CN108539798A - Energy-storage system Secondary Control strategy based on Model Predictive Control - Google Patents
Energy-storage system Secondary Control strategy based on Model Predictive Control Download PDFInfo
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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- H—ELECTRICITY
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- H—ELECTRICITY
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Abstract
The invention discloses a kind of energy-storage system Secondary Control strategy based on Model Predictive Control.Existing frequency, the PI controls of voltage Secondary Control scheme generally use, in load fluctuation, frequency can not be stablized in reference frequency value, and frequency and the transient response of voltage are slower.The technical solution adopted by the present invention is：First, the inverter of energy-storage system and its state-space model of controlling unit are derived, secondly, establishes the prediction model of angular frequency and voltage；Then through rolling optimization and feedback compensation, the control variable of angular frequency and voltage, is finally respectively added on active power/angular frequency and reactive power/voltage droop control by the control variable for obtaining angular frequency and voltage, realize that frequency retrieval to the precise proportions of a reference value and reactive power distribute.There is the present invention faster frequency, voltage transient to respond, and control accuracy is high, highly practical.
Description
Technical Field
The invention belongs to the technical field of distributed power generation and micro-grids, and particularly relates to a secondary voltage regulation and frequency modulation strategy of an energy storage system based on model predictive control.
Background
The micro-grid is composed of devices such as a Distributed Generator (DG) including an energy storage system and a diesel generator, a power converter and a load, and impact caused when the micro-grid is connected to a power grid can be relieved through interactive supplementation with a large power grid. When a microgrid is in an island mode, a plurality of DGs are usually operated in parallel to provide power for loads, and droop control with a plug and play characteristic is the most common control mode. The traditional P-f droop characteristic cannot stabilize the frequency at a rated value when the load changes, and the line impedance in the low-voltage microgrid is usually resistive or resistive, so that the traditional Q-V droop control cannot realize proportional distribution of reactive power, and each output voltage deviates from a voltage reference value.
In order to improve the distribution accuracy of reactive power, some scholars introduce virtual impedance at the output end of an inverter, change the characteristics of output impedance and improve the power distribution accuracy of the inverter, but the virtual impedance cannot solve the influence of different output lines on the current sharing of a system, so that some scholars adopt a secondary regulation strategy, and add secondary regulation optimization quantities on reference values of frequency and voltage respectively to realize the frequency recovery to a rated value and the proportional distribution of the reactive power according to the rated capacity; however, the traditional secondary regulation strategy adopts PI control, which affects the transient characteristics of frequency and voltage when the load fluctuates.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a secondary regulation strategy of an energy storage system based on model predictive control so as to realize the optimal control of the voltage and the frequency of the energy storage system.
Therefore, the invention adopts the following technical scheme: the model predictive control-based energy storage system secondary regulation strategy comprises the following steps:
deducing a state space model of the inverter and a control link of the inverter, and establishing a prediction model of angular frequency and voltage;
respectively obtaining an optimized objective function of angular frequency and voltage according to an expected value, obtaining a plurality of control increments when the function takes a minimum value through rolling optimization of the optimized objective function, obtaining an output predicted value by combining an initial predicted value at the last sampling moment, and obtaining a corrected output predicted value after feedback correction with an actual measured value to be used as an initial predicted value at the next moment; and simultaneously, taking the instant control increment to form a control variable as a secondary regulating variable for regulating the angular frequency and the voltage.
Because the PI controller can only utilize past and current measured values, and adopts a secondary regulation strategy based on PI control, transient characteristics are easily influenced when the frequency and voltage of an energy storage device which is connected into a microgrid and serves as a distributed power supply are regulated. Research and analysis have found that Model Predictive Control (MPC) predicts future information using not only current and past measurements but also a predictive model, adjusts control variables through roll optimization, and feedback corrects the predicted output to maintain the output at a desired value, taking into account the constraints associated with the controlled object.
The method utilizes the characteristic that Model Predictive Control (MPC) can predict future process information and accurately track an expected value, establishes a prediction model of angular frequency and voltage by deducing a state space model of an inverter of an energy storage system and a control link thereof, obtains control variables of the angular frequency and the voltage through rolling optimization and feedback correction, and realizes frequency recovery to a reference value and accurate proportional distribution of reactive power as secondary adjustment optimization quantities of active power/angular frequency and reactive power/voltage droop characteristics. Compared with a secondary regulation strategy of PI control, the secondary regulation strategy based on MPC has faster frequency and voltage transient response characteristics when the load fluctuates, the frequency is always maintained at a frequency reference value when the load fluctuates, the deviation of each output voltage and the reference voltage is greatly reduced, and meanwhile, the reactive power is accurately distributed according to the rated capacity proportion.
