CN115864462A - Composite energy storage system and control method thereof - Google Patents
Composite energy storage system and control method thereof Download PDFInfo
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Abstract
The invention discloses a control method of a composite energy storage system, which comprises the following steps: s100: separating the low-frequency power signal and the high-frequency power signal through a first-order Butterworth filter, and respectively distributing the signals to the lithium battery energy storage module and the super capacitor energy storage module as reference power; s200: outputting and recording actual power through respective protection, power limitation and lower layer control strategies; s300: updating a filter time constant of the first order butterworth filter by fuzzy inference; s400: and separating the low-frequency power signal and the high-frequency power signal and respectively distributing the signals to the lithium battery energy storage module and the super capacitor energy storage module. The invention can distribute power more reasonably and improve the system stability.
Description
Technical Field
The invention relates to the field of energy storage, in particular to a composite energy storage system and a control method thereof.
Background
With the explosion of a new energy revolution, the occupancy of renewable energy resources represented by solar energy and wind energy is increasing year by year. The stability of the power grid is challenged due to the high permeability of renewable energy sources, and the demand of energy storage devices is increasing due to the characteristics of unstable output and difficult prediction. The fluctuation of the renewable energy power generation can be well smoothed through the energy storage device, the power balance of the system is kept, and the stability of a power grid is improved. The energy storage device can be divided into an energy type and a power type, the energy type energy storage device mainly comprises a lead-acid battery, a sodium ion battery, a lithium ion battery, a flow battery and the like, and the power type energy storage device mainly comprises a flywheel energy storage device, a super capacitor, a lithium ion capacitor and the like. The energy type energy storage device has the advantages of high energy density and long energy storage time, but has low power density and short cycle life; the power type energy storage device has the advantages of high power density, high response speed and long cycle life, but has low energy density and high self-discharge rate. Therefore, it is currently difficult for a single energy storage technology to simultaneously satisfy all the above advantages to meet the requirements of different application modes. One feasible method is to perform technical complementation on the two energy storage devices to form a composite energy storage system so as to fully exert the advantages of the two single energy storage methods. Among the various composite energy storage technologies, the hybridization technology of lithium batteries and super capacitors has been widely studied in the past decades, and becomes a classic composite energy storage system composition mode, and is also a research object in the present invention.
The research aiming at the composite energy storage system mainly comprises two aspects: on one hand, the capacity optimization configuration problem is realized, and the purpose of the capacity optimization configuration problem of the composite energy storage system is embodied by an objective function and is usually developed around economy, reliability and volatility. Another aspect is the power allocation strategy problem, and real-time allocation of power commands is a primary problem in the control of the composite energy storage system. Common power allocation strategies include wavelet decomposition, empirical mode, moving average, and low-pass filtering, among others. The power distribution method based on low-pass filtering is the most researched and widely used method at present, and the method is simple in principle, reliable in calculation and high in applicability. Such power fluctuation property-based allocation methods are very sensitive to filtering parameters, and proper filtering parameters are key to determining the performance of such power allocation methods. In addition, the operating parameters of the energy storage system may change with the operating state, and there is an inherent gap between this dynamic process and the fixed filtering parameters, so that the power distribution strategy may not well perform the function of the composite energy storage system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a control method of a composite energy storage system.
A control method of a composite energy storage system comprises a lithium battery module, a super capacitor module and a photovoltaic power generation system, wherein the lithium battery module and the super capacitor module are respectively connected with an alternating current bus through two bidirectional DC/AC converters, and the photovoltaic power generation system is connected with the alternating current bus through a photovoltaic inverter, and comprises the following steps: s100: for a real-time power signal in a current control period, separating a low-frequency power signal and a high-frequency power signal through a first-order Butterworth filter, and respectively distributing the low-frequency power signal and the high-frequency power signal to a lithium battery energy storage module and a super capacitor energy storage module as reference power; s200: outputting and recording actual power through respective protection, power limitation and lower layer control strategies; s300: calculating the input of a fuzzy inference device based on the state of charge (SOC) of the lithium battery module and the super capacitor module respectively, and updating the filtering time constant of the first-order Butterworth filter through fuzzy inference; s400: entering the next control period and continuously circulating until the control is finished, finishing the frequency division control of the real-time power signal of the composite energy storage system, and separating and respectively distributing the low-frequency power signal and the high-frequency power signal to the lithium battery energy storage module and the super capacitor energy storage module.
