Modular small signal modeling method and system for direct current distribution network with multiple hybrid energy storage

Abstract

The application provides a modular small signal modeling method and system for a multi-mixed energy storage DC power distribution network, comprising the following steps: S1: dividing the multi-mixed energy storage DC power distribution network into four mixed energy storage sub-modules; S2: dividing a single mixed energy storage sub-module into a step-up circuit sub-module, a converter control sub-module and a distributed control sub-module, and modeling the step-up circuit sub-module, the converter control sub-module and the distributed control sub-module respectively, and then obtaining a small signal model of the single mixed energy storage sub-module; and S3: obtaining a complete small signal model of the multi-mixed energy storage DC power distribution network by simultaneously solving the small signal models of the four mixed energy storage sub-modules.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of electrical engineering, in particular, to a modular small-signal modeling method and system for a multi-hybrid energy storage DC distribution network. BACKGROUND

[0002] With the increasing attention to sustainable development and environmental protection in society and the full implementation of the "3060 double carbon" goal in China's energy field, it is inevitable 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 supply and distribution systems, which is of great significance to energy saving and emission reduction and sustainable development.

[0003] However, in AC-DC hybrid microgrids, due to the high proportion of renewable energy and the high proportion of power electronics, there are power, frequency and voltage fluctuations on both sides of the source and load, which poses risks to the safe and reliable operation of the microgrid. The multi-hybrid energy storage system connected to the microgrid can suppress power fluctuations, taking advantage of the high efficiency and fast energy storage of supercapacitors, and making up for the short service life and low output capacity rate of batteries. By using a modular small-signal modeling method for a multi-hybrid energy storage DC distribution network, coordinated control between multiple hybrid energy storage systems in a DC distribution network can be effectively achieved, and sensitive parameters that cause instability in a multi-hybrid energy storage system microgrid can be effectively identified.

[0004] The modular small-signal modeling method for a multi-hybrid energy storage DC distribution network mainly includes two parts: distributed control of the hybrid energy storage system and modular small-signal modeling. In a multi-hybrid energy storage DC distribution network, each supercapacitor control unit updates its state locally, and the battery control unit updates its state through data information from adjacent units to achieve the goal of collaborative control. In terms of coordinated control of a multi-hybrid energy storage system DC distribution network, there is currently little research on modular small-signal modeling that takes into account the coordination of distributed droop control.

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

[0006] Li W, Xu C, Yu H, et al. Frequency-division coordination control strategy for hybrid energy storage in DC microgrid system[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 84-92. A frequency-division coordination control strategy is proposed for the DC microgrid system. The power fluctuation is extracted by the feedback of bus voltage. Three "invisible" filters, high-pass filter, band-pass filter and low-pass filter, are set by the DC bus voltage loop and super capacitor voltage loop. The high, middle and low frequency components of bus power fluctuation are responded by bus capacitor, super capacitor and battery in turn, so as to make full use of the advantages of high energy density of battery, high power density and long cycle life of super capacitor, and effectively improve the performance of energy storage system. Moreover, the implementation of invisible filter is completely controlled by software, which has good flexibility and adaptability, and does not need to increase the cost of additional hardware. Finally, the effectiveness of the proposed hybrid energy storage control strategy is verified by experiment. The paper proposes a scheme of extracting power fluctuation information by bus voltage for frequency division control, and carries out power disturbance experiment for the proposed control strategy, which proves the improvement of dynamic and static performance of the system. But the control effectiveness is limited to the system containing a group of hybrid energy storage units, while the integral droop coordination with distributed control theory of the invention is applicable to the system containing multiple groups of hybrid energy storage units, realizing the reasonable distribution of unbalanced power among different units of the same energy storage medium.

[0007] Su H, Zhang JC, Wang N, 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-energy storage medium coordination and multi-energy storage unit power distribution in large-capacity hybrid energy storage system (HESS), a double-layer energy management strategy of "energy storage system-medium group-energy storage unit" hierarchical optimization is proposed. The improved low-pass filter algorithm integrating the state of charge (SOC) of super capacitor group is adopted in the central distribution layer to obtain the power instruction of each medium group. Through state identification and artificial intervention, the deficiency that the medium group power state is opposite caused by the algorithm is made up, and the battery group power climbing limit Plimit is introduced to further smooth the power output of the battery group. The concepts of energy storage system continuous schedulability and operation economy are introduced in the local distribution layer, and the battery group and super capacitor group power distribution models are established respectively to complete the optimization and distribution of the upper layer power instruction among each energy storage unit in the group. Taking the actual output of a certain energy storage power station from 13:00 to 13:30 on a certain day as an example, the AMPL / IPOPT solver is used for solution. The results show that the proposed strategy can not only meet the power demand of the grid for the energy storage system, but also reasonably distribute the power of each energy storage unit, so that the SOC is concentrated in the interval of 0.4-0.8, ensuring that each unit has enough charging and discharging margin, and saving the operation cost of the battery group by 17.7%. The double-layer energy management strategy of "energy storage system-medium group-energy storage unit" hierarchical optimization is proposed in the literature, which solves the problem of "one charging and one discharging" of battery and super capacitor in the traditional filter algorithm, and realizes the reasonable distribution of unbalanced power among each energy storage unit through the control center. However, the control strategy used in the literature is centralized control, while the bottom layer control of the present application adopts voltage power droop control and integral droop control of super capacitor to cooperate, which has higher autonomous coordination operation ability. SUMMARY

[0008] In view of the defects in the prior art, the purpose of the present application is to provide a modular small-signal modeling method and system for a multi-hybrid energy storage DC distribution network.

[0009] According to the modular small-signal modeling method for a multi-hybrid energy storage DC distribution network provided by the present application, the method comprises the following steps:

[0010] Step S1: dividing the multi-hybrid energy storage DC distribution network into four hybrid energy storage sub-modules;

[0011] Step S2: dividing a 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, and then obtaining a small-signal model of the single hybrid energy storage sub-module;

[0012] Step S3: Obtain the complete small signal model of the multi-hybrid energy storage DC distribution network by simultaneously solving the small signal models of the four hybrid energy storage sub-modules.

[0013] Preferably, the method further comprises: in the multi-hybrid energy storage DC distribution network, adopting a distributed control strategy for the hybrid energy storage sub-modules;

[0014] The distributed control strategy comprises: configuring voltage-power droop control for the battery pack, and configuring integral droop control for the super capacitor.

[0015] Preferably, the voltage-power droop control for the battery pack comprises:

[0016]

[0017] The integral droop control for the super capacitor comprises:

[0018]

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

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

[0021]

[0022] wherein P Load is a load power of the multi-hybrid energy storage DC distribution network, the battery pack power is a low-frequency component of the load power, and the super capacitor pack power is a high-frequency component of the load power;

[0023] A local distributed compensator for voltage-power droop control of the battery is allocated to generate compensation δ i and ε i are respectively superimposed on the nominal voltage and the rated power in the voltage-power droop control equation;

[0024]

[0025] wherein δ i is a nominal voltage compensation of the i-th battery; SOC Bi is a state of charge of the i-th battery; δj is the nominal voltage compensation of the jth battery; SOC Bj is the state of charge of the jth battery; a i , b i and g i are constants; g i is the damping control gain; V b represents the 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;

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

[0027]

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

[0029] After considering the compensation amounts d and e, the voltage-power droop formula is updated as:

[0030] V ref1 = V th -mP B +d+e (10).

[0031] Preferably, the step S1 comprises: dividing the multi-energy storage hybrid DC power distribution network into four energy storage hybrid sub-modules, and simplifying the four energy storage hybrid sub-modules into state space equations;

[0032]

[0033]

[0034] where 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 energy storage hybrid unit sub-module, u sysiinput variables of the i-th hybrid energy storage unit sub-module; A sysi system matrix of the i-th hybrid energy storage unit sub-module, C sys1_4 output matrix corresponding to the output variables of the fourth hybrid energy storage unit sub-module generating input of the first hybrid energy storage unit sub-module, C sys2_1 output matrix corresponding to the output variables of the first hybrid energy storage unit sub-module generating input of the second hybrid energy storage unit sub-module, C sys3_2 output matrix corresponding to the output variables of the second hybrid energy storage unit sub-module generating input of the third hybrid energy storage unit sub-module, C sys4_3 output matrix corresponding to the output variables of the third hybrid energy storage unit sub-module generating input of the fourth hybrid energy storage unit sub-module.