As a supplement to the above technical solution, the step of constructing the prediction model of angular frequency and voltage includes:
step 1): sampling to obtain the voltage u at the output end of the inverteriAnd current iiThe voltage u at the output end of the filteroAnd current ioVoltage of common connection point is Vs；uoConversion into dq-axis variable u by parkod、uoqIn the same way, get io、ui、iiAnd VsDq axis variable of (1); the angular frequency of the output voltage is set to omega when the inverter operates stably0Derivation of ii、uoAnd ioThe equation of state of (a) is:
and
wherein L isf、CfAnd RfRespectively a filter inductor, a capacitor and a resistor; l, R are respectively line inductance and resistance; omegan＝2πfnAt a nominal angular frequency, wherein fn＝50Hz；
Step 2): calculating active power and reactive power, p ═ u respectivelyodiod+uoqioq，q＝uoqiod-uodioqWith a cut-off angular frequency of ωcThe low-pass filter has an average power of P, Q, i.e.s is a Laplace transform differential operator;
using P, Q to obtain droop control parameters of angular frequency and voltage, and adding secondary regulation quantity to obtainWhere ω is 2 π f, ωn＝2πfn，fn、UnReference values of frequency and voltage are respectively; pn、QnRespectively corresponding active power and reactive power when the frequency and the voltage are at reference values; m and n are P-omega and Q-V droop coefficients respectively; omegas、uMRespectively an added angular frequency variable and a voltage variable; using the voltage value generated by droop control as the reference of the voltage control outer loopSignals, i.e.
u* od＝Un+n(Qn-Q)+uM、u* oq＝0；
Step 3): defining a voltage difference state variableNamely, pair (u)* od-uod) Integration is performed to obtain a variableSame reason pair (u)* oq-uoq) Integral derived variableDesign voltage outer loop control equationWherein k isvp、kviRespectively are the proportional and integral parameters of the voltage outer ring; h is the regulated output current ioThe current signal i generated by the voltage outer loop control equation* id、i* iqAs a current inner loop reference signal;
defining a current difference state variableNamely pair (i)* id-iid) Integration to obtain the variable gammadThe same thing is to (i)* iq-iiq) Integration to obtain the variable gammaq. Designing the control equation of the current inner loop asWherein k isip、kiiRespectively are the proportion and integral parameters of the inner ring; neglecting the dynamic influence of the switching part at higher frequencies, the voltage signal u generated by the current inner loop control equation is considered* id、u* iqAre respectively equal to inversionOutput voltage u of the deviceid、uiq；
Step 4): from ω to ωn+m(Pn-P)+ωsObtaining an angular frequency, and obtaining an angle δ of a dq axis coordinate defined by the inverter through integration, namely δ ═ ω dt, wherein all variables are defined under the dq axis coordinate system, and a common bus voltage is (V)d，Vq) Will (V)d，Vq) Transforming to dq axis coordinate system defined by the inverter to obtain
According to delta, P, Q,γdq(γd、γq)、iidq(iid、iiq)、uodq(uod、uoq)、iodq(iod、ioq) Establishing small signal state space model of inverter and control link thereofWhereinIs a state variable, Δ u ═ Δ ωsΔuMΔVdΔVq]TIn order to control the variables of the plant,
matrix ADG、BDGIn (I)id、Iiq、Iod、Ioq、Vod、VoqAre respectively state space variablesiid、iiq、iod、ioq、uod、uoqCorresponding state values when the inverter operates stably;
step 5): obtaining the frequency and voltage U output by each distributed power supply by using a central controller, and calculating the average angular frequencyAnd average voltageEstablishing an angular frequency prediction model with an output prediction value as an average angular frequencyWherein, Cω＝[0-m 0 0 0 0 0 0 0 0 0 0 0]，Dω＝[1 0 0]，Δuω＝[ΔωsΔVdΔVq]T，
Matrix BDGωIn (II)id、Iiq、Iod、Ioq、Vod、VoqAre respectively a state space variable iid、iiq、iod、ioq、uod、uoqCorresponding state values when the inverter operates stably;
establishing a voltage prediction model with an output prediction value as an average voltage
Wherein, CU＝[0 0–n 0 0 0 0 0 0 0 0 0 0]，DU＝[1 0 0]，ΔuU＝[ΔuMΔVdΔVq]T，
With a sampling period TsDiscretizing the prediction models of the angular frequency and the voltage to respectively obtain
Wherein
In the formula, the triangular symbol Δ represents a small signal change amount of a certain variable.