Optionally, the power balance relationship of the system is as follows:
P Load -P Grid ＝P PV +P LB +P SC
wherein, P Grid Is the grid output on the bus, P Load Is the load power, P PV Is the power of photovoltaic power generation, P LB And P SC The discharge power distributed to the lithium battery module and the super capacitor module respectively; the reference power of the composite energy storage system is P HESS ：
P HESS ＝P Load -P Grid -P PV
Will P HESS Distributing the energy storage modules to the lithium battery energy storage module and the super capacitor energy storage module; the transfer function H(s) of the first order Butterworth filter is:
wherein, T f Represents the filter time constant, s is the differential operator; by said first order Butterworth filter pair P HESS Filtering to obtain low-frequency fluctuation component of the power instruction P serving as power instruction P of the lithium battery energy storage module LB_ref The high-frequency fluctuation component is used as a power instruction P of the super-capacitor energy storage module SC_ref (ii) a The state of charge of the system is as follows:
wherein SOC (t) and SOC (0) represent time t and initial time, respectivelyState of charge, P, of the energy storage module in this state out Representing the output power of the energy storage module, E max And E min Respectively representing the maximum energy storage electric quantity and the minimum energy storage electric quantity of the energy storage module; the super capacitor energy storage module has fixed polarity, and the relationship between the stored energy and the terminal voltage is as follows:
wherein E is SC Is the energy stored by the super capacitor module, C is the equivalent capacitance of the super capacitor module, U SC Is the terminal voltage at its two ends; the charge state of the super capacitor module is as follows:
therein, SOC SC (t) and U SC (t) respectively representing the charge state and the terminal voltage of the super capacitor module at the time t,
and &>
Respectively representing the maximum terminal voltage and the minimum terminal voltage of the super capacitor module; will SOC SC (t) as an input to a fuzzy reasoner; distributing power signals in real time by combining the change rate of the charge state of the lithium battery energy storage module and the charge state of the super capacitor module; the change rate of the charge state of the lithium battery energy storage module is as follows:
therein, SOC LB ' (t) and SOC LB (t) respectively representing the charge state change rate and the charge state of the lithium battery energy storage module,
the output power of the lithium battery energy storage module is represented; the output of the fuzzy inference engine is a first-order Butterworth filter time constant T f Change amount of (Δ T) f I.e. the filter time constant at the present moment is T f (t)＝T f (t-1)+ΔT f 。
The invention has the beneficial effects that: the invention provides a control strategy capable of automatically distributing power required to be consumed by a composite energy storage system. The invention filters the real-time power of the composite energy storage system through the first-order Butterworth filter, and distributes the high-frequency part of the real-time power instruction to the super-capacitor energy storage module and the low-frequency part to the lithium battery module. So as to coordinate the power distribution between the two energy storage modules as much as possible while ensuring the power balance of the system. Meanwhile, in order to better distribute power in the composite energy storage system and improve the adjusting capability of the energy storage system, the filtering time constant of the first-order Butterworth filter is adjusted in real time through fuzzy reasoning according to the working states of the lithium battery and the super capacitor, so that the power is distributed more reasonably, the system stability is improved, and reliable guarantee is provided for the long-term stable operation of the composite energy storage system.
Drawings
FIG. 1 is a general block diagram of a composite energy storage system according to an embodiment of the invention;
FIG. 2 is a diagram of an overall control framework for a hybrid energy storage system according to an embodiment of the present invention;
FIG. 3 is an overall scheme of the fuzzy dynamic conditioner of the embodiment of the present invention;
FIG. 4 is a fuzzy inference input and output membership functions of an embodiment of the present invention;
FIG. 5 is a flow chart of the fuzzy dynamics tuning-first order Butterworth filtering cooperative control of the embodiment of the present invention.
