CN114970108A - Chemical battery energy storage application analysis method based on data driving - Google Patents
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Abstract
The invention relates to the field of large-scale energy storage application, in particular to a chemical battery energy storage application analysis method based on data driving, which consists of a corresponding design flow, a model base and a database; the method is based on the application requirements of customers and customer data input, carries out data sorting and cleaning, combines an application class library, a component (comprising batteries, an energy storage converter and auxiliary equipment) characteristic library and data of an economic analysis database, establishes a control strategy for an energy storage system through data analysis, optimizes an energy storage proportioning relation, and achieves a balance of system performance and economic benefit; the method avoids the analysis of the electrochemical reaction and failure mechanism inside the lithium ion battery, can overcome the problems of low dynamic precision and poor generalization capability based on a model method to a certain extent, and is widely applied to many fields by the flexibility and the usability of a data driving method.
Description
Technical Field
The invention relates to the field of large-scale energy storage application, in particular to a chemical battery energy storage application analysis method based on data driving.
Background
Energy safety and environmental pollution become factors restricting economic development, the development of an energy storage technology is very likely to become a great trend of new energy development, the development and application of the energy storage technology are beneficial to the access and consumption of distributed energy, the demand and the time-interval load of a matched power transmission line are reduced, and the operation risk is reduced. Among various energy storage technologies, the battery energy storage technology has various types, novel technology and high development speed, so that the battery energy storage technology can be widely applied to a new energy distributed power generation system and a microgrid in the future, the peak regulation pressure of the power grid is reduced, and the stability of a power system is guaranteed. With the environmental concerns and the evolution of energy systems, especially the increasing scale of renewable energy sources, it has become very important and urgent on a global scale how to perform power storage. In recent years, the construction of various advanced large-scale energy storage projects represented by sodium-sulfur, lithium batteries and all-vanadium liquid flows in the global scope marks that battery energy storage becomes a new round of global investment hotspot and rises to the national strategic planning level.
The technical route of large-scale energy storage is that system integration is completed through integration technology along the extension from a long-life large-capacity single battery to an energy storage unit group and a battery management system and an energy storage converter matched with the energy storage unit group. Namely, the battery body technology is taken as a basis, battery grouping is taken as a main means, and integrated application is taken as effective guarantee.
The energy storage application analysis is a bridge connecting customer requirements and battery body manufacturers/control providers, and provides an energy storage proportioning principle and a system control strategy so as to guide design development and implementation of an energy storage system. The traditional energy storage system modeling increases modeling errors by modeling each energy storage unit and then enlarging the scale of the energy storage unit to the level of the energy storage system by using simplified conditions. At present, the application analysis in the aspect of chemical battery energy storage is basically based on empirical data and some literature data, a structured, reliable and quantitative analysis method is not available, unnecessary waste is caused by too large or insufficient energy storage proportion, the performance of energy storage is influenced by lack of a system control strategy, and further impact is caused on the aspects of safety, reliability and durability concerned by the operation of an energy storage system.
The data driving is a model establishing method based on historical operation data, modeling is carried out on external characteristics of the energy storage system without considering internal mechanism changes, and after model training is completed, the model has higher model precision and faster calculation speed. Therefore, a data-driven modeling technology of the lithium battery energy storage system can be researched, and the simulation precision of the model established for the energy storage system is improved, so that the operation control of the energy storage system when the energy storage system is connected to a power grid is guided. With the rise of big data technology, the accumulation of massive operation data of chemical batteries and the improvement of distributed storage and computing power, a plurality of data-driven analysis methods appear. The analysis method is not limited by data quality to a certain extent, does not need to establish a complex model, and is widely applied to engineering practice. However, data-driven analysis methods still have a number of difficulties. Firstly, the chemical battery data has wide sources and various structures, and the fusion and pretreatment of multi-source heterogeneous data are difficult. Secondly, under the application scene of large-scale chemical batteries, due to variable working conditions and the collection quality far inferior to that of experimental data, accurate fault identification and health state management are difficult to realize, and the current urgent need is to select the worst chemical battery as soon as possible; then, for an operation scene with high user demand and data quality meeting conditions, accurate SOC estimation of the health state is required; finally, although a plurality of battery big data platforms are put into use, most research teams of colleges and enterprises have difficulty in directly utilizing the data platforms to carry out work.
In view of research and exploration on chemical battery energy storage and data driving technologies, the invention provides a chemical battery energy storage application analysis method based on data driving.
