CN115425636A

CN115425636A - Flywheel energy storage-containing direct current microgrid virtual inertia self-adaptive control method

Info

Publication number: CN115425636A
Application number: CN202211139071.0A
Authority: CN
Inventors: 宋丽; 宋玲燕; 赵兴勇; 高兰香; 王雨祺
Original assignee: Shanxi University
Current assignee: Shanxi University
Priority date: 2022-09-19
Filing date: 2022-09-19
Publication date: 2022-12-02

Abstract

The invention relates to the technical field of virtual inertia control of a direct-current microgrid and discloses a virtual inertia self-adaptive control method of the direct-current microgrid with flywheel energy storage. Firstly, a linear relation between a voltage change rate and a droop curve intercept adjustment is constructed, and virtual inertia control is achieved. Secondly, the hyperbolic tangent function of the nested power function is adopted to realize self-adaptive adjustment on the virtual inertia, so that the linear relation meets the actual flexible requirement, the transient response time is shortened when the voltage change rate is small, the larger virtual inertia supporting capacity is ensured when the voltage change rate is large, and the output of the converter is prevented from exceeding the limit value. And meanwhile, the rotating speed of a rotor of the flywheel is controlled to be rapidly adjusted, auxiliary power is provided for the system in time, and the inertial support of the direct-current micro-grid is improved. And finally, constructing an optical storage direct current micro-grid control system by using Matlab/Simulink, introducing additional virtual inertia control for simulation under the condition of sudden power change, and verifying the feasibility and effectiveness of the control method.

Description

Self-adaptive control method for virtual inertia of direct-current micro-grid containing flywheel energy storage

Technical Field

The invention relates to the technical field of virtual inertia control of a direct-current microgrid, in particular to a virtual inertia self-adaptive control method of the direct-current microgrid with flywheel energy storage.

Background

The problem that the inertia of a power grid is reduced due to large-scale photovoltaic grid connection limits the development of photovoltaic to a certain extent, and domestic and foreign scholars provide inertial support for the system by using the capacity of energy storage and rapid charging and discharging. The Flywheel Energy Storage System (FESS) is widely applied by virtue of the advantages of high power density, high response speed, easiness in detection of discharge depth, long service life and the like.

At present, a PI feedback control method is commonly used in the FESS discharge process. The method comprises the following steps that a person wears to build and the like introduces a load power and current feedforward compensation value to voltage closed-loop control so as to eliminate the influence of power mutation; the load current is compensated by adopting a feedforward compensation mode in the flying process, so that the stability of the system is improved, but a load current sensor needs to be additionally arranged; the immersion invariant manifold algorithm introduced by scleral et al can effectively inhibit the load fluctuation at the direct current end, but the parameters of a plurality of controllers need to be corrected. The control is only to regulate the feedback quantity on the basis of the original PI to inhibit the influence of load mutation, the potential inertial support capability of the FESS is not considered, the consideration factors are more, and the overall control is more complex.

In order to excite potential inertia, it is proposed to apply improved droop control in the energy storage system, introducing a voltage differential in the droop coefficient to provide inertial support for the system. A dynamic virtual inertia control strategy is provided by research, and an arctan function is introduced to realize droop coefficient self-adaptation, but the arctan function is sensitive to parameter change and the transient response duration is too long. Further, it has been studied to introduce a hyperbolic tangent function to realize virtual inertia control adaptation based on the above control strategy, and although it has a better convergence rate than the arctangent function when the voltage change rate is large, the determination of the adjustment direction and the relationship between the adjustment amount and the virtual capacitance are complicated. Aiming at the problems, the invention provides a virtual inertia self-adaptive control method for a direct current micro-grid containing flywheel energy storage.