Detailed Description
The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments of the present invention when taken in conjunction with the accompanying drawings. Like reference numerals refer to like parts throughout the drawings. The drawings are not intended to be to scale, emphasis instead being placed upon illustrating the principles of the invention.
The terms and words used in the following description and claims are not limited to the written meaning, but are used only by the inventor to enable a clear and consistent understanding of the invention. Thus, it will be apparent to those skilled in the art that the following descriptions of the various embodiments of the present invention are provided for illustration only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
It is to be understood that the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a module" includes reference to one or more such modules. Advantages and features of the present invention and methods of accomplishing the same may be understood more readily by reference to the following detailed description of embodiments and the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art.
The embodiment provides a first-order Butterworth filtering system control method based on fuzzy dynamic empowerment, which is used for power control of a lithium battery-super capacitor composite energy storage system. Firstly, filtering the real-time power of the composite energy storage system through a first-order Butterworth filter, distributing the high-frequency part of a real-time power instruction to the super-capacitor energy storage module, and distributing the low-frequency part of the real-time power instruction to the lithium battery module, so that the power distribution between the two energy storage modules is coordinated as much as possible under the condition of ensuring the power balance of the system. Meanwhile, in order to better distribute power in the composite energy storage system and improve the adjusting capability of the energy storage system, the filtering time constant of the first-order Butterworth filter is adjusted in real time through fuzzy reasoning according to the working states of the lithium battery and the super capacitor, so that the power is distributed more reasonably, the system stability is improved, and reliable guarantee is provided for the long-term stable operation of the composite energy storage system.
The invention relates to a photovoltaic power flow control system with composite energy storage, and the general structure diagram is shown in figure 1. The lithium battery module and the super capacitor module are respectively connected with the alternating current bus through two bidirectional DC/AC converters, and the photovoltaic power generation system is connected with the alternating current bus through a photovoltaic inverter of the photovoltaic power generation system.
If the energy loss in the system is neglected, the power balance relationship of the system can be expressed as shown in equation (1):
P Load -P Grid ＝P PV +P LB +P SC (1)
wherein, P Grid Is the grid output on the bus, P Load Is the load power, P PV Is the power of photovoltaic power generation, P LB And P SC The discharge power distributed to the lithium battery module and the super capacitor module is respectively.
In order to obtain a given value of power to be consumed by the composite energy storage system, P is defined HESS And (3) representing the reference power of the composite energy storage system, wherein the expression is shown as formula (2).
P HESS ＝P Load -P Grid -P PV (2)
Considering the energy storage characteristics of a super capacitor and a lithium battery and the real-time state in operation, based on the reliability and effectiveness of the power distribution method of the first-order Butterworth filtering, the first-order Butterworth filtering is taken as a basic method for realizing the power frequency division control of the scheme, and P is designed HESS And a control method distributed to the lithium battery energy storage module and the super capacitor energy storage module.
The transfer function H(s) of the first order butterworth filter segment can be expressed as shown in equation (3):
wherein, T f Representing the filter time constant, and s is the differential operator.
First order Butterworth filter pair P HESS Filtering to obtain low-frequency fluctuation component of the power instruction P serving as power instruction P of the lithium battery energy storage module LB_ref The high-frequency fluctuation component is used as a power instruction P of the super-capacitor energy storage module SC_ref From this, the power distribution relation in the available composite energy storage system is as the formula (4)
As shown in equation (4), the key parameter of the power allocation method is the filter time constant T f . When T is f When larger, P HESS Will distribute most of the power to the super capacitor energy storage module when T is f Smaller, P HESS The majority of the power in the lithium battery energy storage module is distributed to the lithium battery energy storage module.
Generally, T f Tuning according to engineering experience, which brings about two main problems: (1) the method generally carries out T by stabilizing the energy storage capacity of the super capacitor near a middle point f The setting is equivalent to reducing the capacity of the super capacitor by half; (2) frequent power fluctuation always occurs in the operation process, the electric quantity of the super capacitor is difficult to maintain near the intermediate point, and when the power instruction has large change, the electric quantity of the super capacitor is easy to oversaturate and undersaturate, so that the lithium battery energy storage module needs to frequently switch the charging and discharging states of the lithium battery energy storage module, the service life of the lithium battery energy storage module is shortened, and the power balance can fluctuate frequently.