Disclosure of Invention
In the current chemical battery energy storage application analysis, an approximate proportion of energy storage proportion can be obtained basically by depending on historical empirical data, and the subsequent energy storage system integration is basically carried out on the basis. Because the chemical battery energy storage application scene is complex, different chemical battery technology selections have great influence on the energy storage performance, empirical data only has referential property and cannot guide detailed design, and a structured method is lacked to systematically analyze energy storage application, so that system control parameters and other indexes are obtained.
Aiming at the defects of the prior art, the invention provides a chemical battery energy storage application analysis method based on data driving, which can firstly define the application requirements of a client for energy storage and client data, combine an application class library, a component (comprising a chemical battery, an energy storage converter and auxiliary equipment) characteristic library and data of an economic analysis database, establish a control strategy for an energy storage system through data driving analysis, optimize the energy storage proportioning relation and achieve the balance of system performance and economic benefit.
The technical scheme of the invention is as follows: a chemical battery energy storage application analysis method based on data driving is composed of a design process, a model base and a database, and is characterized in that: the system comprises a data preprocessing database, an application category database, a component characteristic database and an economic analysis database, a control model, a battery characteristic model, an auxiliary system model and a battery life model, and a set of control strategy;
preferably, the data preprocessing comprises data sorting, data cleaning and data construction; the data sorting is to build data based on time units after data provided by a client is cleaned; the data cleaning comprises the steps of assigning a free variable by adopting an average value or a middle value or adjacent interpolation of certain specific data in a period; checking whether the data meets the requirements by reasonably setting the threshold value of the variable, and deleting or correcting the data which exceeds the normal range; deleting or correcting logically unreasonable or contradictory data by setting mutual constraint and dependency relationship among the data; the data construction comprises the steps of integrating the collected data according to a time sequence;
the application category database comprises wind power grid-connected application, photoelectric grid-connected application, frequency modulation, peak regulation and diesel storage mixed application scenes, and comprises basic requirements on energy storage, effect analysis of actual operation and conventional case analysis in the application scenes;
the component database incorporating battery characteristics includes: lithium battery voltage/current, efficiency, internal resistance, thermal and decay characteristics; the voltage/current, efficiency, internal resistance, heating power and attenuation characteristics of the vanadium redox flow battery; sodium-sulfur cell voltage/current, efficiency, internal resistance, thermal and damping characteristics; zinc-bromine battery voltage/current, efficiency, internal resistance, thermal and damping characteristics.
Preferably, the controller model, the battery model, the auxiliary system model and the battery life model are established on the basis of combining the application type database and the component characteristic database, and a system charging and discharging power logical relationship, a charge state logical relationship, an energy storage converter state energy consumption logical relationship, a battery capacity impedance logical relationship and a system temperature logical relationship are established.
Preferably, the control strategy input is user power generation and load data, and through iterative calculation among a controller model, a battery model and an auxiliary system model, an energy storage system instruction, a corresponding target value, a battery temperature, an energy storage converter, battery energy consumption and a battery charge state are output through discharge power, system energy consumption, a battery charge state, energy storage rectifier state energy consumption, battery capacity impedance and charge-discharge depth indexes of the battery system.
