Adaptive droop controller construction method, island micro-grid control system, device and medium

Abstract

The application relates to a self-adaptive droop controller construction method, an island micro-grid control system, equipment and a medium, and the method comprises the following steps: according to the state of charge of a distributed energy storage unit, adopting a double-quadrant state of charge weighted control method to adaptively correct initial droop coefficients in an I-U droop controller to obtain first droop coefficients; based on the assumption that the product of the initial droop coefficients and the output current of the converter is a constant value, fine-tuning droop coefficients are calculated; the communication relationship among the distributed energy storage units in the direct-current micro-grid is described by using a directed graph, a voltage compensator based on a consistency algorithm is constructed, and the voltage compensation items of each distributed energy storage unit are calculated; the sum of the first droop coefficients and the fine-tuning droop coefficients is used to replace the initial droop coefficients, and the output voltage of the converter after the droop control is compensated by using the voltage compensation items, so that a self-adaptive droop controller is obtained. Compared with the prior art, the application has the advantages that the state of charge of the distributed energy storage unit is balanced, and the control reliability is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of island micro-grid control, and in particular to a self-adaptive droop controller construction method, an island micro-grid control system, equipment and a medium. BACKGROUND

[0002] As an effective carrier of distributed power supply, micro-grid has become an important scheme for large-scale development and utilization of renewable energy due to its flexibility and high scalability. Compared with AC micro-grid, DC micro-grid reduces the conversion equipment and power conversion links, and its construction cost is low and operation efficiency is high, which has attracted more extensive attention.

[0003] The energy storage system (ESS) is an important part of the micro-grid, which is connected to the network through a bidirectional DC-DC converter. It plays an extremely important role in promoting new energy consumption and maintaining power and voltage stability in the DC micro-grid. If the distributed energy storage units (DESUs) continue to work under the condition of unbalanced state of charge (SoC), it may cause overcharging and overdischarging of the energy storage units, which seriously affects the service life of the ESS. Therefore, in order to avoid damage caused by excessive charging and discharging of the energy storage units, the SoC of the ESS needs to be coordinated and controlled to achieve balance.

[0004] The conventional droop control commonly used in micro-grid can only distribute the load power or current to each DESU in a fixed proportion, and cannot guarantee the balance of the state of charge (SoC) of the DESU.

[0005] The self-adaptive droop control of the micro-grid system adjusts the bus voltage, SoC of the energy storage system and other state variables to achieve good voltage recovery and stable operation of the micro-grid, and overcomes the limitations of the traditional droop control.

[0006] However, due to the characteristics of distributed energy storage, there is usually a mismatch in line impedance in practice. The existing control strategies for multiple energy storage systems do not consider the impact of line impedance mismatch, and the reliability and stability of the balancing control need to be further improved. SUMMARY

[0007] The purpose of the present application is to overcome the defects of the prior art and provide a self-adaptive droop controller construction method, control system, equipment and medium with high reliability and stability.

[0008] The purpose of the present application can be achieved by the following technical solutions:

[0009] According to a first aspect of the present invention, an adaptive droop controller construction method is provided for balancing the state of charge of distributed energy storage units in an islanded microgrid system, the method comprising:

[0010] Based on the state of charge of the distributed energy storage unit, the initial droop coefficient in the IU droop controller is adaptively corrected using a dual-quadrant state of charge weighted control method to obtain the first droop coefficient.

[0011] Considering the line impedance differences between different distributed energy storage units, and based on the assumption that the product of the initial droop coefficient and the converter output current is constant, the fine-tuning droop coefficient is calculated.

[0012] A directed graph is used to describe the communication relationship between distributed energy storage units in a DC microgrid. A voltage compensator based on a consensus algorithm is constructed, and the voltage compensation term for each distributed energy storage unit is calculated.

[0013] The initial droop coefficient is replaced by the sum of the first droop coefficient and the fine-tuning droop coefficient, and the output voltage of the converter after droop control is compensated by a voltage compensation term to obtain an adaptive droop controller.