[0035] Preferably, the step S2 models the boost circuit sub-module, the converter control sub-module and the distributed control sub-module respectively, and then obtains a small signal model of a single hybrid energy storage sub-module, including:

[0036] The boost circuit sub-module is simplified into a state space equation as:

[0037]

[0038] Y1=C1X1(19)

[0039] ΔX1=[Δi L1 Δi L2 Δv o ] T (20)

[0040] Δu1=[Δd1Δd2] T (21)

[0041] ΔY1=[Δi L1 Δi L2 Δv o ] T (22)

[0042] wherein, P CPL is the power of the constant power load; C o is the equivalent capacitance of the DC boost converter output port; 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 sub-module; u1 represents the input variable of the boost circuit sub-module; Y1 represents the output variable of the boost circuit sub-module; L1 represents the inductance of the DC boost circuit connected to the battery in the first hybrid energy storage unit sub-module; L2 represents the inductance of the DC boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; represents the inductor current of the DC-boost circuit connected to the battery in the first hybrid energy storage unit sub-module; represents the inductor current of the DC-boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; E1 represents the output port voltage of the battery in the first hybrid energy storage unit sub-module; E2 represents the output port voltage of the super capacitor in the first hybrid energy storage unit sub-module; A1 represents the system matrix of the boost circuit sub-module of the first hybrid energy storage unit sub-module; B1 represents the input matrix of the boost circuit sub-module of the first hybrid energy storage unit sub-module; C1 represents the output matrix of the boost circuit sub-module of the first hybrid energy storage unit sub-module; ΔX1 represents the small signal perturbation of the state variable of the boost circuit sub-module of the first hybrid energy storage unit sub-module; represents the small signal perturbation of the inductor current of the DC-boost circuit connected to the battery in the first hybrid energy storage unit sub-module; represents the small signal perturbation of the inductor current of the DC-boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; Δv o represents the small signal perturbation of the DC bus voltage; Δu1 represents the small signal perturbation of the input variable of the boost circuit sub-module of the first hybrid energy storage unit sub-module; Δd1 represents the small signal perturbation of the duty cycle of the DC-boost circuit connected to the battery in the first hybrid energy storage unit sub-module; Δd2 represents the small signal perturbation of the duty cycle of the DC-boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; ΔY1 represents the small signal perturbation of the output variable of the first hybrid energy storage unit sub-module;

[0043] The converter control sub-module is simplified to a state space equation as follows:

[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] wherein ξ1-ξ4 are state variables of the converter control sub-module; vref1 and v ref2 is a voltage reference signal of the proportional-integral (PI) voltage control loop; i ref1 and i ref2 is a current reference signal of the PI current control loop; k pV is a proportional coefficient of the PI voltage control loop; k iV is an integral coefficient of the PI voltage control loop; X2 represents a state variable of the converter control submodule; u2, u3 represent input variables of the converter control submodule; Y2 represents an output variable of the converter control submodule; A2 represents a system matrix of the converter control submodule of the first hybrid energy storage unit submodule; B2 represents a first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; B3 represents a second input matrix of the converter control submodule of the first hybrid energy storage unit submodule; C2 represents an output matrix of the converter control submodule of the first hybrid energy storage unit submodule; D2 represents a first pass-through matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents a second pass-through matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents a small signal disturbance of the state variable of the converter control submodule of the first hybrid energy storage unit submodule; Δξ1 represents a small signal disturbance of a difference between an output voltage reference value of a direct current boost converter connected to the battery of the first hybrid energy storage unit submodule and a bus voltage; Δξ2 represents a small signal disturbance of an integral signal of a voltage loop PI controller of the direct current boost converter connected to the battery of the first hybrid energy storage unit submodule; Δξ3 represents a small signal disturbance of a difference between an output voltage reference value of a direct current boost converter connected to the super capacitor of the first hybrid energy storage unit submodule and the bus voltage; Δξ4 represents a small signal disturbance of an integral signal of a voltage loop PI controller of the direct current boost converter connected to the super capacitor of the first hybrid energy storage unit submodule; Δu2 represents a small signal disturbance of the first input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; Δv ref1 represents a small signal disturbance of the output voltage reference value of the direct current boost converter connected to the battery of the first hybrid energy storage unit submodule; Δv ref2 represents a small signal disturbance of the output voltage reference value of the direct current boost converter connected to the super capacitor of the first hybrid energy storage unit submodule; Δu3 represents a 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 a small signal disturbance of the inductor current of the direct current 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 direct current boost circuit connected to the super capacitor in the first hybrid energy storage unit submodule;

[0051] The distributed control submodule is simplified into a state space equation as follows:

[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] wherein, a, β, λ and μ are constants; SOC1, SOC3 are the current state of charge of the current hybrid energy storage unit cell and the adjacent hybrid energy storage unit cell; v b1 , v b3 , ε b1 and ε b3 are voltage power droop coefficients; n is an integral droop coefficient; X3 represents the state variable of the distributed control strategy control sub-module; u4, u5 and u6 represent the input variable of the distributed control sub-module; Y3 represents the output variable of the distributed control sub-module; γ represents the state of charge compensation coefficient; v ref3represents a small signal disturbance of the droop control voltage reference signal; Δv ref3 represents a small signal disturbance of the droop control voltage reference signal; Δv b1 represents a small signal disturbance of the battery voltage reference compensation of the first hybrid energy storage unit sub-module; ΔSOC1 represents a small signal disturbance of the battery state of charge of the first hybrid energy storage unit sub-module; Δε b1 represents a small signal disturbance of the battery power reference compensation of the first hybrid energy storage unit sub-module; v ΔSC represents a small signal disturbance of the supercapacitor integral droop voltage variation of the first hybrid energy storage unit sub-module; Δv b3 represents a small signal disturbance of the battery voltage reference compensation of the fourth hybrid energy storage unit sub-module; ΔSOC3 represents a small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit sub-module; Δε b3 represents a small signal disturbance of the battery power reference compensation of the fourth hybrid energy storage unit sub-module; v ΔSC3 represents a small signal disturbance of the supercapacitor integral droop voltage variation of the fourth hybrid energy storage unit sub-module.

[0060] Preferably, the step S3 comprises:

[0061] The state variables of the boost circuit sub-module are:

[0062]

[0063] The state variables of the converter control sub-module are:

[0064]

[0065] The state variables of the distributed control sub-module are:

[0066]

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

[0068]

[0069] Wherein, O 7×6 , O 3×7 , O 3×4 , O 4×7 and O 4×4 are zero matrices of 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] The entire small signal model of the multi-hybrid energy storage DC distribution network is derived by simultaneously solving the four hybrid energy storage sub-module small signal models:

[0071]

[0072] Wherein, O 11×11 is a zero matrix of 11 rows and 11 columns.

[0073] According to the modular small signal modeling system for the multi-hybrid energy storage DC distribution network provided by the application, comprising:

[0074] Module M1: the multi-hybrid energy storage DC distribution network is divided into four hybrid energy storage sub-modules;

[0075] Module M2: the single hybrid energy storage sub-module is divided into a boost circuit sub-module, a converter control sub-module and a distributed control sub-module, and the boost circuit sub-module, the converter control sub-module and the distributed control sub-module are modeled respectively, and then the small signal model of the single hybrid energy storage sub-module is obtained;

[0076] Module M3: the complete small signal model of the multi-hybrid energy storage DC distribution network is obtained by simultaneously solving the small signal models of the four hybrid energy storage sub-modules.

