CN117175647A - New energy storage method and system applied to micro-grid - Google Patents
New energy storage method and system applied to micro-grid Download PDFInfo
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Abstract
The invention discloses a new energy storage method and a system applied to a micro-grid, which relate to the technical field of new energy storage and comprise the following steps: collecting power distribution parameters of a micro-grid, energy storage data and thermal parameters generated in a historical operation process, and extracting data related to a battery, a converter and a flywheel from the energy storage data as associated data; establishing an optimization simulation model based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and selecting an optimal temperature control load point according to a model analysis result; judging the electric energy conversion relation between the battery and the flywheel by using the associated data, and establishing a virtual energy storage model based on the judging result and an optimal temperature control load point; and obtaining real-time analog quantity of the virtual energy storage model by using the filter, and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity. According to the invention, an optimized simulation model is established through thermal parameters, so that the purpose of ensuring the bearing capacity of the micro-grid under the optimal temperature control load is realized.
Description
Technical Field
The invention relates to the technical field of new energy storage, in particular to a new energy storage method and a new energy storage system applied to a micro-grid.
Background
The micro-grid is a power system based on distributed energy resources, can independently operate in a small range, is generally composed of a plurality of distributed energy resources (such as solar photovoltaic, wind power generation, gas power generation and the like) and energy storage equipment (such as a battery, a super capacitor and the like), and realizes energy scheduling and management through power electronic equipment.
The new energy refers to an energy form of replacing traditional fossil energy by renewable energy or non-traditional energy, and comprises solar energy, wind energy, water energy, geothermal energy, biological energy and the like. The new energy has the characteristics of reproducibility, environmental protection, innovation and the like. The development of new energy is emphasized by global energy transformation and sustainable development, and many countries and regions are actively promoting the development and utilization of new energy to slow down climate change, improve energy safety and promote economic development.
The micro-grid combines new energy and energy storage technology, can realize sustainable energy supply and management, new energy resources such as solar photovoltaic and wind power generation can be used as main energy sources of the micro-grid, and the energy storage technology can be used for storing redundant energy and releasing the redundant energy when needed. The energy storage technology can play roles in energy balance, peak Gu Tianping and reserve power supply in the micro-grid, and sustainable utilization and management of energy can be realized by combining new energy and the energy storage technology through the micro-grid, so that energy transformation and sustainable development are promoted.
The existing new energy storage method of the micro-grid cannot consider the temperature control load when the energy storage equipment is operated when in use, but unreasonable temperature control load can bring different degrees of load influence to the micro-grid, and in general, the high-power temperature control load change has larger influence on the bearing capacity, so that the subsequent use is influenced.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
In order to solve the problems, the invention provides a new energy storage method and a system applied to a micro-grid, which realize energy storage maximization and micro-grid load bearing optimization.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a new energy storage method applied to a micro-grid, the new energy storage method applied to the micro-grid comprising the steps of:
s1, collecting power distribution parameters of a micro-grid, energy storage data and thermal parameters generated in a historical operation process, and extracting data related to a battery, a converter and a flywheel from the energy storage data as associated data;
s2, an optimization simulation model is established based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and an optimal temperature control load point is selected according to a model analysis result;
S3, judging the electric energy conversion relation between the battery and the flywheel by using the associated data, and establishing a virtual energy storage model based on a judging result and an optimal temperature control load point;
and S4, obtaining real-time analog quantity of the virtual energy storage model by utilizing the filter, and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity.
Preferably, establishing an optimization simulation model based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and selecting an optimal temperature control load point according to a model analysis result comprises the following steps:
s21, acquiring thermal parameters including ambient temperature, polymerization heat capacity, polymerization thermal resistance and a converter, and establishing an energy storage thermal model according to the thermal parameters to describe thermal characteristics during energy storage operation;
s22, an optimized decision model is built according to the thermal characteristic result and the characteristic parameter of the micro-grid in combination with a decision algorithm, and the variation of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works is input;
s23, judging the influence on the micro-grid according to the analysis result and the change of the bearing capacity, and obtaining influence indexes under each temperature control load condition;
s24, comparing the influence indexes under the temperature control load conditions, and selecting an optimal temperature control load point as a temperature threshold value when the energy storage works.
Preferably, the method for constructing the optimization simulation model according to the thermal characteristic result and the characteristic parameter of the micro-grid in combination with a decision algorithm, and inputting the variation of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works comprises the following steps:
s221, analyzing a steady-state temperature during energy storage operation according to a thermal characteristic result, analyzing the bearing capacity of the micro-grid according to a characteristic parameter of the micro-grid, and combining the steady-state temperature and the bearing capacity into response data;
s222, establishing a response parameter model under the framework of hybrid modeling according to response parameters, and describing the relation between the bearing capacity and the steady-state temperature of the micro-grid according to the output result of the model;
s223, obtaining an estimated value of the response parameter based on a decision algorithm, and carrying out global optimization by utilizing a global optimization algorithm to obtain an optimal response parameter;
s224, establishing an optimized simulation model taking different temperature control data as input and taking the bearing capacity of the micro-grid as target output based on the optimal parameters;
s225, summarizing the bearing capacity output result, and describing the change of the bearing capacity of the micro-grid by utilizing the relation between the bearing capacity of the micro-grid and the steady-state temperature.
