Modularized small signal modeling method and system for DC power distribution network containing multiple hybrid energy storage

Abstract

The invention provides a modular small-signal modeling method and system for a direct-current power distribution network containing multiple hybrid energy storage. The method comprises the steps that S1, the direct-current power distribution network containing the multiple hybrid energy storage is divided into four hybrid energy storage sub-modules; s2, dividing the single hybrid energy storage sub-module into a boost circuit sub-module, a converter control sub-module and a distributed control sub-module, and modeling the boost circuit sub-module, the converter control sub-module and the distributed control sub-module respectively to obtain a small signal model of the single hybrid energy storage sub-module; and S3, obtaining a complete small-signal model of the multi-hybrid energy storage DC power distribution network by simultaneously establishing the small-signal model of the four hybrid energy storage sub-modules.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering, and in particular to a modular small signal modeling method and system for a DC distribution network containing multiple hybrid energy storages. Background Art

[0002] With the increasing attention paid by society to sustainable development and environmental protection and the full implementation of the "3060 dual carbon" goal in my country's energy field, it is a general trend to further increase the use of renewable energy and energy storage. In recent years, the development of renewable clean energy and power electronics technology has continuously promoted the progress of distributed power generation, microgrid and other technologies. Microgrid technology represents the development trend of future distributed energy supply systems and is an important part of future intelligent power distribution systems. It is of great significance to promote energy conservation and emission reduction and achieve sustainable development of energy.

[0003] However, in AC / DC hybrid microgrids, due to the access of a high proportion of renewable energy and power electronics, there are problems such as power, frequency and voltage fluctuations on both sides of the source and load, which bring risks to the safe and reliable operation of the microgrid. The access of multiple hybrid energy storage systems to the microgrid can suppress power fluctuations, which can not only give full play to the high energy storage efficiency and fast energy storage of supercapacitors, but also make up for the shortcomings of short service life and low output capacity rate of batteries. By adopting a modular small signal modeling method for DC distribution networks containing multiple hybrid energy storage, the coordinated control between DC distribution networks containing multiple hybrid energy storage systems can be effectively realized, and the sensitive parameters that cause instability in microgrids containing multiple hybrid energy storage systems can be effectively identified.

[0004] The modular small signal modeling method for DC distribution networks with multiple hybrid energy storage systems mainly includes two parts: distributed control of hybrid energy storage systems and modular small signal modeling. In a DC distribution network with multiple hybrid energy storage systems, each supercapacitor control unit updates its status by its own local control, and the battery control unit updates its own status through the data information of the adjacent units, jointly achieving the goal of coordinated control. In terms of the coordinated control of DC distribution networks with multiple hybrid energy storage systems, there are currently few studies on modular small signal modeling that take into account the coordinated cooperation of distributed droop control.

[0005] Based on the coordinated cooperation of voltage power droop control and integral droop control in a DC microgrid under a distributed control strategy, the present invention proposes a modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages, which realizes the compensation of system unbalanced power and the regulation of DC bus voltage, improves the control flexibility and stability analysis effectiveness of the DC distribution network containing multiple hybrid energy storages, and saves system investment costs.

[0006] Li Wuhua, Xu Chi, Yu Hongbin, et al. Frequency division coordination control strategy of hybrid energy storage in DC microgrid system [J]. Transactions of China Electrotechnical Society, 2016, 31(14): 84-92. According to the characteristics of DC microgrid architecture, a frequency division response control strategy is designed. The power fluctuation is extracted by bus voltage feedback, and three "invisible" filters, namely high-pass filter, band-pass filter and low-pass filter, are set by using DC bus voltage loop and supercapacitor voltage loop. The high, medium and low frequency components of bus power fluctuation are responded by bus capacitor, supercapacitor and battery in turn, thus making full use of the advantages of high energy density of battery and high power density and long cycle life of supercapacitor, effectively improving the performance of energy storage system. Moreover, the implementation of invisible filter is completely completed by software control, which has good flexibility and adaptability, and does not require additional hardware cost. Finally, the effectiveness of the proposed hybrid energy storage control strategy is verified by experiments. This paper proposes a scheme of frequency division control by extracting power fluctuation information through bus voltage, and conducts power disturbance experiments on the proposed control strategy, which proves the improvement of dynamic and static performance of the system. However, its control effectiveness is limited to systems containing one group of hybrid energy storage units, while the integrated droop coordinated distributed control theory of the present invention is applicable to systems containing multiple groups of hybrid energy storage units, realizing the reasonable distribution of unbalanced power among the same energy storage medium of different units.

[0007] Su Hao, Zhang Jiancheng, Wang Ning, et al. Energy management strategy for large-capacity hybrid energy storage system based on hierarchical optimization [J]. High Voltage Technology, 2018, 44(04): 1177-1186. Aiming at the problems of multi-storage medium coordination and multi-storage unit power allocation in large-capacity hybrid energy storage system (HESS), a two-layer energy management strategy of "energy storage system-medium group-energy storage unit" step-by-step optimization is proposed. The central distribution layer adopts an improved low-pass filtering algorithm that integrates the state of charge (SOC) of the supercapacitor group to obtain the power instructions of each medium group. Through state recognition and manual intervention, the deficiency that the algorithm easily leads to the opposite power state of the medium group is compensated, and the battery group power climbing limit Pl imit is introduced to further smooth the power output of the battery group. The local distribution layer introduces the concepts of continuous dispatchability and operation economy of the energy storage system, establishes power allocation models for battery groups and supercapacitor groups respectively, and completes the optimal allocation of the upper-level power instructions among the energy storage units in the group. The actual output of a certain energy storage power station from 13:00 to 13:30 on a certain day was used as an example, and the AMPL / IPOPT solver was used for solution. The results show that the proposed strategy can reasonably allocate the power of each energy storage unit while meeting the power demand of the power grid for the energy storage system, so that its SOC is concentrated in the range of 0.4-0.8, ensuring that each unit has sufficient charge and discharge margin, while saving 17.7% of the battery pack operating costs. The document proposes a two-layer energy management strategy of "energy storage system-medium group-energy storage unit" step-by-step optimization, which solves the problem of "one charge and one discharge" of batteries and supercapacitors in traditional filtering algorithms, and realizes the reasonable distribution of unbalanced power among various energy storage units through the control center, but the control strategy used is centralized control, while the underlying control of the present invention adopts the coordinated cooperation of voltage power droop control and integral droop control of supercapacitors, and has more efficient autonomous and coordinated operation capabilities. Summary of the invention

[0008] In view of the defects in the prior art, the object of the present invention is to provide a modular small signal modeling method and system for a DC distribution network containing multiple hybrid energy storages.

[0009] A modular small signal modeling method for a DC distribution network containing multiple hybrid energy storage devices is provided according to the present invention, comprising:

[0010] Step S1: Divide the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage sub-modules;

[0011] Step S2: dividing a single hybrid energy storage submodule into a boost circuit submodule, a converter control submodule and a distributed control submodule, and respectively modeling the boost circuit submodule, the converter control submodule and the distributed control submodule, thereby obtaining a small signal model of the single hybrid energy storage submodule;

[0012] Step S3: By combining the small signal models of the four hybrid energy storage submodules, a complete small signal model of the DC distribution network containing multiple hybrid energy storage is obtained.

[0013] Preferably, the method further comprises: in a DC distribution network containing multiple hybrid energy storages, adopting a distributed control strategy for the hybrid energy storage submodules;

[0014] The distributed control strategy includes: configuring voltage power droop control for the battery pack and configuring integral droop control for the supercapacitor.