[0077] Preferably, the system further comprises: in the multi-hybrid energy storage DC distribution network, a distributed control strategy is adopted for the hybrid energy storage sub-module;

[0078] The distributed control strategy comprises: configuring voltage power droop control for the battery pack and configuring integral droop control for the super capacitor;

[0079] The voltage power droop control for the battery pack comprises:

[0080]

[0081] The integral droop control for the super capacitor comprises:

[0082]

[0083] where V ref1 and V ref2 are the input voltage reference signals of the battery and supercapacitor, respectively; V th is the microgrid nominal voltage, m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV max is the allowed maximum 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 is the Laplace operator;

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

[0085]

[0086] where P Load is the load power of the multi-hybrid energy storage DC distribution network, the battery pack power is the low-frequency component of the load power, and the supercapacitor pack power is the high-frequency component of the load power;

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

[0088]

[0089]

[0090] where δ i is the nominal voltage compensation amount of the i-th battery; SOC Bi is the state of charge of the i-th battery; δ j is the nominal voltage compensation amount of the j-th battery; SOC Bj is the state of charge of the j-th battery; α i , β i and γ i are constants; g i is the damping control gain; V b denotes the bus voltage; P Bj denotes the output power of the j-th battery, P Bjmax denotes the rated power of the j-th battery, P Bi denotes the rated output power of the i-th battery, P Bimax denotes the rated output power of the i-th battery;

[0091] The state of charge of the i-th battery is:

[0092]

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

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

[0095] V ref1 = V th -mP B + δ + ε (10).

[0096] Preferably, the module M1 comprises: dividing the multi-energy storage hybrid DC power distribution network into four energy storage hybrid sub-modules, and simplifying the four energy storage hybrid sub-modules into state space equations;

[0097]

[0098] where 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 denotes the state variable of the i-th energy storage hybrid unit sub-module, u sysi denotes the input variable of the i-th energy storage hybrid unit sub-module; A sysi denotes the system matrix of the i-th energy storage hybrid unit sub-module, C sys1_4 denotes the output matrix corresponding to the output variable of the fourth energy storage hybrid unit sub-module generating the input of the first energy storage hybrid unit sub-module, C sys2_1 denotes the output matrix corresponding to the output variable of the first energy storage hybrid unit sub-module generating the input of the second energy storage hybrid unit sub-module, C sys3_2 denotes the output matrix corresponding to the output variable of the second energy storage hybrid unit sub-module generating the input of the third energy storage hybrid unit sub-module, C sys4_3 denotes the output matrix corresponding to the output variable of the third energy storage hybrid unit sub-module generating the input of the fourth energy storage hybrid unit sub-module.

[0099] Preferably, the module M2 models the boost circuit sub-module, the converter control sub-module and the distributed control sub-module respectively, and then obtains a small signal model of a single energy storage hybrid sub-module, comprising:

[0100] The state space equation of the boost circuit sub-module is simplified as:

[0101]

[0102] Y1=C1X1(19)

[0103] ΔX1=[Δi L1 Δi L2 Δv o ] T (20)

[0104] Δu1=[Δd1Δd2] T (21)

[0105] ΔY1=[Δi L1 Δi L2 Δv o ] T (22)

[0106] wherein, P CPL is the power of the constant power load; C o is the equivalent capacitance of the DC boost converter output port; 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 sub-module; u1 represents the input variable of the boost circuit sub-module; Y1 represents the output variable of the boost circuit sub-module; L1 represents the inductance of the DC boost circuit connected to the storage battery in the first hybrid energy storage unit sub-module; L2 represents the inductance of the DC boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; represents the inductance current of the DC boost circuit connected to the storage battery in the first hybrid energy storage unit sub-module; represents the inductance current of the DC boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; E1 represents the output port voltage of the storage battery in the first hybrid energy storage unit sub-module; E2 represents the output port voltage of the super capacitor in the first hybrid energy storage unit sub-module; A1 represents the boost circuit sub-module system matrix of the first hybrid energy storage unit sub-module; B1 represents the boost circuit sub-module input matrix of the first hybrid energy storage unit sub-module; C1 represents the boost circuit sub-module output matrix of the first hybrid energy storage unit sub-module; ΔX1 represents the small signal disturbance of the boost circuit sub-module state variable of the first hybrid energy storage unit sub-module; represents the small signal disturbance of the inductance current of the DC boost circuit connected to the storage battery in the first hybrid energy storage unit sub-module; represents the small signal disturbance of the inductance current of the DC boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module; Δv orepresents a small signal disturbance of the DC bus voltage; Δu1 represents a small signal disturbance of the input variable of the boost circuit submodule of the first hybrid energy storage unit submodule; Δd1 represents a small signal disturbance of the DC boost circuit duty cycle connected with the battery in the first hybrid energy storage unit submodule; Δd2 represents a small signal disturbance of the DC boost circuit duty cycle connected with the super capacitor in the first hybrid energy storage unit submodule; ΔY1 represents a small signal disturbance of the output variable of the first hybrid energy storage unit submodule;

[0107] The converter control submodule is simplified to a state space equation as follows:

[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] wherein, ξ1-ξ4 are state variables of the converter control submodule; v ref1 and v ref2 are voltage reference signals of the proportional integral (PI) voltage control loop; i ref1 and i ref2 are current reference signals of the PI current control loop; k pV is a 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, u3 represent the input variable of the converter control submodule; Y2 represents the output variable of the converter control submodule; A2 represents the system matrix of the converter control submodule 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 output matrix of the converter control submodule of the first hybrid energy storage unit submodule; D2 represents the first pass-through matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second pass-through matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the small signal disturbance of the state variable of the converter control submodule of 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 voltage booster connected with the first hybrid energy storage unit submodule and the battery and the bus voltage; Δξ2 represents the small signal disturbance of the integral signal of the voltage loop PI controller of the DC voltage booster connected with the first hybrid energy storage unit submodule and the battery; Δξ3 represents the small signal disturbance of the difference between the output voltage reference value of the DC voltage booster connected with the first hybrid energy storage unit submodule and the super capacitor and the bus voltage; Δξ4 represents the small signal disturbance of the integral signal of the voltage loop PI controller of the DC voltage booster connected with the first hybrid energy storage unit submodule and the super capacitor; 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 represents the small signal disturbance of the output voltage reference value of the DC voltage booster connected with the first hybrid energy storage unit submodule and the battery; ref2 represents the small signal disturbance of the output voltage reference value of the DC voltage booster connected with the first hybrid energy storage unit submodule and the super capacitor; 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 voltage booster circuit connected with the first hybrid energy storage unit submodule and the battery; L2 represents the small signal disturbance of the inductor current of the DC voltage booster circuit connected with the first hybrid energy storage unit submodule and the super capacitor;

[0115] The distributed control submodule is simplified as a 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] wherein, α, β, λ and μ are constants; SOC1, SOC3 are the current state of charge of the current hybrid energy storage unit battery and the adjacent hybrid energy storage unit battery; v b1 , v b3 , ε b1 and ε b3 are voltage power droop coefficients; n is an integral droop coefficient; X3 represents the state variable of the distributed control strategy control submodule; u4, u5 and u6 represent the input variable 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 state of charge of the battery; E2 represents the output port voltage of the super capacitor 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 output matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D4 represents the first pass-through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second pass-through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third pass-through 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 a small signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit sub-module; ΔSOC1 represents a small signal disturbance of the battery state of charge of the first hybrid energy storage unit sub-module; Δε b1 represents a small signal disturbance of the battery rated power compensation of the first hybrid energy storage unit sub-module; v ΔSC represents a small signal disturbance of the super capacitor integral droop voltage variation of the first hybrid energy storage unit sub-module; Δv b3 represents a small signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit sub-module; ΔSOC3 represents a small signal disturbance of the battery state of charge of the fourth hybrid energy storage unit sub-module; Δε b3 represents a small signal disturbance of the battery rated power compensation of the fourth hybrid energy storage unit sub-module; v ΔSC3 represents a small signal disturbance of the super capacitor integral droop voltage variation of the fourth hybrid energy storage unit sub-module;

[0124] The module M3 comprises:

[0125] The state variable of the boost circuit sub-module is:

[0126]

[0127] The state variable of the converter control sub-module is:

[0128]

[0129] The state variable of the distributed control sub-module is:

[0130]

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

[0132]

[0133] Wherein, O 7×6 , O 3×7 , O 3×4 , O 4×7 and O 4×4 are zero matrices of 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] The four hybrid energy storage unit sub-module small signal models are combined to derive the small signal model of the entire multi-hybrid energy storage DC distribution network as:

[0135]

[0136] Wherein, O 11×11 is a zero matrix of 11 rows and 11 columns.

