Distributed cooperative control method and device considering line loss optimization scheduling

Abstract

This invention relates to the field of power grid optimization technology and provides a distributed cooperative control method and apparatus that considers line loss optimization scheduling. The method includes: acquiring microgrid parameters; establishing an optimization model considering line losses based on the parameters; solving for the microgrid's optimality conditions based on the optimization model; adjusting the frequency of distributed power sources in the microgrid according to the optimality conditions; and determining the microgrid's stability conditions based on the current communication status of the microgrid. This invention considers line losses and communication delays, enabling optimal scheduling of the microgrid.

Description

Technical Field

[0001] This invention belongs to the field of power grid optimization technology, and in particular relates to a distributed collaborative control method and device that considers line loss optimization scheduling. Background Technology

[0002] With the development of distributed renewable energy, a large number of distributed power sources are connected to microgrids. Therefore, distributed cooperative algorithms are more suitable than centralized algorithms for the optimal scheduling of microgrids.

[0003] Existing distributed optimization scheduling methods may have one or more of the following drawbacks:

[0004] (1) Real-time information about the load is required, which is difficult to obtain;

[0005] (2) Line losses were not considered in the optimized scheduling, which may lead to the deviation between the system operating point and the optimal solution. In addition, the impact of cable resistance on stability was not considered.

[0006] (3) Optimal scheduling depends on communication. If a communication link fails, resulting in no spanning tree in the communication network, it cannot be achieved.

[0007] (4) Low-bandwidth communication is often delayed, which may lead to instability. Summary of the Invention

[0008] In view of this, embodiments of the present invention provide a distributed cooperative control method and apparatus that considers line loss optimization scheduling to achieve optimal scheduling of microgrids, taking into account line losses and communication delays.

[0009] A first aspect of this invention provides a distributed cooperative control method for optimizing scheduling while considering line loss, comprising:

[0010] Obtain the parameters of the microgrid and establish an optimization model that considers line losses based on the parameters;

[0011] The optimality conditions of the microgrid are solved based on the optimization model;

[0012] Frequency regulation of distributed power sources in microgrids based on optimality conditions;

[0013] Based on the current communication status of the microgrid, the stability conditions of the microgrid are determined.

[0014] Optionally, the optimized model is:

[0015]

[0016]

[0017] 0≤p i ≤pmax,i ;

[0018] In the formula, p i Let f be the output power of the i-th distributed power source. i (p i Let be the operating cost function of the i-th distributed power source, and let a be the operating cost function of the i-th distributed power source. i b i c i p is a coefficient load p represents the active power of the load. loss For line loss, p max,i Let be the maximum output power of the i-th distributed power source, and n be the number of distributed power sources in the microgrid.

[0019] Optional, the optimality condition is:

[0020]

[0021] In the formula, X i ,α i Let be the impedance magnitude and impedance angle of the i-th line, respectively, and ε be the maximum allowable voltage deviation percentage of the node voltage. i Let f be the output power of the i-th distributed power source. i (p i Let be the operating cost function of the i-th distributed power source.

[0022] Optionally, frequency regulation of distributed generation in the microgrid can be performed based on optimality conditions, including:

[0023]

[0024] In the formula, m is a preset coefficient, θ is the frequency of the distributed power source, d is the gain coefficient, ω* is the reference frequency, and K=diag(1-βcotα) i ) -1 ,1 n =[1 1 … 1] T L is the Laplace matrix of the communication network.

[0025] Optionally, based on the current communication status of the microgrid, the stability conditions of the microgrid are determined, including:

[0026] When a microgrid contains no communication or has communication with no delay, the stability condition of the microgrid is |θ i -θ L | less than a preset threshold; where |θ i -θ L | represents the phase angle difference in steady state.

[0027] Optionally, based on the current communication status of the microgrid, the stability conditions of the microgrid are determined, including:

[0028] When communication exists in a microgrid and there is a communication delay, the stability condition of the microgrid is: Where τ is the communication delay, d is the gain coefficient, and λ max Let L be the largest eigenvalue of the Laplace matrix L.

[0029] A second aspect of the present invention provides a distributed cooperative control device for optimizing scheduling while considering line loss, comprising:

[0030] The acquisition module is used to acquire the parameters of the microgrid and establish an optimization model that takes line losses into account based on the parameters;

[0031] The solution module is used to solve for the optimality conditions of the microgrid based on the optimization model;

[0032] The regulation module is used to regulate the frequency of distributed power sources in the microgrid according to optimality conditions;

[0033] The determination module is used to determine the stability conditions of the microgrid based on the current communication status of the microgrid.

[0034] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the distributed cooperative control method for optimized scheduling considering line loss as described in the first aspect above.

[0035] A fourth aspect of the present invention provides 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 optimized scheduling considering line loss as described in the first aspect above.