Preferably, the chemical battery energy storage application analysis method based on data driving specifically comprises the following steps:
firstly, a special data integration processing mode is adopted, customer data are cleaned according to rules of vacant assignment, error value removal, cross inspection and the like, and data reconstruction is carried out according to a time sequence;
secondly, designing a flow: the method is based on analysis of customer application requirements and an energy storage application category library constructed by the system, analyzes the power generation and load curves of customers, identifies application categories, quantifies system operation parameters and limiting conditions, and formulates a design optimization objective function. The energy storage application class library comprises wind power integration, photoelectric integration, frequency modulation, peak shaving and diesel storage mixing. Wind power integration scenes include wind power smoothing, automatic active power regulation, power generation plan tracking and power curtailment management. The energy storage capacity of different application scenes can be roughly planned in the category library;
thirdly, battery BMS: selecting the type of the used battery, namely a lithium battery, a vanadium redox flow battery or other batteries, wherein different batteries have different power characteristics and energy characteristics, designing a system control strategy according to different battery selections or battery bundling, wherein the system control strategy comprises energy scheduling, charge-discharge control and battery charge state management, and the system control strategy comprises a system overall control chart, wherein the system input is a power generation and load curve of a user, and the output is a control instruction, response, battery temperature, energy consumption of an energy storage converter and a battery, and battery charge state change of a battery system;
fourthly, model library: establishing a system operation model including a battery system model, an energy storage converter model and other access equipment models according to a component database, including battery voltage and current characteristics, battery efficiency characteristics, internal resistance, heat, battery attenuation, energy storage converter efficiency and auxiliary cooling system data, and establishing a battery life analysis model and a battery capacity maintenance planning model;
fifthly, extracting characteristic parameters: carrying out simulation analysis, carrying out iterative calculation on the charge and discharge power of a battery system, the charge state of a battery, the capacity and impedance of the battery, the state of an energy storage converter, the energy consumption of the battery and the charge and discharge energy, searching and optimizing design parameters, carrying out sensitivity analysis, adjusting the scale of energy storage and controlling the parameters;
sixthly, predicting the degradation trend: determining the scale of a battery energy storage system meeting the application requirements of a client, considering the changes of power and the like caused by battery attenuation, and making a capacity maintenance plan according to life analysis;
and seventhly, identifying and analyzing the fault: and according to the component cost and the operation cost of the economic analysis database, combining the economic data of the client, calculating the cost and analyzing the economic feasibility to obtain the return on investment of the energy storage system.
The invention has the beneficial effects that: an energy storage application database is established, energy storage application requirements are analyzed in a data-driven mode, functional indexes and economic indexes of energy storage are quantized, and landing and maintenance of an energy storage scheme are guided more effectively. The method comprises a corresponding design flow, a model base and a database; based on the application requirements of customers and customer data input, data sorting and cleaning are carried out, data of an application class library, a component (comprising batteries, an energy storage converter and auxiliary equipment) characteristic library and an economic analysis database are combined, and through data analysis, an energy storage system control strategy is established, an energy storage proportioning relation is optimized, and balance between system performance and economic benefits is achieved; the analysis of electrochemical reaction and failure mechanism inside the lithium ion battery is avoided, the problems of low dynamic precision and poor generalization capability based on a model method can be overcome to a certain extent, and the data driving method is widely applied in many fields by the flexibility and the usability of the data driving method; the data-driven prediction method occupies a higher position in the fault prediction method because the internal mechanism of the battery does not need to be researched, and the main prediction process of the method is as follows: collecting data, extracting characteristic parameters, predicting degradation trend, identifying and analyzing faults.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of the system architecture of the present invention;
FIG. 3 is a block diagram of a system control strategy model of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings:
as shown in fig. 1 to 3, a chemical battery energy storage application analysis method based on data driving is composed of a design process, a model library and a database, and is characterized in that: the system comprises a data preprocessing database, an application category database, a component characteristic database and an economic analysis database, a control model, a battery characteristic model, an auxiliary system model and a battery life model, and a set of control strategy;
preferably, the data preprocessing comprises data sorting, data cleaning and data construction; the data arrangement is to construct data based on time units after data provided by a client is cleaned; the data cleaning comprises the steps of assigning a free variable by adopting an average value or a middle value or adjacent interpolation of certain specific data in a period; checking whether the data meets the requirements by reasonably setting the threshold value of the variable, and deleting or correcting the data which exceeds the normal range; deleting or correcting logically unreasonable or contradictory data by setting mutual constraint and dependency relationship among the data; the data construction comprises the steps of integrating the collected data according to a time sequence;
the application category database comprises wind power grid-connected application, photoelectric grid-connected application, frequency modulation, peak regulation and diesel storage mixed application scenes, and comprises basic requirements on energy storage, effect analysis of actual operation and conventional case analysis in the application scenes;
the component database incorporating battery characteristics includes: lithium battery voltage/current, efficiency, internal resistance, thermal and decay characteristics; the voltage/current, efficiency, internal resistance, heating power and attenuation characteristics of the vanadium redox flow battery; sodium-sulfur cell voltage/current, efficiency, internal resistance, thermal and damping characteristics; zinc-bromine battery voltage/current, efficiency, internal resistance, thermal and damping characteristics.
Preferably, the controller model, the battery model, the auxiliary system model and the battery life model are established on the basis of combining the application type database and the component characteristic database, and a system charging and discharging power logical relationship, a charge state logical relationship, an energy storage converter state energy consumption logical relationship, a battery capacity impedance logical relationship and a system temperature logical relationship are established.