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

[0138] 1、 The present application constructs a DC power distribution network modular small signal model containing four mixed energy storage unit modules, and the sub-module model is solved to obtain a DC power distribution network small signal model containing multiple mixed energy storage systems, which greatly reduces the complexity of unified small signal modeling of the system;

[0139] 2、 The present application adopts integral droop control for supercapacitors, and through spontaneous backflow of dynamic power released by supercapacitors in steady state, terminal voltage over-limiting in transient state is avoided, and spontaneous state of charge recovery of multiple mixed energy storage systems is realized;

[0140] 3、 The present application adopts distributed control for batteries, which relieves the DC bus voltage deviation caused by droop control in the multiple mixed energy storage DC power distribution network, realizes the state of charge balance among batteries, prolongs the service life of the battery pack, and improves the control flexibility of the multiple mixed energy storage DC power distribution network;

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

[0142] 5、 The integral droop cooperative distributed control strategy proposed by the present application is a good compromise of centralized control, distributed control and consistency theory control, and has good control structure stability, which is conducive to stable operation of the system;

[0143] 6、 The present application adopts distributed control for batteries, which relieves the DC bus voltage deviation caused by droop control in the multiple mixed energy storage system, thereby realizing the state of charge balance among batteries, and prolonging the service life of the battery pack. Replacing the supercapacitor distributed controller with a supercapacitor local controller can save an average of 1600 yuan in installation cost for each supercapacitor, effectively saving the system investment cost;

[0144] 7、 The modular small signal modeling method proposed by the present application can effectively reduce the complexity of the unified model of the multiple mixed energy storage DC power distribution network, and the acquisition of the state matrix is simplified from single solving of 44-order differential equations in the traditional method to single solving of 4-order differential equations in the present method, which is conducive to identifying sensitive parameters causing instability of the microgrid multiple mixed energy storage system;

[0145] 8、The application is characterized in that the distributed voltage power droop control of the battery pack and the local integral droop control of the super capacitor are cooperated, the super capacitor responds to the high-frequency power fluctuation, and the rapid charge-discharge characteristics of the super capacitor are fully utilized. BRIEF DESCRIPTION OF DRAWINGS

[0146] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments, when read in conjunction with the accompanying drawings:

[0147] Figure 1 It is a structure diagram of the multi-mixed energy storage DC micro-grid.

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

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

[0150] Figure 4 It is a module block diagram of the converter control.

[0151] Figure 5 It is a module block diagram of the distributed control.

[0152] Figure 6 It is a P CPL System root locus diagram when the power varies from 22kW to 60kW.

[0153] Figure 7 It is an output power diagram of the battery and the super capacitor.

[0154] 1-First mixed energy storage unit sub-module; 2-Second mixed energy storage unit sub-module; 3-Third mixed energy storage unit sub-module; 4-Fourth mixed energy storage unit sub-module. DETAILED DESCRIPTION

[0155] The application will be described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the application, but do not limit the application in any form. It should be pointed out that those skilled in the art can make several changes and improvements without departing from the concept of the application. These all belong to the protection scope of the application.

[0156] Example 1

[0157] The application provides a modular small signal modeling method and system for a multi-hybrid energy storage DC power distribution network, comprising: the integral droop cooperative distributed control strategy is a good compromise of centralized control, distributed control and consistency theory cooperative control, and is realized by cooperative cooperation of the distributed voltage power droop control of the battery pack and the local integral droop control of the super capacitor pack. The modular small signal modeling mainly comprises control loop modeling of integral droop control and proportional integral control and hybrid energy storage unit modeling. By simultaneously solving four system sub-module small signal models, a small signal model of the multi-hybrid energy storage DC power distribution network can be obtained, and then system stability and sensitivity analysis can be carried out, and the maximum load for stable operation of the system and sensitive parameters causing instability can be effectively identified.

[0158] The modular small signal modeling method for the multi-hybrid energy storage DC power distribution network comprises:

[0159] Step S1: In the multi-hybrid energy storage DC power distribution network, a distributed control strategy is adopted for the hybrid energy storage sub-module, and the strategy specifically comprises: the battery pack is configured with voltage power droop control, and the super capacitor is configured with integral droop control. By using the distributed control strategy, the source-load mismatch problem in the multi-hybrid energy storage DC power distribution network can be effectively solved.

[0160] Step S2: The multi-hybrid energy storage DC power distribution network is divided into four hybrid energy storage sub-modules, and then a single hybrid energy storage sub-module is divided into three layers and the dynamics in the three layers are modeled respectively, and then a small signal model of the hybrid energy storage sub-module is obtained.

[0161] Step S3: By simultaneously solving the small signal models of the four hybrid energy storage sub-modules, a complete small signal model of the multi-hybrid energy storage system DC power distribution network can be obtained, and based on the small signal model, the system stability margin and the influence mechanism of the main parameters of the system on the stability can be analyzed, thereby providing a reference for control-related researches for the multi-hybrid energy storage DC power distribution network.

[0162] The step S1 comprises:

[0163] In the multi-hybrid energy storage system, the voltage power droop formula of the battery is shown as formula (1), and the integral droop formula of the super capacitor is shown as formula (2):

[0164]

[0165]

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

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

[0168]

[0169] where, P Load is the load power of the multi-energy storage DC distribution network, the battery pack power is the low-frequency component of the load power, and the supercapacitor pack 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 voltage-power droop control of the battery to generate compensation δ i and ε i are respectively superimposed on the nominal voltage and rated power in the voltage-power droop control equation. δ i obeys equations (6)-(7), and ε i obeys equations (8)-(9):

[0171]

[0172] where, δ i is the nominal voltage compensation of the i-th battery, SOC Bi is the state of charge of the i-th battery, δ j is the nominal voltage compensation of the j-th battery, SOC Bj is the state of charge of the j-th battery. α i , β i and γ i are constants, g i is a damping control gain, which can be 1 or 0; V b denotes the bus voltage; P Bj denotes the output power of the j-th battery, P Bjmax denotes the rated power of the j-th battery, P Bi denotes the rated output power of the i-th battery, P Bimax denotes the rated output power of the i-th battery.