[0015] Preferably, configuring voltage and power droop control on the battery pack includes:

[0016]

[0017] The step of configuring the integral droop control on the supercapacitor includes:

[0018]

[0019] Among them, V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor respectively; V th is the nominal voltage of the microgrid, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the maximum allowable voltage deviation, P Bmax is the rated power of the battery, P B and P SC are the output power of the battery and supercapacitor respectively; s is the Laplace operator;

[0020] The power sharing relationship between the battery and the supercapacitor is described as:

[0021]

[0022] Among them, P Load is the load power of the DC distribution network containing multiple hybrid energy storage, the battery group power is the low-frequency component of the load power, and the supercapacitor group power is the high-frequency component of the load power;

[0023] Assign a local distributed compensator to the battery voltage power droop control to generate the compensation amount δ i and ε i They are respectively superimposed on the nominal voltage and rated power in the voltage and power droop control equation;

[0024]

[0025] Among them, δ i is the nominal voltage compensation of the ith battery; SOC Bi is the state of charge of the ith battery; δj is the nominal voltage compensation of the jth battery; SOC Bj is the state of charge of the jth battery; α i , β i and γ i is a constant; g i is the control gain; V b Indicates bus voltage; P Bj represents the output power of the jth battery, P Bjmax represents the rated power of the jth battery, P Bi represents the rated output power of the ith battery, P Bimax represents the rated output power of the i-th battery;

[0026] The state of charge of the ith battery is:

[0027]

[0028] Among them, P Bi is the output power of the ith battery, SOC Bi0 and are the initial state of charge and rated charging capacity of the i-th battery respectively;

[0029] After considering the compensation amounts δ and ε, the voltage power droop formula is updated as follows:

[0030] V ref1 =V th -mP B +δ+ε(10).

[0031] Preferably, step S1 comprises: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage submodules, and simplifying the four hybrid energy storage submodules into state space equations;

[0032]

[0033]

[0034] Among them, u sys1 =C sys1_4 X sys4 ;u sys2 =C sys2_1 X sys1 ;u sys3 =C sys3_2 X sys2 ;u sys4 =C sys4_3 X sys3 ;X sysi represents the state variable of the ith hybrid energy storage unit submodule, u sysiA represents the input variable of the i hybrid energy storage unit submodule; sysi represents the system matrix of hybrid energy storage unit submodule i, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit submodule generated by the fourth hybrid energy storage unit submodule, C sys2_1 represents the output matrix corresponding to the output variable generated by the first hybrid energy storage unit submodule and input into the second hybrid energy storage unit submodule, C sys3_2 represents the output matrix corresponding to the output variable generated by the second hybrid energy storage unit submodule and input into the third hybrid energy storage unit submodule, C sys4_3 It represents the output matrix corresponding to the output variable generated by the third hybrid energy storage unit submodule and input into the fourth hybrid energy storage unit submodule.

[0035] Preferably, in step S2, the boost circuit submodule, the converter control submodule and the distributed control submodule are modeled respectively, so as to obtain a small signal model of a single hybrid energy storage submodule, including:

[0036] The boost circuit submodule is simplified to the state space equation:

[0037]

[0038] Y1=C1X1(19)

[0039] ΔX1=[Δi L1 Δi L2 Δv o ] T (20)

[0040] Δu1=[Δd1Δd2] T (twenty one)

[0041] ΔY1=[Δi L1 Δi L2 Δv o ] T (twenty two)

[0042] Among them, P CPL is the power of the constant power load; C o is the equivalent capacitance of the output port of the DC boost converter; v o is the DC bus voltage; d1 and d2 are the duty cycles of the DC boost converter; X1 represents the state variable of the boost circuit submodule; u1 represents the input variable of the boost circuit submodule; Y1 represents the output variable of the boost circuit submodule; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A1 represents the boost circuit submodule system matrix of the first hybrid energy storage unit submodule; B1 represents the boost circuit submodule input matrix of the first hybrid energy storage unit submodule; C1 represents the boost circuit submodule output matrix of the first hybrid energy storage unit submodule; ΔX1 represents the small signal disturbance of the boost circuit submodule state variable of the first hybrid energy storage unit submodule; Indicates the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv o represents the small signal disturbance of the DC bus voltage; Δu1 represents the small signal disturbance of the input variable of the boost circuit submodule of the first hybrid energy storage unit submodule; Δd1 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δd2 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; ΔY1 represents the small signal disturbance of the output variable of the first hybrid energy storage unit submodule;

[0043] The converter control submodule is simplified into the state space equation:

[0044]

[0045] Y2=C2X2+D2u2+D3u3(28)

[0046] ΔX2=[Δξ1Δξ2Δξ3Δξ4] T (29)

[0047] Δu2=[Δv ref1 Δv ref2 ] T (30)

[0048] Δu3=[Δi L1 Δi L2 Δv o ] T (31)

[0049] ΔY2=[Δd1Δd2] T (32)

[0050] Among them, ξ1~ξ4 are the state variables of the converter control submodule; vref1 and v ref2 is the voltage reference signal of the proportional integral PI voltage control loop; i ref1 and i ref2 is the current reference signal of the PI current control loop; k pV is the proportional coefficient of the PI voltage control loop; k iV is the integral coefficient of the PI voltage control loop; X2 represents the state variable of the converter control submodule; u2 and u3 represent the input variables of the converter control submodule; Y2 represents the output variable of the converter control submodule; A2 represents the converter control submodule system matrix of the first hybrid energy storage unit submodule; B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; B3 represents the second input matrix of the converter control submodule of the first hybrid energy storage unit submodule; C2 represents the converter control submodule output matrix of the first hybrid energy storage unit submodule; D2 represents the first direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule ; Δξ1 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the battery and the bus voltage of the first hybrid energy storage unit submodule; Δξ2 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the battery of the first hybrid energy storage unit submodule; Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the supercapacitor and the bus voltage of the first hybrid energy storage unit submodule; Δξ4 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu2 represents the small signal disturbance of the first input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δv ref1 A small signal disturbance representing the output voltage reference value of the DC boost converter connected to the battery of the first hybrid energy storage unit submodule; Δv ref2 represents the small signal disturbance of the output voltage reference value of the DC boost converter connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu3 represents the small signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δi L1 represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 A small signal disturbance representing the inductive current of a DC boost circuit connected to a supercapacitor in a first hybrid energy storage unit submodule;

[0051] The distributed control submodule is simplified into the state space equation:

[0052]

[0053] Y3=C3X3+D4u4+D5u5+D6u6(38)

[0054] ΔX3=[Δv b1 ΔSOC1 Δε b1 v ΔSC ] T (39)

[0055] Δu4=Δv ref3 (40)

[0056] Δu5=[Δi L1 Δi L2 Δv o ] T (41)

[0057] Δu6=[Δv b3 ΔSOC3 Δε b3 v ΔSC3 ] T (42)

[0058] ΔY3=[Δv ref1 Δv ref2 ] T (43)

[0059] Among them, α, β, λ and μ are constants; SOC1 and SOC3 are the current charge states of the hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery; v b1 、v b3 , ε b1 and ε b3 is the voltage power droop coefficient; n is the integral droop coefficient; X3 represents the state variable of the distributed control strategy control submodule; u4, u5 and u6 represent the input variables of the distributed control submodule; Y3 represents the output variable of the distributed control submodule; γ represents the state of charge compensation coefficient; v ref3represents the droop control voltage reference signal; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; SOC0 represents the initial charge state of the battery; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A3 represents the distributed control submodule system matrix of the first hybrid energy storage unit submodule; B4 represents the first input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; C3 represents the distributed control submodule output matrix of the first hybrid energy storage unit submodule; D4 represents the first direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 represents the small signal disturbance of the droop control voltage reference signal; Δv b1 represents the small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; ΔSOC1 represents the small signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; Δε b1 represents the small signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC Δv represents the small signal disturbance of the supercapacitor integrated droop voltage change of the first hybrid energy storage unit submodule; b3 represents the small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; ΔSOC3 represents the small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; Δε b3 represents the small signal disturbance of the battery rated power compensation amount of the fourth hybrid energy storage unit submodule; v ΔSC3 Represents the small signal disturbance of the supercapacitor integrated droop voltage change of the fourth hybrid energy storage unit submodule.

[0060] Preferably, step S3 comprises:

[0061] The state variables of the boost circuit submodule are:

[0062]

[0063] The state variables of the converter control submodule are:

[0064]

[0065] The state variables of the distributed control submodule are:

[0066]

[0067] The small signal model of the first hybrid energy storage unit module is:

[0068]

[0069] Among them, O 7×6 , O 3×7 , O 3×4 , O 4×7 With O 4×4 The zero matrices are 7 rows and 6 columns, 3 rows and 7 columns, 3 rows and 4 columns, 4 rows and 7 columns, and 4 rows and 4 columns respectively;

[0070] By combining the small signal models of the four hybrid energy storage unit submodules, the small signal model of the entire DC distribution network containing multiple hybrid energy storage is derived as follows:

[0071]

[0072] Among them, O 11×11 is a zero matrix with 11 rows and 11 columns.

