CN113285488A - Hybrid energy storage coordination control method based on multi-level architecture - Google Patents

Hybrid energy storage coordination control method based on multi-level architecture Download PDF

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CN113285488A
CN113285488A CN202110577522.8A CN202110577522A CN113285488A CN 113285488 A CN113285488 A CN 113285488A CN 202110577522 A CN202110577522 A CN 202110577522A CN 113285488 A CN113285488 A CN 113285488A
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CN113285488B (en
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杨帆
张章
李桂鑫
徐晶
徐科
窦晓波
夏冬
胡源
孙阔
刘英英
赵长伟
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention discloses a hybrid energy storage coordination control method based on a multi-hierarchy architecture, which comprises the following steps: determining an electrochemical energy storage local-level operation response model; determining a local-level operation response model of the superconducting energy storage; determining an energy storage system local level automatic response control strategy; determining a power distribution network hybrid energy storage, distributed photovoltaic, load and line overall equipment interconnection relation model; determining an energy storage system cooperative level automatic response control strategy; acquiring a cooperative-level automatic response control instruction by using a model prediction control method, and issuing the cooperative-level automatic response control instruction to the hybrid energy storage system; the invention effectively solves the problems of voltage fluctuation and energy storage control caused by high permeability of distributed new energy in the power distribution network.

Description

Hybrid energy storage coordination control method based on multi-level architecture
Technical Field
The invention relates to the field of energy storage system operation control of a power system, in particular to a hybrid energy storage coordination control method based on a multi-level architecture.
Background
With the continuous development of renewable energy technologies, the wide application of renewable energy technologies becomes a necessary choice for power grid development, and in the face of the serious challenge of energy sustainable development, how to solve a series of randomness and volatility problems caused by the massive grid connection of renewable energy is a big difficulty to be overcome urgently by the current power system. According to local conditions, different types of energy storage systems are developed and connected into the power distribution network, so that the space-time decoupling of renewable energy sources can be realized, the renewable energy sources can be fully consumed on the spot, and the function of improving the overall voltage operation level of the power distribution network is exerted. However, the application of the energy storage system in the power system is gradually brought into the positive orbit, such as participating in the frequency adjustment of the power grid, improving the quality of the electric energy, and improving the stability of the system, and the application of the energy storage system will bring a positive influence on the safe and stable operation of the power grid. However, at present, the operation control of the hybrid energy storage system in the operation of the power distribution network is in a primary stage, and the flexibility of energy storage is difficult to exert to dynamically control the voltage, so that the research on the cooperative control of the hybrid energy storage system is significant. In the past, the steady-state economic optimization control strategy is mainly used in the research on the operation control strategy of the energy storage system, the problem of convergence solving exists, the local optimization is easy to fall into, and in other researches, the problem of power regulation and stability of grid connection of a single energy storage device is only considered, and the problem of interactivity between the energy storage system and a power grid is ignored.
Therefore, based on the problems, a hybrid energy storage coordination control method based on a multi-level architecture is provided, and the problem of rapid voltage fluctuation caused by large access scale of distributed new energy in a power distribution network can be effectively solved.
Disclosure of Invention
In order to improve the real-time performance of model predictive control, the invention provides a method for improving the control effect of a calculation optimization instruction process through a double-level control structure. According to the invention, a local-level and cooperative-level double-level control framework is established, the real-time control performance of model predictive control is improved, and a new solution thought is provided for solving the problem of safe and stable voltage operation of a power distribution network containing large-scale distributed new energy access.
The invention relates to a hybrid energy storage coordination control method based on a multi-level architecture, which comprises the following steps:
determining an electrochemical energy storage local-level operation response model;
determining a local-level operation response model of the superconducting energy storage;
determining an energy storage system local level automatic response control strategy;
determining a power distribution network hybrid energy storage, distributed photovoltaic, load and line overall equipment interconnection relation model;
determining an energy storage system cooperative level automatic response control strategy;
and acquiring a cooperative automatic response control instruction by using a model prediction control method, and issuing the cooperative automatic response control instruction to the hybrid energy storage system.