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

[0174]

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

[0176] Considering the compensation amounts δ and ε, the voltage power droop formula can be updated as shown in equation (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 comprises four hybrid energy storage unit sub-module small signal models. The simplified state space equations are shown in equations (8)-(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] where X sysi represents the state variable of the i-th hybrid energy storage unit sub-module, u sysi represents the input variable of the i-th hybrid energy storage unit sub-module; A sysi represents the system matrix of the first hybrid energy storage unit sub-module, C sys1_4 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit sub-module generating the input of the fourth hybrid energy storage unit sub-module, C sys2_1 represents the output matrix corresponding to the output variable of the first hybrid energy storage unit sub-module generating the input of the second hybrid energy storage unit sub-module, C sys3_2 represents the output matrix corresponding to the output variable of the second hybrid energy storage unit sub-module generating the input of the third hybrid energy storage unit sub-module, C sys4_3An output matrix corresponding to output variables of the third hybrid energy storage unit submodule generating an input of the fourth hybrid energy storage unit submodule

[0186] In the first hybrid energy storage unit submodule, three submodules are included, which are a boost circuit submodule, a converter control submodule and a distributed control submodule. The simplified state space equations of the boost circuit submodule are shown in equations (16)-(23):

[0187]

[0188] Y1=C1X1(20)

[0189] ΔX1=[Δi L1 Δi L2 Δv o ] T (21)

[0190] Δu1=[Δd1Δd2] T (22)

[0191] ΔY1=[Δi L1 Δi L2 Δv o ] T (23)

[0192] where P CPL is the power of the constant power load, C o is the equivalent capacitance of the DC boost converter output port. 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 super capacitor in the first hybrid energy storage unit submodule, represents the inductance current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule, represents the inductance current of the DC boost circuit connected to the super capacitor 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 super capacitor 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 a small-signal perturbation of the inductor current of the DC voltage boost circuit connected to the battery in the first hybrid energy storage unit sub-module, represents a small-signal perturbation of the inductor current of the DC voltage boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module, Δv o represents a small-signal perturbation of the DC bus voltage, Δu1represents a small-signal perturbation of the input variable of the boost circuit sub-module of the first hybrid energy storage unit sub-module, Δd1represents a small-signal perturbation of the duty cycle of the DC voltage boost circuit connected to the battery in the first hybrid energy storage unit sub-module, Δd2represents a small-signal perturbation of the duty cycle of the DC voltage boost circuit connected to the super capacitor in the first hybrid energy storage unit sub-module, and ΔY1represents a small-signal perturbation of the output variable of the first hybrid energy storage unit sub-module;

[0193] The simplified state-space equations of the converter control sub-module are shown in equations (24)-(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] wherein ξ1-ξ4are state variables of the converter control sub-module, v ref1 and v ref2 are voltage reference signals of proportional-integral (PI) voltage control loops. i ref1 and i ref2 are current reference signals of PI current control loops. k pV is a proportional coefficient of the PI voltage control loop, and k iVis the integral coefficient of the PI voltage control loop. X2 represents the state variable of the converter control submodule, u2, u3 represent the input variable of the converter control submodule, Y2 represents the output variable of the converter control submodule. A2 represents the system matrix of the converter control submodule of the first hybrid energy storage unit submodule, B2 represents the input matrix 1 of the converter control submodule of the first hybrid energy storage unit submodule, B3 represents the input matrix 2 of the converter control submodule of the first hybrid energy storage unit submodule, C2 represents the output matrix of the converter control submodule of the first hybrid energy storage unit submodule, D2 represents the pass-through matrix 1 of the converter control submodule of the first hybrid energy storage unit submodule, D3 represents the pass-through matrix 2 of the converter control submodule of the first hybrid energy storage unit submodule, ΔX2 represents the small signal disturbance of the state variable of the converter control submodule of 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 voltage booster connected with the battery of the first hybrid energy storage unit submodule and the bus voltage, Δξ2 represents the small signal disturbance of the integral signal of the voltage loop PI controller of the DC voltage booster connected with 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 voltage booster connected with the super capacitor of the first hybrid energy storage unit submodule and the bus voltage, Δξ4 represents the small signal disturbance of the integral signal of the voltage loop PI controller of the DC voltage booster connected with the super capacitor of 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 represents the small signal disturbance of the output voltage reference value of the DC voltage booster connected with 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 voltage booster connected with the super capacitor 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 voltage booster circuit connected with the battery in the first hybrid energy storage unit submodule, Δi L2 represents the small signal disturbance of the inductor current of the DC voltage booster circuit connected with the super capacitor in the first hybrid energy storage unit submodule,

[0201] The simplified state space equations of the distributed control strategy control submodule are shown in formula (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] wherein α, β, λ and μ are constants; SOC1, SOC3 are the current state of charge of the present hybrid energy storage unit cell and the adjacent hybrid energy storage unit cell.v b1 , v b3 , ε b1 and ε b3 are voltage power droop coefficients, n is an integral droop coefficient. X3 represents the state variable of the distributed control strategy control sub-module, u4, u5 and u6 represent the input variable of the distributed control strategy control sub-module, Y3 represents the output variable of the distributed control strategy control sub-module. 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 sub-module, SOC0 represents the initial state of charge of the battery, E2 represents the output port voltage of the super capacitor in the first hybrid energy storage unit sub-module, A3 represents the distributed control sub-module system matrix of the first hybrid energy storage unit sub-module, B4 represents the distributed control sub-module input matrix 1 of the first hybrid energy storage unit sub-module, B5 represents the distributed control sub-module input matrix 2 of the first hybrid energy storage unit sub-module, B6 represents the distributed control sub-module input matrix 3 of the first hybrid energy storage unit sub-module, C3 represents the distributed control sub-module output matrix of the first hybrid energy storage unit sub-module, D4 represents the distributed control sub-module pass-through matrix 1 of the first hybrid energy storage unit sub-module, D5 represents the distributed control sub-module pass-through matrix 2 of the first hybrid energy storage unit sub-module, D6 represents the distributed control sub-module pass-through matrix 3 of the first hybrid energy storage unit sub-module, Δv ref3a small signal perturbation of the droop control voltage reference signal, Δv b1 a small signal perturbation of the battery state of charge of the first hybrid energy storage unit sub-module, ΔSOC1 b1 a small signal perturbation of the battery power rating compensation of the first hybrid energy storage unit sub-module, v ΔSC a small signal perturbation of the supercapacitor integral droop voltage change of the first hybrid energy storage unit sub-module, Δv b3 a small signal perturbation of the battery state of charge of the fourth hybrid energy storage unit sub-module, ΔSOC3 b3 a small signal perturbation of the battery power rating compensation of the fourth hybrid energy storage unit sub-module, v ΔSC3 a small signal perturbation of the supercapacitor integral droop voltage change of the fourth hybrid energy storage unit sub-module.

[0211] The step S3 comprises:

[0212] As can be seen from the state space equations of the above sub-modules, there are same cases for state variables, output variables and input variables of different modules. The state variable of the boost circuit sub-module can be represented as shown in equation (45):

[0213]

[0214] The state variable of the converter control sub-module can be represented as shown in equation (46):

[0215]

[0216] The state variable of the distributed control strategy control sub-module can be represented as shown in equation (47):

[0217]

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

[0219]

[0220] wherein O 7×6 , O 3×7 , O 3×4 , O 4×7 and O 4×4 are zero matrices of 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 formula (48), and the small signal model of the entire multi-hybrid energy storage DC distribution network can be derived by combining the small signal models of the four hybrid energy storage unit modules, as shown in formula (49):

[0222]

[0223] Wherein, O 11×11 is a zero matrix of 11 rows and 11 columns.

[0224] The application also provides a modular small signal modeling system for a multi-hybrid energy storage DC distribution network, which can be realized by executing the process steps of the modular small signal modeling method for the multi-hybrid energy storage DC distribution network, that is, the modular small signal modeling method for the multi-hybrid energy storage DC distribution network can be understood as the preferred embodiment of the modular small signal modeling system for the multi-hybrid energy storage DC distribution network by those skilled in the art.

[0225] Example 2

[0226] Example 2 is a preferred example of example 1

[0227] In the multi-hybrid energy storage DC microgrid system as shown in Figure 1 , the correctness of the modular small signal modeling method for the multi-hybrid energy storage DC distribution network and the stability analysis result is verified. The system constant power load is set to increase over time in the form of table 1, and it is observed whether the output power of the battery and the super capacitor can recover to steady state after each constant power load power increase until the system is unstable, and then the stability boundary of the multi-hybrid energy storage DC distribution network is obtained. The constant power load change is shown in table 1, and the sensitivity analysis result is shown in table 2.