Disclosure of Invention

The invention provides a virtual inertia self-adaptive control method of a direct-current micro-grid containing flywheel energy storage, aiming at the problems that the potential inertia of the direct-current micro-grid cannot be improved by adopting a traditional control method of a flywheel energy storage system, and the dynamic performance and stability of bus voltage are poor when the system has sudden power change. According to the method, droop curve intercept adjustment quantity is combined with the voltage change rate, a hyperbolic tangent function and a power function are introduced, self-adaptive virtual inertia control is achieved, finally, under the condition of sudden power change, the droop-additionally improved virtual inertia control is compared with the traditional control without virtual inertia in a simulation mode, and the effectiveness of the method is verified.

In order to achieve the purpose, the invention adopts the following technical scheme:

the invention provides a self-adaptive control method for virtual inertia of a direct-current microgrid with flywheel energy storage, which comprises the following steps:

step 1, analyzing virtual inertia of a direct current micro-grid containing flywheel energy storage;

step 2, constructing a linear relation between the voltage change rate and the droop curve intercept adjustment quantity to realize virtual inertia control;

step 3, self-adaptive adjustment is realized on the virtual inertia by adopting a hyperbolic tangent function of a nested power function, so that the linear relation meets the actual flexible requirement, and the transient response time is shortened when the voltage change rate is small; when the voltage change rate is large, ensuring that the virtual inertial support capacity is large and preventing the output of the converter from exceeding the limit value;

and 4, controlling the rotor speed of the flywheel to be rapidly adjusted, providing auxiliary power for the system in time, and improving the inertial support of the direct-current microgrid.

Further, the specific process of step 1 is as follows:

the inertia time constant of the direct current microgrid is as follows:

in the formula: w _ei Represented as a parallel capacitance C _i (ii) stored energy; s _Nci Is C _i A capacity base value of (a); u is a direct current side voltage value;

H _{dc_microgrid} the physical meaning of (A) is as follows: in thatThe time required by the capacitor to release the stored electric energy under the rated voltage and the capacitance C of the DC side _i Small, inertia time constant H _{dc_microgrid} Small, system inertia is insufficient;

the power relation of the direct current capacitor side of the direct current microgrid is as follows:

in the formula, P _r And P _s Respectively representing the required total power and the output power of the energy storage unit; p _c And U _dc Power and dc voltage flowing into the capacitor, respectively;

U _dc when kept constant, has P _r ＝P _s It is true that the first and second sensors,

when the direct current micro-grid is disturbed, the flywheel energy storage system is rapidly charged and discharged under the additional control action to provide inertia for the system, and a larger capacitance, namely C, is virtualized on the direct current side _vir And making the power source provide auxiliary power Δ P _s To reduce the voltage change speed, Δ P _s Comprises the following steps:

of formula (II) to (III)' _s The output power of the flywheel energy storage under the additional control action;

at load output power fluctuation Δ P _r In time, there are:

P' _s -(P _r +ΔP _r )＝P _c +ΔP _s (4)

the binding formulae (2) and (3) give:

after the virtual inertia control is added, the capacitance value on the direct current side is increased from C to C + Cvir, and as can be seen from equation (1), the inertia time constant of the system becomes:

further, the specific process of step 2 is as follows:

when the flywheel energy storage system adopts droop control based on P-U characteristics, there are:

in the formula of U _ref Represents the longitudinal intercept of the sag curve; 1/k is the droop coefficient.

Because the P-U characteristic droop control cannot reflect the sensitivity of the flywheel energy storage system to the voltage change and cannot provide inertia, the droop control method has the advantages that the droop curve intercept is adjusted to control the converter to quickly absorb and release power, and virtual inertia control is realized;

let the droop intercept adjust delta U _ref And the voltage change rate dU/dt are combined, so that the voltage change rate dU/dt is changed along with the change of dU/dt; when dU/dt is positive, reduce U _ref To prevent further voltage increase, otherwise U should be increased _ref (ii) a The following can be obtained:

in the formula of U _ref0 Is the initial sag curve intercept; k is a radical of _d To adjust the parameters, and k _d >0；

Substituting formula (8) for formula (7) to obtain:

recombined formula (7) of U _ref0 The power required by the voltage variation generated by the U is P' _s Therefore, the following can be obtained:

comparing formula (3) with formula (10), eliminating dU/dt to obtain:

U _ref linear control with dU/dt is C _vir Constant virtual inertia control, and k _d The greater, C _vir The larger the size.