Preferably, obtaining an estimated value of the response parameter based on a decision algorithm, and performing global optimization by using a global optimization algorithm to obtain an optimal response parameter includes the following steps:
S2231, constructing posterior distribution of response parameters, and generating an initial state of a decision algorithm by the posterior distribution through distribution transformation;
s2232, generating a new state in a decision algorithm through a transfer function, judging whether the new state meets a preset condition, if so, receiving the transfer state, and if not, refusing the transfer state;
s2233, generating response parameter estimated values which are uniformly distributed by utilizing a decision algorithm according to the new transfer state, and solving parameter optimal settings in the estimated values by utilizing a global optimizing algorithm;
s2234, obtaining an optimal value of the response parameter according to the parameter optimal setting, and generating an optimal corresponding parameter.
Preferably, the method for judging the electric energy conversion relation between the battery and the flywheel by using the associated data and establishing the virtual energy storage model based on the judging result and the optimal temperature control load point comprises the following steps:
s31, analyzing the charge and discharge parameters and the electric energy of the battery according to the operation data of the battery, the flywheel and the converter in the related data;
s32, analyzing the change parameters of the rotating speed of the flywheel and the stored energy, and comparing the parameter change time sequence of the battery and the flywheel to judge the electric energy conversion rule so as to obtain an electric energy conversion relation;
s33, acquiring a judgment result according to the electric energy conversion relation, and constructing a virtual energy storage model by utilizing the judgment result and an optimal temperature control load point combined optimizing algorithm;
S34, inputting data parameters of the preset battery, flywheel and converter in operation into the virtual energy storage model, analyzing the change of the energy storage capacity, and sorting the capacity change to select the optimal energy storage capacity and corresponding data parameters for recording.
Preferably, the method for constructing the virtual energy storage model by utilizing the judgment result and the optimal temperature control load point combined optimizing algorithm comprises the following steps of:
s331, acquiring a judging result of an electric energy conversion relation and an optimal temperature control load point, and presetting the judging result and the optimal temperature control load point as a junction point and a node in an optimizing algorithm respectively;
s332, randomly initializing the intersection points and the nodes, and selecting the intersection points through an optimization strategy;
s333, selecting a junction point for random walk according to an optimization strategy by the node, and setting the direction and the distance of the random walk to be controlled by a random function;
s334, selecting an optimal intersection point according to the direction and the distance of the random walk, setting an updating mechanism to update the node, and calculating the adaptability of the current intersection point after the position of the node is updated;
and S335, selecting the current junction as an optimal virtual value if the fitness is superior to the node, and continuously repeating the steps S333 to S334 if the fitness is inferior to the node, wherein the optimal virtual value is used as the input of the model, and the energy storage capacity is used as the output to complete the construction of the virtual energy storage.
Preferably, the expression of the update mechanism is:
；
in the method, in the process of the invention,represent the firstbThe junction is at the firstaDistance of secondary trip->Represent the firstaDistance of sub-trip time optimization strategy selection node, < >>Represent the firstaThe distance of the node at the time of the secondary trip,arepresenting the number of current iterations and,Eindicating the total number of iterations that should be performed,randexpressed in interval +.>Is a random number of (a) in the memory.
Preferably, the data parameters of the battery, the flywheel and the converter in operation are input into the virtual energy storage model, the change of the energy storage capacity is analyzed, and the capacity change is sequenced to select the optimal energy storage capacity and the corresponding data parameters for recording, and the method comprises the following steps:
s341, consulting a production manual of the battery, flywheel and converter equipment to know the rated parameter range of the production manual, and assuming steady-state operation parameters under the condition of normal energy storage and critical parameters under the extreme condition;
s342, presetting a parameter set of each device in operation according to rated parameters, steady-state operation parameters and critical parameters as reference ranges, and inputting the parameter set into a virtual energy storage model to output an energy storage result;
s343, generating initial capacity change data of an energy storage result by using topology sequencing to obtain a priority matrix, and selecting data with the highest capacity change in the priority matrix as a top value of the priority matrix;
S344, selecting preset data values of the batteries, the flywheel and the converter equipment in the priority matrix to judge whether the generated energy storage result is a top value or not;
and S345, setting the energy storage capacity to be optimal if the energy storage capacity corresponds to the data parameter, recording the corresponding data parameter, and repeating the step S343 and the step S344 if the energy storage capacity does not correspond to the data parameter until the result corresponds to the data parameter.
Preferably, the method for obtaining the real-time analog quantity of the virtual energy storage model by using the filter and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity comprises the following steps:
s41, adding a filtering function in the virtual energy storage model, and setting acquisition parameters of a filter to be connected with the filtering function;
s42, transmitting data generated during operation to a filter by the virtual model through a filtering function, and collecting real-time analog quantity by the filter;
s43, the filter identifies the optimal temperature control load point, the electric energy of the battery and the rotating speed of the flywheel during energy storage according to the real-time analog quantity to form an adjustment set;
s44, acquiring a temperature control point, the electric energy of a battery and the rotating speed of a flywheel in the energy storage process of the existing energy storage scheme, and implementing adjustment according to various parameters in an adjustment set, so as to achieve the purposes of maximizing the energy storage in the energy storage process and improving the bearing capacity of the micro-grid.