Preferably, the control strategy input is user power generation and load data, and through iterative calculation among a controller model, a battery model and an auxiliary system model, an energy storage system instruction, a corresponding target value, a battery temperature, an energy storage converter, battery energy consumption and a battery charge state are output through discharge power, system energy consumption, a battery charge state, energy storage rectifier state energy consumption, battery capacity impedance and charge-discharge depth indexes of the battery system.
Preferably, the chemical battery energy storage application analysis method based on data driving specifically comprises the following steps:
firstly, a special data integration processing mode is adopted, customer data are cleaned according to rules of vacant assignment, error value removal, cross inspection and the like, and data reconstruction is carried out according to a time sequence;
secondly, designing a flow: the method is based on analysis of customer application requirements and an energy storage application category library constructed by the system, analyzes the power generation and load curves of customers, identifies application categories, quantifies system operation parameters and limiting conditions, and formulates a design optimization objective function. The energy storage application class library comprises wind power integration, photoelectric integration, frequency modulation, peak shaving and diesel storage mixing. Wind power integration scenes include wind power smoothing, automatic active power regulation, power generation plan tracking and power curtailment management. The energy storage capacity of different application scenes can be roughly planned in the category library;
thirdly, battery BMS: selecting the type of the used battery, namely a lithium battery, a vanadium redox flow battery or other batteries, wherein different batteries have different power characteristics and energy characteristics, designing a system control strategy according to different battery selections or battery bundling, wherein the system control strategy comprises energy scheduling, charge-discharge control and battery charge state management, and the system control strategy comprises a system overall control chart, wherein the system input is a power generation and load curve of a user, and the output is a control instruction, response, battery temperature, energy consumption of an energy storage converter and a battery, and battery charge state change of a battery system;
fourthly, model library: establishing a system operation model including a battery system model, an energy storage converter model and other access equipment models according to a component database, including battery voltage and current characteristics, battery efficiency characteristics, internal resistance, heat, battery attenuation, energy storage converter efficiency and auxiliary cooling system data, and establishing a battery life analysis model and a battery capacity maintenance planning model;
fifthly, extracting characteristic parameters: performing simulation analysis, performing iterative calculation on the charge and discharge power, the battery charge state, the battery capacity and impedance, the energy storage converter state, the battery energy consumption and the charge and discharge energy of the battery system, searching for optimized design parameters, performing sensitivity analysis, adjusting the energy storage scale and controlling the parameters;
sixthly, predicting the degradation trend: determining the scale of a battery energy storage system meeting the application requirements of a client, considering the changes of power and the like caused by battery attenuation, and making a capacity maintenance plan according to life analysis;
and seventhly, identifying and analyzing the fault: and according to the component cost and the operation cost of the economic analysis database, combining the economic data of the client, calculating the cost and analyzing the economic feasibility to obtain the return on investment of the energy storage system.
In practical application, according to the proposed energy storage requirement (namely what the energy storage needs to do), a recommended chemical battery model selection and a rough proportioning principle are given by combining an application class library; according to the provided power generation and load curves, the goal of battery model selection is further limited, and after the battery type is confirmed, a preliminary system proportion, a control strategy and a capacity maintenance plan are formulated; and establishing a control model, a battery characteristic model, an auxiliary system model and a battery life model according to the application category library and the component characteristic library. And modeling and simulating the performance indexes specified by the control strategy, if the system performance can not be met, re-formulating the battery model selection, the system proportion, the control strategy and the capacity maintenance, and if the system performance can be met, performing benefit analysis. And (4) analyzing the return on investment by utilizing the economic analysis database and the economic data of the client, if the return on investment is not met, reformulating the battery type selection, the system proportion, the control strategy and the capacity maintenance, and if the return on investment is not met, outputting the final result to the implementation scheme.
The foregoing embodiments and description have been presented only to illustrate the principles and preferred embodiments of the invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention as hereinafter claimed.
Claims (5)
1. A chemical battery energy storage application analysis method based on data driving is composed of a design process, a model base and a database, and is characterized in that: the system specifically comprises three databases, namely a data preprocessing database, an application type database, a component characteristic database and an economic analysis database, a control model, a battery characteristic model, an auxiliary system model and a battery life model, and a set of control strategies.