In addition to the above-described solutions, the desired values ω are respectively specifiedn＝2πfn，fn50Hz and Un311V, the target optimization function for angular frequency is Wω、WsRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LωRepresenting the predicted step size, M, of the angular frequency objective optimization functionωRepresenting the control step with the constraint ofωsmax＝2π×0.5rad/s、ωsminEach of-2 π × 0.5rad/s is a control variable ωs(k) Upper and lower limits of (d); control increment Δ ωs(k) Respectively is Δ ωsmax、Δωsmin(ii) a The upper limit and the lower limit of the predicted output quantity are respectively Determination of a control increment [ Delta omega ] from an objective optimization function of angular frequencys(k),…,Δωs(k+Mω-1)]TTaking the real-time control increment delta omegas(k) Angular frequency control variable omega with last sampling moments(k-1) constitutes a k-time controlled variable ωs(k) And adding the angular frequency control circuit into active power/angular frequency droop control to realize secondary regulation of the angular frequency.
In addition to the above technical solution, the objective optimization function of the voltage is
WU、WuRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LURepresenting the predicted step size, M, of the voltage objective optimization functionURepresents a control step size; the constraint condition is
The maximum deviation allowed by the output voltage is 5 percent of the reference value, and u is takenMmin＝-15V，uMmax15V, thenDetermination of a control increment [ Delta u ] from a target optimization function of the voltageM(k),…,ΔuM(k+MU-1)]TTaking the real-time control increment delta uM(k) With the voltage control variable u at the last sampling instantM(k-1) constituent k time controlled variable uM(k) Added to the reactive power/voltage droop control to obtain a resultant voltage, i.e. Un+n(Qn-Q)+uMAnd the influence of load fluctuation on the deviation of the output voltage from the reference voltage is reduced.
In addition to the above-described solution, the deviation of the combined voltage from the actually measured average voltage is PI-regulated, i.e. the deviation isIn the formula, kQpFor proportional coefficient of PI regulation, kQiFor integral coefficient of PI regulation, s is Laplace transform differential operator, and the obtained regulating quantity delta UQAnd adding the voltage to the synthesized voltage to serve as a final voltage reference value so as to inhibit the influence of line impedance on the non-uniform distribution of the reactive power and realize the proportional distribution of the reactive power according to the rated capacity.
The method utilizes the characteristic that a model predictive control algorithm has accurate tracking on an expected value to obtain the secondary regulating quantity of angular frequency and voltage, and respectively adds the secondary regulating quantity into active power/angular frequency droop control and reactive power/voltage droop control, so that the system frequency is always maintained at a frequency reference value while the reactive power is distributed according to the rated capacity proportion, and the deviation of each output voltage and the reference voltage is greatly reduced.
Detailed Description
The present invention will be further described with reference to the following embodiments.
The embodiment provides an energy storage system secondary regulation strategy based on model predictive control, which comprises the following steps:
step 1): sampling to obtain the voltage u at the output end of the inverteriAnd current iiThe voltage u at the output end of the filteroAnd current ioVoltage of common connection point is Vs；uoConversion into dq-axis variable u by parkod、uoqIn the same way, get io、ui、iiAnd VsDq axis variable of (1); the angular frequency of the output voltage is set to omega when the inverter operates stably0Derivation of ii、uoAnd ioThe equation of state of (a) is:
and
wherein L isf、CfAnd RfRespectively a filter inductor, a capacitor and a resistor; l, R are respectively line inductance and resistance; omegan＝2πfnAt a nominal angular frequency, wherein fn＝50Hz。
Step 2): calculating active power and reactive power, p ═ u respectivelyodiod+uoqioq，q＝uoqiod-uodioqWith a cut-off angular frequency of ωcThe low-pass filter has an average power of P, Q, i.e.
s is a Laplace transform differential operator;
using P, Q to obtain droop control parameters of angular frequency and voltage, and adding secondary regulation quantity to obtainWhere ω is 2 π f, ωn＝2πfn，fn、UnReference values of frequency and voltage are respectively; pn、QnRespectively corresponding active power and reactive power when the frequency and the voltage are at reference values; m and n are P-omega and Q-V droop coefficients respectively; omegas、uMRespectively an added angular frequency variable and a voltage variable; the voltage value generated by droop control is used as a reference signal for the outer loop of the voltage control, i.e.