In a second aspect, the invention also provides a new energy storage system applied to the micro-grid, which comprises an acquisition and extraction module, an analysis and selection module, a conversion judging module and an adjustment implementing module;
the system comprises an acquisition and extraction module, an analysis and selection module, a conversion judging module, an adjustment implementation module and a control module, wherein the acquisition and extraction module is connected with the analysis and selection module;
the acquisition and extraction module is used for acquiring power distribution parameters of the micro-grid, energy storage data and thermal parameters generated in the historical operation process, and extracting data related to a battery, a converter and a flywheel in the energy storage data as associated data;
the analysis and selection module is used for establishing an optimization simulation model based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and selecting an optimal temperature control load point according to a model analysis result;
the conversion judging module is used for judging the electric energy conversion relation between the battery and the flywheel by utilizing the associated data, and establishing a virtual energy storage model based on the judging result and the optimal temperature control load point;
and the adjustment implementation module is used for obtaining the real-time analog quantity of the virtual energy storage model by utilizing the filter and implementing adjustment on the existing new energy storage scheme according to the real-time analog quantity.
One or more technical solutions provided in the embodiments of the present invention at least have the following technical effects or advantages:
1. according to the invention, an optimization simulation model is built through thermal parameters, and then the influence of energy storage collaborative operation under different temperature control loads on the micro-grid is analyzed, so that the purpose of ensuring the bearing capacity of the micro-grid under the optimal temperature control loads is realized, the load of the micro-grid is predicted and controlled better, so that the running efficiency and stability of the micro-grid are improved, and meanwhile, the energy storage efficiency of the whole system is improved by searching the optimal battery electric energy and flywheel rotating speed to realize the maximized energy storage, the maximization of the energy storage capacity of new energy is realized, strong support is provided for sustainable energy development, the energy storage maximization and the micro-grid bearing optimization are ensured, and the stable running capacity and the new energy utilization rate of the micro-grid are improved.
2. According to the invention, the thermal characteristics during energy storage operation are accurately described through thermal parameters, and an optimization decision model is established to input different temperature control analysis micro-grid bearing capacity changes during energy storage operation, so that the operation of the micro-grid can be optimized, the bearing capacity of the micro-grid is improved, and finally, the optimal temperature control load point is selected as a temperature threshold value during energy storage operation, so that the operation efficiency of energy storage equipment can be improved, the performance and efficiency of the micro-grid can be improved, the operation cost is reduced, and the operation safety of the equipment is ensured.
3. According to the invention, the operation data of the battery and the flywheel are accurately analyzed by utilizing the associated data, the optimal rule of electric energy conversion is found, the operation efficiency of the energy storage device is improved, the operation parameters of the operating optimal battery and the flywheel are found by utilizing the optimizing algorithm, the maximization of the energy storage capacity is realized on the premise of ensuring the energy storage efficiency, and the service life of the micro-grid is prolonged.
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 specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flowchart of a new energy storage method applied to a micro grid according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a new energy storage system applied to a micro grid according to an embodiment of the present invention.
In the figure:
1. the collecting and extracting module; 2. an analysis and selection module; 3. a conversion judging module; 4. and adjusting the implementation module.
Detailed Description
The invention is further described below with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms also are intended to include the plural forms, and furthermore, it is to be understood that the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, such as, for example, processes, methods, systems, products or devices that comprise a series of steps or units, are not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or inherent to such processes, methods, products or devices.
Embodiments of the invention and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, the invention provides a new energy storage method applied to a micro-grid, which comprises the following steps:
s1, collecting power distribution parameters of a micro-grid, energy storage data and thermal parameters generated in a historical operation process, and extracting data related to a battery, a converter and a flywheel from the energy storage data as related data.
In this embodiment, collecting the distribution parameters of the micro-grid and the energy storage data and thermal parameters generated in the historical operation process, and extracting the data related to the battery, the converter and the flywheel in the energy storage data as the associated data includes the following steps:
electrical parameters, including capacity, voltage, etc., of the critical distribution equipment (e.g., transformers, lines, etc.) of the microgrid are collected and recorded, and thermal parameters of the load equipment of the microgrid are collected and recorded.
And extracting real-time load data in the historical operation process from the micro-grid energy management system or monitoring equipment, and extracting real-time operation data of the battery pack, the converter and the flywheel from the energy storage management system.
And extracting control index data related to the load matching degree of the micro-grid according to an operation control strategy of the energy storage equipment, sorting and storing the collected and extracted related parameters, and constructing a database support for the operation of the micro-grid and the energy storage system.
S2, an optimization simulation model is established based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and an optimal temperature control load point is selected according to a model analysis result.
In this embodiment, an optimization simulation model is established based on thermal parameters to analyze the influence of energy storage collaborative operation on a micro-grid under different temperature control loads, and an optimal temperature control load point is selected according to a model analysis result, including the following steps:
S21, acquiring thermal parameters including ambient temperature, polymerization heat capacity, polymerization thermal resistance and a converter, and establishing an energy storage thermal model according to the thermal parameters to describe thermal characteristics during energy storage operation.
S22, an optimized decision model is built according to the thermal characteristic result and the characteristic parameter combination decision algorithm of the micro-grid, and the variation of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works is input.