[0036] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0037] This invention first establishes an optimization model considering line losses, obtaining optimality conditions. Secondly, cost optimization and frequency regulation are implemented in the first and second level control stages, thus achieving optimal scheduling even without communication, with only frequency recovery dependent on communication. Simultaneously, system stability considering cable resistance and communication delay is analyzed, and stability conditions are given based on the current communication status of the microgrid. The optimal scheduling algorithm of this invention does not depend on communication; optimal scheduling can be achieved even without communication. The control scheme proposed in this invention does not require real-time load information. If communication is normal, the control scheme proposed in this invention can simultaneously achieve optimal scheduling and frequency recovery. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram illustrating the implementation process of the distributed cooperative control method for optimized scheduling considering line loss provided in an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of the equivalent model of a microgrid provided in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram of the structure of the distributed cooperative control device for optimized scheduling considering line loss provided in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0043] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0044] To illustrate the technical solution described in this invention, specific embodiments are described below.

[0045] Economic dispatch and frequency regulation are crucial for the stable and economical operation of power systems. There are generally three types of optimal dispatch methods: centralized, decentralized, and distributed optimization methods.

[0046] Centralized optimal dispatch typically involves two processes (i.e., "decision-making" and "control"). In the first process, a central processing unit gathers global information, solves the optimization problem, and generates references regarding the output voltage and power of distributed generation. The second process tracks these references via a PI controller. The primary task of centralized optimal dispatch is to develop algorithms to solve the optimization problem. Here, the optimization problem is typically represented using global parameters such as line impedance, flow constraints, cost functions, and load parameters. Centralized optimization is usually based on traditional optimization techniques, such as convex optimization and heuristic optimization methods. Due to low power constraints, economic dispatch problems are often non-convex. To overcome this, some convex relaxation methods, such as semidefinite programming and second-order cone programming, have been proposed. For economic dispatch problems with more non-convexity and stricter constraints, heuristic optimization methods, such as genetic algorithms, particle swarm optimization, and deep neural network methods, have been proposed to find solutions to the economic dispatch problem. For economic dispatch problems with a centralized framework, the optimization model is usually detailed and accurate, thus often providing global or near-global optimal solutions in practical systems. However, it heavily relies on dense communication and often suffers from single-point-of-failure problems, which increase communication costs and reduce the reliability of the microgrid. Distributed algorithms based on droop control reduce the communication costs of economic dispatch problems, and the biggest advantage of distributed methods is that they only use local information and do not require communication. With the development of distributed renewable energy generation, a large number of distributed generators are connected to microgrids. Distributed optimization strategies only require neighbor communication. Moreover, compared with decentralized methods, distributed optimization strategies can simultaneously achieve optimal dispatch and frequency regulation. Therefore, distributed cooperative algorithms are more suitable for the optimal dispatch of microgrids than centralized algorithms.

[0047] For these reasons, this invention investigates the economic dispatch problem of microgrids considering line losses and communication delays. Specifically, this embodiment proposes a distributed cooperative dispatch method considering line losses to achieve optimal dispatch and frequency regulation. First, an optimization model considering line losses is established, and optimal conditions are obtained. Second, cost optimization and frequency regulation are implemented in the first and second level control stages. Therefore, optimal dispatch can be achieved without communication, and only frequency recovery depends on communication. The system stability considering cable resistance and communication delay is analyzed, and stability conditions are given: First, the stability conditions for the communication-free system are derived. Second, based on the singular perturbation theorem, the stability conditions for the time-delay-free system are obtained. Finally, the stability analysis conditions for the system under time delay are given, providing guidance for system design.

[0048] See Figure 1 As shown, the distributed cooperative control method considering line loss optimization scheduling includes:

[0049] Step S101: Obtain the parameters of the microgrid and establish an optimization model considering line losses based on the parameters.

[0050] In this embodiment, the optimization model is:

[0051]

[0052]

[0053] 0≤p i ≤p max,i ;

[0054] In the formula, p i Let f be the output power of the i-th distributed power source. i (p i Let be the operating cost function of the i-th distributed power source, and let a be the operating cost function of the i-th distributed power source. i b i c i p is a coefficient load p represents the active power of the load. loss For line loss, p max,i Let be the maximum output power of the i-th distributed power source, and n be the number of distributed power sources in the microgrid.

[0055] Step S101: Solve for the optimality conditions of the microgrid based on the optimization model.