u* od＝Un+n(Qn-Q)+uM、u* oq＝0。
Step 3): defining a voltage difference state variableNamely, pair (u)* od-uod) Integration is performed to obtain a variableSame reason pair (u)* oq-uoq) Integral derived variableDesign voltage outer loop control equationWherein k isvp、kviRespectively are the proportional and integral parameters of the voltage outer ring; h is the regulated output current ioThe current signal i generated by the voltage outer loop control equation* id、i* iqAs a current inner loop reference signal;
defining a current difference state variableNamely pair (i)* id-iid) Integration to obtain the variable gammadThe same thing is to (i)* iq-iiq) Integration to obtain the variable gammaq. Designing the control equation of the current inner loop as
Wherein k isip、kiiRespectively are the proportion and integral parameters of the inner ring; neglecting the dynamic influence of the switching part at higher frequencies, the voltage signal u generated by the current inner loop control equation is considered* id、u* iqAre respectively equal to the output voltage u of the inverterid、uiq。
Step 4): from ω to ωn+m(Pn-P)+ωsObtaining an angular frequency, and obtaining an angle δ of a dq axis coordinate defined by the inverter through integration, namely δ ═ ω dt, wherein all variables are defined under the dq axis coordinate system, and a common bus voltage is set to be ^ ω dt(Vd，Vq) Will (V)d，Vq) Transforming to dq axis coordinate system defined by the inverter to obtain
According to delta, P, Q,γdq(γd、γq)、iidq(iid、iiq)、uodq(uod、uoq)、iodq(iod、ioq) Establishing small signal state space model of inverter and control link thereofWhereinIs a state variable, Δ u ═ Δ ωsΔuMΔVdΔVq]TIn order to control the variables of the plant,
matrix ADG、BDGIn (I)id、Iiq、Iod、Ioq、Vod、VoqAre respectively a state space variable iid、iiq、iod、ioq、uod、uoqAnd corresponding state values when the inverter operates stably.
Step 5): obtaining the frequency and voltage U output by each distributed power supply by using a central controller, and calculating the average angular frequencyAnd average voltageEstablishing an angular frequency prediction model with an output prediction value as an average angular frequencyWherein, Cω＝[0-m 0 0 0 0 0 0 0 0 0 0 0]，Dω＝[1 0 0]，Δuω＝[ΔωsΔVdΔVq]T，
Matrix BDGωIn (II)id、Iiq、Iod、Ioq、Vod、VoqAre respectively a state space variable iid、iiq、iod、ioq、uod、uoqAnd corresponding state values when the inverter operates stably.
Establishing a voltage prediction model with an output prediction value as an average voltage
Wherein, CU＝[0 0–n 0 0 0 0 0 0 0 0 0 0]，DU＝[1 0 0]，ΔuU＝[ΔuMΔVdΔVq]T，
With a sampling period TsDiscretizing the prediction models of the angular frequency and the voltage to respectively obtain,
wherein
In the formula, the triangular symbol Δ represents a small signal change amount of a certain variable.
Respectively given desired values ωn＝2πfn，fn50Hz and Un311V, the target optimization function for angular frequency isWω、WsRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LωRepresenting the predicted step size, M, of the angular frequency objective optimization functionωIndicating the control step size.
The constraint condition isωsmax＝2π×0.5rad/s、ωsminEach of-2 π × 0.5rad/s is a control variable ωs(k) Upper and lower limits of (d); control increment Δ ωs(k) Respectively is Δ ωsmax、Δωsmin(ii) a The upper limit and the lower limit of the predicted output quantity are respectively Determination of a control increment [ Delta omega ] from an objective optimization function of angular frequencys(k),…,Δωs(k+Mω-1)]TTaking the real-time control increment delta omegas(k) Angular frequency control variable omega with last sampling moments(k-1) constitutes a k-time controlled variable ωs(k) And adding the angular frequency control circuit into active power/angular frequency droop control to realize secondary regulation of the angular frequency.