2. The chemical battery energy storage application analysis method based on data driving as claimed in claim 1, characterized in that: the data preprocessing comprises data sorting, data cleaning and data construction; the data sorting is to build data based on time units after data provided by a client is cleaned; the data cleaning comprises the steps of assigning a free variable by adopting an average value or a middle value or adjacent interpolation of certain specific data in a period; checking whether the data meets the requirements by reasonably setting the threshold value of the variable, and deleting or correcting the data which exceeds the normal range; deleting or correcting logically unreasonable or contradictory data by setting mutual constraint and dependency relationship among the data; the data construction comprises the steps of integrating the collected data according to a time sequence;
the application category database comprises wind power grid-connected application, photoelectric grid-connected application, frequency modulation, peak regulation and diesel storage mixed application scenes, and comprises basic requirements on energy storage, effect analysis of actual operation and conventional case analysis in the application scenes;
the component database incorporating battery characteristics includes: lithium battery voltage/current, efficiency, internal resistance, thermal and decay characteristics; the voltage/current, efficiency, internal resistance, heating power and attenuation characteristics of the vanadium redox flow battery; sodium-sulfur cell voltage/current, efficiency, internal resistance, thermal and damping characteristics; zinc-bromine battery voltage/current, efficiency, internal resistance, thermal and damping characteristics.
3. The chemical battery energy storage application analysis method based on data driving as claimed in claim 1, characterized in that: the controller model, the battery model, the auxiliary system model and the battery life model are established on the basis of combining the application type database and the component characteristic database, and a system charging and discharging power logic relation, a charge state logic relation, an energy storage converter state energy consumption logic relation, a battery capacity impedance logic relation and a system temperature logic relation are established.
4. The chemical battery energy storage application analysis method based on data driving as claimed in claim 1, characterized in that: the control strategy input is user power generation and load data, and through iterative calculation among a controller model, a battery model and an auxiliary system model, an energy storage system instruction, a corresponding target value of battery temperature, an energy storage converter and battery energy consumption and a target value of battery charge state are output through discharge power, system energy consumption, battery charge state, energy storage rectifier state energy consumption, battery capacity impedance and charge-discharge depth indexes of a battery system.
5. The chemical battery energy storage application analysis method based on data driving as claimed in claim 1, characterized in that: the method specifically comprises the following steps:
firstly, a special data integration processing mode is adopted, customer data are cleaned according to rules of vacant assignment, error value removal, cross inspection and the like, and data reconstruction is carried out according to a time sequence;
secondly, designing a flow: the method is based on analysis of customer application requirements and an energy storage application category library constructed by the system, analyzes the power generation and load curves of customers, identifies application categories, quantifies system operation parameters and limiting conditions, and formulates a design optimization objective function. The energy storage application class library comprises wind power integration, photoelectric integration, frequency modulation, peak shaving and diesel storage mixing. Wind power integration scenes include wind power smoothing, automatic active power regulation, power generation plan tracking and power curtailment management. The energy storage capacity of different application scenes can be roughly planned in the category library;
thirdly, battery BMS: selecting the type of the used battery, namely a lithium battery, a vanadium redox flow battery or other batteries, wherein different batteries have different power characteristics and energy characteristics, designing a system control strategy according to different battery selections or battery bundling, wherein the system control strategy comprises energy scheduling, charge-discharge control and battery charge state management, and the system control strategy comprises a system overall control chart, wherein the system input is a power generation and load curve of a user, and the output is a control instruction, response, battery temperature, energy consumption of an energy storage converter and a battery, and battery charge state change of a battery system;
fourthly, model library: establishing a system operation model including a battery system model, an energy storage converter model and other access equipment models according to a component database, including battery voltage and current characteristics, battery efficiency characteristics, internal resistance, heat, battery attenuation, energy storage converter efficiency and auxiliary cooling system data, and establishing a battery life analysis model and a battery capacity maintenance planning model;
fifthly, extracting characteristic parameters: performing simulation analysis, performing iterative calculation on the charge and discharge power, the battery charge state, the battery capacity and impedance, the energy storage converter state, the battery energy consumption and the charge and discharge energy of the battery system, searching for optimized design parameters, performing sensitivity analysis, adjusting the energy storage scale and controlling the parameters;
sixthly, predicting the degradation trend: determining the scale of a battery energy storage system meeting the application requirements of a client, considering the changes of power and the like caused by battery attenuation, and making a capacity maintenance plan according to life analysis;
and seventhly, identifying and analyzing the fault: and according to the component cost and the operation cost of the economic analysis database, combining the economic data of the client, calculating the cost and analyzing the economic feasibility to obtain the return on investment of the energy storage system.
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