Further, the specific process of step 3 is as follows:

the linear function droop curve intercept adjustment algorithm in the formula (8) is used for adjusting the parameter k _d The method is constant, and flexible application under actual conditions cannot be realized; therefore, Δ U is designed _ref Regarding a nonlinear function of dU/dt passing through an origin, nesting a power function in a hyperbolic tangent function, and shortening transient duration when | dU/dt | is smaller; when the value of | dU/dt | is larger, larger virtual inertial support is ensured, and the output of the converter is prevented from exceeding the limit value;

the improved intercept adjustment algorithm is as follows:

in the formula of U _ref.max And U _ref.min Are respectively U _ref Maximum and minimum values of k ₁ And k ₂ Adjusting parameters for the virtual inertia;

the method is obtained by the formula (12), the intercept of the droop curve is adjusted by using the nesting function, the adaptive capacity is certain, and the slope k of a secant line of the curve passing through the origin point _d Can represent C _vir The size of (d); when k is ₂ When =1, the slope of the curve secant gradually decreases with the increase of | dU/dt |; it can be seen that, with the hyperbolic tangent function, C increases with | dU/dt | during the transient after the load sudden change _vir Are getting smaller and smaller; when k is ₂ >1, when | dU/dt | is small, k _d The change trend of the absolute value is the same as that of the absolute value of dU/dt'; as | dU/dt | continues to increase to the right of point e,k _d decreases with increasing | dU/dt |; the coordinate of the e point indicates that the amount of voltage variation is | | | dU/dt- _max | the longitudinal intercept of the sag curve of the converter should be close to its limit | Δ U _ref ，limit|；

Therefore adopt k ₂ >The intercept adjustment algorithm of 1 can provide smaller virtual inertia and reduce transient response time when | dU/dt | is smaller, and the converter outputs larger auxiliary power when | dU/dt | is larger so as to strengthen the virtual inertia support capability of the system; when the value of | dU/dt | is large, the amplitude limiting capability of the hyperbolic function is utilized, the output power is reduced, and the out-of-limit of the converter is effectively avoided.

Further, the specific process of step 4 is as follows:

since the flywheel is running with a speed limit, the flywheel releases its maximum power limit as:

in the formula, J _F Is the moment of inertia of the rotor; omega _f Is the rotor rotational angular velocity;

in the same way, U _ref The adjustment of the flywheel energy storage system is also limited in a limited range, the flywheel energy storage system supplies auxiliary power during disturbance, the rotation speed of the flywheel energy storage system is limited, and the droop curve can be raised to L at most _high ，L _high Expressed as the maximum value of the vertical intercept of the droop curve under the maximum power which can be provided by the flywheel energy storage for the system under the additional action under the condition that the slope of the original P-U droop characteristic curve is not changed, and the U is known because 1/k is constant _ref.max Comprises the following steps:

similarly, the lowest sag curve can be translated to L _low ，L _low Expressed as the minimum value of the longitudinal intercept of the droop curve under the minimum power which can be provided by the flywheel energy storage system under the additional action under the condition that the slope of the original P-U droop characteristic curve is not changed, so that U _ref.min Comprises the following steps:

compared with the prior art, the invention has the following advantages:

(1) The invention provides a virtual inertia self-adaptive control method of a direct-current micro-grid containing flywheel energy storage, which combines a droop curve intercept adjustment quantity with a voltage change rate, introduces a hyperbolic tangent function and a power function to realize self-adaptive virtual inertia control, and finally carries out simulation comparison on additional improved droop virtual inertia control and traditional control without virtual inertia under the condition of power sudden change to verify the effectiveness of the method.

(2) The virtual inertia self-adaptive control method provided by the invention can utilize the F-VSC to provide inertia support at the moment of sudden load change, and shorten the transient reaction time limit according to the dynamic performance requirement of the system.