Further, the method for determining the electrochemical energy storage local-level operation response model comprises the following steps:
the energy stored by the energy storage system may be expressed as:
Figure BDA0003084975720000021
in the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000022
for the initial energy of the electrochemical energy storage system, PBESSCharging and discharging power for an electrochemical energy storage system;
then there is an electrochemical energy storage system model as follows
Figure BDA0003084975720000023
In the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000024
reference active command deviation, Δ P, for electrochemical energy storage systemsintIs composed of
Figure BDA0003084975720000025
And PBESSThe integral of the deviation is then calculated,
Figure BDA0003084975720000026
and
Figure BDA0003084975720000027
proportional and integral parameters, T, for the outer loop PI controlleridTime constant, T, for inner loop controlfdIs the filter time constant, Δ iLFor electrochemical energy storage of the current deviation, U, on the DC sideBESSIs electricityThe voltage at DC side of chemical energy storage, s is differential operator, Δ PBESSFor the amount of deviation of the output power of the electrochemical energy storage system,
Figure BDA0003084975720000028
is the initial output power deviation, Delta C, of the electrochemical energy storage systemBESSStoring the energy deviation for the electrochemical energy storage system;
written as a state space equation of the form:
Figure BDA0003084975720000029
in the formula, xESIs the state quantity of the electrochemical energy storage system uESFor controlling the quantity of the electrochemical energy storage system, dESDisturbance variable of electrochemical energy storage system, AESSystem matrix being an electrochemical energy storage system, BESA control matrix for an electrochemical energy storage system, EESA perturbation matrix of the electrochemical energy storage system;
further, the method for determining the superconducting energy storage local-level operation response model comprises the following steps:
energy E stored in superconducting energy storagesmCan be expressed as:
Figure BDA0003084975720000031
in the formula, LsmIs an inductor, IsmdThe current is the direct current side current of the superconducting energy storage;
the direct current can be represented as
Figure BDA0003084975720000032
In the formula, PsmRepresenting the output active power of the superconducting energy storage, xi is the power loss coefficient, Ismd0Is the initial value of the direct current. Thus, the output active and reactive power of the superconducting energy storage can be expressed as
Figure BDA0003084975720000033
In the formula, QsmRepresenting the output reactive power of superconducting energy storage, UsmAnd IsmIs the amplitude, alpha, of the AC voltage and current of the convertersmIs a voltage UsmAnd current IsmM is the modulation degree of the converter;
suppose Psm,rAnd Qsm,rThe reference values of active power and reactive power of the superconducting energy storage respectively are
Figure BDA0003084975720000034
Therefore, the superconducting energy storage system can be expressed as a second-order model
Figure BDA0003084975720000035
In the formula, TsmcIs response time constant, mu, of superconducting energy storage convertersmαAnd musmmIs a control signal.
Written as a state space equation of the form:
Figure BDA0003084975720000041
in the formula, xSMIs a state quantity of a superconducting energy storage system, uSMFor controlling the quantity of the superconducting energy storage system, dSMDisturbance quantity of superconducting energy storage system, ASMSystem matrix being a superconducting energy storage system, BSMA control matrix for a superconducting energy storage system, ESMIs a disturbance matrix of the superconducting energy storage system.
Further, the method for determining the local-level automatic response control strategy of the energy storage system comprises the following steps:
the electrochemical energy storage and superconducting energy storage local control strategy adopts a PI control mode of grid-connected point voltage deviation feedback, and realizes local-level automatic response of an energy storage system by changing a power reference instruction through a voltage-active/reactive droop characteristic curve, and can be realized by the following modes:
Figure BDA0003084975720000042
wherein R is the voltage reactive droop coefficient, QdFor the reactive regulation of energy storage, V is the actual voltage of the grid-connected point of energy storage, VcorAs a correction amount of voltage, QmaxFor maximum reactive power output of stored energy, QminIn order to store energy and output the minimum reactive power,
Figure BDA0003084975720000043
and
Figure BDA0003084975720000044
the lower limit and the upper limit of the voltage regulation dead zone,
Figure BDA0003084975720000045
and
Figure BDA0003084975720000046
the lower limit and the upper limit of the voltage safe operation range.