[0073] According to the present invention, a modular small signal modeling system for a DC distribution network containing multiple hybrid energy storage devices is provided, comprising:

[0074] Module M1: Divide the DC distribution network with multiple hybrid energy storage into four hybrid energy storage sub-modules;

[0075] Module M2: Divide a single hybrid energy storage submodule into a boost circuit submodule, a converter control submodule and a distributed control submodule, and model the boost circuit submodule, the converter control submodule and the distributed control submodule respectively, thereby obtaining a small signal model of the single hybrid energy storage submodule;

[0076] Module M3: By combining the small signal models of the four hybrid energy storage sub-modules, a complete small signal model of the DC distribution network containing multiple hybrid energy storage is obtained.

[0077] Preferably, the system further comprises: in a DC distribution network containing multiple hybrid energy storages, adopting a distributed control strategy for the hybrid energy storage submodules;

[0078] The distributed control strategy includes: configuring voltage and power droop control for the battery pack and configuring integral droop control for the supercapacitor;

[0079] The step of configuring voltage and power droop control on the battery pack includes:

[0080]

[0081] The step of configuring the integral droop control on the supercapacitor includes:

[0082]

[0083] Among them, V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor respectively; V th is the nominal voltage of the microgrid, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the maximum allowable voltage deviation, P Bmax is the rated power of the battery, P B and P SC are the output power of the battery and supercapacitor respectively; s is the Laplace operator;

[0084] The power sharing relationship between the battery and the supercapacitor is described as:

[0085]

[0086] Among them, P Load is the load power of the DC distribution network containing multiple hybrid energy storage, the battery group power is the low-frequency component of the load power, and the supercapacitor group power is the high-frequency component of the load power;

[0087] Assign a local distributed compensator to the battery voltage power droop control to generate the compensation amount δ i and ε i They are respectively superimposed on the nominal voltage and rated power in the voltage and power droop control equation;

[0088]

[0089]

[0090] Among them, δ i is the nominal voltage compensation of the ith battery; SOC Bi is the state of charge of the ith battery; δ j is the nominal voltage compensation of the jth battery; SOC Bj is the state of charge of the jth battery; α i , β i and γ i is a constant; g i is the control gain; V b Indicates bus voltage; P Bj represents the output power of the jth battery, P Bjmax represents the rated power of the jth battery, P Bi represents the rated output power of the ith battery, P Bimax represents the rated output power of the i-th battery;

[0091] The state of charge of the ith battery is:

[0092]

[0093] Among them, P Bi is the output power of the ith battery, SOC Bi0 and are the initial state of charge and rated charging capacity of the i-th battery respectively;

[0094] After considering the compensation amounts δ and ε, the voltage power droop formula is updated as follows:

[0095] V ref1 =V th -mP B +δ+ε(10).

[0096] Preferably, the module M1 comprises: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage submodules, and simplifying the four hybrid energy storage submodules into state space equations;

[0097]

[0098] Among them, u sys1 =C sys1_4 X sys4 ;u sys2 =C sys2_1 X sys1 ;u sys3 =C sys3_2 X sys2 ;u sys4 =C sys4_3 X sys3 ;X sysi represents the state variable of the ith hybrid energy storage unit submodule, u sysi A represents the input variable of the i hybrid energy storage unit submodule; sysi represents the system matrix of hybrid energy storage unit submodule i, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit submodule generated by the fourth hybrid energy storage unit submodule, C sys2_1 represents the output matrix corresponding to the output variable generated by the first hybrid energy storage unit submodule and input into the second hybrid energy storage unit submodule, C sys3_2 represents the output matrix corresponding to the output variable generated by the second hybrid energy storage unit submodule and input into the third hybrid energy storage unit submodule, C sys4_3 It represents the output matrix corresponding to the output variable generated by the third hybrid energy storage unit submodule and input into the fourth hybrid energy storage unit submodule.

[0099] Preferably, the boost circuit submodule, the converter control submodule and the distributed control submodule are modeled in the module M2 respectively, so as to obtain a small signal model of a single hybrid energy storage submodule, including:

[0100] The boost circuit submodule is simplified to the state space equation:

[0101]

[0102] Y1=C1X1(19)

[0103] ΔX1=[Δi L1 Δi L2 Δv o ] T (20)

[0104] Δu1=[Δd1Δd2] T (twenty one)

[0105] ΔY1=[Δi L1 Δi L2 Δv o ] T (twenty two)

[0106] Among them, P CPL is the power of the constant power load; C o is the equivalent capacitance of the output port of the DC boost converter; v o is the DC bus voltage; d1 and d2 are the duty cycles of the DC boost converter; X1 represents the state variable of the boost circuit submodule; u1 represents the input variable of the boost circuit submodule; Y1 represents the output variable of the boost circuit submodule; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A1 represents the boost circuit submodule system matrix of the first hybrid energy storage unit submodule; B1 represents the boost circuit submodule input matrix of the first hybrid energy storage unit submodule; C1 represents the boost circuit submodule output matrix of the first hybrid energy storage unit submodule; ΔX1 represents the small signal disturbance of the boost circuit submodule state variable of the first hybrid energy storage unit submodule; Indicates the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv orepresents the small signal disturbance of the DC bus voltage; Δu1 represents the small signal disturbance of the input variable of the boost circuit submodule of the first hybrid energy storage unit submodule; Δd1 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δd2 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; ΔY1 represents the small signal disturbance of the output variable of the first hybrid energy storage unit submodule;

[0107] The converter control submodule is simplified into the state space equation:

[0108]

[0109] Y2=C2X2+D2u2+D3u3(28)

[0110] ΔX2=[Δξ1Δξ2Δξ3Δξ4] T (29)

[0111] Δu2=[Δv ref1 Δv ref2 ] T (30)

[0112] Δu3=[Δi L1 Δi L2 Δv o ] T (31)

[0113] ΔY2=[Δd1Δd2] T (32)

[0114] Among them, ξ1~ξ4 are the state variables of the converter control submodule; v ref1 and v ref2 is the voltage reference signal of the proportional integral PI voltage control loop; i ref1 and i ref2 is the current reference signal of the PI current control loop; k pV is the proportional coefficient of the PI voltage control loop; k iVis the integral coefficient of the PI voltage control loop; X2 represents the state variable of the converter control submodule; u2 and u3 represent the input variables of the converter control submodule; Y2 represents the output variable of the converter control submodule; A2 represents the converter control submodule system matrix of the first hybrid energy storage unit submodule; B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; B3 represents the second input matrix of the converter control submodule of the first hybrid energy storage unit submodule; C2 represents the converter control submodule output matrix of the first hybrid energy storage unit submodule; D2 represents the first direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule ; Δξ1 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the battery and the bus voltage of the first hybrid energy storage unit submodule; Δξ2 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the battery of the first hybrid energy storage unit submodule; Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the supercapacitor and the bus voltage of the first hybrid energy storage unit submodule; Δξ4 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu2 represents the small signal disturbance of the first input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δv ref1 A small signal disturbance representing the output voltage reference value of the DC boost converter connected to the battery of the first hybrid energy storage unit submodule; Δv ref2 represents the small signal disturbance of the output voltage reference value of the DC boost converter connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu3 represents the small signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δi L1 represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 A small signal disturbance representing the inductive current of a DC boost circuit connected to a supercapacitor in a first hybrid energy storage unit submodule;

[0115] The distributed control submodule is simplified into the state space equation:

[0116]

[0117] Y3=C3X3+D4u4+D5u5+D6u6 (38)

[0118] ΔX3=[Δv b1 ΔSOC1 Δεb1 v ΔSC ] T (39)

[0119] Δu4=Δv ref3 (40)

[0120] Δu5=[Δi L1 Δi L2 Δv o ] T (41)