(3) The control method of the invention not only can improve the voltage transient phenomenon, but also can reduce the power impact caused to the alternating current power grid.

Drawings

Fig. 1 is a diagram of a dc microgrid topology.

Fig. 2 is a power relationship diagram of the dc capacitor side.

Fig. 3 is a graph of droop curve intercept adjustment.

FIG. 4 is a characteristic curve of the change of the intercept of the droop curve, wherein (a) is a slope diagram of the secant line, and (b) is an adjustment parameter k _d The graph is varied.

FIG. 5 is a graph of the droop curve intercept adjustment range.

FIG. 6 is a block diagram of the F-VSC control.

FIG. 7 is a graph of transient response time test results of voltage and power at a power ramp; wherein: (a) Is a DC bus voltage U _dc And (b) is the drooping curve intercept U _ref (c) is F-VSC output power P _F (d) is G-VSC output power P _G And (e) is P _PV Output power, (f) is P _Load And outputting the power.

FIG. 8 is a test result diagram of transient response time under different values of k1 and k2 when load is suddenly changed; wherein: (a) Is k ₁ Varying DC bus voltage U _dc And (b) is k ₁ F-VSC output power P during change _F And (c) is k ₂ Varying DC bus voltage U _dc And (d) is k ₂ F-VSC output power P during change _F 。

Detailed Description

The technical solution in the embodiments of the present invention will be specifically and specifically described below with reference to the embodiments of the present invention and the accompanying drawings. It should be noted that variations and modifications can be made by those skilled in the art without departing from the principle of the present invention, and these should also be construed as falling within the scope of the present invention.

The self-adaptive control method of the virtual inertia of the direct current micro-grid containing flywheel energy storage comprises the following steps:

the direct-current microgrid topological structure is shown in fig. 1 and mainly comprises the following parts: photovoltaic module, energy storage module, converter, alternating current electric network and load module.

The photovoltaic power generation unit is connected with a direct-current power distribution network through a direct-current converter PV-DC, and Maximum Power Point Tracking (MPPT) control is adopted during operation; the FESS and the alternating current power grid are respectively connected into a direct current power distribution network through a bidirectional AC/DC converter F-VSC and a bidirectional AC/DC converter G-VSC, and the converters adopt droop control to adjust direct current voltage; the alternating current load is connected with the direct current bus through a converter L-VSC.

1. Virtual inertia analysis of DC micro-grid containing flywheel energy storage

(1) Direct current microgrid inertia definition

Inertia is the resistance of any substance to its change in velocity. In an ac system, inertia can prevent abrupt changes in the system frequency. In a conventional synchronous generator set, the inertia time constant H _s Can be described as:

in the formula, W _k And S _N Are respectively stored in the rotorJ refers to the rotational inertia of the rotor, and ω is the rotor speed.

Similar to the inertia definition of an ac system, the inertia of a dc microgrid may be defined as the ability to suppress sudden changes in system voltage. By extending the formula (1) to the dc microgrid, the inertia time constant can be expressed as:

in the formula, W _ei Expressed as a parallel capacitance C _i (ii) stored energy; s. the _Nci Is C _i A capacity base value of (a); u is the DC side voltage value.

H _{dc_microgrid} The physical meaning of (A) is as follows: the time required for the capacitor to discharge the stored energy at the rated voltage. DC side capacitance C _i Small, inertia time constant H _{dc_microgrid} Small, system inertia is not sufficient.

(2) Inertial analysis of flywheel energy storage system

FESS utilizes flywheel masses that rotate at high speeds for energy storage. Kinetic energy E thereof _F Comprises the following steps:

in the formula, J _F Expressed as the moment of inertia of the rotor; omega _f Is the rotor rotational angular velocity.

The output power variation caused by the FESS rotor speed change is:

when power unbalance occurs on the direct current side of the system, the charge and discharge power of a capacitor on the direct current side is as follows:

in the formula, P _r And P _s The power at the input side is respectively a direct current capacitor.