Aiming at the problems existing in the practical method, the invention provides a scheme for fuzzy dynamic adjustment to dynamically set the filtering time constant T in real time f The overall frame is shown in fig. 2. The working principle is that the optimal T at the current moment is calculated in real time through fuzzy reasoning according to the real-time charge state of the super capacitor module and the real-time charge state change rate of the lithium battery module f 。
The state of charge is defined as the ratio of the current capacity to the total capacity of the energy storage module, and the expression is shown as formula (5):
wherein SOC (t) and SOC (0) respectively represent the state of charge of the energy storage module at time t and in an initial state, P out Representing the output power of the energy storage module, E max And E min Respectively representing the maximum energy storage electric quantity and the minimum energy storage electric quantity of the energy storage module.
Because the super capacitor energy storage module has fixed polarity, the relation between the stored energy and the terminal voltage is shown as the formula (6):
wherein E is SC Is the energy stored by the super capacitor module, C is the equivalent capacitance of the super capacitor module, U SC Is the terminal voltage across it.
The state of charge of the super capacitor is shown as formula (7):
therein, SOC SC (t) and U SC (t) respectively representing the charge state and the terminal voltage of the super capacitor module at the time t,
and &>
The maximum terminal voltage and the minimum terminal voltage of the super capacitor module are respectively represented. Real-time charge state SOC (state of charge) of super-capacitor energy storage module capable of being obtained from above SC (t) and as an input for fuzzy inference.
For the lithium battery energy storage module, the charge state can also be directly calculated according to the formula (5). In the composite energy storage module, the lithium battery energy storage module is a main part and is responsible for most of load output and power absorption, and the super capacitor module is responsible for smoothing power signals of the lithium battery module when the power is suddenly changed. Therefore, the power signal can be distributed in real time by combining the change rate of the charge state of the lithium battery energy storage module and the charge state of the super capacitor module. The change rate of the state of charge of the lithium battery energy storage module can be calculated by the formula (8):
wherein, SOC' LB (t) and SOC LB (t) respectively representing the charge state change rate and the charge state of the lithium battery energy storage module,
and the output power of the lithium battery energy storage module is represented, and meanwhile, the calculated change rate of the state of charge of the lithium battery energy storage module is used as another input of the fuzzy reasoning.
The equations (7) and (8) are two input signals of the fuzzy dynamic regulator in the scheme, and the output of the fuzzy dynamic regulator is the filtering time constant T f Change amount of (Δ T) f I.e. the filter time constant at the present moment is T f (t)＝T f (t-1)+ΔT f 。
The overall structure of the fuzzy inference is shown in fig. 3, the fuzzy inference type adopts a Mamdan i type, wherein the and adopts minimum operation, or adopts maximum operation, the implication adopts minimum operation, and the fuzzy rule aggregation adopts maximum operation. The region-centroid method is used in the defuzzification process and based on this calculation a clear output is obtained.
The fuzzy control input membership function in the scheme is respectively shown as (a) diagram and (b) diagram in fig. 4, wherein UOD _ SOC is shown in the diagram LB ' is the domain of the rate of change of the state of charge of the lithium battery module, UOD _ SOC SC For the domain of the state of charge of the super capacitor module, the fuzzy control output membership function is shown as (c) in FIG. 4UOD_ΔT f Is the domain of the filter time constant.
When SOC is reached LB ' when (t) is small, the lithium battery energy storage module is in a discharging state, and the SOC is required to be adjusted at the moment SC (t) adjusting to a lower state to account for power spikes that may occur, and SOC LB The smaller the value of' (t), the SOC SC (t) the lower the state should be adjusted; when SOC is reached SC (t) when the state is already in the low state, the state may be maintained.