Further, the method for determining the interconnection relation model of the hybrid energy storage, distributed photovoltaic, load and line overall equipment of the power distribution network comprises the following steps:
Figure BDA0003084975720000047
in the formula,. DELTA.Pi、ΔQiRespectively the active and reactive power injection quantity of the ith node, and the sensitivity factor SδP、SUPRespectively representing the influence of active output fluctuation on the phase angle and amplitude of the voltage, SδQ、SUQThe influence of reactive power output fluctuation on node voltage phase angle and amplitude is respectively shown, and the voltage amplitude variation delta U and an active and reactive power variation sequence delta P of a system containing N PQ nodes meet the following conditions:
ΔU＝SUPΔP+SUQΔQ (28)
the cooperative peer system operation model is formed as follows:
Figure BDA0003084975720000048
in the formula, xsIs the state quantity u of the distribution network systemsFor controlling the system of the distribution network, dsFor disturbance of the distribution network system, ysFor the output of the distribution network system, AsSystem matrix for distribution network system, BsFor control matrices of the distribution network system, EsDisturbance matrix for distribution network systems, CsAn output matrix of the power distribution network system; and has the following components:
Figure BDA0003084975720000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000053
is NESA system matrix of individual battery energy storage systems,
Figure BDA0003084975720000054
is NSMA system matrix of superconducting energy storage systems;
Figure BDA0003084975720000055
in the formula (I), the compound is shown in the specification,
Figure BDA00030849757200000511
is NESA control matrix for each of the battery energy storage systems,
Figure BDA0003084975720000057
is NSMA control matrix for each superconducting energy storage system;
Figure BDA0003084975720000058
in the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000059
is NESA disturbance matrix of each battery energy storage system;
Figure BDA00030849757200000510
is NSMA disturbance matrix of the superconducting energy storage system.
And setting voltage reference values of access nodes of the energy storage systems according to the running state of the power distribution network, outputting reactive suppression voltage deviation by the energy storage systems when node voltage generates deviation, and calculating a control command through cooperative automatic response control if the voltage deviation continues to increase. The automatic energy storage response control strategy of the cooperative stage mainly depends on real-time measurement data of the power distribution network, the voltage is controlled within a safe operation range (0.95pu. -1.05 pu.) more strictly, and the contribution of each energy storage is adjusted.
Further, the method for obtaining the cooperative automatic response control instruction by using the model predictive control method and sending the cooperative automatic response control instruction to the hybrid energy storage system comprises the following steps:
discretizing the system operation model (8) according to the cooperative level to obtain:
Figure BDA0003084975720000051
in the formula, A, Bu、BdAre respectively As、Bs、EsIn the form of a discretized matrix of, Δ xs(k)、Δus(k)、Δds(k) Are respectively xs、us、dsK denotes the k sampling instants.
The objective function for modeling prediction is as follows:
min J(x(k),ΔU(k))＝||Γy(Yp,s(k+1|k))-R(k+1)||2+||ΓuΔU(k)||2 (34)
in the formula, gammayAnd ΓuIs a weighting matrix, R (k +1) is a given control output reference sequence, Δ U (k) is a control increment sequence, p is a predicted step size of the MPC, m is a control step size, Y is a control step sizep,s(k +1| k) is the p-step control output at time k based on model (8) predictions, where:
Figure BDA0003084975720000061
in the formula,. DELTA.us(k)、Δus(k+1)、…、Δus(k + m +1) is the control increment of k, k +1, …, k + m +1 time respectively;
Figure BDA0003084975720000062
in the formula, r (k +1), r (k +2), … and r (k + p) are reference output vectors at the time of k +1, … and k + p respectively;
Figure BDA0003084975720000063
in the formula, the prediction output vectors at time k for time k +1, time …, and time k + p for time y (k +1| k), y (k +2| k), …, and y (k + p | k), respectively;
finally solving the control vector delta u of each moments(k) And issuing the data to each energy storage system to realize the automatic response control of the cooperative energy storage system.
The hybrid energy storage cooperative control method based on the multilevel architecture has the beneficial effects that:
1. according to the hybrid energy storage cooperative control method based on the multi-level architecture, the voltage deviation of an access node is reduced and the overall voltage level of a power distribution network is kept together according to the cooperative cooperation of the local level control and the cooperative level control, the problem that the distributed power supply affects the voltage operation level when being accessed to the power distribution network in a large scale is solved, the control architecture considers the quality of electric energy from local to global, and the consumption efficiency of the distributed power supply can be fully improved on the premise that the power distribution network operates safely and stably;
2. according to the hybrid energy storage cooperative control method based on the multi-level architecture, the secondary control instruction of the hybrid energy storage system which is optimal in real time is obtained by using the model predictive control method, the problem of adaptability of a conventional method to model accuracy under the condition of solving the scale of a power distribution network is solved, the problem of performance reduction caused by inaccuracy of the model in a large-scale system is solved, and efficient optimized operation of accessing a large-scale distributed power supply into the power distribution network is realized.
