through the current PID adjustment value, the lowest voltage value of the single body and the highest temperature value of the single body in the battery cluster, and P (t+1) =PID (P (t), cv_min and ct_max) so that the cv_min does not exceed the single body discharging cut-off voltage and the ct_max does not exceed the discharging cut-off temperature.
Transmitting the corrected energy storage system power reference value to an energy storage converter corresponding to a battery management and energy management integrated system node in the direct-current cabin through EMS energy management service;
and outputting according to the corrected power reference value of the energy storage system through the energy storage converter to obtain the actual output power of the battery system in the direct-current cabin until the actual output power of the renewable energy system is adjusted to the expected stabilizing value of the renewable energy system.
In order to guarantee high availability of cluster service management, the method further comprises the following steps:
If the Kubernetes cluster service in the direct current cabin finds out that the software service in the main controller fails, starting a new service and closing an abnormal service;
if the Kubernetes cluster service in the direct current cabin finds out the hardware service fault in the main controller, the new starting service in other main controllers is allocated, and the subordinate system of the fault main controller is taken over.
Example 2
For a detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated herein. As shown in fig. 2 to 5, the system comprises battery management and energy management integrated system nodes deployed in each direct current cabin, the battery management and energy management integrated system nodes in each direct current cabin are in communication connection with corresponding energy storage converters and coordination controllers, the battery management and energy management integrated system nodes comprise cluster service management, database middleware and distributed database nodes, the cluster service management, database middleware and distributed database nodes are deployed in a main controller, and all monitoring management services are uniformly managed and monitored by Kubernetes cluster service. The main controller is in communication connection with the plurality of battery cluster controllers, the battery cluster controllers are in communication connection with the plurality of battery pack controllers, and the main controller is in communication connection with the battery cluster controllers and the battery pack controllers through Ethernet and a switch or through Ethernet ring network communication. The system supports dual star networks or ring network networking. The battery pack controller is used for acquiring and controlling data of the battery pack, all monitoring management services of the battery management and energy management integrated system node are uniformly managed and monitored by a Kubernetes cluster service, the Kubernetes cluster service comprises an EMS energy management service and a BMS monitoring management service, and the data acquired by the EMS energy management service and the BMS monitoring management service are stored into a distributed database through a database middleware;
The Kubernetes cluster service is used for responding to a user request, and checking and controlling the operation parameters, the alarm states and the operation modes of all node data and equipment in the management system; the user can log in the battery management and energy management integrated system through the browser to check and control the operation parameters, alarm states, operation modes and the like of all node data and equipment in the management system. When the software service fault in the main controller is found, starting a new service and closing an abnormal service; when the hardware service fault in the main controller is found, the new starting service in other main controllers is allocated, the subordinate system of the fault main controller is taken over, and the high availability of the service is ensured. Because the system adopts distributed services and nodes, multi-pair multi-data acquisition and storage can be realized, and data interaction can be more efficiently carried out than the traditional centralized main and standby redundant system of the centralized EMS, and the real-time performance and high availability of the system are ensured.
The database middleware is used for setting and managing the redundant backup proportion of each distributed database node, and realizing high-speed data reading and writing and node hot standby. When the main controller fails, the cluster service management program and the database middleware can allocate the newly started service in other controllers, take over the subordinate system of the failed main controller and improve the availability of the system.
The invention adopts distributed service management and distributed database storage, and realizes efficient data acquisition and storage by a plurality of monitoring management services corresponding to a plurality of PCS and a plurality of battery systems. The system can automatically recover when any main controller fails or service fails or database node fails through service and node monitoring scheduling, the whole operation of the system is not affected, and high availability is realized. All other system data information within the same local network and within the same cluster may be accessed by any one system node.
The system has no centralized data storage equipment or central server, and the data is stored in each system node in the cluster, and belongs to the decentralization system. And the distributed data acquisition monitoring management and data storage are supported, the data acquisition pressure is distributed to each sub-node service, the real-time performance and the reliability of the system data are greatly improved, meanwhile, a BMS and EMS integrated comprehensive management mode is adopted, the BMS and EMS monitoring management service share the controller hardware and the data storage system, and the construction cost of a user system is reduced.
The invention greatly improves the data acquisition monitoring efficiency and the availability of the battery management and energy management system of the large-scale energy storage cluster, and can greatly reduce the construction cost of users because an EMS energy management system is not required to be additionally deployed and constructed.