[0121] Δu6=[Δv b3 ΔSOC3 Δε b3 v ΔSC3 ] T (42)

[0122] ΔY3=[Δv ref1 Δv ref2 ] T (43)

[0123] Among them, α, β, λ and μ are constants; SOC1 and SOC3 are the current charge states of the hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery; v b1 、v b3 , ε b1 and ε b3 is the voltage power droop coefficient; n is the integral droop coefficient; X3 represents the state variable of the distributed control strategy control submodule; u4, u5 and u6 represent the input variables of the distributed control submodule; Y3 represents the output variable of the distributed control submodule; γ represents the state of charge compensation coefficient; v ref3 represents the droop control voltage reference signal; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; SOC0 represents the initial charge state of the battery; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A3 represents the distributed control submodule system matrix of the first hybrid energy storage unit submodule; B4 represents the first input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; C3 represents the distributed control submodule output matrix of the first hybrid energy storage unit submodule; D4 represents the first direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 represents the small signal disturbance of the droop control voltage reference signal; Δv b1represents the small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; ΔSOC1 represents the small signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; Δε b1 represents the small signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC Δv represents the small signal disturbance of the supercapacitor integrated droop voltage change of the first hybrid energy storage unit submodule; b3 represents the small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; ΔSOC3 represents the small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; Δε b3 represents the small signal disturbance of the battery rated power compensation amount of the fourth hybrid energy storage unit submodule; v ΔSC3 A small signal disturbance representing the variation of the supercapacitor integrated droop voltage of the fourth hybrid energy storage unit submodule;

[0124] The module M3 comprises:

[0125] The state variables of the boost circuit submodule are:

[0126]

[0127] The state variables of the converter control submodule are:

[0128]

[0129] The state variables of the distributed control submodule are:

[0130]

[0131] The small signal model of the first hybrid energy storage unit module is:

[0132]

[0133] Among them, O 7×6 , O 3×7 , O 3×4 , O 4×7 With O 4×4 The zero matrices are 7 rows and 6 columns, 3 rows and 7 columns, 3 rows and 4 columns, 4 rows and 7 columns, and 4 rows and 4 columns respectively;

[0134] By combining the small signal models of the four hybrid energy storage unit submodules, the small signal model of the entire DC distribution network containing multiple hybrid energy storage is derived as follows:

[0135]

[0136] Among them, O 11×11 is a zero matrix with 11 rows and 11 columns.

[0137] Compared with the prior art, the present invention has the following beneficial effects:

[0138] 1. The present invention constructs a modular small signal model of a DC distribution network composed of four hybrid energy storage unit modules. The combined sub-module models obtain a small signal model of a DC distribution network containing multiple hybrid energy storage systems, which greatly reduces the complexity of unified small signal modeling of the system;

[0139] 2. The present invention adopts integral droop control for supercapacitors, and releases dynamic power spontaneously in steady state to avoid terminal voltage exceeding the limit in transient state, thereby realizing spontaneous charge state recovery of the multi-hybrid energy storage system;

[0140] 3. The present invention adopts distributed control for the battery, which alleviates the DC bus voltage deviation caused by droop control in the DC distribution network containing multiple hybrid energy storage, realizes the charge state balance between batteries, prolongs the service life of the battery pack and improves the control flexibility of the DC distribution network containing multiple hybrid energy storage;

[0141] 4. The present invention effectively reduces the complexity of the unified model of the DC distribution network containing multiple hybrid energy storage, effectively realizes the spontaneous state of charge recovery capability of the multiple hybrid energy storage system, effectively adjusts the compensation of the system unbalanced power and the DC bus voltage, effectively improves the control flexibility and stability analysis effectiveness of the multiple hybrid energy storage system, and effectively saves the system investment cost;

[0142] 5. The integrated droop coordinated distributed control strategy proposed in the present invention is a good compromise among centralized control, distributed control and control based on consistency theory. The control structure has good stability and is conducive to stable operation of the system.

[0143] 6. The present invention adopts distributed control for the battery, which alleviates the DC bus voltage deviation caused by droop control in the multi-hybrid energy storage system, thereby achieving charge state balance between batteries, which is beneficial to extending the service life of the battery pack. Replacing the supercapacitor distributed controller with a supercapacitor local controller can save an average of 1,600 yuan for the installation cost of each supercapacitor, effectively saving the system investment cost;

[0144] 7. The modular small signal modeling method proposed in the present invention can effectively reduce the complexity of the unified model of the DC distribution network containing multiple hybrid energy storage. The acquisition of the state matrix is ​​simplified from the single solution of the 44th order differential equation in the traditional method to the single solution of the 4th order differential equation in the present method, and then the system stability and sensitivity analysis is carried out, which is conducive to identifying the sensitive parameters that cause the instability of the microgrid multi-hybrid energy storage system;

[0145] 8. The present invention coordinates the distributed voltage power droop control of the battery pack and the local integral droop control of the supercapacitor, so that the supercapacitor responds to high-frequency power fluctuations, which is conducive to giving full play to the fast charging and discharging characteristics of the supercapacitor. BRIEF DESCRIPTION OF THE DRAWINGS

[0146] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0147] Figure 1 This is a structural diagram of a DC microgrid with multiple hybrid energy storage systems.

[0148] Figure 2 This is the topology diagram of the DC boost converter.

[0149] Figure 3 This is the block diagram of the battery local distributed controller module.

[0150] Figure 4 This is the block diagram of the converter control submodule.

[0151] Figure 5 This is the block diagram of the distributed control submodule.

[0152] Figure 6 P CPL Schematic diagram of the system root locus when changing from 22kW to 60kW.

[0153] Figure 7 Schematic diagram of the output power of batteries and supercapacitors.

[0154] Among them, 1-first hybrid energy storage unit submodule; 2-second hybrid energy storage unit submodule; 3-third hybrid energy storage unit submodule; 4-fourth hybrid energy storage unit submodule. DETAILED DESCRIPTION

[0155] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0156] Example 1

[0157] The present invention provides a modular small signal modeling method and system for a DC distribution network containing multiple hybrid energy storage, including: the integral droop coordinated distributed cooperative control strategy is a good compromise of centralized control, distributed control and consistency theory cooperative control, and is achieved by the coordinated cooperation of distributed voltage power droop control of the battery group and the local integral droop control of the supercapacitor group. Modular small signal modeling mainly includes control loop modeling including integral droop control and proportional integral control, as well as hybrid energy storage unit modeling. By combining the small signal models of four system submodules, a small signal model of a DC distribution network containing multiple hybrid energy storage can be obtained, and then system stability and sensitivity analysis can be carried out to effectively identify the maximum load for stable operation of the system and the sensitive parameters that cause instability.

[0158] The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages comprises:

[0159] Step S1: In a DC distribution network containing multiple hybrid energy storages, a distributed control strategy is adopted for the hybrid energy storage submodules. The strategy is specifically embodied as follows: voltage and power droop control is configured for the battery pack, and integral droop control is configured for the supercapacitor. By using a distributed control strategy, the source-load mismatch problem in a DC distribution network containing multiple hybrid energy storages can be effectively solved;

[0160] Step S2: by dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage submodules, and then dividing a single hybrid energy storage submodule into three layers and modeling the dynamics in the three layers respectively, thereby obtaining a small signal model of the hybrid energy storage submodule;

[0161] Step S3: By combining the small signal models of the four hybrid energy storage submodules, a complete small signal model of the DC distribution network containing multiple hybrid energy storage systems can be obtained. Based on the small signal model, the system stability margin and the influence mechanism of the main system parameters on the stability can be analyzed, providing a reference for control-related research on DC distribution networks containing multiple hybrid energy storage systems.

[0162] Wherein, the step S1 comprises:

[0163] In the multi-hybrid energy storage system proposed by the present invention, the voltage power droop formula of the battery is shown in formula (1), and the integral droop formula of the supercapacitor is shown in formula (2):

[0164]

[0165]

[0166] Among them, V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor, V this the nominal voltage of the microgrid, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the maximum allowable voltage deviation, P Bmax is the rated power of the battery, P B and P SC are the output powers of the battery and supercapacitor respectively; s represents the Laplace operator;

[0167] The power sharing relationship between the battery and the supercapacitor can be described as shown in formula (3):

[0168]

[0169] Among them, P Load is the load power of the DC distribution network containing multiple hybrid energy storages, the battery pack power is the low-frequency component of the load power, and the supercapacitor group power is the high-frequency component of the load power.

