CN111555328A - Intelligent state judgment and mode switching method for high-voltage direct-hanging energy storage system - Google Patents

Intelligent state judgment and mode switching method for high-voltage direct-hanging energy storage system Download PDF

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CN111555328A
CN111555328A CN202010502359.4A CN202010502359A CN111555328A CN 111555328 A CN111555328 A CN 111555328A CN 202010502359 A CN202010502359 A CN 202010502359A CN 111555328 A CN111555328 A CN 111555328A
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energy storage
soc
factor
storage system
voltage direct
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张亮
李丹栋
水恒华
侯凯
杨波
陶以彬
何安然
管益涛
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Nanjing Institute of Technology
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management

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Abstract

The invention discloses a method for intelligently judging the state and switching modes of a high-voltage direct-hanging energy storage system. Aiming at the H-bridge cascade high-voltage direct-hanging type energy storage system, the safe and stable operation of the high-voltage direct-hanging type energy storage system can be seriously influenced by different states such as voltage drop caused by external power grid faults, deviation of SOC balance degree of an internal battery pack, too low SOC caused by excessive discharge of the battery pack and the like, so that a factor representing the voltage drop amplitude of a power grid, a factor tau representing the SOC balance degree of the energy storage battery pack and a factor rho representing the SOC discharge depth of the energy storage battery pack are defined to carry out multi-factor system state function derivation, a comprehensive multi-factor high-voltage direct-hanging type energy storage system state intelligent judgment and mode switching method is provided, and the operation reliability of the system.

Description

Intelligent state judgment and mode switching method for high-voltage direct-hanging energy storage system
Technical Field
The invention relates to the technical field of high-voltage direct-hanging energy storage, in particular to a comprehensive multi-factor intelligent state judgment and mode switching method for a high-voltage direct-hanging energy storage system.
Background
The multilevel technology is widely concerned and researched in the application occasions of medium-high voltage high-power conversion. The H-bridge cascaded multilevel converter is one of the most typical representatives, and is also a power conversion topology which is commonly used in a high-voltage direct-hanging high-capacity battery energy storage system. The high-voltage direct-hanging type battery energy storage system has the advantages of high access efficiency and the like, but the requirement for operation safety protection of the high-voltage direct-hanging type battery energy storage system is stricter. Different states such as voltage drop caused by external power grid faults, deviation of SOC balance degree of an internal battery pack, and too low SOC caused by excessive discharge of the battery pack can all bring challenges to safety and stability of a system, intelligent early warning is needed, and intelligent conversion is carried out on the operation mode of the system.
Disclosure of Invention
The invention aims to provide a method for intelligently judging the state and switching the mode of a high-voltage direct-hanging energy storage system, which comprises the steps of establishing a factor for representing the voltage drop amplitude of a power grid, a factor tau for representing the SOC balance degree of an energy storage battery pack and a factor rho for representing the SOC discharge depth of the energy storage battery pack, constructing a system state comprehensive function f (z), intelligently judging the fault type and the severity, constructing a system operation decision function g (x) and intelligently switching the operation mode through the decision function.
In order to achieve the object, the present disclosure provides a method for intelligently determining a state and switching modes of a high-voltage direct-hanging energy storage system, including an H-bridge cascaded high-voltage direct-hanging energy storage system, where the H-bridge cascaded high-voltage direct-hanging energy storage system includes: the power grid, the H-bridge cascaded high-voltage direct-hanging PCS, the grid-connected inductor and the battery unit, wherein the grid-connected inductor filters harmonic waves of output current of the H-bridge cascaded high-voltage direct-hanging PCS; the H-bridge cascaded high-voltage direct-hanging PCS main circuit adopts a star connection method, consists of N H-bridge units and converts direct current and alternating current into each other; the battery unit is composed of a plurality of battery packs and is charged and discharged by the power unit module;
when an alternating current power grid normally operates, acquiring a factor representing the voltage drop amplitude of the power grid, a factor tau representing the SOC balance degree of an energy storage battery pack and a factor rho representing the SOC discharge depth of the energy storage battery pack, defining a state factor z of a high-voltage direct-hanging energy storage system, calculating a system comprehensive state function f (z), further obtaining a system operation decision function g (x), intelligently judging the state and the fault severity of the system, and selecting corresponding mode switching control.