[0014] Preferably, the initial droop coefficient is adaptively corrected using a dual-quadrant state-of-charge weighted control method based on the state of charge of the distributed energy storage unit to obtain the first droop coefficient, expressed as:

[0015]

[0016] In the formula: R is the first droop coefficient corresponding to the i-th distributed energy storage unit. v_i Let be the initial droop coefficient corresponding to the i-th distributed energy storage unit; SoC ave Average state of charge of distributed energy storage units; SoC i Let n be the state of charge of the i-th distributed energy storage unit; n is the convergence coefficient of the state of charge; i dc >0 indicates the discharge state, i dc <0 indicates the charging status.

[0017] Preferably, considering the line impedance differences between different distributed energy storage units, and based on the assumption that the product of the initial droop coefficient and the converter output current is a constant, the fine-tuning droop coefficient is calculated using a first PI controller, and the calculation expression is:

[0018]

[0019] In the formula: R n_i K is the fine-tuning droop coefficient corresponding to the i-th distributed energy storage unit; n η represents the parameters of the first PI controller.i R is the initial droop coefficient corresponding to the i-th distributed energy storage unit in the original IU droop control model. v_i With DC / DC converter output current I o_i The product of η ave This represents the average of the initial droop coefficients and converter output currents for all distributed energy storage units in the droop control.

[0020] Preferably, the description of the communication relationships between distributed energy storage units in the DC microgrid using a directed graph specifically involves using a directed graph G = (V G E G A G This represents the communication layer between distributed energy storage units in a DC microgrid; where each distributed energy storage unit is considered a node and labeled as a point set V. G ={v1 v2···v n}; Transmission links between nodes are used This means that when (v i ,v j )∈E i When, it means that node i can interact with its neighboring node j, N i ={i∈V G |(j,i)∈E G} represents node v i The adjacency matrix A is the set of all adjacent nodes; a communication network formed by communication lines between adjacent nodes has an adjacency matrix A defined as A. G =[a ij ]∈R N a ij Let a be the communication weight between nodes i and j. If node j sends information to node i, it means that node j is adjacent to node i. ij >0, otherwise, a ij =0; diagonal matrix D G =diag|d ij Let L be the degree matrix of a directed graph G, and let L be the Laplacian matrix of the directed graph G. G =D G -A G .

[0021] Preferably, in the construction of the voltage compensator based on the consensus algorithm, the average bus voltage estimated by the i-th voltage compensator is... The calculation expression is:

[0022]

[0023] In the formula: V busi Let be the bus voltage at the output of the i-th converter.

[0024] Preferably, the second PI controller is used to calculate the voltage compensation term Δu of each distributed energy storage unit, and the calculation expression is as follows:

[0025]

[0026] wherein K p and K i are parameters of the second PI controller; V ref is the reference output voltage of the DC / DC converter in the no-load state; is the average bus voltage estimated by the i-th voltage compensator.

[0027] Preferably, the initial droop coefficient is replaced by the sum of the first droop coefficient and the fine-tuned droop coefficient, and the output voltage of the converter is compensated by the voltage compensation term, so as to obtain an improved I-U droop controller, and the mathematical expression is as follows:

[0028]

[0029] wherein V o_i and I o_i respectively represent the output voltage and the output current of the i-th DC / DC converter after droop control; V ref represents the reference output voltage of the DC / DC converter in the no-load state; and Δu is the voltage compensation term of each distributed energy storage unit.

[0030] According to a second aspect of the present application, there is provided an island micro-grid control system, which comprises an adaptive droop controller constructed by using any of the above methods.

[0031] According to a third aspect of the present application, there is provided an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement any of the above methods.

[0032] According to a fourth aspect of the present application, there is provided a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement any of the above methods.

[0033] Compared with the prior art, the present application has the following beneficial effects:

[0034] 1) The present application couples the droop coefficient with the state of charge of the energy storage unit by using a dual-quadrant state of charge weighting control method, so as to adaptively correct the droop coefficient according to the state of charge of the energy storage unit, thereby improving the reliable stability of the state of charge balancing control of the distributed energy storage unit of the island DC micro-grid.

[0035] 2) Considering the influence of line impedance mismatch on state of charge balancing, the initial droop coefficient is introduced to make the current accurate distribution and state of charge balancing, and the fine droop coefficient is calculated based on PI controller according to the line impedance difference between different energy storage units, so that the state of charge can quickly reach the balanced state.

[0036] 3) Considering the introduction of fine droop coefficient and the influence of line impedance, it is inevitable to cause the deviation of DC bus voltage, and the voltage compensator based on consistency algorithm is adopted to make the DC bus voltage always keep around the rated value, which improves the reliability of microgrid system. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a DC microgrid structure diagram;

[0038] Figure 2 is a simplified model diagram of parallel DESUs;

[0039] Figure 3 is a double-quadrant SoC weighted control method diagram;

[0040] Figure 4 is a general scheme diagram of control strategy;

[0041] Figure 5 is a simplified droop control block diagram;

[0042] Figure 6 is a root locus diagram under parameter variation;

[0043] Figure 7 is a microgrid stable charging experiment result diagram;

[0044] Figure 8 is a microgrid stable discharging experiment result diagram;

[0045] Figure 9 is a microgrid impact load experiment result diagram;

[0046] Figure 10 is a microgrid photovoltaic fluctuation experiment result diagram. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0048] Embodiment 1

[0049] The embodiment provides a self-adaptive droop controller construction method for balancing the state of charge of distributed energy storage units in an island micro-grid system.

[0050] According to the state of charge of the distributed energy storage units, an initial droop coefficient in an I-U droop controller is adaptively corrected by using a double-quadrant state of charge weighted control method to obtain a first droop coefficient.

[0051] Considering the line impedance difference between different distributed energy storage units, a fine-tuning droop coefficient is calculated based on the assumption that the product of the initial droop coefficient and the output current of the converter is a constant value.

[0052] A directed graph is used to describe the communication relationship between the distributed energy storage units in the DC micro-grid, and a voltage compensator based on a consensus algorithm is constructed to calculate the voltage compensation term of each distributed energy storage unit.

[0053] The sum of the first droop coefficient and the fine-tuning droop coefficient is used to replace the initial droop coefficient, and the output voltage of the converter after droop control is compensated by using the voltage compensation term, so that the self-adaptive droop controller is obtained.

[0054] The embodiment also provides an island micro-grid control system, which comprises the self-adaptive droop controller constructed by using the method.

[0055] The electronic device of the present application includes a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The CPU, ROM, and RAM are connected to each other through a bus. An input / output (I / O) interface is also connected to the bus.

[0056] A plurality of components in the device are connected to the I / O interface, including: an input unit such as a keyboard, a mouse, etc.; an output unit such as various types of displays, a loudspeaker, etc.; a storage unit such as a magnetic disk, an optical disk, etc.; and a communication unit such as a network card, a modem, a wireless communication transceiver, etc. The communication unit allows the device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0057] The processing units perform the various methods and processes described above. For example, in some embodiments, the methods can be implemented as a computer software program tangibly embodied in a machine readable medium, such as a storage unit. In some embodiments, portions of the computer program, or all of the computer program, can be loaded onto the device via, for example, the ROM and / or the communications unit. When the computer program is loaded onto the RAM and executed by the CPU, one or more of the steps of the methods described above can be performed. Alternatively, in other embodiments, the CPU can be configured to perform the methods by way of other means, such as by way of firmware.

[0058] The functionality described herein above can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0059] Program code for carrying out methods of the present application can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, causes the machine to perform the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0060] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of a computer program code, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0061] Example 2

[0062] This embodiment provides a method for constructing an adaptive droop controller for balancing the state of charge of distributed energy storage units in an islanded microgrid system. The method includes:

[0063] (1) Model establishment

[0064] DC microgrids with distributed power sources, such as Figure 1 As shown, each distributed power source is connected to the DC bus via a DC / DC converter and a DC load. The DC microgrid is connected to the external distribution network through a grid-connected converter to achieve switching between islanded and grid-connected modes. When the DC microgrid is operating in islanded mode, due to the lack of external grid support, the stable operation of the system depends on the coordinated operation of the internal distributed power sources. At the same time, in order to ensure the power quality and reliable operation of the islanded DC microgrid, energy storage is configured to reduce power fluctuations and maintain source-load balance.

[0065] Since there are no frequency and reactive power control issues in DC microgrid systems, it is only necessary to maintain the stability of the DC bus voltage and power balance. Typically, an IU droop control strategy with the microgrid DC bus voltage as a reference is adopted, as shown in equation (1):

[0066] V o_i =V ref -R v_i I o_i (1)

[0067] In the formula: V o_i and I o_i V represents the output voltage and output current of the i-th DC / DC converter after droop control, respectively; ref R represents the reference output voltage of the DC / DC converter under no-load conditions. v_i R is the droop factor for the i-th DC / DC converter. Typically, R... v_i The design current is inversely proportional to the rated current, and its calculation is as follows:

[0068]

[0069] In the formula, ΔV max I is the maximum allowable voltage deviation of this system. ratei This is the rated output current of DESU.

[0070] During the charging and discharging process of DC microgrid DESUs, the state of charge (SoC) is an important indicator for measuring the available capacity of DESUs. The Coulomb counting method is usually used to estimate the state of charge of each DESU.

[0071]

[0072] In the formula, SoC i and SoCi_0 respectively represent the current SoC and initial SoC of the i-th DESU; V o_i and I o_i respectively represent the output voltage and output current of the i-th DESU; V Bat_i and C Bat_i respectively represent the terminal voltage and capacity of the i-th battery; Taking the derivative of both sides of equation (3) gives:

[0073]

[0074] As can be seen from equation (4), the SoC charging and discharging rate of the i-th DESU is determined by the output voltage, output current and capacity.

[0075] However, in an actual islanded DC microgrid, due to the existence of line impedance, the traditional droop control method cannot achieve accurate current distribution, which further affects the SoC balance. For ease of illustration, two groups of DESUs operating in parallel are taken as an example for analysis.

[0076] According to Figure 2 , the outlet currents of the two DC / DC converters are:

[0077]

[0078] By combining equation (1) and equation (5), the output currents of the two converters can be expressed as:

[0079]

[0080] The output voltages of each DESU through the DC / DC converter are respectively

[0081]

[0082] By combining equation (4), equation (6) and equation (7), the ratio of the SoC conversion rates of the two energy storage units is:

[0083]

[0084] According to equation (7), the state of charge of different DESUs mainly depends on the line impedance R line , the energy storage capacity C Bat and the droop coefficient R v ; in fact, when the DESU capacity in the DC microgrid is relatively fixed, the line impedance mismatch will have a great impact on the SoC balance effect. Therefore, it is necessary to design an adaptive droop control strategy to achieve the SoC balance of the energy storage system by adaptively adjusting the droop coefficient.

[0085] (2) Improved droop control

[0086] Since the current sharing of DESU in ideal state is strictly proportional to the inverse of droop coefficient, the DESU with larger SoC wants to output larger current and the DESU with smaller SoC wants to output smaller current in charging state. In discharging state, the DESU with smaller SoC wants to absorb larger current and the DESU with larger SoC wants to absorb smaller current. Therefore, this paper proposes a dual-quadrant SoC weighted control method to modify the droop coefficient, which can be expressed as:

[0087]

[0088] In formula (9), R v_i is the initial droop coefficient, is the modified droop coefficient, SoC ave is the average state of charge of DESU, and n is the SoC convergence coefficient. Larger n has faster convergence speed at the initial moment, but too large n will cause some DESU to exceed the power limit, so its selection needs to be balanced.

[0089] For better explanation, define the virtual rated current

[0090]

[0091] wherein, is the virtual rated current. From formula (10), it can be seen that in discharging state, when SoC is greater than the average value, its virtual rated current is greater than the real rated current; when SoC is less than the average value, its virtual rated current is less than the real rated current. In charging state, when SoC is greater than the average value, its virtual rated current is less than the real rated current; when SoC is less than the average value, its virtual rated current is greater than the real rated current.

[0092] Combining the above formula, we can get

[0093]

[0094] It can be seen from formula (11) that the essence of the dual-quadrant SoC weighted control method is to make the current sharing proportional to the virtual rated current.

[0095] (3) Fine-tuning droop coefficient

[0096] In practical application, line impedance exists in islanded DC microgrid, which affects the current distribution, i.e.

[0097]

[0098] In order to eliminate the influence of line resistance and ensure accurate current distribution and SOC balance, by adding fine-tuning droop coefficient R n , we have

[0099]

[0100] Since the line impedance of an islanded DC microgrid is difficult to measure accurately during actual operation, a fine-tuning droop coefficient can be obtained by combining the initial droop coefficient with the output current. This fine-tuning coefficient is defined as η. i and η j

[0101]

[0102]

[0103] Where, η i and η j Let η represent the product of the droop coefficient and the output current in the i-th and j-th droop controls, respectively. ave Indicates η i and η j The average value.

[0104] Then, the fine-tuning droop coefficient is calculated based on the PI controller.

[0105]

[0106]

[0107] Among them, K n A PI controller is used; through PI control, the following can be obtained:

[0108] η i =η j =η ave (18)

[0109] By introducing a fine-tuned droop coefficient to achieve precise current distribution and SOC balancing, the improved IV droop control can be rewritten as follows:

[0110]

[0111] (4) Voltage compensator based on consensus algorithm

[0112] In a DC microgrid, the communication layer between DESUs can be represented by a directed graph G = (V G E G A G In this paper's control method, each DESU is considered a node and denoted as a point set V. G ={v1 v2···v n The transmission links between nodes use... This means that when (v i ,v j )∈E iAt time t, node i can interact with its neighboring node j, N i = {i∈V G |(j,i)∈E G} denotes the union of all neighboring nodes of node v i . There is a communication link between neighboring nodes, and the communication network formed has an adjacency matrix A defined as:

[0113] A = [a ij ]∈R N (20)

[0114] where a ij is the communication weight between nodes i and j. If node j sends information to node i, it means that node j is adjacent to node i, a ij > 0, otherwise a ij = 0.

[0115] The diagonal matrix D G = diag|d ij | is the degree matrix of the directed graph G, and the Laplacian matrix of the directed graph G is defined as L G = D G -A G .

[0116] The consensus algorithm is simple in form, and its application range is very wide. The continuous-time consensus algorithm can be defined as:

[0117]

[0118]

[0119] where x i and x j represent the state variables of nodes i and j, respectively; u i represents the control input variable of node i, and a ij is the adjacency matrix coefficient.

[0120] Introducing the Laplacian matrix, equations (18) and (19) can be obtained

[0121]

[0122] where,

[0123] Under the action of the controller (19), when t→∞, the consensus is achieved, and the final convergence value is:

[0124]

[0125] where ξ = [ξ1, ξ2, ξ3,..., ξN ] T is the eigenvector corresponding to the eigenvalue 0 of the Laplacian matrix.

[0126] From the above analysis, we can know that the state variable can achieve asymptotic consensus as long as the communication topology is connected.

[0127] The improved droop control is used to achieve the SoC balance of the energy storage system. Due to the introduction of the fine-tuning droop coefficient, it is inevitable to cause the deviation of the DC bus voltage. To restore the bus voltage, the traditional method first samples the common bus voltage, then compares it with the rated voltage to obtain the deviation, and finally obtains the correction amount of the secondary voltage through the PI controller. However, this method not only depends too much on the communication line between DESU and the bus, but also needs to continuously sample the bus voltage. Therefore, in order to reduce the communication cost and improve the reliability of the DC microgrid, this paper designs a voltage compensator based on the distributed consensus algorithm to improve the problem of bus voltage deviation from the rated value.

[0128] The information of local nodes and neighbor nodes is processed through the consensus algorithm, and the compensation of the bus voltage difference is realized based on the communication between adjacent DESUs.

[0129] The expression of the consensus voltage compensator used in this paper is as follows

[0130]

[0131] In the formula, is the average observation value of the bus voltage obtained by the i-th DESU through the consensus voltage compensator, and V busi is the bus voltage at the outlet of the i-th DC / DC converter. As shown in section 3.1, when t→∞, will converge to the average value of V busi .

[0132] Therefore, the voltage recovery term added in the droop control can be expressed as

[0133]

[0134] In the formula, k p and k i are the PI controller coefficients, and V ref represents the reference output voltage of the DC / DC converter in the no-load state.

[0135] By comparing the average bus voltage estimated by the consensus voltage compensator with the reference voltage V ref , the voltage compensation term of each DESU is calculated based on the PI controller. Finally, the expression of the droop control is:

[0136]

[0137] The voltage compensator based on the consensus algorithm ensures that the DESUs in the DC microgrid operate at the same average bus voltage level, eliminates the bus voltage deviation caused by the fine droop coefficient, and improves the operation stability of the system.

[0138] After the fine droop coefficient is added, the total droop coefficient will change accordingly, which will affect the operation stability of the DC microgrid system. In this embodiment, the discharging process of two distributed energy storage units (DESU1 and DESU2) with the same capacity is taken as an example to analyze the stability of the system under the proposed control strategy.

[0139] According to the superposition principle, the relationship between the output current and voltage of the two parallel DESUs is Figure 2

[0140]

[0141] Among them, α1, α2, β parameters are as shown in formula (29).

[0142]

[0143] In formula (29), R line1 , R line2 , R load respectively represent the outlet line resistance and load resistance of DESU1 and DESU2.

[0144] The fine droop coefficient is combined with the original droop coefficient, and the simplified droop control block diagram is as shown in Figure 5 .

[0145] Among them, the current I o i filtered through the low-pass filter with a cutoff frequency of ω c , the real-time SoC of the energy storage unit is obtained through the SoC estimation link. In the DC voltage closed-loop transfer function, since the bandwidth of the droop control loop is much smaller than the switching frequency, the time constant τ is very small, so G conv (s)≈1. The relationship between the outlet voltages of the two DC / DC converters is:

[0146]

[0147] By simultaneously solving equations (25)-(27), the characteristic equation is obtained as:

[0148] as 4 +bs 3 +cs 2 +ds+e=0 (31)

[0149] ​where each coefficient is

[0150]

[0151] According to the Routh criterion, if the system is in a stable state, each coefficient a, b, c, d, e needs to be greater than 0, and the cutoff frequency ω c and the combined droop coefficient R v has an important influence on the stability of the system. By changing the parameter R v and the parameter ω c , the stability of the system is analyzed, and the specific parameters are shown in Table 1.

[0152] Table 1

[0153] Parameter Value Unit [R line1 ]]> 1 Ω [R line2 ]]> 2 Ω [R load ]]> 120 Ω [R v ]]> 0.1-10 Ω k p_V ]]> 2 - k I_V ]]> 0.5 - c ]]> ​ 20-100 rad / s

[0154] When ω c = 50 rad / s, increasing R v , the system root locus diagram is shown in Figure 6 (a), and when the droop coefficient changes in the range of 0.1-10 Ω, the system is stable. When R v = 1 Ω, increasing ω c , the system root locus is shown in Figure 6 (b), and from the root locus, it can be seen that increasing ω c will not affect the stability of the system.

[0155] (5) Example analysis

[0156] 1) Example description

[0157] In order to verify the effect of the proposed control scheme, the laboratory is built based on the OPAL-RT experimental platform as shown in Figure 7 The experimental platform is composed of an RTLAB real-time simulator (OP5700), a DSP controller (OP8665), a PC host computer, and an oscilloscope. The tested islanded DC microgrid includes three distributed energy storage units (DESUS1, DESUS2, DESUS3), a photovoltaic power generation model, and a load. Under three typical examples, the improved droop control strategy based on fine-tuning of the droop coefficient proposed in this paper is compared with the control strategy proposed in the existing scheme (Accurate power sharing with balanced battery state of charge in distributed DC microgrid[J]. IEEE Transactions on Industrial Electronics). The specific parameters of the system are shown in Table 2.

[0158] Table 2

[0159] Parameter Symbol Value System rated voltage V ref ]]> 400V desu1 line resistance [R line_1 ]]> 0.1 Ω desu2 line resistance [R line_2 ]]> 0.2 Ω desu3 line resistance [R line_3 ]]> 0.3 Ω Load [R load ]]> 100 Ω Convergence coefficient n 5 Photovoltaic rated power P pv ]]> 3 kw DESU capacity B .]]> ​ 0.5 Ah Initial droop coefficient [R v ]]> 0.2

[0160] 2) Results Analysis

[0161] Under normal charging conditions, each DESU initially has a SoC of 40%. To simulate the impact of line load mismatch on the system, a traditional droop control method is used for the first 3 seconds. Figure 8 As shown in (a), due to inconsistent line impedance in the system and the inability of traditional droop control to adaptively change the droop coefficient, deviations occur in the output current and SOC, making it impossible to maintain balance. After 3 seconds, the traditional droop control strategy switches to an improved droop control strategy based on fine-tuning the droop coefficient. Each DESU adaptively adjusts the droop coefficient and uses the fine-tuning strategy, ultimately achieving SoC balance for the DESU. Figure 8 As shown in (c), the introduction of the fine-tuning droop coefficient and the presence of line impedance cause a deviation in the DC bus voltage. Experimental results show that the voltage compensator based on the consensus algorithm is very effective in stabilizing the DC bus voltage. The simulation results in this paper are compared with those of existing schemes, as follows... Figure 8 As shown in (b), DESUs using the existing scheme cannot achieve SoC and current balance, and the bus voltage deviates from the reference value.

[0162] DESUs stable discharge experiment Figure 9 As shown, the initial SoC of each DESU is 70%. Within 0-3 seconds, the DESUs operate under traditional droop control, resulting in a deviation between the output current and SoC, making it difficult to achieve balancing. At 3 seconds, the DESUs switch to the control strategy proposed in this paper, ultimately achieving SoC balancing for each DESU. However, SoC and current balancing cannot be achieved, and the bus voltage deviates from the reference value. The charging simulation results are compared with those of existing schemes. The operation process is as follows: Figure 9 As shown in (b), SoC balancing ultimately could not be achieved. Figure 9 In (c), the voltage compensator based on the consensus algorithm proposed in this paper maintains the bus voltage stability, while the bus voltage deviates significantly under the control method of the existing scheme.

[0163] To verify the effectiveness of the proposed control strategy in the case of a surge load in an islanded microgrid, the surge load was set to occur at 8 seconds and last for 2 seconds. The initial SoC of each DESU was 70%. From 0 to 6 seconds, the DESUs were in conventional droop control mode, and the output current and SoC deviated. At 6 seconds, the DESUs switched to the proposed control strategy, and the SoC gradually balanced. Then, at 8 seconds, the microgrid system experienced a surge load, and it returned to normal at 10 seconds. The specific process is as follows: Figure 10As shown in (a), the proposed strategy can still achieve output current and SoC balance even after an impact load. The charging simulation results in this paper are compared with those of existing solutions. The operation process is as follows: Figure 10 As shown in (b), the SoC gradually reaches equilibrium at 6 seconds. After the impact load, the existing control method is unable to achieve SoC equilibrium. Figure 10 As shown in (c), the bus voltage waveform also reflects that the control strategy in this paper will not cause large transient waveforms when facing impact loads, thus ensuring stable system operation. In contrast, the existing control methods cause significant deviations in the bus voltage.

[0164] In this example, the effectiveness of the proposed control strategy is verified when the photovoltaic unit power fluctuates. When the photovoltaic unit output power is less than the load power consumption, the DESUs are in a discharging state. Then, due to increased light intensity, the photovoltaic unit output power increases, and the DESUs transition from a discharging state to a charging state. The experimental results for photovoltaic unit power fluctuations are as follows: Figure 10 As shown, at t = 0-12s, DESUs operate in discharge mode, supporting stable operation of the DC microgrid, such as... Figure 10 As shown in (a) and (b), the DESUs initially operate under the traditional droop control method. Due to the limitations of traditional droop control and line impedance mismatch, differences in SoC (System-on-Chips) emerge. At t = 6s, the DESUs switch to the control strategy proposed in this paper, and the differences between SoCs gradually decrease. At t = 12s, the output power of the photovoltaic unit increases, and the DESUs transition to a charging state, absorbing excess power in the system. The SoC and output current remain balanced. Simulation results using the existing scheme are shown below. Figure 10 As shown in (c) and (d), under conditions of fluctuating photovoltaic power output, there is a significant deviation between the SoC and the output current, making it difficult to achieve balance. The DC bus voltage results for the two methods are as follows: Figure 10 As shown in (e) and (f), the DC bus voltage remains stable and does not exceed the limit under the voltage compensation method based on the consensus algorithm proposed in this paper, and is not affected by the power fluctuation of the photovoltaic unit. However, the DC bus voltage exceeds the limit severely under the control method of the existing scheme, which affects the stable operation of the microgrid system.

[0165] The other settings in this embodiment are the same as in Embodiment 1.

[0166] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A self-adaptive droop controller construction method for equalization control of state of charge of distributed energy storage units in an islanded microgrid system, characterized in that, The method comprises: According to the state of charge of the distributed energy storage unit, a double-quadrant state of charge weighting control method is used to adaptively correct the initial droop coefficient in the I-U droop controller to obtain a first droop coefficient; wherein, is the first droop coefficient corresponding to the th distributed energy storage unit. Considering the line impedance difference between different distributed energy storage units, the fine tuning droop coefficient is calculated based on the assumption that the product of the initial droop coefficient and the converter output current is a constant value; wherein, is the fine tuning droop coefficient corresponding to the mth distributed energy storage unit, is the fine tuning droop coefficient corresponding to the mth distributed energy storage unit. The communication relationship between each distributed energy storage unit in the direct current micro-grid is described by using a directed graph, a voltage compensator based on a consistency algorithm is constructed, and a voltage compensation term of each distributed energy storage unit is calculated; The sum of the first droop coefficient and the fine-tuning droop coefficient is used to replace the initial droop coefficient, and the output voltage of the converter after droop control is compensated by using the voltage compensation term to obtain an adaptive droop controller.

2. The method of claim 1, wherein, According to the state of charge of the distributed energy storage unit, a double-quadrant state of charge weighted control method is used to adaptively correct the initial droop coefficient to obtain the first droop coefficient, and the expression is: , In the formula: is the initial droop coefficient corresponding to the jth distributed energy storage unit; is the initial droop coefficient corresponding to the jth distributed energy storage unit; is the average state of charge of the distributed energy storage unit; is the average state of charge of the distributed energy storage unit; is the state of charge of the jth distributed energy storage unit; is the state of charge of the jth distributed energy storage unit; represents a discharging state, represents a charging state.

3. The method of claim 1, wherein, Considering the line impedance difference between different distributed energy storage units, based on the assumption that the product of the initial droop coefficient and the output current of the converter is a constant value, a first PI controller is used to calculate the fine-tuning droop coefficient, and the calculation expression is: ， In the formula: is a parameter of the first PI controller; is the initial droop coefficient corresponding to the i-th distributed energy storage unit in the original I-U droop control model is the initial droop coefficient corresponding to the i-th distributed energy storage unit in the original I-U droop control model is the product of the DC / DC converter output current is the product of the DC / DC converter output current is the average value of the product of the initial droop coefficient corresponding to all distributed energy storage units in the droop control and the converter output current.

4. The method of claim 1, wherein, The communication relationship between the distributed energy storage units in the direct current micro-grid is described by using a directed graph, and specifically, a directed graph G is used to represent the communication relationship between the distributed energy storage units in the direct current micro-grid The communication layer between the distributed energy storage units in the direct current micro-grid is represented by using a directed graph Each distributed energy storage unit is regarded as a node and marked as a point set The transmission link between each node is represented by using a directed graph When the node i exchanges information with its adjacent node j, it is represented by using a directed graph The set of all adjacent nodes of the node i is represented by using a directed graph There is a communication line between the adjacent nodes, and the communication network formed thereby has an adjacent matrix A The adjacent matrix is defined as , The communication weight between the nodes i and j is represented by using a directed graph, if the node j sends information to the node i, it is represented by using a directed graph >0, otherwise, =0; the diagonal matrix is the degree matrix of the directed graph G, and the Laplacian matrix of the directed graph G is defined as .

5. The method of claim 4, wherein, The construction of the voltage compensator based on the consistency algorithm is then The average bus voltage estimated by the first The calculation expression is: , In the formulae: is the bus voltage at the outlet of the th transformer.

6. The method of claim 1, wherein, The voltage compensation term of each distributed energy storage unit is calculated by using a second PI controller The calculation expression is: , wherein: and are parameters of the second PI controller; is the reference output voltage in the no-load state of the DC / DC converter; is the average bus voltage estimated by the first voltage compensator.

7. The method of claim 1, wherein, The sum of the first droop coefficient and the fine-tuning droop coefficient is used to replace the initial droop coefficient, and the output voltage of the converter after droop control is compensated by using the voltage compensation term to obtain an improved I-U droop controller, and the mathematical expression is: , In the formula: and respectively represent the output voltage and output current of the DC / DC converter after droop control; represents the reference output voltage of the DC / DC converter in the no-load state; is the voltage compensation term of each distributed energy storage unit.

8. An islanded microgrid control system, characterized by, The system comprises an adaptive droop controller constructed by using the method of any one of claims 1-7.

9. An electronic device comprising a memory and a processor, said memory having stored thereon a computer program, characterized in that, The processor executes the program to implement the method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1-7.

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