Drawings
Fig. 1 is a flowchart of a hybrid energy storage coordination control method based on a multi-level architecture according to an embodiment of the present invention;
fig. 2 is a voltage fluctuation diagram of the node 19 before and after control by using the method provided by the embodiment of the invention.
Detailed Description
The method for hybrid energy storage coordination control based on a multi-level architecture provided by the invention has a method flow as shown in fig. 1, and as can be seen from fig. 1, the method comprises the following steps:
determining an electrochemical energy storage local-level operation response model;
determining a local-level operation response model of the superconducting energy storage;
determining an energy storage system local level automatic response control strategy;
determining a power distribution network hybrid energy storage, distributed photovoltaic, load and line overall equipment interconnection relation model;
determining an energy storage system cooperative level automatic response control strategy;
and acquiring a cooperative automatic response control instruction by using a model prediction control method, and issuing the cooperative automatic response control instruction to the hybrid energy storage system.
Further, the method for determining the electrochemical energy storage local-level operation response model comprises the following steps:
the energy stored by the energy storage system may be expressed as:
Figure BDA0003084975720000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000072
for the initial energy of the electrochemical energy storage system, PBESSCharging and discharging power for an electrochemical energy storage system;
then there is an electrochemical energy storage system model as follows
Figure BDA0003084975720000081
In the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000082
reference active command deviation, Δ P, for electrochemical energy storage systemsintIs composed of
Figure BDA0003084975720000083
And PBESSThe integral of the deviation is then calculated,
Figure BDA0003084975720000084
and
Figure BDA0003084975720000085
proportional and integral parameters, T, for the outer loop PI controlleridTime constant, T, for inner loop controlfdIs the filter time constant, Δ iLFor electrochemical energy storage of the current deviation, U, on the DC sideBESSThe voltage at the direct current side of the electrochemical energy storage is used, and s is a differential operator;
written as a state space equation of the form:
Figure BDA0003084975720000086
in the formula, xESIs the state quantity of the electrochemical energy storage system uESFor controlling the quantity of the electrochemical energy storage system, dESDisturbance variable of electrochemical energy storage system, AESFor electrochemical storageSystem matrix of energy systems, BESA control matrix for an electrochemical energy storage system, EESA perturbation matrix of the electrochemical energy storage system;
further, the method for determining the superconducting energy storage local-level operation response model comprises the following steps:
energy E stored in superconducting energy storagesmCan be expressed as:
Figure BDA0003084975720000087
in the formula, LsmIs an inductor, IsmdThe current is the direct current side current of the superconducting energy storage;
the direct current can be represented as
Figure BDA0003084975720000088
In the formula, PsmRepresenting the output active power of the superconducting energy storage, xi is the power loss coefficient, Ismd0Is the initial value of the direct current. Thus, the output active and reactive power of the superconducting energy storage can be expressed as
Figure BDA0003084975720000089
In the formula, QsmRepresenting the output reactive power of superconducting energy storage, UsmAnd IsmIs the amplitude, alpha, of the AC voltage and current of the convertersmIs a voltage UsmAnd current IsmM is the modulation degree of the converter;
suppose Psm,rAnd Qsm,rThe reference values of active power and reactive power of the superconducting energy storage respectively are
Figure BDA0003084975720000091
Therefore, the superconducting energy storage system can be expressed as a second-order model
Figure BDA0003084975720000092
In the formula, TsmcIs response time constant, mu, of superconducting energy storage convertersmαAnd musmmIs a control signal.
Written as a state space equation of the form:
Figure BDA0003084975720000093
in the formula, xSMIs a state quantity of a superconducting energy storage system, uSMFor controlling the quantity of the superconducting energy storage system, dSMDisturbance quantity of superconducting energy storage system, ASMSystem matrix being a superconducting energy storage system, BSMA control matrix for a superconducting energy storage system, ESMIs a disturbance matrix of the superconducting energy storage system.