Defining a factor representing the voltage drop amplitude of the power grid:
Figure BDA0002525280050000021
wherein, USdFor positive sequence d-axis component, U, of the grid voltage before sagTdIs the positive sequence d-axis component of the grid voltage after the dip.
Defining a factor tau representing the SOC balance degree of the energy storage battery pack:
Figure BDA0002525280050000022
in the formula: SOCi_maxThe maximum SOC capacity and SOC in the high-voltage direct-hanging energy storage system clusteri_minThe minimum SOC capacity in the high-voltage direct-hanging energy storage system cluster is represented as i, a, b and c; SOCrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
According to the discharging residual capacity of the SOC of the battery pack, defining a factor rho representing the SOC discharging depth of the energy storage battery pack:
Figure BDA0002525280050000031
in the formula: SOCijThe real-time capacity of the SOC of the i-phase j battery module is i-a, b, c, j-1, 2 … n, n is the number of power units in one phase, and the SOC isrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
And combining a factor representing the voltage drop amplitude of a power grid, a factor tau representing the SOC balance degree of the energy storage battery pack and a factor rho representing the SOC discharge depth of the energy storage battery pack to define a state factor z of the high-voltage direct-hanging energy storage system:
z＝ω0+ω1*+ω2*τ+ω3*ρ (4)
in the formula: omega0Is an initial value; omega1The weight coefficient is the grid voltage drop factor; omega2Weighting coefficient of SOC balance factor; omega3Is SOC discharge factor weight coefficient; is a power grid drop amplitude factor; tau is a SOC balance factor; and rho is an SOC (state of charge) depth factor of the energy storage battery pack.
According to a state factor z of the high-voltage direct-hanging energy storage system, defining a system comprehensive state function f (z)
Figure BDA0002525280050000032
And intelligently judging the current state by using the output value of the system comprehensive state function, wherein the current state comprises whether an external power grid has faults or not, whether the SOC balance of an internal battery pack is good or not and whether the battery has over-discharge or not.
Defining a system operation decision function g (x):
Figure BDA0002525280050000033
according to the output value of the state function f (z) of the high-voltage direct-hanging battery energy storage system, judging that the type of the system fault-1 represents the locking and tripping of the energy storage system, 1 represents a reactive support mode, the support system does not disconnect during the period of the grid fault, and 0 represents the normal operation of the energy storage system.
By adopting the technical scheme, based on the H-bridge cascade high-voltage direct-hanging energy storage system, the influence on the safe and stable operation of the high-voltage direct-hanging energy storage system under different states such as voltage drop caused by external power grid faults, deviation of SOC balance degree of an internal battery pack, and low SOC caused by over discharge of the battery pack is utilized, a voltage drop depth factor, an SOC balance degree factor tau and an SOC discharge depth factor rho are defined in advance to carry out multi-factor system state function derivation, and the method for intelligently judging and switching the states of the high-voltage direct-hanging energy storage system by integrating multiple factors is provided, so that the operation reliability of the system is improved. The judgment mode is simple, and the judgment result is accurate.
Drawings
In order to more clearly describe the specific embodiments of the present invention, the drawings, which are required to be used for the embodiments of the present invention, will be briefly described.
Fig. 1 is a schematic diagram of an H-bridge cascaded high-voltage direct-hanging energy storage system.
Fig. 2 is a schematic diagram of the system state intelligent judgment and operation mode decision principle of the integrated multi-factor system.
Fig. 3 is a diagram of a system status determination function.
Fig. 4 is a diagram of a system decision function.
Detailed Description
The following will more clearly and completely describe the detailed embodiments of the present invention in conjunction with the attached drawings in the examples of the present invention.
The H-bridge cascaded high-voltage direct-hanging energy storage system shown in fig. 1 mainly comprises: the system comprises a power grid, an H-bridge cascaded high-voltage direct-hanging PCS, a grid-connected inductor and a battery unit. The grid-connected inductor filters H-bridge cascaded high-voltage direct hanging PCS output current harmonic waves; the H-bridge cascaded high-voltage direct-hanging PCS main circuit adopts a star-shaped connection method, consists of N H-bridge units and mainly has the function of converting direct current and alternating current into each other; the battery unit is composed of a plurality of battery packs, and is charged and discharged by the power unit module.