[0170] In order to alleviate the problem of inaccurate battery power distribution, a local distributed compensator is assigned to the battery voltage power droop control to generate the compensation amount δ i and ε i They are respectively superimposed on the nominal voltage and rated power in the voltage and power droop control equation. i Following equations (6) to (7), ε i Following formulas (8) to (9):

[0171]

[0172] Among them, δ i is the nominal voltage compensation of the ith battery, SOC Bi is the state of charge of the ith battery, δ j is the nominal voltage compensation of the jth battery, SOC Bj is the state of charge of the jth battery. i , β i and γ i is a constant, g i is the control gain, which can be 1 or 0; V b Indicates bus voltage; P Bj represents the output power of the jth battery, P Bjmax represents the rated power of the jth battery, P Bi represents the rated output power of the ith battery, P Bimax represents the rated output power of the ith battery.

[0173] The state of charge of the ith battery is shown in equation (10):

[0174]

[0175] Among them, P Bi is the output power of the ith battery, SOC Bi0 and E rate Bi are the initial state of charge and rated charging capacity of the i-th battery respectively.

[0176] After considering the compensation amounts δ and ε, the voltage power droop formula can be updated as shown in formula (11):

[0177] V ref1 =V th -mP B +δ+ε(11)

[0178] The step S2 comprises:

[0179] The modular small signal model of the multi-hybrid energy storage system includes four hybrid energy storage unit submodule small signal models. The simplified state space equations are shown in equations (8) to (15):

[0180]

[0181] u sys1 =C sys1_4 X sys4 (12)

[0182] u sys2 =C sys2_1 X sys1 (13)

[0183] u sys3 =C sys3_2 X sys2 (14)

[0184] u sys4 =C sys4_3 X sys3 (15)

[0185] Among them, X sysi represents the state variable of the ith hybrid energy storage unit submodule, u sysi A represents the input variable of the i hybrid energy storage unit submodule; sysi represents the system matrix of the first hybrid energy storage unit submodule, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit submodule generated by the fourth hybrid energy storage unit submodule, C sys2_1 represents the output matrix corresponding to the output variable generated by the first hybrid energy storage unit submodule and input into the second hybrid energy storage unit submodule, C sys3_2 represents the output matrix corresponding to the output variable generated by the second hybrid energy storage unit submodule and input into the third hybrid energy storage unit submodule, C sys4_3The output matrix corresponding to the output variable generated by the third hybrid energy storage unit submodule and input into the fourth hybrid energy storage unit submodule is represented

[0186] The first hybrid energy storage unit submodule includes three submodules, namely, a boost circuit submodule, a converter control submodule and a distributed control submodule. The simplified state space equation of the boost circuit submodule is shown in equations (16) to (23):

[0187]

[0188] Y1=C1X1(20)

[0189] ΔX1=[Δi L1 Δi L2 Δv o ] T (twenty one)

[0190] Δu1=[Δd1Δd2] T (twenty two)

[0191] ΔY1=[Δi L1 Δi L2 Δv o ] T (twenty three)

[0192] Among them, P CPL is the power of the constant power load, C o is the equivalent capacitance of the DC boost converter output port. o is the DC bus voltage, d1 and d2 are the duty cycles of the DC boost converter. X1 represents the state variable of the boost circuit submodule, u1 represents the input variable of the boost circuit submodule, and Y1 represents the output variable of the boost circuit submodule; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, L2 represents the inductance of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule, represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule, E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule, E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule, A1 represents the boost circuit submodule system matrix of the first hybrid energy storage unit submodule, B1 represents the boost circuit submodule input matrix of the first hybrid energy storage unit submodule, C1 represents the boost circuit submodule output matrix of the first hybrid energy storage unit submodule, ΔX1 represents the small signal disturbance of the boost circuit submodule state variable of the first hybrid energy storage unit submodule, represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, represents the small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule, Δv o represents a small signal disturbance of the DC bus voltage, Δu1 represents an input variable small signal disturbance of the boost circuit submodule of the first hybrid energy storage unit submodule, Δd1 represents a small signal disturbance of the duty cycle of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, Δd2 represents a small signal disturbance of the duty cycle of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule, and ΔY1 represents a small signal disturbance of the output variable of the first hybrid energy storage unit submodule;

[0193] The simplified state space equations of the converter control submodule are shown in equations (24) to (33):

[0194]

[0195] Y2=C2X2+D2u2+D3u3(29)

[0196] ΔX2=[Δξ1Δξ2Δξ3Δξ4] T (30)

[0197] Δu2=[Δv ref1 Δv ref2 ] T (31)

[0198] Δu3=[Δi L1 Δi L2 Δv o ] T (32)

[0199] ΔY2=[Δd1Δd2] T (33)

[0200] Among them, ξ1~ξ4 are the state variables of the converter control submodule, v ref1 and v ref2 is the voltage reference signal for the proportional-integral (PI) voltage control loop. ref1 and i ref2 It is the current reference signal of the PI current control loop. pV is the proportional coefficient of the PI voltage control loop, k iVis the integral coefficient of the PI voltage control loop. X2 represents the state variable of the converter control submodule, u2 and u3 represent the input variables of the converter control submodule, and Y2 represents the output variable of the converter control submodule. A2 represents the converter control submodule system matrix of the first hybrid energy storage unit submodule, B2 represents the converter control submodule input matrix 1 of the first hybrid energy storage unit submodule, B3 represents the converter control submodule input matrix 2 of the first hybrid energy storage unit submodule, C2 represents the converter control submodule output matrix of the first hybrid energy storage unit submodule, D2 represents the converter control submodule direct-pass matrix 1 of the first hybrid energy storage unit submodule, D3 represents the converter control submodule direct-pass matrix 2 of the first hybrid energy storage unit submodule, ΔX2 represents the small signal disturbance of the converter control submodule state variable of the first hybrid energy storage unit submodule, and Δξ1 represents the first hybrid energy storage unit submodule and The small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the battery and the bus voltage, Δξ2 represents the small signal disturbance of the integral signal of the PI controller of the voltage loop of the DC boost converter connected to the battery by the first hybrid energy storage unit submodule, Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the supercapacitor by the first hybrid energy storage unit submodule and the bus voltage, Δξ4 represents the small signal disturbance of the integral signal of the PI controller of the voltage loop of the DC boost converter connected to the supercapacitor by the first hybrid energy storage unit submodule, Δu2 represents the small signal disturbance of the input variable matrix 1 of the converter control submodule of the first hybrid energy storage unit submodule, Δv ref1 The small signal disturbance of the output voltage reference value of the DC boost converter connected to the battery of the first hybrid energy storage unit submodule, Δv ref2 represents the small signal disturbance of the output voltage reference value of the DC boost converter connected to the supercapacitor of the first hybrid energy storage unit submodule, Δu3 represents the small signal disturbance of the input variable matrix 2 of the converter control submodule of the first hybrid energy storage unit submodule, Δi L1 represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, Δi L2 represents a small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule,

[0201] The simplified state space equations of the distributed control strategy control submodule are shown in equations (34) to (44):

[0202]

[0203]

[0204] Y3=C3X3+D4u4+D5u5+D6u6(39)

[0205] ΔX3=[Δv b1 ΔSOC1Δε b1 v ΔSC ] T (40)

[0206] Δu4=Δv ref3 (41)

[0207] Δu5=[Δi L1 Δi L2 Δv o ] T (42)

[0208] Δu6=[Δv b3 ΔSOC3Δε b3 v ΔSC3 ] T (43)

[0209] ΔY3=[Δv ref1 Δv ref2 ] T (44)