[0228] Table 1 Constant power load change

[0229]

[0230] In order to analyze the stability of the multi-hybrid energy storage DC distribution network, the eigenvalues of the system matrix A need to be calculated. The stability condition of the system is that all the eigenvalues of the matrix A have negative real parts, that is, all the eigenvalues are located in the left half plane. As shown in Figure 6 , when the constant power load is gradually increased to about 60kW, a pair of eigenvalues crosses the imaginary axis and moves to the right half plane, making the system unstable, and the simulation result is shown in Figure 7As shown, at 85s, the power of the constant power load is increased from 46kW to 52kW. The hybrid energy storage DC power distribution system remains stable. However, at 105s, the power of the constant power load is increased from 52kW to 60kW. The system starts to become unstable, which is consistent with the theoretical analysis result.

[0231] Table 2 System eigenvalues and dominant variables

[0232]

[0233] By corresponding the variation of the eigenvalue trajectory to the system state variable, the influence of the constant power load power variation on the system characteristics can be obtained. As shown in Fig. 6, with the increase of the constant power load, λ5~λ Figure 6 move to the right half plane, and the dominant state variables are I 12 , I L1 , I L3 and I L5 , which means that the inductor currents are continuously close to the instability critical value.

[0234] Those skilled in the art understand that, in addition to implementing the system and each device, module and unit thereof provided by the present application in the form of pure computer readable program code, the system and each device, module and unit thereof provided by the present application can also be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. by logically programming the method steps to achieve the same functions. Therefore, the system and each device, module and unit thereof provided by the present application can be considered as a hardware component, and the devices, modules and units included therein for achieving various functions can also be considered as structures within the hardware component; the devices, modules and units for achieving various functions can also be considered as both software modules for implementing the method and structures within the hardware component.

[0235] The specific embodiments of the present application are described above. It needs to be understood that the present application 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 essential content of the present application. The embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other without conflict.

Claims

1. A modular small-signal modeling method for DC distribution networks containing multiple hybrid energy storage systems, 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: 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, converter control submodule, and distributed control submodule respectively to obtain the small signal model of a single hybrid energy storage submodule; Step S3: Obtain a complete small-signal model of a DC distribution network containing multiple hybrid energy storage modules by simultaneously establishing the small-signal models of the four hybrid energy storage sub-modules; Step S3 includes: The state variables of the boost circuit submodule are: Where A1 represents the system matrix of the boost circuit submodule of the first hybrid energy storage unit submodule, B1 represents the input matrix of the boost circuit submodule of the first hybrid energy storage unit submodule, D2 represents the first through matrix of the converter control submodule of the first hybrid energy storage unit submodule, D5 represents the second through matrix of the distributed control submodule of the first hybrid energy storage unit submodule, D3 represents the second through matrix of the converter control submodule of the first hybrid energy storage unit submodule, ΔX1 represents the small-signal disturbance of the state variables of the boost circuit submodule of the first hybrid energy storage unit submodule, C2 represents the output matrix of the converter control submodule of the first hybrid energy storage unit submodule, ΔX2 represents the small-signal disturbance of the state variables of the converter control submodule of the first hybrid energy storage unit submodule, C3 represents the output matrix of the distributed control submodule of the first hybrid energy storage unit submodule, ΔX3=[Δv b1 ΔSOC1Δε b1 v ΔSC ] T ;Δv b1 ΔSOC1 represents the small-signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; b1 This indicates the small-signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC D4 represents the small-signal disturbance of the supercapacitor integral droop voltage change in the first hybrid energy storage unit submodule; D4 represents the first through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δu4 = Δv ref3 ;Δv ref3 This represents a small-signal disturbance in the droop control voltage reference signal; The state variables of the converter control submodule are: Wherein, B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; D5 represents the second through matrix of the distributed 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; A2 represents the system matrix of the converter control submodule of the first hybrid energy storage unit submodule; and B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule. The state variables of the distributed control submodule are: Where B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; A3 represents the system matrix of the distributed control submodule 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; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δu6=[Δv b3 ΔSOC3Δε b3 v ΔSC3 ] T ;Δv b3 ΔSOC3 represents the small-signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; b3 This indicates the small-signal disturbance of the battery rated power compensation amount for the fourth hybrid energy storage unit submodule; v ΔSC3 This indicates a small signal disturbance in the integral droop voltage change of the supercapacitor in the fourth hybrid energy storage unit submodule; The small-signal model of the first hybrid energy storage unit module is as follows: Among them, O 7×6 O 3×7 O 3×4 O 4×7 With O 4×4 These are zero matrices with dimensions of 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 sub-modules, the small-signal model of the entire DC distribution network containing multiple hybrid energy storage units is derived as follows: Among them, O 11×11 A is an 11-row, 11-column zero matrix; sysi The system matrix represents the hybrid energy storage unit submodule i; C sys3_2 This represents the output matrix corresponding to the input variables of the second hybrid energy storage unit submodule and the output variables of the third hybrid energy storage unit submodule.

2. The modular small-signal modeling method for DC distribution networks with multiple hybrid energy storage as described in claim 1, characterized in that, The method further includes: in a DC distribution network containing multiple hybrid energy storage modules, 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 integral droop control for the supercapacitor.

3. The modular small-signal modeling method for DC distribution networks with multiple hybrid energy storage as described in claim 2, characterized in that, The configuration of voltage and power droop control for the battery pack includes: The configuration of integral droop control for the supercapacitor includes: Among them, V ref1 and V ref2 These are the input voltage reference signals for the battery and the supercapacitor, respectively; V th This is the nominal voltage of the microgrid, where m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV. max It is the maximum allowable voltage deviation, P Bmax It is the battery's rated power, P B and P SC These are the output power of the battery and the supercapacitor, respectively; s is the Laplace operator. The power-sharing relationship between the battery and the supercapacitor is described as follows: Among them, P Load For DC distribution networks containing multiple hybrid energy storage loads, the battery pack power is the low-frequency component of the load power, and the supercapacitor pack power is the high-frequency component of the load power. Local distributed compensators are assigned to control the voltage-power droop of the battery to generate a compensation amount δ. i and ε i These are respectively superimposed on the nominal voltage and rated power in the voltage-power droop control equation; Where, δ i It is the nominal voltage compensation of the i-th battery; SOC Bi It represents the state of charge of the i-th battery; δ j It is the nominal voltage compensation of the j-th battery; SOC Bj It represents the state of charge of the j-th battery; α i β i and γ i It is a constant; g i It is the restraint control gain; V b P represents the bus voltage; Bj P represents the output power of the j-th battery. Bjmax P represents the rated power of the j-th battery. Bi P represents the output power of the i-th battery. Bimax This represents the rated power of the i-th battery; The state of charge of the i-th battery is: Among them, P Bi It is the output power of the i-th battery, SOC Bi0 and These 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 (14)。 4. The modular small-signal modeling method for DC distribution networks with multiple hybrid energy storage as described in claim 1, characterized in that, Step S1 includes: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage sub-modules, and simplifying the four hybrid energy storage sub-modules 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 Let u represent the state variable of the i-th hybrid energy storage unit submodule. sysi A represents the input variables of i hybrid energy storage unit submodules; sysi C represents the system matrix of hybrid energy storage unit submodule i. sys1_4 C represents the output matrix corresponding to the output variables of the first hybrid energy storage unit submodule, generated by the fourth hybrid energy storage unit submodule. sys2_1 C represents the output matrix corresponding to the output variables generated by the first hybrid energy storage unit submodule and input to the second hybrid energy storage unit submodule. sys3_2 C represents the output matrix corresponding to the input variables of the second hybrid energy storage unit submodule to the third hybrid energy storage unit submodule. sys4_3 This represents the output matrix corresponding to the input variables of the third hybrid energy storage unit submodule and the output variables of the fourth hybrid energy storage unit submodule.