Example 3
A computer readable storage medium having stored thereon program instructions that when executed implement the above-described method, as shown in fig. 6. For detailed description of the method, reference may be made to corresponding descriptions in the above method embodiments, and details are not repeated here.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description is illustrative of embodiments of the present disclosure and is not to be construed as limiting one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of one or more embodiments of the present disclosure, are intended to be included within the scope of the claims of one or more embodiments of the present disclosure.
Claims (10)
1. The energy storage battery array cluster monitoring method is characterized by comprising the following steps of:
the method comprises the steps that actual output parameters of a renewable energy system are collected in real time through EMS energy management service in a DC cabin battery management and energy management integrated system node;
Acquiring the last actual output parameter of the renewable energy system in the corresponding distributed database node through EMS energy management service;
predicting ultra-short-term power of the renewable energy system according to the last actual output parameter and the current actual output parameter of the renewable energy system by using an EMS energy management service;
judging whether the fluctuation amount of the actual output parameters of the renewable energy system in the preset time is within the grid-connected power limit range of the renewable energy system or not through EMS energy management service;
if not, decomposing the actual output power of the renewable energy system through the EMS energy management service to obtain a stabilization expected value of the renewable energy system and a power reference value of the energy storage system;
calculating the SOC value of the battery system in the direct-current cabin in real time through BMS energy management service in the node of the battery management and energy management integrated system in the direct-current cabin;
judging whether the calculated SOC value of the battery system in the direct-current cabin reaches an SOC limit value or not through EMS energy management service, or whether the actual output power of the battery system reaches a charge-discharge power limit value or not;
if yes, respectively correcting the power reference values of the energy storage system in the charge-discharge mode through EMS energy management service to obtain corrected power reference values of the energy storage system;
Transmitting the corrected energy storage system power reference value to an energy storage converter corresponding to a battery management and energy management integrated system node in the direct-current cabin through EMS energy management service;
and outputting according to the corrected power reference value of the energy storage system through the energy storage converter to obtain the actual output power of the battery system in the direct-current cabin.
2. The method for monitoring the energy storage battery array cluster according to claim 1, wherein: the method comprises the steps that the ultra-short-term power of a renewable energy system is predicted according to the last actual output parameter and the current actual output parameter of the renewable energy system through an EMS energy management service, and the ultra-short-term power of the renewable energy system is predicted according to the actual output power and the last actual output power of the renewable energy system through a renewable energy system power prediction model in the EMS energy management service; and the renewable energy system power prediction model is a BP neural network prediction model.
3. The method for monitoring the energy storage battery array cluster according to claim 1, wherein the step of determining, by the EMS energy management service, whether the fluctuation amount of the ultra-short-term power of the renewable energy system within the preset time is within the grid-connected power limit range of the renewable energy system includes the steps of:
Calculating the fluctuation amount of the actual output power of the renewable energy system within 1min and 10 min;
judging whether the fluctuation amount of the actual output power of the renewable energy system within 1min exceeds 1/10 of the rated power or not;
if yes, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to the step of decomposing the actual output power of the renewable energy system through EMS energy management service;
judging whether the fluctuation amount of the actual output power of the renewable energy system within 10min exceeds 1/3 of the rated power or not;
if yes, judging that the actual output power of the renewable energy system exceeds the grid-connected power limit range of the renewable energy system, and jumping to the step of decomposing the actual output power of the renewable energy system through the EMS energy management service.
4. A method of monitoring a cluster of energy storage cells as claimed in claim 1 or 3, wherein said decomposing the actual output power of the renewable energy system by the EMS energy management service comprises the steps of:
carrying out a plurality of layers of wavelet packet decomposition on the actual output power of the renewable energy system by adopting the wavelet packet to obtain a low-frequency component and a high-frequency component;
Judging whether the frequency corresponding to the obtained low-frequency component is within the grid-connected power limit range of the renewable energy system or not;
if yes, determining the current decomposition layer number, taking the frequency corresponding to the low-frequency component corresponding to the current decomposition layer number as a stabilizing expected value of the renewable energy system, and taking the frequency corresponding to the high-frequency component corresponding to the current decomposition layer number as a power reference value of the energy storage system.
5. The method for monitoring the energy storage battery array cluster according to claim 4, wherein the calculating the SOC value of the battery system in the dc compartment in real time through the BMS energy management service in the node of the integrated battery management and energy management system in the dc compartment comprises the steps of:
in the running process of the battery system of the direct current cabin, estimating the SOC of the corresponding battery cluster through the battery cluster controller;
and acquiring the estimated battery cluster SOC value of the battery cluster controller through EMS energy management service, and averaging all the acquired battery cluster SOC value estimation values to obtain the SOC value of the battery system in the direct-current cabin.