It should be noted that, the decision algorithm related to the present invention may be a markov chain monte carlo algorithm, which is an algorithm for sampling probability distribution, and the main markov chain monte carlo algorithm includes: random walk monte carlo, bayesian monte carlo, gibbs sampling.
Specifically, an optimization simulation model is constructed according to a thermal characteristic result and a characteristic parameter combination decision algorithm of the micro-grid, and the change of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works is input, and the method comprises the following steps:
s221, analyzing the steady-state temperature during energy storage operation according to the thermal characteristic result, analyzing the bearing capacity of the micro-grid according to the characteristic parameter of the micro-grid, and combining the steady-state temperature and the bearing capacity into response data.
S222, establishing a response parameter model under the framework of hybrid modeling according to the response parameters, and describing the relation between the bearing capacity and the steady-state temperature of the micro-grid according to the output result of the model.
S223, obtaining an estimated value of the response parameter based on the decision algorithm, and carrying out global optimization by utilizing the global optimization algorithm to obtain the optimal response parameter.
It should be noted that, the global optimizing algorithm related in the invention can be a genetic hybrid algorithm, which is an optimizing method of hybrid genetic algorithm and optimizing algorithm, and is mainly based on basic framework of genetic algorithm, and adopts the idea of population evolution to solve the problem, and other optimizing algorithms, such as simulated annealing algorithm, particle swarm algorithm, etc., are combined in the selection, crossing and variation of genetic operators.
The method for obtaining the optimal response parameters based on the decision algorithm and global optimization by using the global optimization algorithm comprises the following steps:
s2231, constructing posterior distribution of response parameters, and generating an initial state of a decision algorithm by the posterior distribution through distribution transformation;
s2232, generating a new state in a decision algorithm through a transfer function, judging whether the new state meets a preset condition, if so, receiving the transfer state, and if not, refusing the transfer state;
S2233, generating response parameter estimated values which are uniformly distributed by utilizing a decision algorithm according to the new transfer state, and solving parameter optimal settings in the estimated values by utilizing a global optimizing algorithm;
s2234, obtaining an optimal value of the response parameter according to the parameter optimal setting, and generating an optimal corresponding parameter.
S224, based on the optimal parameters, building an optimal simulation model taking different temperature control data as input and taking the bearing capacity of the micro-grid as target output.
S225, summarizing the bearing capacity output result, and describing the change of the bearing capacity of the micro-grid by utilizing the relation between the bearing capacity of the micro-grid and the steady-state temperature.
S23, judging the influence on the micro-grid according to the analysis result and the change of the bearing capacity, and obtaining the influence index under each temperature control load condition.
Specifically, the influence on the micro-grid is judged according to the analysis result and the change of the bearing capacity, and the method for obtaining the influence index under each temperature control load condition comprises the following steps:
defining working conditions of temperature control loads of different types and scales, establishing a micro-grid operation model, respectively setting different load modes in the operation model according to the temperature control load conditions, and carrying out simulation analysis on each load mode by using power grid analysis software or a custom program.
In the simulation process, indexes such as voltage quality, line loss change, load satisfaction rate and the like are monitored and recorded, the change amplitude of each index and the reference state under different load modes are respectively compared, and the influence of loads of different scales on the micro-grid is compared according to the change degree of the indexes.
And selecting the voltage quality reduction rate, the maximum load satisfaction rate reduction amplitude and the like as influence judgment standards, classifying and counting influence indexes under each load mode to obtain influence on the micro-grid, and analyzing, comparing and summarizing characteristic rules of influence of each load type on the micro-grid.
S24, comparing the influence indexes under the temperature control load conditions, and selecting an optimal temperature control load point as a temperature threshold value when the energy storage works.
Specifically, comparing the influence indexes under each temperature control load condition, and selecting the optimal temperature control load point as the temperature threshold value when the energy storage works comprises the following steps:
and carrying out detailed comparison analysis on the influence indexes under each temperature control load mode according to the analysis result, selecting key indexes with larger influence degree, such as voltage quality reduction rate, maximum load satisfaction rate reduction amplitude and the like, comparing and analyzing the values of the key indexes under each load mode, and excluding modes with influence obviously larger than a threshold value.
And comprehensively evaluating the residual modes, giving a score or grade corresponding to each mode, setting constraint conditions in consideration of factors such as safety, efficiency and the like of the energy storage system, selecting the mode with the highest score or grade from the modes meeting the constraint conditions, and setting a temperature range corresponding to a temperature control load in the mode as a temperature threshold value during energy storage operation.
Verifying whether the influence of the work of the energy storage in the temperature range on the micro-grid meets the requirement, if necessary, ensuring that the work of the energy storage is in an optimal condition by optimizing the fine-tuning temperature threshold range, and writing the temperature threshold into the energy storage management system.
In summary, the invention accurately describes the thermal characteristics during the energy storage operation through thermal parameters, establishes an optimization decision model to input different temperature control analysis energy storage operation, so as to optimize the operation of the micro-grid, improve the bearing capacity of the micro-grid, and finally select the optimal temperature control load point as the temperature threshold during the energy storage operation, thereby improving the operation efficiency of the energy storage device.
And S3, judging the electric energy conversion relation between the battery and the flywheel by using the associated data, and establishing a virtual energy storage model based on the judging result and the optimal temperature control load point.