5. The modular small-signal modeling method for DC distribution networks with multiple hybrid energy storage as described in claim 1, characterized in that, In step S2, the boost circuit submodule, converter control submodule, and distributed control submodule are modeled respectively, thereby obtaining a small-signal model of a single hybrid energy storage submodule, including: The boost circuit submodule can be simplified to the following state-space equation: Y1=C1X1 (23) ΔX1=[Δi L1 Δi L2 Δv o ] T (24) Δu1=[Δd1 Δd2] T (25) ΔY1=[Δi L1 Δi L2 Δv o ] T (26) Among them, P CPL It is the power of a constant power load; C o It is the equivalent capacitance of the output port of the DC-DC boost converter; v o d1 and d2 are the DC bus voltage; d1 and d2 are the duty cycles of the DC-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-DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC-DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule. This represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; E1 represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; A1 represents the system matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; B1 represents the input matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; C1 represents the output matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; ΔX1 represents the small-signal perturbation of the state variables of the boost circuit submodule of the first hybrid energy storage unit submodule. This indicates a small-signal disturbance in the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; This represents the small-signal disturbance of the inductor current in the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv o Δu1 represents the small-signal disturbance of the DC bus voltage; Δd1 represents the small-signal disturbance of the input variable of the boost circuit submodule of 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 battery 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 can be simplified to the following state-space equation: Y2=C2X2+D2u2+D3u3 (32) ΔX2=[Δξ1 Δξ2 Δξ3 Δξ4] T (33) Δu2=[Δv ref1 Δv ref2 ] T (34) Δu3=[Δi L1 Δi L2 Δv o ] T (35) ΔY2=[Δd1 Δd2] T (36) Where ξ1~ξ4 are the state variables of the converter control submodule; v ref1 and v ref2 It is the voltage reference signal for the proportional-integral (PI) voltage control loop; i ref1 and i ref2 It is the current reference signal for the PI current control loop; k pV It is the proportional coefficient of the PI voltage control circuit; k iV X1 represents 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 system matrix of the converter control submodule 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 output matrix of the converter control submodule of the first hybrid energy storage unit submodule; D2 represents the first through matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second through matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule... The small-signal disturbance of the state variables of the converter control submodule; Δξ1 represents the small-signal disturbance of the difference between the output voltage reference value and the bus voltage of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δξ2 represents the small-signal disturbance of the integral signal of the voltage loop PI controller of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δξ3 represents the small-signal disturbance of the difference between the output voltage reference value and the bus voltage of the DC-DC boost converter connected to the supercapacitor in the first hybrid energy storage unit submodule; Δξ4 represents the small-signal disturbance of the integral signal of the voltage loop PI controller of the DC-DC boost converter connected to the supercapacitor in 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 This represents the small-signal disturbance of the output voltage reference value of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δv ref2 Δu3 represents the small-signal disturbance of the output voltage reference value of the DC-DC boost converter connected to the supercapacitor in the first hybrid energy storage unit submodule; Δi represents the small-signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; L1 This represents a small-signal disturbance in the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 This represents a small-signal disturbance in the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; The distributed control submodule can be simplified to the following state-space equation: Y3=C3X3+D4u4+D5u5+D6u6(42) ΔX3=[Δv b1 ΔSOC1No b1 v ΔSC ] T (43) Δu4=Δv ref3 (44) Δu5=[Δi L1 Δi L2 Δv o ] T (45) Δu6=[Δv b3 ΔSOC3 No b3 v ΔSC3 ] T (46) ΔY3=[Δv ref1 Δv ref2 ] T (47) Where α, β, λ, and μ are constants; SOC1 and SOC3 are the current states of charge of the battery in this hybrid energy storage unit and the battery in the neighboring hybrid energy storage unit; v b1 v b3 ε b1 and ε b3 γ 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 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 system matrix of the distributed control submodule 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 output matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D4 represents the first through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 This represents a small-signal disturbance in the droop control voltage reference signal; Δv b1 ΔSOC1 represents the small-signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; b1 This indicates the small-signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC This represents the small-signal disturbance of the integral droop voltage change of the supercapacitor in the first hybrid energy storage unit submodule; Δv b3 ΔSOC3 represents the small-signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; b3 This indicates the small-signal disturbance of the battery rated power compensation amount for the fourth hybrid energy storage unit submodule; v ΔSC3 This indicates a small signal disturbance in the integral droop voltage change of the supercapacitor in the fourth hybrid energy storage unit submodule.

6. A modular small-signal modeling system for DC distribution networks containing multiple hybrid energy storage systems, characterized in that, include: Module M1: Divides the DC distribution network containing 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, converter control submodule, and distributed control submodule respectively to obtain the small-signal model of a single hybrid energy storage submodule; Module M3: By combining the small-signal models of four hybrid energy storage sub-modules, a complete small-signal model of a DC distribution network containing multiple hybrid energy storage modules is obtained; The module M3 includes: The state variables of the boost circuit submodule are: Where A1 represents the system matrix of the boost circuit submodule of the first hybrid energy storage unit submodule, B1 represents the input matrix of the boost circuit submodule of the first hybrid energy storage unit submodule, D2 represents the first through matrix of the converter control submodule of the first hybrid energy storage unit submodule, D5 represents the second through matrix of the distributed control submodule of the first hybrid energy storage unit submodule, D3 represents the second through matrix of the converter control submodule of the first hybrid energy storage unit submodule, ΔX1 represents the small-signal disturbance of the state variables of the boost circuit submodule of the first hybrid energy storage unit submodule, C2 represents the output matrix of the converter control submodule of the first hybrid energy storage unit submodule, ΔX2 represents the small-signal disturbance of the state variables of the converter control submodule of the first hybrid energy storage unit submodule, C3 represents the output matrix of the distributed control submodule of the first hybrid energy storage unit submodule, ΔX3=[Δv b1 ΔSOC1Δε b1 v ΔSC ] T ;Δv b1 ΔSOC1 represents the small-signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; b1 This indicates the small-signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC D4 represents the small-signal disturbance of the supercapacitor integral droop voltage change in the first hybrid energy storage unit submodule; D4 represents the first through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δu4 = Δv ref3 ;Δv ref3 This represents a small-signal disturbance in the droop control voltage reference signal; The state variables of the converter control submodule are: Wherein, B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule; D5 represents the second through matrix of the distributed 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; A2 represents the system matrix of the converter control submodule of the first hybrid energy storage unit submodule; and B2 represents the first input matrix of the converter control submodule of the first hybrid energy storage unit submodule. The state variables of the distributed control submodule are: Where B5 represents the second input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; A3 represents the system matrix of the distributed control submodule 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; B6 represents the third input matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δu6=[Δv b3 ΔSOC3 Δε b3 v ΔSC3 ] T ;Δv b3 ΔSOC3 represents the small-signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; b3 This indicates the small-signal disturbance of the battery rated power compensation amount for the fourth hybrid energy storage unit submodule; v ΔSC3 This indicates a small signal disturbance in the integral droop voltage change of the supercapacitor in the fourth hybrid energy storage unit submodule; The small-signal model of the first hybrid energy storage unit module is as follows: Among them, O 7×6 O 3×7 O 3×4 O 4×7 With O 4×4 These are zero matrices with dimensions of 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 sub-modules, the small-signal model of the entire DC distribution network containing multiple hybrid energy storage units is derived as follows: Among them, O 11×11 A is an 11-row, 11-column zero matrix; sysi The system matrix represents the hybrid energy storage unit submodule i; C sys3_2 This represents the output matrix corresponding to the input variables of the second hybrid energy storage unit submodule and the output variables of the third hybrid energy storage unit submodule.