When SOC is reached LB ' (t) when bigger, the lithium battery energy storage module is in a charging state, and the SOC is required to be adjusted at the moment SC (t) adjusting to a higher state to cope with a possible power dip, and SOC LB The larger the value of' (t), the SOC SC (t) the higher the state should be adjusted; when SOC is reached SC (t) when the state is already high, the state may be maintained.
The rule base of the fuzzy inference is obtained by carrying out derivation based on the expert experience and fine tuning according to the experimental result, and is shown in table 1. VS in the table indicates that the state of charge of the super capacitor is extremely small; s represents that the charge state of the super capacitor module is small; m represents the charge state of the super capacitor module is medium; l represents that the charge state of the super capacitor module is large; VL represents the state of charge of the super capacitor module is extremely large; NL represents that the change rate of the state of charge of the lithium battery module is large; NS indicates that the change rate of the state of charge of the lithium battery module is small; z represents zero change rate of the state of charge of the lithium battery module; PS represents that the change rate of the state of charge of the lithium battery module is small; PL represents that the change rate of the state of charge of the lithium battery module is large; NL1 indicates that the filter time constant is negative large; NM1 represents the filter time constant minus or plus; NS1 represents the filter time constant minus minimum; z1 represents a filter time constant of zero; PL1 indicates that the filter time constant positive is large; PM1 denotes the filter time constant plus or minus; PS1 indicates that the filter time constant is positive minimum.
TABLE 1 fuzzy rule Table
According to the state of charge of the lithium battery module input in the fuzzy controllerThe change rate and the charge state of the super capacitor module are combined with a fuzzy rule table to obtain dynamically adjusted T f Finally, the number of times of charge and discharge switching of the lithium battery energy storage module during operation is reduced, and the capacity of the super capacitor energy storage module is fully utilized, so that the dynamic stability of power balance during system operation is improved, the construction cost of the composite energy storage system is reduced, and the economic benefit is improved.
As shown in fig. 5, for the real-time power signal, the low-frequency power signal and the high-frequency power signal are separated by the first-order butterworth filtering and are respectively distributed to the lithium battery energy storage module and the super capacitor energy storage module as reference power; outputting and recording actual power through respective protection, power limitation and lower layer control strategies; calculating the input of a fuzzy inference device based on the state of charge (SOC) of the two energy storage modules respectively, and updating a filtering time constant of a first-order Butterworth filter through fuzzy inference; entering the next control period and continuously circulating until the control is finished, finishing the frequency division control of the real-time power signal of the composite energy storage system, and separating and respectively distributing the low-frequency power signal and the high-frequency power signal to the lithium battery energy storage module and the super capacitor energy storage module.
The invention relates to a fuzzy dynamic regulation first-order Butterworth filtering cooperative control method for a lithium battery-super capacitor composite energy storage system. The power distribution between the two energy storage modules is coordinated as much as possible under the condition of ensuring the power balance of the system; according to the working states of the lithium battery and the super capacitor, the filtering time constant of the first-order Butterworth filter is adjusted in real time through fuzzy reasoning, power is distributed more reasonably, system stability is improved, and reliable guarantee is provided for long-term stable operation of the composite energy storage system.
Although the technology has been illustrated and described with respect to one or more implementations, alterations and/or modifications may be made to the illustrated examples without departing from the spirit and scope of the appended claims. In particular regard to the various functions performed by the above described components or structures (assemblies, devices, circuits, systems, etc.), the terms (including a reference to a "means") used to describe such components are intended to correspond, unless otherwise indicated, to any component or structure which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated example implementations. In addition, while a particular feature may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms "includes," including, "" has, "" containing, "or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.
In the previous description, numerous specific details were set forth in order to provide a thorough understanding of the present invention. The foregoing description is only a preferred embodiment of the invention, which can be embodied in many different forms than described herein, and therefore the invention is not limited to the specific embodiments disclosed above. And that those skilled in the art may, using the methods and techniques disclosed above, make numerous possible variations and modifications to the disclosed embodiments, or modify equivalents thereof, without departing from the scope of the claimed embodiments. Any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the scope of the technical solution of the present invention.