At the moment of load fluctuation, the charging and discharging power supplied to the dc side capacitor from the FESS is converted into the rotational speed fluctuation, regardless of the loss. The combinations of formulae (4) and (5) give:

integrating the two sides of the formula (6) to obtain:

in the formula, ω _f1 ，ω _f0 ，U _dc1 ，U _dc0 And respectively changing the mechanical angular speed and the direct current bus voltage of the FESS rotor at the initial moment.

Taking the rotation speed omega of the FESS at the rated voltage value at the moment after transformation _f1 Performing per unit for the rated rotating speed to obtain:

then omega _{f1_pu} 、ω _{f0_pu} The rotor angular speed per unit value after FESS conversion and at the beginning are respectively; e _C Is U _N The lower capacitor stores electrical energy. Due to E _C ＜＜E _F Thus, there is no significant change in flywheel speed as the voltage changes. Therefore, the additional inertia control is adopted to act on the FESS, so that the rotation speed change can be increased on the original basis, the charging and discharging power of the flywheel is controlled, and the inertia support is provided for the system.

(3) Virtual inertia analysis of DC microgrid

As shown in fig. 2, the equivalent circuit of the converter on the dc side is outside the dashed box. Wherein, P _r And P _s Respectively representing the required total power and the output power of the energy storage unit; p _c And U _dc Respectively the power flowing into the capacitor and the dc voltage.

From FIG. 2, it can be seen that:

U _dc when kept constant, has P _r ＝P _s This is true.

When the direct current micro-grid is disturbed, the FESS charges and discharges rapidly under the additional control action to provide inertia for the system, and a larger capacitance, namely C, is virtualized at the direct current side _vir And making the power source provide auxiliary power Δ P _s To reduce the speed of voltage change. Delta P _s Comprises the following steps:

at the fluctuation of load output power DeltaP _r In time, there are:

P' _s -(P _r +ΔP _r )＝P _c +ΔP _s (11)

the combined formulae (9) and (10) give:

after the virtual inertia control is added, the capacitance value on the dc side is increased from C to C + Cvir, and as can be seen from equation (2), the inertia time constant of the system becomes:

in the formula, C _{vir_i} Is the virtual capacitance value of the i-th converter participating in the control.

2. Constructing a linear relation between the voltage change rate and the droop curve intercept adjustment quantity to realize virtual inertia control

When FESS employs droop control based on P-U characteristics, there are:

in the formula of U _ref Represents the longitudinal intercept of the droop curve; 1/k is the droop coefficient.

The P-U characteristic droop control cannot reflect the sensitivity of the FESS to voltage change and cannot provide inertia, so that the droop curve intercept is adjusted to control the converter to quickly absorb and release power, and virtual inertia control is realized. The schematic diagram is as follows:

as shown in FIG. 3, a is the initial operating point when the load increases to P _b Detecting U _dc Below the desired value, the droop curve intercept U is increased _ref The converter outputs auxiliary power to delay the reduction speed of the direct-current voltage; and vice versa. It can be seen that there is a linear correlation between the amount of adjustment of the droop curve intercept and the amount of change in converter output power.

Substituting equation (15) into equation (14) yields:

recombined formula (14) of U _ref0 With the amount of voltage change required by UPower is P' _s Therefore, the following can be obtained:

comparing formula (10) with formula (17), eliminating dU/dt to obtain:

3. The hyperbolic tangent function of the nested power function is adopted to realize self-adaptive adjustment on the virtual inertia, so that the linear relation meets the actual flexible requirement, and the transient response time is shortened when the voltage change rate is small; when the voltage change rate is large, ensuring that the virtual inertial support capacity is large and preventing the output of the converter from exceeding the limit value;

the linear function droop curve intercept adjustment algorithm in the formula (15) adjusts the parameter k _d The method is constant, and flexible application under the actual condition cannot be realized; design of Δ U _ref Regarding a nonlinear function of dU/dt passing through an origin, nesting a power function in a hyperbolic tangent function, and shortening transient duration when | dU/dt | is smaller; when the value of | dU/dt | is larger, larger virtual inertia support is ensured, and the output of the converter is prevented from exceeding the limit value;

the improved intercept adjustment algorithm is as follows:

the intercept of the droop curve is adjusted by using the nesting function according to the formula (19), so that the droop curve has certain self-adaption capability, and when the k value is changed, the intercept of the droop curve is changed as shown in fig. 4.