Further, the method for determining the local-level automatic response control strategy of the energy storage system comprises the following steps:
the electrochemical energy storage and superconducting energy storage local control strategy adopts a PI control mode of grid-connected point voltage deviation feedback, and realizes local-level automatic response of an energy storage system by changing a power reference instruction through a voltage-active/reactive droop characteristic curve, and can be realized by the following modes:
Figure BDA0003084975720000094
wherein R is the voltage reactive droop coefficient, QdFor the reactive regulation of energy storage, V is the actual voltage of the grid-connected point of energy storage, VcorAs a correction amount of voltage, QmaxFor maximum reactive power output of stored energy, QminIn order to store energy and output the minimum reactive power,
Figure BDA0003084975720000101
and
Figure BDA0003084975720000102
the lower limit and the upper limit of the voltage regulation dead zone,
Figure BDA0003084975720000103
and
Figure BDA0003084975720000104
the lower limit and the upper limit of the voltage safe operation range.
Further, the method for determining the interconnection relation model of the hybrid energy storage, distributed photovoltaic, load and line overall equipment of the power distribution network comprises the following steps:
Figure BDA0003084975720000105
in the formula,. DELTA.Pi、ΔQiRespectively the active and reactive power injection quantity of the ith node, and the sensitivity factor SδP、SUPRespectively representing the influence of active output fluctuation on the phase angle and amplitude of the voltage, SδQ、SUQThe influence of reactive power output fluctuation on node voltage phase angle and amplitude is respectively shown, and the voltage amplitude variation delta U and an active and reactive power variation sequence delta P of a system containing N PQ nodes meet the following conditions:
ΔU＝SUPΔP+SUQΔQ (49)
the cooperative peer system operation model is formed as follows:
Figure BDA0003084975720000106
in the formula, xsIs the state quantity u of the distribution network systemsFor controlling the system of the distribution network, dsFor disturbance of the distribution network system, ysFor the output of the distribution network system, AsSystem matrix for distribution network system, BsFor control matrices of the distribution network system, EsDisturbance matrix for distribution network systems, CsAn output matrix of the power distribution network system; and has the following components:
Figure BDA0003084975720000107
in the formula (I), the compound is shown in the specification,
Figure BDA0003084975720000108
is NESA system matrix of individual battery energy storage systems,
Figure BDA0003084975720000109
is NSMA system matrix of superconducting energy storage systems;
Figure BDA00030849757200001010
in the formula (I), the compound is shown in the specification,
Figure BDA00030849757200001011
is NESA control matrix for each of the battery energy storage systems,
Figure BDA00030849757200001012
is NSMA control matrix for each superconducting energy storage system;
Figure BDA00030849757200001013
in the formula (I), the compound is shown in the specification,
Figure BDA00030849757200001014
is NESA disturbance matrix of each battery energy storage system;
Figure BDA00030849757200001015
is NSMA disturbance matrix of the superconducting energy storage system.
And setting voltage reference values of access nodes of the energy storage systems according to the running state of the power distribution network, outputting reactive suppression voltage deviation by the energy storage systems when node voltage generates deviation, and calculating a control command through cooperative automatic response control if the voltage deviation continues to increase. The automatic energy storage response control strategy of the cooperative stage mainly depends on real-time measurement data of the power distribution network, the voltage is controlled within a safe operation range (0.95pu. -1.05 pu.) more strictly, and the contribution of each energy storage is adjusted.
Further, the method for obtaining the cooperative automatic response control instruction by using the model predictive control method and sending the cooperative automatic response control instruction to the hybrid energy storage system comprises the following steps:
discretizing the system operation model (8) according to the cooperative level to obtain:
Figure BDA0003084975720000111
in the formula, A, Bu、BdAre respectively As、Bs、EsIn the form of a discretized matrix of, Δ xs(k)、Δus(k)、Δds(k) Are respectively xs、us、dsK denotes the k sampling instants.
The objective function for modeling prediction is as follows:
min J(x(k),ΔU(k))＝||Γy(Yp,s(k+1|k))-R(k+1)||2+||ΓuΔU(k)||2 (55)
in the formula, gammayAnd ΓuIs a weighting matrix, R (k +1) is a given control output reference sequence, Δ U (k) is a control increment sequence, p is a predicted step size of the MPC, m is a control step size, Y is a control step sizep,s(k +1| k) is the p-step control output at time k based on model (8) predictions, where:
Figure BDA0003084975720000112
in the formula,. DELTA.us(k)、Δus(k+1)、…、Δus(k + m +1) is the control increment of k, k +1, …, k + m +1 time respectively;
Figure BDA0003084975720000113
in the formula, r (k +1), r (k +2), … and r (k + p) are reference output vectors at the time of k +1, … and k + p respectively;
Figure BDA0003084975720000114
in the formula, the prediction output vectors at time k for time k +1, time …, and time k + p for time y (k +1| k), y (k +2| k), …, and y (k + p | k), respectively;
finally solving the control vector delta u of each moments(k) And issuing the data to each energy storage system to realize the automatic response control of the cooperative energy storage system.