The objective optimization function of the voltage is
WU、WuRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LURepresenting the predicted step size, M, of the voltage objective optimization functionUIndicating the control step size. The constraint condition is
The maximum deviation allowed by the output voltage is 5 percent of the reference value, and u is takenMmin＝-15V，uMmax15V, thenDetermination of a control increment [ Delta u ] from a target optimization function of the voltageM(k),…,ΔuM(k+MU-1)]TTaking the real-time control increment delta uM(k) With the voltage control variable u at the last sampling instantM(k-1) constituent k time controlled variable uM(k) Added to the reactive power/voltage droop control to obtain a resultant voltage, i.e. Un+n(Qn-Q)+uMAnd the influence of load fluctuation on the deviation of the output voltage from the reference voltage is reduced.
The deviation of the resulting voltage from the actually measured average voltage is subjected to PI regulation (proportional-integral regulation), i.e.In the formula, kQpFor proportional coefficient of PI regulation, kQiFor the integral coefficient of PI regulation, s is the Laplace transform differential operator. The adjustment amount DeltaU to be obtainedQAnd adding the voltage to the synthesized voltage to serve as a final voltage reference value so as to inhibit the influence of line impedance on the non-uniform distribution of the reactive power and realize the proportional distribution of the reactive power according to the rated capacity.
The above-mentioned embodiments only express the embodiments of the present invention, and therefore, should not be interpreted as limiting the scope of the present invention, and should not be interpreted as limiting the structure of the present invention in any way. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (5)
1. The energy storage system secondary regulation strategy based on model predictive control is characterized by comprising the following steps:
deducing a state space model of the inverter and a control link of the inverter, and establishing a prediction model of angular frequency and voltage;
respectively obtaining an optimized objective function of angular frequency and voltage according to an expected value, obtaining a plurality of control increments when the function takes a minimum value through rolling optimization of the optimized objective function, obtaining an output predicted value by combining an initial predicted value at the last sampling moment, and obtaining a corrected output predicted value after feedback correction with an actual measured value to be used as an initial predicted value at the next moment; and simultaneously, taking the instant control increment to form a control variable as a secondary regulating variable for regulating the angular frequency and the voltage.
2. The model predictive control-based energy storage system quadratic regulation strategy of claim 1, wherein the step of constructing the predictive model of angular frequency and voltage comprises:
step 1): sampling to obtain the voltage u at the output end of the inverteriAnd current iiThe voltage u at the output end of the filteroAnd current ioVoltage of common connection point is Vs；uoConversion into dq-axis variable u by parkod、uoqIn the same way, get io、ui、iiAnd VsDq axis variable of (1); the angular frequency of the output voltage is set to omega when the inverter operates stably0Derivation of ii、uoAnd ioThe equation of state of (a) is:
and
wherein L isf、CfAnd RfRespectively a filter inductor, a capacitor and a resistor; l, R are respectively line inductance and resistance; omegan＝2πfnAt a nominal angular frequency, wherein fn＝50Hz；
Step 2): calculating active power and reactive power, p ═ u respectivelyodiod+uoqioq，q＝uoqiod-uodioqWith a cut-off angular frequency of ωcThe low-pass filter has an average power of P, Q, i.e.s is a Laplace transform differential operator;
by usingP, Q obtaining droop control parameters of angular frequency and voltage, and adding secondary adjustment amount to obtain final productWhere ω is 2 π f, ωn＝2πfn，fn、UnReference values of frequency and voltage are respectively; pn、QnRespectively corresponding active power and reactive power when the frequency and the voltage are at reference values; m and n are P-omega and Q-V droop coefficients respectively; omegas、uMRespectively an added angular frequency variable and a voltage variable; the voltage value generated by droop control is used as a reference signal of a voltage control outer ring, i.e. u* od＝Un+n(Qn-Q)+uM、u* oq＝0；
Step 3): defining a voltage difference state variableNamely, pair (u)* od-uod) Integration is performed to obtain a variableSame reason pair (u)* oq-uoq) Integral derived variableDesign voltage outer loop control equationWherein k isvp、kviRespectively are the proportional and integral parameters of the voltage outer ring; h is the regulated output current ioThe current signal i generated by the voltage outer loop control equation* id、i* iqAs a current inner loop reference signal;