As shown in fig. 2, a schematic diagram of a system state intelligent judgment and operation mode decision principle of multi-factor integration is shown. When an alternating current power grid normally operates, acquiring a factor representing the voltage drop amplitude of the power grid, a factor tau representing the SOC balance degree of an energy storage battery pack and a factor rho representing the SOC discharge depth of the energy storage battery pack, calculating a state function f (z), intelligently judging the state and the fault severity of a system, selecting corresponding mode switching control according to a system operation decision function g (x), generating alternating current voltage drop faults of the power grid, and supporting the energy storage system not to be disconnected during short-time faults of the power grid by adopting a low-voltage ride-through strategy; and locking and tripping the energy storage system when the SOC balance fault or the SOC over-discharge fault occurs.
As shown in fig. 2, a schematic diagram of a system state intelligent judgment and operation mode decision principle of a comprehensive multi-factor system detects a grid voltage in real time and calculates a grid voltage positive sequence d-axis component value, wherein a calculation formula of a positive sequence component is as follows:
Figure BDA0002525280050000051
defining a grid voltage sag depth factor of
Figure BDA0002525280050000052
Wherein, USdFor positive sequence d-axis component, U, of the grid voltage before sagTdIs the positive sequence d-axis component of the grid voltage after the dip.
Defining a factor tau representing the SOC balance degree of the energy storage battery pack:
Figure BDA0002525280050000061
in the formula: SOCi_maxThe maximum SOC capacity and SOC in the high-voltage direct-hanging energy storage system clusteri_minThe minimum SOC capacity in the high-voltage direct-hanging energy storage system cluster is represented as i, a, b and c; SOCrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
The more unbalanced the internal capacity of the SOC is, the larger the difference value of the individual capacities of the batteries is, and the larger the value of the balance factor is. The SOC capacity fluctuation range is affected by battery materials, the number of battery pack chains and other factors, and normally, the SOC allowable fluctuation range is 20% to 90%, and the upper limit value of the equalization factor can be set to about 0.78.
When the imbalance condition occurs in the battery pack with the energy storage module connected in series, different coping strategies are selected according to the SOC balance factor tau, when the SOC balance factor tau is 0.5 times of the upper limit threshold value, the system keeps normal operation and sends out early warning signals, and the controller adjusts the SOC comprehensive balance control coefficient to inhibit the slight imbalance of the SOC; and if the SOC balance factor tau exceeds the upper limit threshold value, the energy storage system selects a locking tripping machine.
The SOC comprehensive balance control strategy comprises SOC interphase balance and SOC cluster balance, and the SOC interphase balance control comprises the following steps:
Figure BDA0002525280050000062
in the formula:
Figure BDA0002525280050000063
is an interphase SOC balance component; k is a radical of0Balancing and adjusting coefficients for the interphase SOC; delta SOCα、ΔSOCβThe three-phase SOC mean value is obtained by Clark transformation; i.e. id、iqD and q components of the current of the power grid are connected to the high-voltage direct-hanging energy storage system, and omega is the angular frequency of the power grid.
The SOC inter-cluster balance control is as follows:
Figure BDA0002525280050000071
in the formula VijBalancing components for inter-cluster SOC; k is a radical of1For inter-cluster SOC balance adjustment coefficient, Δ SOCi＝SOCi-SOCij，i＝a,b,c；
Figure BDA0002525280050000072
The phase angle is the difference between the a phase, the b phase and the c phase
Figure BDA0002525280050000073
j is 1,2 … n, and n is the number of power cells included in a certain phase.
The high-voltage direct-hanging type energy storage SOC comprehensive balance control comprises the following steps:
Figure BDA0002525280050000074
in the formula:
Figure BDA0002525280050000075
control after SOC cluster balancing for i-phase jth power unitAn amount; vi *Is SOC control quantity before balance;
Figure BDA0002525280050000076
is an interphase SOC balance component; vijBalancing components for inter-cluster SOC; i ═ a, b, c; j is 1,2 … n; n is the number of power cells included in a certain phase.