In this embodiment, the method for determining the electric energy conversion relationship between the battery and the flywheel by using the associated data, and establishing the virtual energy storage model based on the determination result and the optimal temperature control load point includes the following steps:
s31, analyzing the charge and discharge parameters and the electric energy of the battery according to the operation data of the battery, the flywheel and the converter in the related data.
Specifically, according to the operation data of the battery, the flywheel and the converter in the associated data, the analysis of the charge and discharge parameters and the electric energy of the battery comprises the following steps:
and collecting and arranging operation data of the battery, the flywheel and the converter, wherein the operation data comprise parameters such as voltage, current, capacitance, rotating speed and the like, and counting parameters such as start-stop time, charging depth, charging power and the like of a charging and discharging period of the battery according to the data.
And calculating the electric energy absorbed or released by the battery in each charge-discharge period, grouping the charge-discharge periods of the battery according to the electric energy, and analyzing factors influencing the electric energy of the battery, such as the charge depth, the charge power and the like, for each electric energy.
And establishing a regression or correlation analysis model between the electric energy and the rotating speed of the flywheel, judging main technical parameters influencing the electric energy of the battery, obtaining the technical parameter optimization direction influencing the charge and discharge electric quantity of the battery according to the analysis result, and repeating the steps to analyze the operation data of the flywheel and the converter to obtain a correlation conclusion influencing the energy storage size.
S32, analyzing the change parameters of the rotating speed of the flywheel and the stored energy, and comparing the parameter change time sequence of the battery and the flywheel to judge the electric energy conversion rule so as to obtain an electric energy conversion relation.
Specifically, analyzing the variation parameters of the rotation speed of the flywheel and the magnitude of the stored and released energy, comparing the parameter variation time sequence of the battery and the flywheel to judge the electric energy conversion rule and obtain the electric energy conversion relation, and the method comprises the following steps:
collecting flywheel rotation speed data and battery charging and discharging current of a battery, performing time sequence analysis on the change of the flywheel rotation speed to obtain rotation energy of the flywheel, performing time sequence analysis on the charging and discharging data of the battery, and observing the change trend of the current and the voltage.
According to the change of the current and the voltage, the charge or discharge energy of the battery at each moment is calculated, the energy change time sequence of the flywheel and the battery is compared, and the correlation is searched to obtain the electric energy conversion relation.
Through the steps, the relation between the rotation speed of the flywheel and the charge and discharge energy of the battery is obtained, so that the conversion rule of electric energy between the flywheel and the battery is revealed, and the method has important significance for optimizing the design and control of an energy system.
And S33, acquiring a judging result according to the electric energy conversion relation, and constructing a virtual energy storage model by utilizing the judging result and an optimal temperature control load point combined optimizing algorithm.
It should be noted that, the optimizing algorithm related in the invention can be selected from ant lion algorithm, which is an optimizing algorithm simulating ant lion behavior in nature, and is used for solving the problem of multivariable optimization.
Specifically, the method for constructing the virtual energy storage model by utilizing the judgment result and the optimal temperature control load point combined optimizing algorithm comprises the following steps of:
s331, acquiring a judging result of an electric energy conversion relation and an optimal temperature control load point, and presetting the judging result and the optimal temperature control load point as a junction point and a node in an optimizing algorithm respectively;
s332, randomly initializing the intersection points and the nodes, and selecting the intersection points through an optimization strategy;
s333, selecting a junction point for random walk according to an optimization strategy by the node, and setting the direction and the distance of the random walk to be controlled by a random function;
s334, selecting an optimal intersection point according to the direction and the distance of the random walk, setting an updating mechanism to update the node, and calculating the adaptability of the current intersection point after the position of the node is updated;
And S335, selecting the current junction as an optimal virtual value if the fitness is superior to the node, and continuously repeating the steps S333 to S334 if the fitness is inferior to the node, wherein the optimal virtual value is used as the input of the model, and the energy storage capacity is used as the output to complete the construction of the virtual energy storage.
Wherein, the expression of the update mechanism is:
；
in the method, in the process of the invention,represent the firstbThe junction is at the firstaDistance of secondary trip->Represent the firstaDistance of sub-trip time optimization strategy selection node, < >>Represent the firstaThe distance of the node at the time of the secondary trip,arepresenting the number of current iterations and,Eindicating the total number of iterations that should be performed,randexpressed in interval +.>Is a random number of (a) in the memory.
S34, inputting data parameters of the preset battery, flywheel and converter in operation into the virtual energy storage model, analyzing the change of the energy storage capacity, and sorting the capacity change to select the optimal energy storage capacity and corresponding data parameters for recording.
Specifically, data parameters of a preset battery, a flywheel and a converter during operation are input into a virtual energy storage model, the change of energy storage capacity is analyzed, and the capacity change is sequenced to select the optimal energy storage capacity and the corresponding data parameters for recording, and the method comprises the following steps:
s341, consulting a production manual of the battery, flywheel and converter equipment to know the rated parameter range of the production manual, and assuming steady-state operation parameters under the condition of normal energy storage and critical parameters under the extreme condition;
S342, presetting a parameter set of each device in operation according to rated parameters, steady-state operation parameters and critical parameters as reference ranges, and inputting the parameter set into a virtual energy storage model to output an energy storage result;
s343, generating initial capacity change data of an energy storage result by using topology sequencing to obtain a priority matrix, and selecting data with the highest capacity change in the priority matrix as a top value of the priority matrix;
s344, selecting preset data values of the batteries, the flywheel and the converter equipment in the priority matrix to judge whether the generated energy storage result is a top value or not;
and S345, setting the energy storage capacity to be optimal if the energy storage capacity corresponds to the data parameter, recording the corresponding data parameter, and repeating the step S343 and the step S344 if the energy storage capacity does not correspond to the data parameter until the result corresponds to the data parameter.