[0210] Among them, α, β, λ and μ are constants; SOC1 and SOC3 are the current charge states of the hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery. b1 、v b3 , ε b1 and ε b3 is the voltage power droop coefficient, n is the integral droop coefficient. X3 represents the state variable of the distributed control strategy control submodule, u4, u5 and u6 represent the input variables of the distributed control strategy control submodule, and Y3 represents the output variable of the distributed control strategy control submodule. γ represents the state of charge compensation coefficient, v ref3 represents the droop control voltage reference signal, E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule, SOC0 represents the initial state of charge of the battery, E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule, A3 represents the distributed control submodule system matrix of the first hybrid energy storage unit submodule, B4 represents the distributed control submodule input matrix 1 of the first hybrid energy storage unit submodule, B5 represents the distributed control submodule input matrix 2 of the first hybrid energy storage unit submodule, B6 represents the distributed control submodule input matrix 3 of the first hybrid energy storage unit submodule, C3 represents the distributed control submodule output matrix of the first hybrid energy storage unit submodule, D4 ​​represents the distributed control submodule direct matrix 1 of the first hybrid energy storage unit submodule, D5 represents the distributed control submodule direct matrix 2 of the first hybrid energy storage unit submodule, D6 represents the distributed control submodule direct matrix 3 of the first hybrid energy storage unit submodule, Δv ref3represents the small signal disturbance of the droop control voltage reference signal, Δv b1 represents the small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule, ΔSOC1 represents the small signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule, Δε b1 represents the small signal disturbance of the battery rated power compensation of the first hybrid energy storage unit submodule, v ΔSC represents the small signal disturbance of the supercapacitor integrated droop voltage change of the first hybrid energy storage unit submodule, Δv b3 represents the small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule, ΔSOC3 represents the small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule, Δε b3 represents the small signal disturbance of the battery rated power compensation of the fourth hybrid energy storage unit submodule, v ΔSC3 Represents the small signal disturbance of the supercapacitor integrated droop voltage change of the fourth hybrid energy storage unit submodule.

[0211] The step S3 comprises:

[0212] It can be seen from the state space equations of the above modules that the state variables, output variables and input variables of different modules are the same. The state variables of the boost circuit submodule can be expressed as shown in formula (45):

[0213]

[0214] The state variables of the converter control submodule can be expressed as shown in equation (46):

[0215]

[0216] The state variables of the distributed control strategy control submodule can be expressed as shown in formula (47):

[0217]

[0218] Since u6 is the state variable of the distributed control submodule in the fourth hybrid energy storage unit module, the small signal model of the first hybrid energy storage unit module can be expressed as shown in formula (48):

[0219]

[0220] Among them, O 7×6 , O 3×7 , O 3×4 , O 4×7 With O 4×4 The zero matrices are 7 rows and 6 columns, 3 rows and 7 columns, 3 rows and 4 columns, 4 rows and 7 columns, and 4 rows and 4 columns respectively.

[0221] The small signal models of the four hybrid energy storage unit modules are similar to those of formula (48). By combining the small signal models of the four hybrid energy storage unit submodules, the small signal model of the entire DC distribution network containing multiple hybrid energy storage can be derived as shown in formula (49):

[0222]

[0223] Among them, O 11×11 is a zero matrix with 11 rows and 11 columns.

[0224] The present invention also provides a modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages. The modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages can be realized by executing the process steps of the modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages, that is, those skilled in the art can understand the modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages as a preferred implementation of the modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages.

[0225] Example 2

[0226] Embodiment 2 is a preferred embodiment of Embodiment 1

[0227] In such Figure 1 The modular small signal modeling method and stability analysis results for the DC distribution network with multiple hybrid energy storage are verified in the DC microgrid system with multiple hybrid energy storage shown in the figure. Set the system constant power load to increase over time in the form of Table 1, and observe whether the output power of the battery and supercapacitor can recover to a steady state after each constant power load power increase until the system becomes unstable, and then obtain the stability boundary of the DC distribution network with multiple hybrid energy storage. The constant power load changes are shown in Table 1, and the sensitivity analysis results are shown in Table 2.

[0228] Table 1 Constant power load changes

[0229]

[0230] In order to analyze the stability of the DC distribution network with multiple hybrid energy storage, it is necessary to calculate the eigenvalues ​​of the system matrix A. The stability condition of the system is if and only if the real part of all eigenvalues ​​of the matrix A is negative, that is, all eigenvalues ​​are located in the left half plane. Figure 6 As shown in Figure 2, when the constant power load gradually increases to about 60 kW, a pair of eigenvalues ​​cross the imaginary axis and move to the right half plane, making the system unstable. The simulation results are shown in Figure 2. Figure 7As shown in Figure 1, at the 85th second, the power of the constant power load increases from 46kW to 52kW. The DC distribution network with multi-hybrid energy storage remains stable. However, at the 105th second, the power of the constant power load increases from 52kW to 60kW. The system begins to become unstable, which is consistent with the theoretical analysis results.

[0231] Table 2 System eigenvalues ​​and dominant variables

[0232]

[0233] By matching the changes in the eigenvalue trajectory with the system state variables, we can obtain the impact of the constant power load power change on the system characteristics. Figure 6 , with the increase of constant power load, λ5~λ 12 Moving to the right half plane, the dominant state variable is I L1 ,I L3 and I L5 , which means that the inductor current is constantly approaching the instability critical value.

[0234] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0235] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A modular small signal modeling method for a DC distribution network with multiple hybrid energy storage, characterized in that: include: Step S1: Divide the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage sub-modules; Step S2: dividing a single hybrid energy storage submodule into a boost circuit submodule, a converter control submodule and a distributed control submodule, and respectively modeling the boost circuit submodule, the converter control submodule and the distributed control submodule, thereby obtaining a small signal model of the single hybrid energy storage submodule; Step S3: By combining the small signal models of the four hybrid energy storage submodules, a complete small signal model of the DC distribution network containing multiple hybrid energy storage is obtained.

2. The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages according to claim 1 is characterized in that: The method further comprises: in a DC distribution network containing multiple hybrid energy storages, adopting a distributed control strategy for the hybrid energy storage submodules; The distributed control strategy includes: configuring voltage power droop control for the battery pack and configuring integral droop control for the supercapacitor.

3. The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages according to claim 2 is characterized in that: The step of configuring voltage and power droop control on the battery pack includes: The step of configuring the integral droop control on the supercapacitor includes: Among them, V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor respectively; V th is the nominal voltage of the microgrid, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the maximum allowable voltage deviation, P Bmax is the rated power of the battery, P B and P SC are the output power of the battery and supercapacitor respectively; s is the Laplace operator; The power sharing relationship between the battery and the supercapacitor is described as: Among them, P Load is the load power of the DC distribution network containing multiple hybrid energy storage, the battery group power is the low-frequency component of the load power, and the supercapacitor group power is the high-frequency component of the load power; Assign a local distributed compensator to the battery voltage power droop control to generate the compensation amount δ i and ε i They are respectively superimposed on the nominal voltage and rated power in the voltage and power droop control equation; Among them, δ i is the nominal voltage compensation of the ith battery; SOC Bi is the state of charge of the ith battery; δ j is the nominal voltage compensation of the jth battery; SOC Bj is the state of charge of the jth battery; α i , β i and γ i is a constant; g i is the control gain; V b Indicates bus voltage; P Bj represents the output power of the jth battery, P Bjmax represents the rated power of the jth battery, P Bi represents the rated output power of the ith battery, P Bimax represents the rated output power of the i-th battery; The state of charge of the ith battery is: Among them, P Bi is the output power of the ith battery, SOC Bi0 and are the initial state of charge and rated charging capacity of the i-th battery respectively; After considering the compensation amounts δ and ε, the voltage power droop formula is updated as follows: V ref1 =V th -mP B +δ+ε(10)。 4. The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages according to claim 1 is characterized in that: The step S1 comprises: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage submodules, and simplifying the four hybrid energy storage submodules into state space equations; Among them, u sys1 =C sys1_4 X sys4 ;u sys2 =C sys2_1 X sys1 ;u sys3 =C sys3_2 X sys2 ;u sys4 =C sys4_3 X sys3 ;X sysi represents the state variable of the ith hybrid energy storage unit submodule, u sysi A represents the input variable of the i hybrid energy storage unit submodule; sysi represents the system matrix of hybrid energy storage unit submodule i, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit submodule generated by the fourth hybrid energy storage unit submodule, C sys2_1 represents the output matrix corresponding to the output variable generated by the first hybrid energy storage unit submodule and input into the second hybrid energy storage unit submodule, C sys3_2 represents the output matrix corresponding to the output variable generated by the second hybrid energy storage unit submodule and input into the third hybrid energy storage unit submodule, C sys4_3 It represents the output matrix corresponding to the output variable generated by the third hybrid energy storage unit submodule and input into the fourth hybrid energy storage unit submodule.