7. The modular small-signal modeling system for DC distribution networks with multiple hybrid energy storage as described in claim 6, characterized in that, The system also includes: in a DC distribution network containing multiple hybrid energy storage modules, a distributed control strategy is adopted for the hybrid energy storage submodules; The distributed control strategy includes: configuring voltage and power droop control for the battery pack and integral droop control for the supercapacitor; The configuration of voltage and power droop control for the battery pack includes: The configuration of integral droop control for the supercapacitor includes: Among them, V ref1 and V ref2 These are the input voltage reference signals for the battery and the supercapacitor, respectively; V th This is the nominal voltage of the microgrid, where m and n are the droop coefficients of the battery and supercapacitor, respectively, ΔV. max It is the maximum allowable voltage deviation, P Bmax It is the battery's rated power, P B and P SC These are the output power of the battery and the supercapacitor, respectively; s is the Laplace operator. The power-sharing relationship between the battery and the supercapacitor is described as follows: Among them, P Load For DC distribution networks containing multiple hybrid energy storage loads, the battery pack power is the low-frequency component of the load power, and the supercapacitor pack power is the high-frequency component of the load power. Local distributed compensators are assigned to control the voltage-power droop of the battery to generate a compensation amount δ. i and ε i These are respectively superimposed on the nominal voltage and rated power in the voltage-power droop control equation; Where, δ i It is the nominal voltage compensation of the i-th battery; SOC Bi It represents the state of charge of the i-th battery; δ j It is the nominal voltage compensation of the j-th battery; SOC Bj It represents the state of charge of the j-th battery; α i β i and γ i It is a constant; g i It is the restraint control gain; V b P represents the bus voltage; Bj P represents the output power of the j-th battery. Bjmax P represents the rated power of the j-th battery. Bi P represents the output power of the i-th battery. Bimax This represents the rated power of the i-th battery; The state of charge of the i-th battery is: Among them, P Bi It is the output power of the i-th battery, SOC Bi0 and These 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 (14)。 8. The modular small-signal modeling system for DC distribution networks with multiple hybrid energy storage as described in claim 7, characterized in that, The module M1 includes: dividing the DC distribution network containing multiple hybrid energy storage into four hybrid energy storage sub-modules, and simplifying the four hybrid energy storage sub-modules 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 Let u represent the state variable of the i-th hybrid energy storage unit submodule. sysi A represents the input variables of i hybrid energy storage unit submodules; sysi C represents the system matrix of hybrid energy storage unit submodule i. sys1_4 C represents the output matrix corresponding to the output variables of the first hybrid energy storage unit submodule, generated by the fourth hybrid energy storage unit submodule. sys2_1 C represents the output matrix corresponding to the output variables generated by the first hybrid energy storage unit submodule and input to the second hybrid energy storage unit submodule. sys3_2 C represents the output matrix corresponding to the input variables of the second hybrid energy storage unit submodule to the third hybrid energy storage unit submodule. sys4_3 This represents the output matrix corresponding to the input variables of the third hybrid energy storage unit submodule and the output variables of the fourth hybrid energy storage unit submodule.

9. The modular small-signal modeling system for DC distribution networks with multiple hybrid energy storage as described in claim 6, characterized in that, In module M2, the boost circuit submodule, converter control submodule, and distributed control submodule are modeled respectively, thereby obtaining a small-signal model of a single hybrid energy storage submodule, including: The boost circuit submodule can be simplified to the following state-space equation: Y1=C1X1(23) ΔX1=[Δi L1 Δi L2 Δv o ] T (24) Δu1=[Δd1Δd2] T (25) ΔY1=[Δi L1 Δi L2 Δv o ] T (26) Among them, P CPL It is the power of a constant power load; C o It is the equivalent capacitance of the output port of the DC-DC boost converter; v o d1 and d2 are the DC bus voltage; d1 and d2 are the duty cycles of the DC-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-DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; L2 represents the inductance of the DC-DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule. This represents the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; E1 represents the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; E2 represents the output port voltage of the battery in the first hybrid energy storage unit submodule; A1 represents the system matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; B1 represents the input matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; C1 represents the output matrix of the boost circuit submodule of the first hybrid energy storage unit submodule; ΔX1 represents the small-signal perturbation of the state variables of the boost circuit submodule of the first hybrid energy storage unit submodule. This indicates a small-signal disturbance in the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; This represents the small-signal disturbance of the inductor current in the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; Δv o Δu1 represents the small-signal disturbance of the DC bus voltage; Δd1 represents the small-signal disturbance of the input variable of the boost circuit submodule of 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 battery 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 can be simplified to the following state-space equation: Y2=C2X2+D2u2+D3u3 (32) ΔX2=[Δξ1 Δξ2 Δξ3 Δξ4] T (33) Δu2=[Δv ref1 Δv ref2 ] T (34) Δu3=[Δi L1 Δi L2 Δv o ] T (35) ΔY2=[Δd1 Δd2] T (36) Where ξ1~ξ4 are the state variables of the converter control submodule; v ref1 and v ref2 It is the voltage reference signal for the proportional-integral (PI) voltage control loop; i ref1 and i ref2 It is the current reference signal for the PI current control loop; k pV It is the proportional coefficient of the PI voltage control circuit; k iV X1 represents 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 system matrix of the converter control submodule 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 output matrix of the converter control submodule of the first hybrid energy storage unit submodule; D2 represents the first through matrix of the converter control submodule of the first hybrid energy storage unit submodule; D3 represents the second through matrix of the converter control submodule of the first hybrid energy storage unit submodule; ΔX2 represents the first hybrid energy storage unit submodule... The small-signal disturbance of the state variables of the converter control submodule; Δξ1 represents the small-signal disturbance of the difference between the output voltage reference value and the bus voltage of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δξ2 represents the small-signal disturbance of the integral signal of the voltage loop PI controller of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δξ3 represents the small-signal disturbance of the difference between the output voltage reference value and the bus voltage of the DC-DC boost converter connected to the supercapacitor in the first hybrid energy storage unit submodule; Δξ4 represents the small-signal disturbance of the integral signal of the voltage loop PI controller of the DC-DC boost converter connected to the supercapacitor in 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 This represents the small-signal disturbance of the output voltage reference value of the DC-DC boost converter connected to the battery in the first hybrid energy storage unit submodule; Δv ref2 Δu3 represents the small-signal disturbance of the output voltage reference value of the DC-DC boost converter connected to the supercapacitor in the first hybrid energy storage unit submodule; Δi represents the small-signal disturbance of the second input variable matrix of the converter control submodule of the first hybrid energy storage unit submodule; L1 This represents a small-signal disturbance in the inductor current of the DC boost circuit connected to the battery in the first hybrid energy storage unit submodule; Δi L2 This represents a small-signal disturbance in the inductor current of the DC boost circuit connected to the supercapacitor in the first hybrid energy storage unit submodule; The distributed control submodule can be simplified to the following state-space equation: Y3=C3X3+D4u4+D5u5+D6u6 (42) ΔX3=[Δv b1 ΔSOC1 No b1 v ΔSC ] T (43) Δu4=Δv ref3 (44) Δu5=[Δi L1 Δi L2 Δv o ] T (45) Δu6=[Δv b3 ΔSOC3 No b3 v ΔSC3 ] T (46) ΔY3=[Δv ref1 Δv ref2 ] T (47) Where α, β, λ, and μ are constants; SOC1 and SOC3 are the current states of charge of the battery in this hybrid energy storage unit and the battery in the neighboring hybrid energy storage unit; v b1 v b3 ε b1 and ε b3 γ 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 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 system matrix of the distributed control submodule 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 output matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D4 represents the first through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D5 represents the second through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; D6 represents the third through matrix of the distributed control submodule of the first hybrid energy storage unit submodule; Δv ref3 This represents a small-signal disturbance in the droop control voltage reference signal; Δv b1 ΔSOC1 represents the small-signal disturbance of the battery rated voltage compensation of the first hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the first hybrid energy storage unit submodule; b1 This indicates the small-signal disturbance of the battery rated power compensation amount of the first hybrid energy storage unit submodule; v ΔSC This represents the small-signal disturbance of the integral droop voltage change of the supercapacitor in the first hybrid energy storage unit submodule; Δv b3 ΔSOC3 represents the small-signal disturbance of the battery rated voltage compensation of the fourth hybrid energy storage unit submodule; Δε represents the small-signal disturbance of the battery state of charge of the fourth hybrid energy storage unit submodule; b3 This indicates the small-signal disturbance of the battery rated power compensation amount for the fourth hybrid energy storage unit submodule; v ΔSC3 This indicates a small signal disturbance in the integral droop voltage change of the supercapacitor in the fourth hybrid energy storage unit submodule.

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