FIG. 4 (a) shows 2 curves of the function, which are secants of the curves passing through the origin, the slope k of the secant _d Can represent C _vir Of (c) is used.

When k is ₂ If =1, there is k as | dU/dt | increases _d3 <k _d2 <k _d1 The slope of the curve secant gradually decreases. It can be seen that, with the hyperbolic tangent function, C increases with | dU/dt | during the transient after the load sudden change _vir And are getting smaller and smaller. When k is ₂ >1, when | dU/dt | is small, there is k _d4 <k _d5 ，k _d The change trend of the absolute value is the same as that of the absolute value of dU/dt'; when | dU/dt | continues to increase to the right of point e, there is k _d6 >k _d7 ，k _d Decreases with increasing | dU/dt |.

Therefore adopt k ₂ >The intercept adjustment algorithm of 1 can provide smaller virtual inertia and reduce transient response time when | dU/dt | is smaller, and the converter outputs larger auxiliary power and strengthens the virtual inertia support capability of the system when | dU/dt | is larger; when the value of | dU/dt | is large, the amplitude limiting capability of the hyperbolic function is utilized, the output power is reduced, and the out-of-limit of the converter is effectively avoided.

FIG. 4 (b) shows 3k ₂ >1, and k ₂ The values are different from each other, and it can be seen from the graph that when | dU/dt | are the same, k is _d3 <k _d2 <k _d1 . I.e. the control adjusts the parameter k as a function of the virtual inertia ₂ An increase in value provides a gradual decrease in the virtual inertia provided by the system. It will be appreciated that selection of appropriate tuning parameters can provide the system with appropriate inertial support.

4. The rotor speed of the flywheel is controlled to be rapidly adjusted, auxiliary power is timely provided for the system, and the inertial support of the direct-current micro-grid is improved

From the above analysis, the larger the power released or absorbed by the FESS is, the stronger the voltage stabilizing effect of the dc bus is, but the flywheel has a rotational speed limitation during operation, so the maximum power limitation released by the flywheel is:

in the same way, U _ref Also has a limited range, as shown in fig. 5.

FESS supplies auxiliary power during disturbance, and the droop curve can rise to L at most under the limitation of the rotating speed of FESS _high Since 1/k is constant, U is known _ref.max Comprises the following steps:

similarly, the lowest sag curve can be translated to L _low Therefore is U _ref.min Comprises the following steps:

5. implementation of flywheel energy storage control system

The additional control link acts on an FESS outer ring, and enables the F-VSC to show inertia characteristics of the synchronous generator under the synergistic effect of the additional control link and FESS traditional PI control. In practical applications, since the measurement of the voltage change rate is easily affected by noise and harmonic waves, the present research proposes to perform low-pass filtering on the voltage information, and then replace dU/dt with the voltage change δ U output by the high-pass filtering.

When the system is disturbed, delta U is not equal to 0, and delta U is under the additional control action _ref Adjusting the direction to the reverse direction of delta U, and calculating a new droop curve intercept U by the formula (19) _ref And then obtaining the reference value P of the output power of the current transformer at the moment according to the droop characteristic of the P-U _ref-F Then, the formula (4) is subjected to Laplace transformation to obtain an angular speed reference value omega of flywheel energy storage _ref-F Then, a current inner loop reference value is generated by using PI control, wherein the inner loop adopts i _sd Vector control of = 0. Because the original PI control of the flywheel energy storage system has larger current inner loop bandwidth, the FESS can track the reference value of the output power in time and provide real-time inertial support for the system. The specific control is shown in fig. 6.