For example, in the embodiment, in order to further verify the hybrid energy storage coordination control method based on the multi-level architecture, a standard IEEE33 node power distribution network including three energy storage systems is used as an example to perform simulation, and on the premise that the voltage of each node is ensured to be within an allowable range, the system voltage control effects before and after control are compared. The parameter setting is shown in table 1, and the voltage control capability in the power distribution network is obviously improved after the control is carried out by the method.
TABLE 1 hybrid energy storage parameter configuration for power distribution networks
Serial number Electrochemical energy storage Electrochemical energy storage Superconducting energy storage
Access node 16 19 29
Installed capacity (kW) 400 300 400
Capacitive reactive power (kVar) (0,160) (0,180) (0,160)
Inductive reactive power (kvar) (-160,0) (-180,0) (-160,0)
The foregoing detailed description of the present application has been presented to illustrate the principles and implementations of the present application using simulation, and the above description of the embodiments is only for the purpose of facilitating understanding the method and core concept of the present application, and the present application may be modified in various aspects, and in summary, the present description should not be construed as limiting the present application.

Claims (6)

1. The hybrid energy storage coordination control method based on the multilevel architecture is characterized by comprising the following steps:
determining an electrochemical energy storage local-level operation response model;
determining a local-level operation response model of the superconducting energy storage;
determining an energy storage system local level automatic response control strategy;
determining a power distribution network hybrid energy storage, distributed photovoltaic, load and line overall equipment interconnection relation model;
determining an energy storage system cooperative level automatic response control strategy;
and acquiring a cooperative automatic response control instruction by using a model prediction control method, and issuing the cooperative automatic response control instruction to the hybrid energy storage system.
2. The hybrid energy storage coordination control method based on the multi-hierarchy architecture as claimed in claim 1, wherein the method for determining the electrochemical energy storage local operation response model comprises:
the energy stored by the energy storage system may be expressed as:
Figure FDA0003084975710000011
in the formula (I), the compound is shown in the specification,
Figure FDA0003084975710000012
for the initial energy of the electrochemical energy storage system, PBESSCharging and discharging power for an electrochemical energy storage system;
written as a state space equation of the form:
Figure FDA0003084975710000013
in the formula, xESIs the state quantity of the electrochemical energy storage system uESFor controlling the quantity of the electrochemical energy storage system, dESDisturbance variable of electrochemical energy storage system, AESSystem matrix being an electrochemical energy storage system, BESA control matrix for an electrochemical energy storage system, EESIs a perturbation matrix of the electrochemical energy storage system.
3. The hybrid energy storage coordination control method based on the multi-hierarchy architecture as claimed in claim 1, wherein the method for determining the local operation response model of the superconducting energy storage is as follows:
energy E stored in superconducting energy storagesmCan be expressed as:
Figure FDA0003084975710000014
in the formula, LsmIs an inductor, IsmdThe current is the direct current side current of the superconducting energy storage;
written as a state space equation of the form:
Figure FDA0003084975710000015
in the formula, xSMIs a state quantity of a superconducting energy storage system, uSMFor controlling the quantity of the superconducting energy storage system, dSMDisturbance quantity of superconducting energy storage system, ASMSystem matrix being a superconducting energy storage system, BSMA control matrix for a superconducting energy storage system, ESMIs a disturbance matrix of the superconducting energy storage system.