defining a current difference state variableNamely pair (i)* id-iid) Integration to obtain the variable gammadThe same thing is to (i)* iq-iiq) Integration to obtain the variable gammaqDesigning the control equation of the current inner loop asWherein k isip、kiiRespectively are the proportion and integral parameters of the inner ring; neglecting the dynamic influence of the switching part at higher frequencies, the voltage signal u generated by the current inner loop control equation is considered* id、u* iqAre respectively equal to the output voltage u of the inverterid、uiq；
Step 4): from ω to ωn+m(Pn-P)+ωsObtaining an angular frequency, and obtaining an angle δ of a dq axis coordinate defined by the inverter through integration, namely δ ═ ω dt, wherein all variables are defined in the dq axis coordinate system, and a common bus voltage is (V)d，Vq) Will (V)d，Vq) Transforming to dq axis coordinate system defined by the inverter to obtainAccording to delta, P, Q,γd、γq、iid、iiq、uod、uoq、iod、ioqEstablishing small signal state space model of inverter and control link thereofWhereinIn order to be a state variable, the state variable,
Δu＝[ΔωsΔuMΔVdΔVq]Tfor controlling variables, matrix ADG、BDGIn (I)id、Iiq、Iod、Ioq、Vod、VoqAre respectively a state space variable iid、iiq、iod、ioq、uod、uoqCorresponding state values when the inverter operates stably;
step 5): obtaining the frequency and voltage U output by each distributed power supply by using a central controller, and calculating the average angular frequencyAnd average voltageEstablishing an angular frequency prediction model with an output prediction value as an average angular frequency
Wherein, Cω＝[0 -m 0 0 0 0 0 0 0 0 0 0 0]，Dω＝[1 0 0]，Δuω＝[ΔωsΔVdΔVq]T，
Matrix BDGωIn (II)id、Iiq、Iod、Ioq、Vod、VoqAre respectively a state space variable iid、iiq、iod、ioq、uod、uoqCorresponding state values when the inverter operates stably;
electricity with output prediction value as average voltagePressure prediction model
Wherein, CU＝[0 0 –n 0 0 0 0 0 0 0 0 0 0]，DU＝[1 0 0]，ΔuU＝[ΔuMΔVdΔVq]T，
With a sampling period TsDiscretizing the prediction models of the angular frequency and the voltage to respectively obtain
Wherein,
in the formula, the triangular symbol Δ represents a small signal change amount of a certain variable.
3. The model predictive control-based energy storage system quadratic regulation strategy of claim 2, characterized in that a desired value ω is respectively givenn＝2πfn，fn50Hz and Un311V, the target optimization function for angular frequency isWω、WsRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LωRepresenting the predicted step size, M, of the angular frequency objective optimization functionωRepresenting the control step with the constraint ofωsmax＝2π×0.5rad/s、ωsminControl is carried out for-2 pi x 0.5rad/s respectivelySystem variable omegas(k) Upper and lower limits of (d); control increment Δ ωs(k) Respectively is Δ ωsmax、Δωsmin(ii) a The upper limit and the lower limit of the predicted output quantity are respectively Determination of a control increment [ Delta omega ] from an objective optimization function of angular frequencys(k),…,Δωs(k+Mω-1)]TTaking the real-time control increment delta omegas(k) Angular frequency control variable omega with last sampling moments(k-1) constitutes a k-time controlled variable ωs(k) And adding the angular frequency control circuit into active power/angular frequency droop control to realize secondary regulation of the angular frequency.
4. The energy storage system secondary regulation strategy of claim 3, wherein the objective optimization function of voltage isWU、WuRespectively representing the weight coefficients for suppressing the tracking error and the control increment change, LURepresenting the predicted step size, M, of the voltage objective optimization functionURepresenting the control step with the constraint ofThe maximum deviation allowed by the output voltage is 5 percent of the reference value, and u is takenMmin＝-15V，uMmax15V, thenDetermination of a control increment [ Delta u ] from a target optimization function of the voltageM(k),…,ΔuM(k+MU-1)]TTaking the real-time control increment delta uM(k) With the voltage control variable u at the last sampling instantM(k-1) constituent k time controlled variable uM(k) Added to the reactive power/voltage droop control to obtain a resultant voltage, i.e. Un+n(Qn-Q)+uMAnd the influence of load fluctuation on the deviation of the output voltage from the reference voltage is reduced.
5. The energy storage system secondary regulation strategy of claim 4, characterized in that the deviation of the resulting voltage from the actual measured average voltage is PI regulated, i.e. theIn the formula, kQpFor proportional coefficient of PI regulation, kQiFor integral coefficient of PI regulation, s is Laplace transform differential operator, and obtained regulating quantity delta UQAnd adding the voltage to the synthesized voltage to serve as a final voltage reference value so as to inhibit the influence of line impedance on the non-uniform distribution of the reactive power and realize the proportional distribution of the reactive power according to the rated capacity.
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