And S4, obtaining real-time analog quantity of the virtual energy storage model by utilizing the filter, and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity.
In this embodiment, the method for obtaining the real-time analog quantity of the virtual energy storage model by using the filter and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity includes the following steps:
s41, adding a filtering function in the virtual energy storage model, and setting acquisition parameters of a filter to be connected with the filtering function.
Specifically, adding a filtering function in the virtual energy storage model, and setting the acquisition parameters of the filter to be connected with the filtering function comprises the following steps:
the data type needing filtering is determined, including battery voltage and flywheel rotating speed, proper filter types and parameters are selected, common filters comprise a low-pass filter, a high-pass filter, a band-pass filter and the like, and acquisition parameters of the filters are set, including sampling frequency, filter order, cut-off frequency and the like.
The signal or data to be filtered is input into the filter and the filtered signal is connected to the corresponding part in the virtual energy storage model.
S42, the virtual model transmits data generated during operation to a filter through a filtering function, and the filter collects real-time analog quantity.
Specifically, the virtual model transmits data generated during running to a filter through a filtering function, and the filter collects real-time analog quantities, including:
in the virtual model, real-time analog data are transmitted to a filtering function, the filtering function receives the real-time analog data and performs filtering processing, the filter collects the filtered data and stores the filtered data in a memory or stores the filtered data in other modes, and the virtual model can use the filtered data to perform calculation, analysis or other operations according to requirements.
In the process, the filtering function can help to remove noise or interference in the real-time analog data, so that the quality and accuracy of the data are improved, the filter can carry out filtering processing on the data in a real-time or off-line mode, the filtered data are obtained according to a specific filtering algorithm and parameters, and the virtual model can further use the filtered data to carry out simulation and analysis in a more accurate and reliable mode.
S43, the filter identifies the optimal temperature control load point, the electric energy of the battery and the rotating speed of the flywheel during energy storage according to the real-time analog quantity to form an adjustment set.
S44, acquiring a temperature control point, the electric energy of a battery and the rotating speed of a flywheel in the energy storage process of the existing energy storage scheme, and implementing adjustment according to various parameters in an adjustment set, so as to achieve the purposes of maximizing the energy storage in the energy storage process and improving the bearing capacity of the micro-grid.
Referring to fig. 2, the invention further provides a new energy storage system applied to the micro-grid, which comprises an acquisition and extraction module 1, an analysis and selection module 2, a conversion judging module 3 and an adjustment implementing module 4;
the system comprises an acquisition and extraction module 1, an analysis and selection module 2, a conversion judging module 3 and an adjustment implementing module 4, wherein the acquisition and extraction module 1 is connected with the analysis and selection module 2;
The acquisition and extraction module 1 is used for acquiring power distribution parameters of the micro-grid, energy storage data and thermal parameters generated in the historical operation process, and extracting data related to a battery, a converter and a flywheel in the energy storage data as associated data;
the analysis selection module 2 is used for establishing an optimization simulation model based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and selecting an optimal temperature control load point according to a model analysis result;
the conversion judging module 3 is used for judging the electric energy conversion relation between the battery and the flywheel by utilizing the associated data, and establishing a virtual energy storage model based on the judging result and the optimal temperature control load point;
and the adjustment implementation module 4 is used for obtaining the real-time analog quantity of the virtual energy storage model by utilizing the filter and implementing adjustment on the existing new energy storage scheme according to the real-time analog quantity.
In summary, by means of the technical scheme, the invention establishes the optimized simulation model through the thermal parameters, further analyzes the influence of the energy storage collaborative operation under different temperature control loads on the micro-grid, so that the purpose of ensuring the bearing capacity of the micro-grid under the optimal temperature control loads is achieved, the loads of the micro-grid are predicted and controlled better, the running efficiency and stability of the micro-grid are improved, meanwhile, the energy storage efficiency of the whole system is improved by searching the optimal battery electric energy and flywheel rotating speed to achieve the maximized energy storage, the maximization of the energy storage capacity of the new energy is achieved, strong support is provided for sustainable energy development, the maximization of the energy storage and the optimal bearing of the micro-grid are ensured, and the stable running capacity and the new energy utilization rate of the micro-grid are improved. According to the invention, the thermal characteristics during energy storage operation are accurately described through thermal parameters, and an optimization decision model is established to input different temperature control analysis micro-grid bearing capacity changes during energy storage operation, so that the operation of the micro-grid can be optimized, the bearing capacity of the micro-grid is improved, and finally, the optimal temperature control load point is selected as a temperature threshold value during energy storage operation, so that the operation efficiency of energy storage equipment can be improved, the performance and efficiency of the micro-grid can be improved, the operation cost is reduced, and the operation safety of the equipment is ensured. According to the invention, the operation data of the battery and the flywheel are accurately analyzed by utilizing the associated data, the optimal rule of electric energy conversion is found, the operation efficiency of the energy storage device is improved, the operation parameters of the operating optimal battery and the flywheel are found by utilizing the optimizing algorithm, the maximization of the energy storage capacity is realized on the premise of ensuring the energy storage efficiency, and the service life of the micro-grid is prolonged.