5. The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages according to claim 1 is characterized in that: In the step S2, the boost circuit submodule, the converter control submodule and the distributed control submodule are modeled respectively, so as to obtain a small signal model of a single hybrid energy storage submodule, including: The boost circuit submodule is simplified to the state space equation: Y1=C1X1(19) ΔX1=[Δi L1 Δi L2 Δv o ] T (20) Δu1=[Δd1Δd2] T (21) ΔY1=[Δi L1 Δi L2 Δv o ] T (22) Among them, P CPL is the power of the constant power load; C o is the equivalent capacitance of the output port of the DC boost converter; v o is the DC bus voltage; d1 and d2 are the duty cycles of the DC boost converter; X1 represents the state variable of the boost circuit submodule; u1 represents the input variable of the boost circuit submodule; Y1 represents the output variable of the boost circuit submodule; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A1 represents the boost circuit submodule system matrix of the first hybrid energy storage unit submodule; B1 represents the boost circuit submodule input matrix of the first hybrid energy storage unit submodule; C1 represents the boost circuit submodule output matrix of the first hybrid energy storage unit submodule; ΔX1 represents the small signal disturbance of the boost circuit submodule state variable of the first hybrid energy storage unit submodule; Indicates the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv o represents the small signal disturbance of the DC bus voltage; Δu1 represents the small signal disturbance of the input variable of the boost circuit submodule of the first hybrid energy storage unit submodule; Δd1 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δd2 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; ΔY1 represents the small signal disturbance of the output variable of the first hybrid energy storage unit submodule; The converter control submodule is simplified into the state space equation: Y2=C2X2+D2u2+D3u3(28) ΔX2=[Δξ1Δξ2Δξ3Δξ4] T (29) Δu2=[Δv ref1 Δv ref2 ] T (30) Δu3=[Δi L1 Δi L2 Δv o ] T (31) ΔY2=[Δd1Δd2] T (32) Among them, ξ1~ξ4 are the state variables of the converter control submodule; v ref1 and v ref2 is the voltage reference signal of the proportional integral PI voltage control loop; i ref1 and i ref2 is the current reference signal of the PI current control loop; k pV is the proportional coefficient of the PI voltage control loop; k iV is the integral coefficient of the PI voltage control loop; X2 represents the state variable of the converter control submodule; u2 and u3 represent the input variables of the converter control submodule; Y2 represents the output variable of the converter control submodule; A2 represents the converter control submodule system matrix of the first hybrid energy storage unit submodule; B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; B3 represents the second input matrix of the converter control submodule of the first hybrid energy storage unit submodule; C2 represents the converter control submodule output matrix of the first hybrid energy storage unit submodule; D2 represents the first direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule ; Δξ1 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the battery and the bus voltage of the first hybrid energy storage unit submodule; Δξ2 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the battery of the first hybrid energy storage unit submodule; Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the supercapacitor and the bus voltage of the first hybrid energy storage unit submodule; Δξ4 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu2 represents the small signal disturbance of the first input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δv ref1 A small signal disturbance representing the output voltage reference value of the DC boost converter connected to the battery of the first hybrid energy storage unit submodule; Δv ref2 represents the small signal disturbance of the output voltage reference value of the DC boost converter connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu3 represents the small signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δi L1 represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 A small signal disturbance representing the inductive current of a DC boost circuit connected to a supercapacitor in a first hybrid energy storage unit submodule; The distributed control submodule is simplified into the state space equation: Y3=C3X3+D4u4+D5u5+D6u6(38) ΔX3=[Δv b1 ΔSOC1No b1 v ΔSC ] T (39) Δu4=Δv ref3 (40) Δu5=[Δi L1 Δi L2 Δv o ] T (41) Δu6=[Δv b3 ΔSOC3No b3 v ΔSC3 ] T (42) ΔY3=[Δv ref1 Δv ref2 ] T (43) Among them, α, β, λ and μ are constants; SOC1 and SOC3 are the current charge states of the hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery; v b1 、v b3 , ε b1 and ε b3 is the voltage power droop coefficient; n is the integral droop coefficient; X3 represents the state variable of the distributed control strategy control submodule; u4, u5 and u6 represent the input variables of the distributed control submodule; Y3 represents the output variable of the distributed control submodule; γ represents the state of charge compensation coefficient; v ref3 represents the droop control voltage reference signal; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; SOC0 represents the initial charge state of the battery; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A3 represents the distributed control submodule system matrix of the first hybrid energy storage unit submodule; B4 represents the first input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; C3 represents the distributed control submodule output matrix of the first hybrid energy storage unit submodule; D4 represents the first direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 represents the small signal disturbance of the droop control voltage reference signal; Δv b1 represents the small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; ΔSOC1 represents the small signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; Δε b1 represents the small signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC Δv represents the small signal disturbance of the supercapacitor integrated droop voltage change of the first hybrid energy storage unit submodule; b3 represents the small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; ΔSOC3 represents the small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; Δε b3 represents the small signal disturbance of the battery rated power compensation amount of the fourth hybrid energy storage unit submodule; v ΔSC3 Represents the small signal disturbance of the supercapacitor integrated droop voltage change of the fourth hybrid energy storage unit submodule.

6. The modular small signal modeling method for a DC distribution network containing multiple hybrid energy storages according to claim 1 is characterized in that: The step S3 comprises: The state variables of the boost circuit submodule are: The state variables of the converter control submodule are: The state variables of the distributed control submodule are: The small signal model of the first hybrid energy storage unit module is: Among them, O 7×6 , O 3×7 , O 3×4 , O 4×7 With O 4×4 The zero matrices are 7 rows and 6 columns, 3 rows and 7 columns, 3 rows and 4 columns, 4 rows and 7 columns, and 4 rows and 4 columns respectively; By combining the small signal models of the four hybrid energy storage unit submodules, the small signal model of the entire DC distribution network containing multiple hybrid energy storage is derived as follows: Among them, O 11×11 is a zero matrix with 11 rows and 11 columns.

7. A modular small signal modeling system for a DC distribution network with multiple hybrid energy storage, characterized in that: include: Module M1: Divide the DC distribution network with multiple hybrid energy storage into four hybrid energy storage sub-modules; Module M2: Divide a single hybrid energy storage submodule into a boost circuit submodule, a converter control submodule and a distributed control submodule, and model the boost circuit submodule, the converter control submodule and the distributed control submodule respectively, thereby obtaining a small signal model of the single hybrid energy storage submodule; Module M3: By combining the small signal models of the four hybrid energy storage sub-modules, a complete small signal model of the DC distribution network containing multiple hybrid energy storage is obtained.