The SOC comprehensive balance strategy is invested in the whole process of system operation, and the SOC balance capacity can be adjusted according to the balance factor tau. When the balance factor tau exceeds 0.5 times of the upper limit threshold of the balance factor, the system maintains the normal running state and gives out early warning, and dynamically adjusts the balance adjustment coefficient k of the interphase SOC0And inter-cluster SOC balance adjustment coefficient k1And the SOC balance adjusting effect of the system is improved. And when the balance factor tau exceeds a set threshold value, the energy storage system is locked and switched off.
In the battery module charge-discharge process, the monocell overdischarge causes the SOC undersize, causes the inside SOC of battery module easily unbalanced, still can harm battery life by a wide margin simultaneously, according to the surplus electric quantity of discharging of group battery SOC, defines the factor rho of the characterization energy storage group battery SOC depth of discharge:
Figure BDA0002525280050000077
in the formula: SOCijThe i-phase j battery module SOC real-time capacity is represented by i-phase a, b, c, j-1, 2 … n, n is the number of power units contained in a certain phase, and SOCrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
The more the battery is discharged, the larger the discharge factor ρ is, and the smaller the SOC contained in the battery is. And when the SOC is smaller than the threshold value, the energy storage system is selected to be locked in a tripping mode. In general, the lower threshold of the SOC of the battery cell may be set to about 15%, and the upper threshold of the depth of discharge factor may be set to about 0.83.
And (3) integrating the grid voltage drop amplitude factor, the energy storage battery pack SOC balance factor tau and the energy storage battery pack SOC discharge depth factor rho, and defining a state factor z of the high-voltage direct-hanging energy storage system:
z＝ω0+ω1*+ω2*τ+ω3*ρ (4)
in the formula: omega0Is an initial value; omega1The weight coefficient is the grid voltage drop factor; omega2Weighting coefficient of SOC balance factor; omega3Is SOC discharge factor weight coefficient; is a power grid drop amplitude factor; tau is a SOC balance factor; and rho is an SOC (state of charge) depth factor of the energy storage battery pack.
As shown in the fault determination function diagram of fig. 3, the calculation results of the function value fields of [ -1,1], and f (z) are affected by the factors and the weight coefficients corresponding to the factors. In order to distinguish voltage drop faults from SOC faults, a drop factor takes a positive weight coefficient, and an SOC balance factor tau and an SOC discharge factor rho take a negative weight coefficient. In normal operation, the z value is about 0; when a power grid voltage drop fault occurs and is increased, the z value is positive, and a low voltage ride through strategy or a locking tripping strategy is adopted according to a fault threshold value, wherein the strategy corresponds to a positive half shaft of a transverse shaft in the graph; and (3) when the SOC of the battery has internal fault, increasing tau or rho, enabling the z value to be negative, and if the z value is smaller than a fault threshold value, adopting a locking tripping strategy corresponding to a negative semi-axis of a horizontal axis in the diagram.
Defining a system synthesis state function f (z):
Figure BDA0002525280050000081
and intelligently judging the current state by using the output value of the system comprehensive state function, wherein the current state comprises whether an external power grid has faults or not, whether the SOC balance of an internal battery pack is good or not and whether the battery has over-discharge or not.
Defining a system operation decision function g (x):
Figure BDA0002525280050000091
and judging the type and severity of the system fault according to the output value of the state function f (z) of the high-voltage direct-hanging battery energy storage system, and selecting an operation mode by an operation decision function g (x). As shown in the fault decision diagram of fig. 4, SOC _ offline indicates that the energy storage system enters a locking tripping mode; STATCOM _ model represents a reactive support mode, and the support system is not off-line during the grid fault; normal represents the normal operating mode of the energy storage system.