Claims (10)
1. A control method of a composite energy storage system comprises a lithium battery module, a super capacitor module and a photovoltaic power generation system, wherein the lithium battery module and the super capacitor module are respectively connected with an alternating current bus through two bidirectional DC/AC converters, and the photovoltaic power generation system is connected with the alternating current bus through a photovoltaic inverter, and is characterized by comprising the following steps of:
s100: for a real-time power signal in a current control period, separating a low-frequency power signal and a high-frequency power signal through a first-order Butterworth filter, and respectively distributing the low-frequency power signal and the high-frequency power signal to a lithium battery energy storage module and a super capacitor energy storage module as reference power;
s200: outputting and recording actual power through respective protection, power limitation and lower layer control strategies;
s300: calculating the input of a fuzzy inference device based on the state of charge (SOC) of the lithium battery module and the super capacitor module respectively, and updating the filtering time constant of the first-order Butterworth filter through fuzzy inference;
s400: entering the next control period and continuously circulating until the control is finished, finishing the frequency division control of the real-time power signal of the composite energy storage system, and separating and respectively distributing the low-frequency power signal and the high-frequency power signal to the lithium battery energy storage module and the super capacitor energy storage module.
2. The control method of claim 1, wherein the power balance relationship of the system is:
P Load -P Grid ＝P PV +P LB +P SC
wherein, P Grid Is the grid output on the bus, P Load Is the load power, P PV Is the power of photovoltaic power generation, P LB And P SC The discharge power distributed to the lithium battery module and the super capacitor module is respectively.
3. The control method according to claim 2, wherein the reference power of the composite energy storage system is P HESS ：
P HESS ＝P Load -P Grid -P PV
Will P HESS And distributing the energy to the lithium battery energy storage module and the super capacitor energy storage module.
4. A control method according to claim 3, characterized in that the transfer function H(s) of the first order butterworth filter is:
wherein, T f Representing the filter time constant, and s is the differential operator.
5. Control method according to claim 4, characterized in that P is passed through the pair of first order Butterworth filters HESS Filtering to obtain low-frequency fluctuation component of the power instruction P serving as power instruction P of the lithium battery energy storage module LB_ref The high-frequency fluctuation component is used as a power instruction P of the super-capacitor energy storage module SC_ref 。
6. The control method of claim 5, wherein the state of charge of the system is:
wherein SOC (t) and SOC (0) respectively represent the state of charge of the energy storage module at time t and in an initial state, P out Representing the output power of the energy storage module, E max And E min Respectively representing the maximum energy storage electric quantity and the minimum energy storage electric quantity of the energy storage module.
7. The control method of claim 6, wherein the super capacitor energy storage module has a fixed polarity, and the stored energy is related to the terminal voltage as follows:
wherein E is SC Is the energy stored by the super capacitor module, C is the super capacitor moduleEquivalent capacitance of U SC Is the terminal voltage across it.
8. The control method according to claim 7, wherein the state of charge of the supercapacitor module is as follows:
therein, SOC SC (t) and U SC (t) respectively representing the charge state and the terminal voltage of the super capacitor module at the time t,
and
respectively representing the maximum terminal voltage and the minimum terminal voltage of the super capacitor module; will SOC SC (t) as one input to the fuzzy reasoner.
9. The control method of claim 8, wherein the power signal is distributed in real time in combination with a rate of change of a state of charge of the lithium battery energy storage module and a state of charge of the supercapacitor module.
10. The control method according to claim 9, wherein the rate of change of the state of charge of the lithium battery energy storage module is:
therein, SOC LB ' (t) and SOC LB (t) respectively representing the charge state change rate and the charge state of the lithium battery energy storage module,
indicating lithium battery storageThe output power of the energy module; the output of the fuzzy inference engine is a first-order Butterworth filter time constant T f Change amount of (Δ T) f I.e. the filter time constant at the present moment is T f (t)＝T f (t-1)+ΔT f 。/>
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|---|---|---|---|---|
| CN118336885A (en) * | 2024-06-12 | 2024-07-12 | 西安热工研究院有限公司 | Hybrid power electric energy supply method and system based on super-capacity energy storage |
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