6. Simulation analysis:

in order to verify the feasibility of the self-adaptive control method for the virtual inertia of the direct-current microgrid with the flywheel energy storage function, a model is built in a Matlab/Simulink simulation platform according to the diagram 1, and the control algorithm is applied to the simulation model. The main parameters of the simulation model are shown in table 1.

Table 1 dc microgrid simulation main parameters

In order to verify the effectiveness of the control method, the virtual inertia adjustment parameters (k) under the condition of power mutation and different values are adjusted ₁ 、k ₂ ) The transient response time at load surge was tested.

(1) Power bump test

At the beginning, P _PV =10kW, AC load P _Load 13kW，U _dc The voltage is regulated by F-VSC and G-VSC together at 397.56V. t =1s, P _PV Rising to 15kW; t =2s, p _Load The working condition is shown in FIGS. 7 (e) and (f) when the power is increased by 10kW suddenly. F-VSC control simulation comparison is respectively based on virtual-free inertia control and self-adaptive virtual inertia control. The waveforms are shown in fig. 7. In the figure: p _G And P _F The output power of the G-VSC and the F-VSC respectively.

FIGS. 7 (a) and (b) show U at the time of sudden power change _dc And (3) a transient response test result, when the self-adaptive virtual inertia control is not added, the F-VSC and the G-VSC share power disturbance according to droop characteristics, and the droop curve intercept U _ref Remaining unchanged, system lack of inertia, U _dc The stability is reached quickly; after introducing the adaptive virtual inertia control, U under the control of F-VSC _ref With real-time adjustment of dU/dt, the converter can quickly absorb or release auxiliary power to supply virtual inertial support and make U _dc Slowly tending to stabilize.

Fig. 7 (c) and (d) show power fluctuation curves, and compared with non-virtual inertia control, the FESS with additional control can automatically adjust its charge and discharge speed at the moment of power abrupt change, so that auxiliary power is instantly and rapidly input into the system, thereby improving system inertia and dynamic performance of the system. After the inertia response is finished, the FESS power curve controlled by the virtual inertia is parallel to the G-VSC energy curve not controlled by the virtual inertia, namely the same steady-state power can be continuously provided in the droop control stage so as to ensure the stable operation of the system.

The simulation experiment results of the two controls are compared, the auxiliary power provided by the energy storage side can be utilized by adopting the additional virtual inertia control, the inertia supporting capacity of the system is increased, the power output of the alternating current power grid is effectively slowed down, and the overall stability of the system is enhanced.

(2)k ₁ 、k ₂ Different effects on transient response

Initial time, P _PV ＝15kW，P _Load ＝13kW，U _dc The F-VSC and the G-VSC act at 400.04V; t =1s, P _Load The operating condition is shown in figure 8, with a sudden increase of 23 kW. To compare the excess response time after a load jump, k is adjusted ₁ And k ₂ Make the load suddenly increase the instant U _ref The same is true. The waveform is shown in fig. 8.

As can be seen from the bus voltage and FESS converter output power curve shown in fig. 8, as the k value changes, the virtual inertia provided by the virtual inertia control to the system also changes, and the specific experimental data are shown in tables 2 and 3.

TABLE 2 k ₂ Constant value, k ₁ Transient response test results in value changes

TABLE 3 transient response test results with constant k1 value and varying k2 value

At the moment of sudden load increase, the F-VSC immediately outputs auxiliary power to relieve the influence caused by sudden power change so as to ensure the stability of the systemIt is also good. From the data in Table 2, when k is ₂ At constant value, with k ₁ The larger the value, the larger the auxiliary power provided by the F-VSC, the longer the transient response time, and the smaller the bus voltage deviation ratio.

From the data in Table 3, when k ₁ At constant value, with k ₂ The virtual inertia control adopted for reducing the value increases the inertia provided for the system, reduces the deviation of the bus voltage and improves the stability of the direct current bus voltage.

Thus, by choosing the appropriate k ₁ And k ₂ The F-VSC can provide larger inertial support for the system at the moment of sudden load change, and can reduce delta U according to the dynamic performance requirement of the system _ref The sensitivity to small voltage change rates reduces transient response time.