6. The method of claim 5, wherein the estimating the SOC of the corresponding battery cluster by the battery cluster controller comprises the steps of:
Establishing an equivalent circuit model of the battery cluster;
identifying model parameters by a forgetting factor least square method based on HPPC test data at-20 ℃ to 25 ℃; the HPPC test data based on the temperature of-20 ℃ to 25 ℃ is a data set of HPPC test of the battery cluster at the temperature of-20 ℃ to 25 ℃, wherein the data set comprises temperature, voltage and current;
establishing a cell database of the environmental temperature through the model parameter identification result;
and estimating the SOC of the battery cluster data acquired by the battery cluster controller through a battery core database.
7. The method for monitoring the energy storage battery array cluster according to claim 5, wherein the step of determining whether the calculated SOC value of the battery system in the dc compartment reaches the SOC limit value by the EMS energy management service comprises the steps of:
if the power reference value of the energy storage system is negative, judging that the working state of the energy storage system is a discharging state, and judging whether the calculated SOC value of the battery system in the direct current cabin reaches a preset value at the end of discharging;
if yes, jumping to the step of respectively correcting the power reference values of the energy storage system in the charge and discharge modes through the EMS energy management service;
if the power reference value of the energy storage system is a positive number, judging that the working state of the energy storage system is a charging state, and judging whether the calculated SOC value of the battery system in the direct current cabin reaches a preset value at the end of charging;
If yes, jumping to the step of respectively correcting the power reference values of the energy storage system in the charge and discharge modes through the EMS energy management service.
8. The method for monitoring the energy storage battery array cluster as set forth in claim 7, wherein the step of respectively correcting the power reference values of the energy storage system in the charge and discharge mode by the EMS energy management service comprises the steps of:
calculating SOC percentages respectively through a preset value at the end of charge and a preset value at the end of discharge;
correcting the power reference value of the energy storage system in the charge-discharge mode through the SOC percentage and the correction coefficient corresponding to the SOC;
obtaining a monomer highest voltage value, a monomer lowest voltage value and a monomer highest temperature value through EMS energy management service;
if the highest voltage value of the monomer in the battery system is larger than the monomer charge cut-off voltage and/or the lowest voltage value of the monomer is smaller than the monomer discharge cut-off voltage and/or the highest temperature value of the monomer is larger than the charge and discharge cut-off temperature, PID adjustment is carried out on the power reference value of the energy storage system in the charge and discharge mode through the current PID adjustment value and the highest voltage value, the lowest voltage value and the highest temperature value of the monomer in the battery system;
the method also comprises the following steps:
If the Kubernetes cluster service in the direct current cabin finds out that the software service in the main controller fails, starting a new service and closing an abnormal service;
if the Kubernetes cluster service in the direct current cabin finds out the hardware service fault in the main controller, the new starting service in other main controllers is allocated, and the subordinate system of the fault main controller is taken over.
9. A computer readable storage medium, having stored thereon program instructions which, when executed, implement the method of claim 1.
10. An energy storage cell array cluster monitoring system for implementing the method of claim 1, wherein: comprises battery management and energy management integrated system nodes arranged in each direct-current cabin, wherein the battery management and energy management integrated system nodes in each direct-current cabin are in communication connection with each other, the battery management and energy management integrated system nodes in each direct-current cabin are in communication connection with corresponding energy storage converters and coordination controllers, the battery management and energy management integrated system nodes comprise cluster service management, database middleware and distributed database nodes, the cluster service management, database middleware and distributed database nodes are arranged in a main controller, the main controller is in communication connection with a plurality of battery cluster controllers, the battery cluster controllers are in communication connection with a plurality of battery pack controllers, the main controller is used for management control of the direct current cabin, the battery cluster controllers are used for corresponding battery cluster data acquisition and control, the battery pack controllers are used for battery cell data acquisition, all monitoring management services of the battery management and energy management integrated system node are uniformly managed and monitored by a Kubernetes cluster service, the Kubernetes cluster service comprises an EMS energy management service and a BMS monitoring management service, and data acquired by the EMS energy management service and the BMS monitoring management service are stored into a distributed database through a database middleware;
The Kubernetes cluster service is used for responding to a user request, and checking and controlling operation parameters, alarm states and operation modes of all node data and equipment in the management system; when the software service fault in the main controller is found, starting a new service and closing an abnormal service; when the hardware service fault in the main controller is found, the new starting service in other main controllers is allocated, and the subordinate system of the fault main controller is taken over;
the database middleware is used for setting and managing the redundant backup proportion of each distributed database node, allocating the newly started service in other main controllers and taking over the subordinate system of the fault main controller.
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