8. The modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages according to claim 7, characterized in that: The system further comprises: in a DC distribution network containing multiple hybrid energy storages, adopting a distributed control strategy for the hybrid energy storage submodules; The distributed control strategy includes: configuring voltage and power droop control for the battery pack and configuring integral droop control for the supercapacitor; The step of configuring voltage and power droop control on the battery pack includes: The step of configuring the integral droop control on the supercapacitor includes: Among them, V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor respectively; V th is the nominal voltage of the microgrid, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the maximum allowable voltage deviation, P Bmax is the rated power of the battery, P B and P SC are the output power of the battery and supercapacitor respectively; s is the Laplace operator; The power sharing relationship between the battery and the supercapacitor is described as: Among them, P Load is the load power of the DC distribution network containing multiple hybrid energy storage, the battery group power is the low-frequency component of the load power, and the supercapacitor group power is the high-frequency component of the load power; Assign a local distributed compensator to the battery voltage power droop control to generate the compensation amount δ i and ε i They are respectively superimposed on the nominal voltage and rated power in the voltage and power droop control equation; Among them, δ i is the nominal voltage compensation of the ith battery; SOC Bi is the state of charge of the ith battery; δ j is the nominal voltage compensation of the jth battery; SOC Bj is the state of charge of the jth battery; α i , β i and γ i is a constant; g i is the control gain; V b Indicates bus voltage; P Bj represents the output power of the jth battery, P Bjmax represents the rated power of the jth battery, P Bi represents the rated output power of the ith battery, P Bimax represents the rated output power of the i-th battery; The state of charge of the ith battery is: Among them, P Bi is the output power of the ith battery, SOC Bi0 and are the initial state of charge and rated charging capacity of the i-th battery respectively; After considering the compensation amounts δ and ε, the voltage power droop formula is updated as follows: V ref1 =V th -mP B +δ+ε(10)。 9. The modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages according to claim 7, characterized in that: The module M1 comprises: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage submodules, and simplifying the four hybrid energy storage submodules into state space equations; Among them, u sys1 =C sys1_4 X sys4 ;u sys2 =C sys2_1 X sys1 ;u sys3 =C sys3_2 X sys2 ;u sys4 =C sys4_3 X sys3 ;X sysi represents the state variable of the ith hybrid energy storage unit submodule, u sysi A represents the input variable of the i hybrid energy storage unit submodule; sysi represents the system matrix of hybrid energy storage unit submodule i, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit submodule generated by the fourth hybrid energy storage unit submodule, C sys2_1 represents the output matrix corresponding to the output variable generated by the first hybrid energy storage unit submodule and input into the second hybrid energy storage unit submodule, C sys3_2 represents the output matrix corresponding to the output variable generated by the second hybrid energy storage unit submodule and input into the third hybrid energy storage unit submodule, C sys4_3 It represents the output matrix corresponding to the output variable generated by the third hybrid energy storage unit submodule and input into the fourth hybrid energy storage unit submodule.

10. The modular small signal modeling system for a DC distribution network containing multiple hybrid energy storages according to claim 7, characterized in that: The module M2 models the boost circuit submodule, the converter control submodule and the distributed control submodule respectively, thereby obtaining a small signal model of a single hybrid energy storage submodule, including: The boost circuit submodule is simplified to the state space equation: Y1=C1X1(19) ΔX1=[Δi L1 Δi L2 Δv o ] T (20) Δu1=[Δd1Δd2] T (21) ΔY1=[Δi L1 Δi L2 Δv o ] T (22) Among them, P CPL is the power of the constant power load; C o is the equivalent capacitance of the output port of the DC boost converter; v o is the DC bus voltage; d1 and d2 are the duty cycles of the DC boost converter; X1 represents the state variable of the boost circuit submodule; u1 represents the input variable of the boost circuit submodule; Y1 represents the output variable of the boost circuit submodule; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A1 represents the boost circuit submodule system matrix of the first hybrid energy storage unit submodule; B1 represents the boost circuit submodule input matrix of the first hybrid energy storage unit submodule; C1 represents the boost circuit submodule output matrix of the first hybrid energy storage unit submodule; ΔX1 represents the small signal disturbance of the boost circuit submodule state variable of the first hybrid energy storage unit submodule; Indicates the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; represents the small signal disturbance of the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv o represents the small signal disturbance of the DC bus voltage; Δu1 represents the small signal disturbance of the input variable of the boost circuit submodule of the first hybrid energy storage unit submodule; Δd1 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δd2 represents the small signal disturbance of the duty cycle of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; ΔY1 represents the small signal disturbance of the output variable of the first hybrid energy storage unit submodule; The converter control submodule is simplified into the state space equation: Y2=C2X2+D2u2+D3u3(28) ΔX2=[Δξ1Δξ2Δξ3Δξ4] T (29) Δu2=[Δv ref1 Δv ref2 ] T (30) Δu3=[Δi L1 Δi L2 Δv o ] T (31) ΔY2=[Δd1Δd2] T (32) Among them, ξ1~ξ4 are the state variables of the converter control submodule; v ref1 and v ref2 is the voltage reference signal of the proportional integral PI voltage control loop; i ref1 and i ref2 is the current reference signal of the PI current control loop; k pV is the proportional coefficient of the PI voltage control loop; k iV is the integral coefficient of the PI voltage control loop; X2 represents the state variable of the converter control submodule; u2 and u3 represent the input variables of the converter control submodule; Y2 represents the output variable of the converter control submodule; A2 represents the converter control submodule system matrix of the first hybrid energy storage unit submodule; B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; B3 represents the second input matrix of the converter control submodule of the first hybrid energy storage unit submodule; C2 represents the converter control submodule output matrix of the first hybrid energy storage unit submodule; D2 represents the first direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second direct-pass matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule ; Δξ1 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the battery and the bus voltage of the first hybrid energy storage unit submodule; Δξ2 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the battery of the first hybrid energy storage unit submodule; Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC boost converter connected to the supercapacitor and the bus voltage of the first hybrid energy storage unit submodule; Δξ4 represents the small signal disturbance of the integral signal of the PI controller of the DC boost converter voltage loop connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu2 represents the small signal disturbance of the first input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δv ref1 A small signal disturbance representing the output voltage reference value of the DC boost converter connected to the battery of the first hybrid energy storage unit submodule; Δv ref2 represents the small signal disturbance of the output voltage reference value of the DC boost converter connected to the supercapacitor of the first hybrid energy storage unit submodule; Δu3 represents the small signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δi L1 represents the small signal disturbance of the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 A small signal disturbance representing the inductive current of a DC boost circuit connected to a supercapacitor in a first hybrid energy storage unit submodule; The distributed control submodule is simplified into the state space equation: Y3=C3X3+D4u4+D5u5+D6u6(38) ΔX3=[Δv b1 ΔSOC1No b1 v ΔSC ] T (39) Δu4=Δv ref3 (40) Δu5=[Δi L1 Δi L2 Δv o ] T (41) Δu6=[Δv b3 ΔSOC3No b3 v ΔSC3 ] T (42) ΔY3=[Δv ref1 Δv ref2 ] T (43) Among them, α, β, λ and μ are constants; SOC1 and SOC3 are the current charge states of the hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery; v b1 、v b3 , ε b1 and ε b3 is the voltage power droop coefficient; n is the integral droop coefficient; X3 represents the state variable of the distributed control strategy control submodule; u4, u5 and u6 represent the input variables of the distributed control submodule; Y3 represents the output variable of the distributed control submodule; γ represents the state of charge compensation coefficient; v ref3 represents the droop control voltage reference signal; E1 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; SOC0 represents the initial charge state of the battery; E2 represents the output port voltage of the supercapacitor in the first hybrid energy storage unit submodule; A3 represents the distributed control submodule system matrix of the first hybrid energy storage unit submodule; B4 represents the first input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; C3 represents the distributed control submodule output matrix of the first hybrid energy storage unit submodule; D4 represents the first direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third direct matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 represents the small signal disturbance of the droop control voltage reference signal; Δv b1 represents the small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; ΔSOC1 represents the small signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; Δε b1 represents the small signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC Δv represents the small signal disturbance of the supercapacitor integrated droop voltage change of the first hybrid energy storage unit submodule; b3 represents the small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; ΔSOC3 represents the small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; Δε b3 represents the small signal disturbance of the battery rated power compensation amount of the fourth hybrid energy storage unit submodule; v ΔSC3 A small signal disturbance representing the variation of the supercapacitor integrated droop voltage of the fourth hybrid energy storage unit submodule; The module M3 comprises: The state variables of the boost circuit submodule are: The state variables of the converter control submodule are: The state variables of the distributed control submodule are: The small signal model of the first hybrid energy storage unit module is: Among them, O 7×6 , O 3×7 , O 3×4 , O 4×7 With O 4×4 The zero matrices are 7 rows and 6 columns, 3 rows and 7 columns, 3 rows and 4 columns, 4 rows and 7 columns, and 4 rows and 4 columns respectively; By combining the small signal models of the four hybrid energy storage unit submodules, the small signal model of the entire DC distribution network containing multiple hybrid energy storage is derived as follows: Among them, O 11×11 is a zero matrix with 11 rows and 11 columns.

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