Claims (7)

1. A method for intelligently judging the state and switching modes of a high-voltage direct-hanging energy storage system is characterized by comprising the following steps: including H bridge cascade high pressure direct-hanging energy storage system, H bridge cascade high pressure direct-hanging energy storage system includes: the power grid, the H-bridge cascaded high-voltage direct-hanging PCS, the grid-connected inductor and the battery unit, wherein the grid-connected inductor filters harmonic waves of output current of the H-bridge cascaded high-voltage direct-hanging PCS; the H-bridge cascaded high-voltage direct-hanging PCS main circuit adopts a star connection method, consists of N H-bridge units and converts direct current and alternating current into each other; the battery unit is composed of a plurality of battery packs and is charged and discharged by the power unit module;
when an alternating current power grid normally operates, acquiring a factor representing the voltage drop amplitude of the power grid, a factor tau representing the SOC balance degree of an energy storage battery pack and a factor rho representing the SOC discharge depth of the energy storage battery pack, defining a state factor z of a high-voltage direct-hanging energy storage system, calculating a system comprehensive state function f (z), further obtaining a system operation decision function g (x), intelligently judging the state and the fault severity of the system, and selecting corresponding mode switching control.
2. The method for intelligently judging the state and switching the mode of the high-voltage direct-hanging energy storage system according to claim 1, wherein the method comprises the following steps: the above definition characterizes the factor of the grid voltage sag amplitude:
Figure FDA0002525280040000011
wherein, USdFor positive sequence d-axis component, U, of the grid voltage before sagTdIs the positive sequence d-axis component of the grid voltage after the dip.
3. The method for intelligently judging the state and switching the mode of the high-voltage direct-hanging energy storage system according to claim 1, wherein the method comprises the following steps: the factor τ representing the SOC balance degree of the energy storage battery pack is defined as follows:
Figure FDA0002525280040000012
in the formula: SOCi_maxThe maximum SOC capacity and SOC in the high-voltage direct-hanging energy storage system clusteri_minThe minimum SOC capacity in the high-voltage direct-hanging energy storage system cluster is represented as i, a, b and c; SOCrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
4. The method for intelligently judging the state and switching the mode of the high-voltage direct-hanging energy storage system according to claim 1, wherein the method comprises the following steps: according to the discharging residual capacity of the SOC of the battery pack, defining a factor rho representing the SOC discharging depth of the energy storage battery pack:
Figure FDA0002525280040000021
in the formula: SOCijThe real-time capacity of the SOC of the i-phase j battery module is i-a, b, c, j-1, 2 … n, n is the number of power units in one phase, and the SOC isrefThe SOC capacity upper limit threshold value of the single battery pack of the high-voltage direct-hanging energy storage system is adopted.
5. The intelligent state judgment and mode switching method for the high-voltage direct-hanging energy storage system according to claim 2, 3 or 4, characterized in that: the factor representing the voltage drop amplitude of the power grid, the factor tau representing the SOC balance degree of the energy storage battery pack and the factor rho representing the SOC discharge depth of the energy storage battery pack define a state factor z of the high-voltage direct-hanging energy storage system:
z＝ω0+ω1*+ω2*τ+ω3*ρ (4)
in the formula: omega0Is an initial value; omega1The weight coefficient is the grid voltage drop factor; omega2Weighting coefficient of SOC balance factor; omega3Is SOC discharge factor weight coefficient; is a power grid drop amplitude factor; tau is a SOC balance factor; and rho is an SOC (state of charge) depth factor of the energy storage battery pack.
6. The method for intelligently judging the state and switching the mode of the high-voltage direct-hanging energy storage system according to claim 5, wherein the method comprises the following steps: according to a state factor z of the high-voltage direct-hanging energy storage system, defining a system comprehensive state function f (z)
Figure FDA0002525280040000022
And intelligently judging the current state by using the output value of the system comprehensive state function, wherein the current state comprises whether an external power grid has faults or not, whether the SOC balance of an internal battery pack is good or not and whether the battery has over-discharge or not.
7. The method for intelligently judging the state and switching the mode of the high-voltage direct-hanging energy storage system according to claim 6, wherein the method comprises the following steps: defining a system operation decision function g (x):
Figure FDA0002525280040000031
according to the output value of the state function f (z) of the high-voltage direct-hanging battery energy storage system, judging that the type of the system fault-1 represents the locking and tripping of the energy storage system, 1 represents a reactive support mode, the support system does not disconnect during the period of the grid fault, and 0 represents the normal operation of the energy storage system.
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