4. The hybrid energy storage coordination control method based on the multi-hierarchy architecture as claimed in claim 1, wherein the method for determining the local-level automatic response control strategy of the energy storage system comprises:
the electrochemical energy storage and superconducting energy storage local control strategy adopts a PI control mode of grid-connected point voltage deviation feedback, and realizes local-level automatic response of an energy storage system by changing a power reference instruction through a voltage-active/reactive droop characteristic curve, and can be realized by the following modes:
Figure FDA0003084975710000021
wherein R is the voltage reactive droop coefficient, QdFor the reactive regulation of energy storage, V is the actual voltage of the grid-connected point of energy storage, VcorAs a correction amount of voltage, QmaxFor maximum reactive power output of stored energy, QminIn order to store energy and output the minimum reactive power,
Figure FDA0003084975710000022
and
Figure FDA0003084975710000023
the lower limit and the upper limit of the voltage regulation dead zone,
Figure FDA0003084975710000024
and
Figure FDA0003084975710000025
the lower limit and the upper limit of the voltage safe operation range.
5. The hybrid energy storage coordination control method based on the multi-hierarchy architecture as claimed in claim 1, wherein the method for determining the power distribution network hybrid energy storage, distributed photovoltaic, load and line overall equipment interconnection relation model comprises:
Figure FDA0003084975710000026
in the formula,. DELTA.Pi、ΔQiRespectively the active and reactive power injection quantity of the ith node, and the sensitivity factor SδP、SUPRespectively representing the influence of active output fluctuation on the phase angle and amplitude of the voltage, SδQ、SUQThe influence of reactive power output fluctuation on node voltage phase angle and amplitude is respectively shown, and the voltage amplitude variation delta U and an active and reactive power variation sequence delta P of a system containing N PQ nodes meet the following conditions:
ΔU＝SUPΔP+SUQΔQ (7)
the cooperative peer system operation model is formed as follows:
Figure FDA0003084975710000027
in the formula, xsIs the state quantity u of the distribution network systemsFor controlling the system of the distribution network, dsFor disturbance of the distribution network system, ysFor power distribution network systemsOutput quantity, AsSystem matrix for distribution network system, BsFor control matrices of the distribution network system, EsDisturbance matrix for distribution network systems, CsAn output matrix of the power distribution network system; and has the following components:
Figure FDA0003084975710000033
in the formula (I), the compound is shown in the specification,
Figure FDA0003084975710000034
is NESA system matrix of individual battery energy storage systems,
Figure FDA0003084975710000035
is NSMA system matrix of superconducting energy storage systems;
Figure FDA0003084975710000036
in the formula (I), the compound is shown in the specification,
Figure FDA0003084975710000037
is NESA control matrix for each of the battery energy storage systems,
Figure FDA0003084975710000038
is NSMA control matrix for each superconducting energy storage system;
Figure FDA0003084975710000039
in the formula (I), the compound is shown in the specification,
Figure FDA00030849757100000310
is NESA disturbance matrix of each battery energy storage system;
Figure FDA00030849757100000311
is NSMA disturbance matrix of the superconducting energy storage system.
6. The hybrid energy storage coordination control method based on the multi-hierarchy architecture as claimed in claim 1, wherein the method for obtaining the cooperative automatic response control command by using the model prediction control method and sending the cooperative automatic response control command to the hybrid energy storage system comprises:
discretizing the system operation model (8) according to the cooperative level to obtain:
Figure FDA0003084975710000031
in the formula, A, Bu、BdAre respectively As、Bs、EsIn the form of a discretized matrix of, Δ xs(k)、Δus(k)、Δds(k) Are respectively xs、us、dsK represents k sampling instants;
the objective function for modeling prediction is as follows:
min J(x(k),ΔU(k))＝||Γy(Yp,s(k+1|k))-R(k+1)||2+||ΓuΔU(k)||2 (13)
in the formula, gammayAnd ΓuIs a weighting matrix, R (k +1) is a given control output reference sequence, Δ U (k) is a control increment sequence, p is a predicted step size of the MPC, m is a control step size, Y is a control step sizep,s(k +1| k) is the p-step control output at time k based on model (8) predictions, where:
Figure FDA0003084975710000032
in the formula,. DELTA.us(k)、Δus(k+1)、…、Δus(k + m +1) is the control increment of k, k +1, …, k + m +1 time respectively;
Figure FDA0003084975710000041
in the formula, r (k +1), r (k +2), … and r (k + p) are reference output vectors at the time of k +1, … and k + p respectively;
Figure FDA0003084975710000042
in the formula, the prediction output vectors at time k for time k +1, time …, and time k + p for time y (k +1| k), y (k +2| k), …, and y (k + p | k), respectively;
finally solving the control vector delta u of each moments(k) And issuing the data to each energy storage system to realize the automatic response control of the cooperative energy storage system.
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