Those of ordinary skill in the art will appreciate that the elements of the various examples described in connection with the present embodiments, i.e., the algorithm steps, can be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
While the foregoing description of the embodiments of the present application has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the application, but rather, it is intended to cover all modifications or variations within the scope of the application as defined by the claims of the present application.
Claims (10)
1. The new energy storage method applied to the micro-grid is characterized by comprising the following steps of:
s1, collecting power distribution parameters of a micro-grid, energy storage data and thermal parameters generated in a historical operation process, and extracting data related to a battery, a converter and a flywheel from the energy storage data as associated data;
S2, an optimization simulation model is established based on thermal parameters to analyze the influence of energy storage cooperative operation on the micro-grid under different temperature control loads, and an optimal temperature control load point is selected according to a model analysis result;
s3, judging the electric energy conversion relation between the battery and the flywheel by using the associated data, and establishing a virtual energy storage model based on a judging result and an optimal temperature control load point;
and S4, obtaining real-time analog quantity of the virtual energy storage model by utilizing the filter, and implementing and adjusting the existing new energy storage scheme according to the real-time analog quantity.
2. The new energy storage method applied to the micro-grid according to claim 1, wherein the method for establishing an optimization simulation model based on thermal parameters to analyze the influence of the energy storage collaborative operation under different temperature control loads on the micro-grid, and selecting the optimal temperature control load point according to the model analysis result comprises the following steps:
s21, acquiring thermal parameters including ambient temperature, polymerization heat capacity, polymerization thermal resistance and a converter, and establishing an energy storage thermal model according to the thermal parameters to describe thermal characteristics during energy storage operation;
s22, an optimized decision model is built according to the thermal characteristic result and the characteristic parameter of the micro-grid in combination with a decision algorithm, and the variation of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works is input;
S23, judging the influence on the micro-grid according to the analysis result and the change of the bearing capacity, and obtaining influence indexes under each temperature control load condition;
s24, comparing the influence indexes under the temperature control load conditions, and selecting an optimal temperature control load point as a temperature threshold value when the energy storage works.
3. The new energy storage method applied to the micro-grid according to claim 2, wherein the steps of constructing an optimized simulation model according to the thermal characteristic result and the characteristic parameter of the micro-grid in combination with a decision algorithm, and inputting the variation of the micro-grid bearing capacity during the operation of different temperature control analysis energy storage works include the following steps:
s221, analyzing a steady-state temperature during energy storage operation according to a thermal characteristic result, analyzing the bearing capacity of the micro-grid according to a characteristic parameter of the micro-grid, and combining the steady-state temperature and the bearing capacity into response data;
s222, establishing a response parameter model under the framework of hybrid modeling according to response parameters, and describing the relation between the bearing capacity and the steady-state temperature of the micro-grid according to the output result of the model;
s223, obtaining an estimated value of the response parameter based on a decision algorithm, and carrying out global optimization by utilizing a global optimization algorithm to obtain an optimal response parameter;
S224, establishing an optimized simulation model taking different temperature control data as input and taking the bearing capacity of the micro-grid as target output based on the optimal parameters;
s225, summarizing the bearing capacity output result, and describing the change of the bearing capacity of the micro-grid by utilizing the relation between the bearing capacity of the micro-grid and the steady-state temperature.
4. The new energy storage method applied to the micro grid according to claim 3, wherein the step of obtaining the estimated value of the response parameter based on the decision algorithm and performing global optimization by using the global optimization algorithm to obtain the optimal response parameter comprises the following steps:
s2231, constructing posterior distribution of response parameters, and generating an initial state of a decision algorithm by the posterior distribution through distribution transformation;
s2232, generating a new state in a decision algorithm through a transfer function, judging whether the new state meets a preset condition, if so, receiving the transfer state, and if not, refusing the transfer state;
s2233, generating response parameter estimated values which are uniformly distributed by utilizing a decision algorithm according to the new transfer state, and solving parameter optimal settings in the estimated values by utilizing a global optimizing algorithm;
s2234, obtaining an optimal value of the response parameter according to the parameter optimal setting, and generating an optimal corresponding parameter.
5. The new energy storage method applied to the micro-grid according to claim 1, wherein the step of determining the electric energy conversion relationship between the battery and the flywheel by using the associated data, and establishing a virtual energy storage model based on the determination result and the optimal temperature control load point comprises the following steps:
s31, analyzing the charge and discharge parameters and the electric energy of the battery according to the operation data of the battery, the flywheel and the converter in the related data;
s32, analyzing the change parameters of the rotating speed of the flywheel and the stored energy, and comparing the parameter change time sequence of the battery and the flywheel to judge the electric energy conversion rule so as to obtain an electric energy conversion relation;
s33, acquiring a judgment result according to the electric energy conversion relation, and constructing a virtual energy storage model by utilizing the judgment result and an optimal temperature control load point combined optimizing algorithm;
s34, inputting data parameters of the preset battery, flywheel and converter in operation into the virtual energy storage model, analyzing the change of the energy storage capacity, and sorting the capacity change to select the optimal energy storage capacity and corresponding data parameters for recording.