Claims

1. A self-adaptive control method for virtual inertia of a direct-current micro-grid containing flywheel energy storage is characterized by comprising the following steps:

step 1, analyzing virtual inertia of a direct-current micro-grid containing flywheel energy storage;

and 4, controlling the rotor speed of the flywheel to be rapidly adjusted, providing auxiliary power for the system in time, and improving the inertial support of the direct-current micro-grid.

2. The self-adaptive control method for the virtual inertia of the direct-current microgrid with a flywheel energy storage function as claimed in claim 1, characterized in that the specific process of the step 1 is as follows:

the inertia time constant of the direct-current microgrid is as follows:

H _{dc_microgrid} the physical meaning of (1) is: the time required for the capacitor to release the stored electric energy under the rated voltage and the DC side capacitance value C _i Small, inertia time constant H _{dc_microgrid} Small, system inertia is insufficient;

the power relation of the direct current side of the direct current micro-grid capacitor is as follows:

U _dc when kept constant, has P _r ＝P _s In the case where it is true,

when the direct current microgrid is disturbed, the flywheel energy storage system is rapidly charged and discharged under the additional control action to provide inertia for the system, and a larger capacitance, namely C, is virtualized at the direct current side _vir And making the power source provide auxiliary power Δ P _s To reduce the voltage change speed, Δ P _s Comprises the following steps:

at the fluctuation of load output power DeltaP _r In time, there are:

P _s ′-(P _r +ΔP _r )＝P _c +ΔP _s (4)

the binding formulae (2) and (3) give:

3. the self-adaptive control method for the virtual inertia of the direct-current microgrid with a flywheel energy storage function as claimed in claim 2, characterized in that the specific process of the step 2 is as follows:

in the formula of U _ref Represents the longitudinal intercept of the droop curve; 1/k is the sag factor.

Substituting formula (8) for formula (7) yields:

recombined formula (7) of U _ref0 The power required by the voltage variation generated by U is P' _s Therefore, the following can be obtained:

comparing formula (3) with formula (10), eliminating dU/dt to obtain:

4. The self-adaptive control method for the virtual inertia of the direct-current microgrid with a flywheel energy storage function as claimed in claim 3, characterized in that the specific process of the step 3 is as follows:

the linear function droop curve intercept adjustment algorithm in the formula (8) is used for adjusting the parameter k _d The method is constant, and flexible application under the actual condition cannot be realized; therefore, Δ U is designed _ref Regarding a nonlinear function of dU/dt passing through an origin, nesting a power function in a hyperbolic tangent function, and shortening transient duration when | dU/dt | is smaller; when the value of | dU/dt | is larger, larger virtual inertia support is ensured, and the output of the converter is prevented from exceeding the limit value;

the improved intercept adjustment algorithm is as follows:

the method is obtained by the formula (12), the intercept of the droop curve is adjusted by using the nesting function, the adaptive capacity is certain, and the slope k of a secant line of the curve passing through the origin point _d Can represent C _vir The size of (d); when k is ₂ When =1, the slope of the curve secant gradually decreases with the increase of | dU/dt |; it can be seen that, with the hyperbolic tangent function, C increases with | dU/dt | during the transient after the load sudden change _vir Are getting smaller and smaller; when k is ₂ >1, when | dU/dt | is small, k _d The change trend of the absolute value is the same as that of the absolute value of dU/dt'; when | dU/dt | continuously increases to the right of point e, k _d Decreases with increasing | dU/dt |; e point coordinate represents as voltage variation amount | | | dU/dt |) _max If the longitudinal intercept of the sagging curve of the converter should be close to its limit value | Δ U _ref ，limit|；

5. The self-adaptive control method for the virtual inertia of the direct-current microgrid with a flywheel energy storage function as claimed in claim 4, wherein the specific process of the step 4 is as follows:

since the flywheel is operating with a speed limit, the flywheel releases its maximum power limit as follows: