A distributed cooperative control method, device and equipment for a direct-current microgrid

Abstract

The application discloses a kind of distributed cooperative control method, device and equipment of direct current microgrid, and each energy storage system is updated using the average voltage estimation model considering random time delay to the average voltage estimation value of ontology;Each energy storage system generates average voltage correction value using the average voltage consistency model considering random time delay;Each energy storage system generates state of charge correction value using the state of charge consistency model considering random time delay;Each energy storage system generates reference voltage value for bottom control using feedback linearization method according to the state of charge correction value and average voltage correction value of ontology, until the current state of charge of each energy storage system realizes consistency, and the updated average voltage estimation value of each energy storage system realizes consistency.The application aims to realize the average voltage recovery and state of charge balance of direct current microgrid considering energy storage under the condition of introducing random time delay.

Description

Technical Field

[0001] This invention relates to the field of microgrid control, and specifically to a distributed collaborative control method, apparatus, and equipment for a DC microgrid. Background Technology

[0002] Against the backdrop of the rapid increase in the penetration rate of distributed renewable energy, the development of distributed collaborative control of DC microgrids considering energy storage is of great significance and urgently needed. Researching distributed collaborative control methods for DC microgrids considering energy storage can help in the rational design of controllers for practical applications such as data centers. This includes ensuring power quality through voltage control, fully utilizing the capacity and extending the lifespan of energy storage devices through state-of-charge control, and promoting the application and development of distributed renewable energy in DC microgrids. This is of great significance for promoting the development of energy storage in my country's power system and the low-carbon transition of energy. The control objectives of DC microgrids considering energy storage include voltage recovery, power sharing, and state-of-charge balance.

[0003] Current research on distributed control methods for DC microgrids that consider energy storage largely fails to consider the time delay in distributed communication or simply treats it as a fixed time delay, lacking discussion on its stochasticity. At the same time, most methods do not improve the control conflict problem between voltage control and state of charge control, thus failing to achieve good overall control performance. Summary of the Invention

[0004] To address the problems existing in the prior art, the present invention provides a distributed cooperative control method, device and equipment for DC microgrids, the purpose of which is to achieve average voltage recovery and state of charge balance of DC microgrids considering energy storage under the condition of introducing random time delay.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] A distributed cooperative control method for a DC microgrid includes:

[0007] S1. Each energy storage system updates its average voltage estimate by using an average voltage estimation model that considers random time delay, based on the current output voltage and average voltage estimate of the system itself, as well as the average voltage estimate of its neighboring energy storage systems.

[0008] S2. Each energy storage system generates an average voltage correction value based on the average voltage estimate of the system itself and the average voltage estimate of its adjacent energy storage systems using an average voltage consistency model that takes into account random time delays.

[0009] S3. Each energy storage system generates a state of charge correction value based on its current state of charge and the current state of charge of its adjacent energy storage systems using a state of charge consistency model that considers random time delay.

[0010] S4. Each energy storage system generates a reference voltage value for underlying control using a feedback linearization method based on its own state of charge correction value and average voltage correction value. S1 to S4 are repeated until the current state of charge of each energy storage system is consistent and the updated average voltage estimate of each energy storage system is consistent.

[0011] Furthermore, the average voltage estimation model considering random time delays is as follows:

[0012]

[0013] In the formula: This is the average voltage estimate; V i od (t) represents the output voltage; Gain for voltage observation information feed; τ is an element of the adjacency matrix. ij (t) represents the random time delay in the communication process of an energy storage system modeled by a Markov chain.

[0014] Furthermore, the average voltage consistency model considering random time delays is as follows:

[0015]

[0016] In the formula: This is the average voltage correction value output by the voltage controller; Gain for feeding voltage control information; To access the pinning gain of the reference node; g i For pinning matrix elements; V mg This is the rated voltage of the microgrid.

[0017] Furthermore, the state-of-charge consistency model considering random time delays is as follows:

[0018]

[0019] In the formula: This is the state-of-charge correction value output by the state-of-charge controller; Gain for feeding state-of-charge control information; For the state variables of the state-of-charge control system, it includes the state of charge combined with the droop coefficient and the rate of change of the state of charge.

[0020] A distributed collaborative control device for a DC microgrid includes:

[0021] The average voltage observation module is used to update the average voltage estimate of each energy storage system based on the current output voltage and average voltage estimate of the system itself, as well as the average voltage estimate of the adjacent energy storage systems, using an average voltage estimation model that takes into account random time delays.

[0022] The average voltage consistency control module is used to generate an average voltage correction value for each energy storage system based on the average voltage estimate of the system itself and the average voltage estimate of the adjacent energy storage systems, using an average voltage consistency model that takes into account random time delays.

[0023] The state of charge consistency control module is used by each energy storage system to generate a state of charge correction value based on the current state of charge of the system itself and the current state of charge of its adjacent energy storage systems, using a state of charge consistency model that takes into account random time delay.

[0024] The calculation module is used by each energy storage system to generate a reference voltage value for the underlying control based on the corrected state of charge and the corrected average voltage value of the system. The DC microgrid continuously calls the average voltage observation module, the average voltage consistency control module, the state of charge consistency control module and the calculation module until the current state of charge of each energy storage system is consistent and the updated average voltage estimate of each energy storage system is consistent.

[0025] Furthermore, each energy storage system is equipped with the aforementioned average voltage observation module, average voltage consistency control module, state of charge consistency control module, and calculation module.

[0026] An apparatus includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the distributed cooperative control method for a DC microgrid.

[0027] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the distributed cooperative control method for a DC microgrid.

[0028] Compared with the prior art, the present invention has at least the following beneficial effects:

[0029] This invention provides a distributed cooperative control method for DC microgrids. Addressing the stochastic time delay problem in distributed average voltage recovery and state-of-charge (POC) balance control of DC microgrids considering energy storage, this method employs an average voltage estimation model considering stochastic time delay, an average voltage consistency model considering stochastic time delay, and a POC consistency model considering stochastic time delay. This reduces the deviation between the output voltage of each energy storage system converter and the rated voltage of the microgrid during POC convergence, weakens the conflict between voltage control and POC control, and better reflects real-world operating scenarios. Specifically, through the coordinated operation of preset average voltage observation, average voltage consistency control, and POC consistency control, the method achieves average voltage recovery and POC balance of DC microgrids considering energy storage under the influence of stochastic time delay.

[0030] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the specific embodiments of the present invention, the drawings used in the description of the specific embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a flowchart of a distributed collaborative control method for a DC microgrid according to the present invention.

[0033] Figure 2 This is a structural diagram of the distributed control method of the present invention.

[0034] Figure 3 This is a schematic diagram of the distributed control and communication topology of the four energy storage systems of the present invention.

[0035] Figure 4 This is a flowchart illustrating the stability verification process of the distributed control system of the present invention.

[0036] Figure 5 This is a schematic diagram of the application simulation model of the present invention.

[0037] Figure 6 This is a schematic diagram of the 4-energy storage system used for simulation verification of the present invention.

[0038] Figure 7 This diagram illustrates the effect of the average voltage consistency control in a 4-energy storage system based on the present invention.

[0039] Figure 8 This diagram illustrates the effect of the present invention on the state-of-charge consistency control in a 4-energy storage system. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] As a specific embodiment of the present invention, combined with Figure 1 and Figure 2 As shown, a distributed cooperative control method for a DC microgrid is as follows:

[0042] S1. Each energy storage system updates its average voltage estimate by using an average voltage estimation model that considers random time delay, based on the current output voltage and average voltage estimate of the system itself, as well as the average voltage estimate of its neighboring energy storage systems.

[0043] The average voltage estimation model considering random time delay is as follows:

[0044]

[0045] In the formula: This is the average voltage estimate; V i od (t) represents the output voltage; Gain for voltage observation information feed; τ is an element of the adjacency matrix. ij (t) represents the random time delay in the communication process of an energy storage system modeled by a Markov chain.

[0046] It should be noted that this requires designing a two-level control communication network topology based on graph theory and the characteristics of distributed multi-agent systems, i.e., the elements of the adjacency matrix. Design: Sparse communication graph G(V) between various BESS G E G ) consisting of a set of nodes Through a series of edges The graph is composed of interconnections. Each node represents a BESS system, and the edges represent communication links for data exchange between them. Based on whether there exists a link (i, j) ∈ E that allows information to flow from node i to node j, the adjacency matrix of the graph reflects the connectivity between nodes. The adjacency matrix is ​​given by the following formula:

[0047]

[0048] In the formula: a ij— The communication coefficient from node j to node i.

[0049] It should be noted that after discretization, stochastic time delays in distributed control systems can be modeled using Markov chains. This assumes that the time delay of each communication link is consistent and bounded, i.e., 0 ≤ τ. ij (k)≤τ, random time delay τ ij The model of (k) is a set of discrete random variables X = {X n Given a random variable θ = {0, 1, ..., τ}, where n > 0, and its exponent set is countable θ = {0, 1, ..., τ}, its conditional probability relation satisfies:

[0050] p(X t+1 |X t ,…,X1)=p(X t+1 |X t (12)

[0051] If a random variable is given at step t, then the random variable at step t+1 will be conditionally independent of all random variables except those at step t. This is the "memoryless property" of a Markov chain, which can be expressed by the formula:

[0052] X t+1 ⊥(X t-1 ,X0)|X t (13) Assume the transition matrix of the random time delay of this system is in and Non-negative, This means the random time delay τ at time k of the communication link from point j to point i. ij (k) will be with The probability of transitioning from state r to state s at time k+1 is [not specified]. The Markov chain model built for the time delay is ergodic, which is reflected in the transition graph as the ergodic chain does not form a directed acyclic graph or a single closed loop. Furthermore, due to homogeneous modeling, it is assumed that all communication link time delays have the same transition matrix Λ. ij =Λ=[λ rs ].

[0053] S2. Each energy storage system generates an average voltage correction value based on the average voltage estimate of the system itself and the average voltage estimate of its adjacent energy storage systems using an average voltage consistency model that takes into account random time delays.

[0054] Specifically, the average voltage consistency model considering random time delays is as follows:

[0055]

[0056] In the formula: This is the average voltage correction value output by the voltage controller; Gain for feeding voltage control information; To access the pinning gain of the reference node; g i For pinning matrix elements; V mg This is the rated voltage of the microgrid.

[0057] It should be noted that the diagonal matrix G = diag{g1,...,g...} N This is called the pinning gain matrix, and the reference point is defined as node 0. Therefore, the weights g are defined as follows: i If the weight g is greater than 0, then the edge (0, i) ∈ E exists. Here, the weight g... i This is called pinning gain, when g i When the value is greater than 0, the corresponding node i is called the pinning reference node and can access the reference value. Adjacent energy storage systems can communicate with each other, while a leader energy storage system only communicates with a small number of agents. By setting the pinning matrix, a leader energy storage system node that can access the voltage reference value can be selected.

[0058] S3. Each energy storage system generates a state of charge correction value based on its current state of charge and the current state of charge of its adjacent energy storage systems using a state of charge consistency model that considers random time delay.

[0059] Specifically, the state-of-charge consistency model considering random time delays is as follows:

[0060]

[0061] In the formula: This is the state-of-charge correction value output by the state-of-charge controller; Gain for feeding state-of-charge control information; For the state variables of the state-of-charge control system, it includes the state of charge combined with the droop coefficient and the rate of change of the state of charge.

[0062] It should be noted that, in order to achieve balanced convergence of the charge levels of each energy storage system, the state of charge (SPC) of each system is used for communication. Furthermore, to mitigate fluctuations during the convergence process, the rate of change of SPC also needs to be communicated simultaneously, making it easier to smoothly reach a balanced charging and discharging state. The rate of change of SPC is also linearly related to the converter inductor current, thus ensuring current balance while balancing the charge levels of each energy storage system.

[0063] S4. Each energy storage system generates a reference voltage value for underlying control using a feedback linearization method based on its own state of charge correction value and average voltage correction value. S1 to S4 are repeated until the current state of charge of each energy storage system is consistent and the updated average voltage estimate of each energy storage system is consistent.

[0064] It should be noted that by using the feedback linearization of input and output, the nonlinear control of the energy storage system can be transformed into linear control. Therefore, the secondary voltage control, which involves complex internal dynamics, can be transformed into a synchronization tracking problem. This invention, based on a general battery model combined with the internal working model of a DC-DC converter, and by replacing the current with the state of charge, yields the voltage controller output:

[0065]

[0066] In the formula: r i drp′ — The corrected virtual droop control impedance.

[0067] Further obtain the state of charge control output

[0068]

[0069] Where: SOC i — The state of charge of the i-th energy storage system.

[0070] Finally, the reference voltage value used for low-level control is obtained.

[0071]

[0072] The stability of the distributed cooperative control method for a DC microgrid of the present invention will be explained below with theoretical analysis. The overall process is as follows: Figure 4 As shown.

[0073] Define the first state variable and observe the voltage deviation.

[0074] Define the second state variable as the output voltage deviation.

[0075] The state-space equation of the distributed average voltage control system of the i-th energy storage system is obtained as follows:

[0076]

[0077] remember

[0078] Select an appropriate sampling period T s =2×10-3 s, transforms the continuous-time model of the state-space equations of the distributed average voltage control system into a discrete-time model.

[0079] The discrete state-space equation of the distributed average voltage control system of the i-th energy storage system is:

[0080]

[0081] The corresponding discrete coefficient matrix can be calculated using the following formula:

[0082]

[0083] The state vector and control vector corresponding to the equation are:

[0084] The control vector is transformed and simplified:

[0085]

[0086] In the formula: —Neighbor gain parameter matrix of average voltage observer and average voltage controller; — Pinning gain parameter matrix of average voltage controller.

[0087] Define the state variables of the global system as follows:

[0088]

[0089] By merging n energy storage systems, the global state-space equations of the distributed average voltage control system of an islanded DC microgrid are obtained:

[0090]

[0091] In the formula: A — diagonal matrix diag{A i};B——Diagonal matrix diag{B i};C——Diagonal matrix diag{C i}

[0092] The difference equations for the control vectors incorporating Markov random time delays are rewritten as stochastic equations. The stochastic equations are shown below:

[0093]

[0094] In the formula: E1 — global controller observation neighbor gain matrix diag{K i E2—Global Controller Pinning Gain Matrix I o ——rank{I o}=rank{X i The identity matrix Φ = 2. m (k)——Random matrix.

[0095] The definition of a random matrix is: m = 0, 1, ..., τ, and the elements of the matrix are defined as follows: At the same time, the random matrix satisfies:

[0096]

[0097] Predictable time delay jumps in adjacent communication links:

[0098]

[0099] Obviously This involves predicting the case where the time delay from node j to node i at time k+1 is 0. This can be calculated by combining the time delay from node j to node i at time k with the corresponding transition probability. Since the network-induced time delays of the microgrid communication links are independent, based on the above analysis, Φ m (k) is a random matrix with Markov properties. Define the state space. It is the set of all possible matrices {{Φ0(k),Φ1(k),…Φ} τ Let l be a combination of (k)}, k = 0, 1, 2, ..., where l is a finite number. For any time k, the following holds:

[0100]

[0101] In the formula: η qp —The transition probability from state q to state p.

[0102] For (4-20), where η qp ≥0 and for all {p,q}∈{1,2,…l}, satisfying

[0103] Therefore, we can conclude that: {{Φ0(k),Φ1(k),…Φ τ If (k)}, k=0,1,2,…} is a Markov process, then it can be predicted that:

[0104]

[0105] The closed-loop equation of the global system can be simplified to:

[0106]

[0107] make: Where m = 0, 1, 2, ..., τ.

[0108] Define a new state variable:

[0109] The obtained microgrid distributed average voltage control closed-loop system model is as follows:

[0110]

[0111] In the formula: ψ(k) — the state coefficient matrix of the closed-loop system.

[0112] The system can be described as:

[0113]

[0114] In the formula: —Initial system state.

[0115] Similar to Φ m The prediction of (k), G m (k) also conforms to the Markov property.

[0116] definition:

[0117]

[0118] have

[0119]

[0120] In the formula: I G ——rank(I G ) = rank(G m The identity matrix of ) ; Λ - single-step transition matrix.

[0121] Based on Markov chain modeling with ergodicity, we have {π(s), s=0,1,…τ} as a Markov chain {τ ij The limiting distribution (stationary distribution) of {(k), k = 0, 1, 2, ...} satisfies:

[0122] π=πΛ (28)

[0123] Further, there are:

[0124]

[0125] The method for calculating the stationary distribution π(s) is as follows:

[0126]

[0127] And this limiting stationary distribution satisfies

[0128] Therefore, we can conclude that: It is a constant matrix.

[0129] The following closed-loop systems exist:

[0130]

[0131] It is the limit system corresponding to the global model of distributed average voltage control.

[0132] Considering that a corrected droop coefficient was introduced in the design of the SOC auxiliary controller, the state variable was chosen as... The state-space equations of the SOC balance control system of the i-th BESS can be written as follows:

[0133]

[0134] Discretize it using the same method:

[0135]

[0136] Then we obtain the global state-space equations for the SOC balance control system of n BESS:

[0137]

[0138] Transform the difference equations of the SOC controller into the form of stochastic equations:

[0139]

[0140] Where: E0 — SOC controller gain coefficient matrix

[0141] Substituting (35) into (34), we can obtain the global closed-loop equation of the SOC balance control system as follows:

[0142]

[0143] Similarly, let m = 0, 1, 2, ..., τ.

[0144] Similarly, based on the derivation of the stationary distribution characteristics of Markov chains mentioned above, the closed-loop equation of the SOC equilibrium control limit system can be obtained as follows:

[0145]

[0146] The state coefficient matrix of the limit system is calculated as follows:

[0147]

[0148] Combining the matrix parameters in (38) and

[0149]

[0150] visible It is a constant matrix.

[0151] The global state-space equations of both distributed average voltage control and distributed state-of-charge control limit systems can be expressed in the following form:

[0152]

[0153] At this point, the necessary and sufficient conditions for the stability of the discrete system can be obtained simply by mapping the stable region of the z-plane, i.e. When all eigenvalues ​​of a matrix lie within the unit circle centered at the origin (meaning there is only one eigenvalue on the unit circle), the system is critically stable of type I. Therefore, the closed-loop stochastic system is asymptotically stable.

[0154] The present invention also provides a distributed cooperative control device for a DC microgrid, comprising:

[0155] The average voltage observation module is used to update the average voltage estimate of each energy storage system based on the current output voltage and average voltage estimate of the system itself, as well as the average voltage estimate of the adjacent energy storage systems, using an average voltage estimation model that takes into account random time delays.

[0156] The average voltage consistency control module is used by each energy storage system to generate an average voltage correction value based on the average voltage estimate of the system itself and the average voltage estimate of its neighboring energy storage systems, using an average voltage consistency model that takes into account random time delays.

[0157] The state of charge consistency control module is used by each energy storage system to generate a state of charge correction value based on the current state of charge of the system itself and the current state of charge of its neighboring energy storage systems, using a state of charge consistency model that takes into account random time delays.

[0158] The calculation module is used by each energy storage system to generate a reference voltage value for the underlying control based on the corrected state of charge and the corrected average voltage value of the system. The DC microgrid continuously calls the average voltage observation module, the average voltage consistency control module, the state of charge consistency control module and the calculation module until the current state of charge of each energy storage system is consistent and the updated average voltage estimate of each energy storage system is consistent.

[0159] Example

[0160] Combination Figure 5The microgrid simulation design model can be built and the simulation verification model can be constructed based on the Simulink environment, such as Figure 6 As shown, the model includes a battery model, a DC-DC converter dual-loop control model, a physical microgrid RL line model, a communication topology model, a new energy power output fluctuation model, and a stochastic time-delay model for Markov chain modeling of the communication link. Here, a stochastic time delay of 0–8 ms is designed, and the corresponding transition matrix is:

[0161] according to Figure 3 As shown in the communication topology link, the adjacency matrix of the communication is designed as follows: The pinning matrix is: g = diag{1,0,0,0}, and the microgrid's rated voltage is 380V. Different load fluctuation scenarios were set at four time points. Figure 7 and Figure 8 This demonstrates the effectiveness of the consistency control system, and Figure 7 The actual average output voltage of each energy storage system converges to the rated voltage of the microgrid of 380V, and it can also cope well with power fluctuations in the DC microgrid, maintaining consistent convergence. Figure 8 The demonstrated energy storage systems achieved consistent state-of-charge convergence, fulfilling the goal of balancing the battery's state of charge. Simulation results illustrate the excellent control performance of this invention.

[0162] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used to implement the operation of a distributed cooperative control method for a DC microgrid.

[0163] In one embodiment of the present invention, a distributed collaborative control method for a DC microgrid, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data.

[0164] The computer storage medium can be any available medium or data storage device that a computer can access, including but not limited to magnetic storage (e.g., floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs)), optical storage (e.g., CDs, DVDs, BDs, HVDs), and semiconductor storage (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0165] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0167] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0169] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and 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 distributed cooperative control method for a DC microgrid, characterized in that, include: S1. Each energy storage system updates its average voltage estimate based on its current output voltage and average voltage estimate, as well as the average voltage estimates of its neighboring energy storage systems, using an average voltage estimation model that considers random time delays. The average voltage estimation model that considers random time delays is as follows: In the formula: This is the average voltage estimate; This refers to the output voltage. Gain for voltage observation information feed; These are elements of the adjacency matrix; Stochastic time delays in the communication process of energy storage systems modeled for Markov chains; S2. Each energy storage system generates an average voltage correction value based on its own average voltage estimate and the average voltage estimates of its adjacent energy storage systems, using an average voltage consistency model that considers random time delays; the average voltage consistency model that considers random time delays is: In the formula: This is the average voltage correction value output by the voltage controller; Gain for feeding voltage control information; To access the pinning gain of the reference node; For pinning matrix elements; This is the rated voltage of the microgrid; S3. Each energy storage system generates a state-of-charge (POC) correction value based on its current POC and the current POC of its adjacent energy storage systems using a POC consistency model that considers random time delays. The POC consistency model considering random time delays is as follows: In the formula: This is the state-of-charge correction value output by the state-of-charge controller; Gain for feeding state-of-charge control information; For the state variables of the state-of-charge control system, it includes the state of charge combined with the droop coefficient and the rate of change of the state of charge. ; S4. Each energy storage system generates a reference voltage value for underlying control using a feedback linearization method based on its own state of charge correction value and average voltage correction value. S1~S4 are repeated until the current state of charge of each energy storage system is consistent and the updated average voltage estimate of each energy storage system is consistent.

2. A distributed collaborative control device for a DC microgrid, characterized in that, include: The average voltage observation module is used to update the average voltage estimate of each energy storage system based on its current output voltage and average voltage estimate, as well as the average voltage estimates of its neighboring energy storage systems, using an average voltage estimation model that considers random time delays. The average voltage estimation model considering random time delays is as follows: In the formula: This is the average voltage estimate; This refers to the output voltage. Gain for voltage observation information feed; These are elements of the adjacency matrix; Stochastic time delays in the communication process of energy storage systems modeled for Markov chains; An average voltage consistency control module is used by each energy storage system to generate an average voltage correction value based on its own average voltage estimate and the average voltage estimates of its neighboring energy storage systems, using an average voltage consistency model that considers random time delays. The average voltage consistency model considering random time delays is as follows: In the formula: This is the average voltage correction value output by the voltage controller; Gain for feeding voltage control information; To access the pinning gain of the reference node; For pinning matrix elements; This is the rated voltage of the microgrid; The state-of-charge (POC) consistency control module is used by each energy storage system to generate a POC correction value based on its own current POC and the current POC of its adjacent energy storage systems, using a POC consistency model that considers random time delays. The POC consistency model considering random time delays is as follows: In the formula: This is the state-of-charge correction value output by the state-of-charge controller; Gain for feeding state-of-charge control information; For the state variables of the state-of-charge control system, it includes the state of charge combined with the droop coefficient and the rate of change of the state of charge. ; The calculation module is used by each energy storage system to generate a reference voltage value for underlying control based on the system's state of charge correction value and average voltage correction value using a feedback linearization method. The DC microgrid continuously calls the average voltage observation module, the average voltage consistency control module, the state of charge consistency control module, and the calculation module until the current state of charge of each energy storage system is consistent, and the updated average voltage estimate of each energy storage system is consistent.

3. The distributed collaborative control device for a DC microgrid according to claim 2, characterized in that, Each energy storage system is equipped with the aforementioned average voltage observation module, average voltage consistency control module, state of charge consistency control module, and calculation module.

4. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the distributed cooperative control method for a DC microgrid as described in claim 1.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the distributed cooperative control method for a DC microgrid as described in claim 1.

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