6. The new energy storage method applied to the micro-grid according to claim 5, wherein the steps of obtaining a judgment result according to the electric energy conversion relation, and constructing a virtual energy storage model by utilizing the judgment result and an optimal temperature control load point combined optimizing algorithm comprise the following steps:
S331, acquiring a judging result of an electric energy conversion relation and an optimal temperature control load point, and presetting the judging result and the optimal temperature control load point as a junction point and a node in an optimizing algorithm respectively;
s332, randomly initializing the intersection points and the nodes, and selecting the intersection points through an optimization strategy;
s333, selecting a junction point for random walk according to an optimization strategy by the node, and setting the direction and the distance of the random walk to be controlled by a random function;
s334, selecting an optimal intersection point according to the direction and the distance of the random walk, setting an updating mechanism to update the node, and calculating the adaptability of the current intersection point after the position of the node is updated;
and S335, selecting the current junction as an optimal virtual value if the fitness is superior to the node, and continuously repeating the steps S333 to S334 if the fitness is inferior to the node, wherein the optimal virtual value is used as the input of the model, and the energy storage capacity is used as the output to complete the construction of the virtual energy storage.
7. The new energy storage method applied to the micro grid according to claim 6, wherein the expression of the update mechanism is:
；
in the method, in the process of the invention,represent the firstbThe junction is at the firstaDistance of secondary trip time;
represent the firstaOptimizing the distance of the nodes by using a strategy for secondary travel time;
Represent the firstaDistance of the secondary travel time node;
arepresenting the number of current iterations;
Erepresenting the total number of iterations;
randis shown in the intervalIs a random number 2 of (c).
8. The new energy storage method applied to the micro-grid according to claim 7, wherein the data parameters of the preset battery, flywheel and converter during operation are input into the virtual energy storage model, the change of the energy storage capacity is analyzed, and the capacity change is sequenced to select the optimal energy storage capacity and the corresponding data parameters for recording, and the method comprises the following steps:
s341, consulting a production manual of the battery, flywheel and converter equipment to know the rated parameter range of the production manual, and assuming steady-state operation parameters under the condition of normal energy storage and critical parameters under the extreme condition;
s342, presetting a parameter set of each device in operation according to rated parameters, steady-state operation parameters and critical parameters as reference ranges, and inputting the parameter set into a virtual energy storage model to output an energy storage result;
s343, generating initial capacity change data of an energy storage result by using topology sequencing to obtain a priority matrix, and selecting data with the highest capacity change in the priority matrix as a top value of the priority matrix;
s344, selecting preset data values of the batteries, the flywheel and the converter equipment in the priority matrix to judge whether the generated energy storage result is a top value or not;
And S345, setting the energy storage capacity to be optimal if the energy storage capacity corresponds to the data parameter, recording the corresponding data parameter, and repeating the step S343 and the step S344 if the energy storage capacity does not correspond to the data parameter until the result corresponds to the data parameter.
9. The new energy storage method applied to the micro grid according to claim 8, wherein the obtaining the real-time analog quantity of the virtual energy storage model by using the filter and performing the adjustment on the existing new energy storage scheme according to the real-time analog quantity comprises the following steps:
s41, adding a filtering function in the virtual energy storage model, and setting acquisition parameters of a filter to be connected with the filtering function;
s42, transmitting data generated during operation to a filter by the virtual model through a filtering function, and collecting real-time analog quantity by the filter;
s43, the filter identifies the optimal temperature control load point, the electric energy of the battery and the rotating speed of the flywheel during energy storage according to the real-time analog quantity to form an adjustment set;
s44, acquiring a temperature control point, the electric energy of a battery and the rotating speed of a flywheel in the energy storage process of the existing energy storage scheme, and implementing adjustment according to various parameters in an adjustment set, so as to achieve the purposes of maximizing the energy storage in the energy storage process and improving the bearing capacity of the micro-grid.
10. A new energy storage system applied to a micro-grid for realizing the new energy storage method applied to the micro-grid according to any one of claims 1-9, wherein the new energy storage system applied to the micro-grid comprises an acquisition and extraction module, an analysis and selection module, a conversion judging module and an adjustment implementing module;
The acquisition and extraction module is connected with the analysis and selection module, the analysis and selection module is connected with the conversion judgment module, and the conversion judgment module is connected with the adjustment implementation module;
the acquisition and extraction module is used for acquiring power distribution parameters of the micro-grid, energy storage data and thermal parameters generated in the historical operation process, and extracting data related to a battery, a converter and a flywheel in the energy storage data as associated data;
the analysis and selection module is used for establishing an optimization simulation model based on thermal parameters to analyze the influence of energy storage cooperative operation under different temperature control loads on the micro-grid, and selecting an optimal temperature control load point according to a model analysis result;
the conversion judging module is used for judging the electric energy conversion relation between the battery and the flywheel by utilizing the associated data, and establishing a virtual energy storage model based on the judging result and the optimal temperature control load point;
the adjustment implementation module is used for obtaining real-time analog quantity of the virtual energy storage model by utilizing the filter and implementing adjustment on the existing new energy storage scheme according to the real-time analog quantity.
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