[0056] In this embodiment, the Lagrange operator for the optimization model is as follows:

[0057]

[0058] Where λ is a Lagrange multiplier with respect to equality constraints, and the optimality condition is:

[0059]

[0060] Typically, the load is a constant power load, and the optimality condition becomes:

[0061]

[0062] let The optimality condition is

[0063] The equivalent model of a microgrid is as follows: Figure 2 As shown. There are n schedulable distributed power sources, and all loads and unschedulable distributed power sources are equivalent to instantaneous constant power loads. Furthermore, the reactive power of the loads is assumed to be zero, and p... load This represents the active power of the load. Indicates the voltage of the distributed power source. V represents the impedance of the cable, where V,α i ,θ iThese are all positive constants and scalars. Let P represent the active power of the load. The complex power of the i-th power source is... From the above formula, the active power of the i-th distributed power source can be obtained as follows:

[0064] Similarly, the complex power of the load is given by the following formula.

[0065] Then, the power balance equation for the load is obtained:

[0066]

[0067]

[0068] Total line loss is Typically |θ i -θ L | is very small, therefore the following approximate condition can be obtained, sin(θ) i -θ L )≈θ i -θ L cos(θ) i -θ L )≈1.

[0069] Under this approximation, we obtain

[0070]

[0071] According to the above formula, we can obtain

[0072] Substituting the above equation into the equation After simplification, we can obtain

[0073]

[0074] So in This represents the maximum acceptable voltage deviation ratio. This leads to the optimal conditions for considering the economic problem of line loss:

[0075]

[0076] Step S103: Frequency regulation of distributed power sources in the microgrid is performed according to the optimality conditions.

[0077] In this embodiment, a distributed cooperative control method is proposed to achieve optimal scheduling and frequency regulation. The proposed distributed cooperative control consists of two layers: primary control and secondary control.

[0078] The main control design is as follows:

[0079]

[0080] Where m is a coefficient that keeps the frequency within an acceptable range.

[0081] x i Designed as:

[0082]

[0083] Where b is a positive constant, and a is a variable when there is communication between the i-th and j-th distributed power sources. ij =1, otherwise a ij =0. Therefore, the compact form of the system dynamics is given by the following equation:

[0084]

[0085] Where K = diag(1-βcotα) i ) -1 ,1 n =[1 1 … 1] T L is the Laplace matrix of the communication network.

[0086] Communication failures can occur, which may negatively impact optimized scheduling. The system can only reach a stable state when its frequency is synchronized. When the system reaches a stable state:

[0087]

[0088] Multiply the second equation in the above equation by the left side. get Combining the above equations, we get Then the second equation above becomes Lx = 0. n According to graph theory, the following equation holds if and only if the communication network has a spanning tree: x1 = x2 = ... = x n Therefore, we get This formula shows This means that optimal scheduling has been achieved. When communication completely fails (i.e., the communication network has no spanning tree), x... i The value will be reset to 0. In steady state, the following equation holds: Call achievable It was established, achieving optimal scheduling.

[0089] Step S104: Determine the stability conditions of the microgrid based on the current communication status of the microgrid.

[0090] In this embodiment, the stability of the system is analyzed.

[0091] (1) When the system does not contain communication, x = 0 n Considering Where A = diag{a i}, b = [b1 b2… b n The dynamic equation of a distributed power source is: Ignore v L The dynamics of the system, the small-signal model is

[0092]

[0093] in M2 = M11 n , This is the phase angle difference at steady state. Δθ L =M3Δθ, Therefore, we obtain the following formula:

[0094]

[0095] The KAM4 system is stable only when it has only one eigenvalue of zero and all other eigenvalues ​​have negative real parts. The specific analysis is as follows: According to Lyapunov's inertia theorem, if there exists a positive definite matrix... If a matrix is ​​a positive semi-definite matrix and has one eigenvalue of 0, then KAM4 has only one eigenvalue of 0. |θ i -θ L | is usually very small, we assume θ i -θ L =0, then It is the Schur complement of the following positive semidefinite matrices with simple zero eigenvalues:

[0096]

[0097] Therefore, if the phase difference |θ i -θ L If the value is less than a certain preset threshold, the system is stable.

[0098] (2) The system has communication and the communication has no delay. The system dynamic equation is:

[0099]

[0100] The small-signal model of the system is Based on the singular perturbation theorem, we derive the system stability conditions. Assuming d is sufficiently small, the small-signal model of the system becomes... This is a singularly perturbated system. If both the boundary system and the reduced-order system are stable, then the system is stable. In fact, the boundary system is... The reduced-order system is It can be easily found that the reduced-order system is stable. Therefore, if the phase difference |θ i -θ L | is less than a certain preset threshold, the system is stable.

[0101] (3) System Stability Analysis under Communication Delay

[0102] Small-signal Model of the System under Communication Delay where τ is the time delay. Since d is very small, this is also a singularly perturbed system. In addition, the boundary system is the same as that in (2), and the reduced-order system is given by the following formula when the system is stable.

[0103] It can be seen that in the embodiment of the present invention, an optimization model considering line loss is first established, and the optimality condition is obtained. Secondly, cost optimization and frequency regulation are implemented in primary and secondary control, so optimal scheduling can be achieved without communication, and only frequency recovery depends on communication. Moreover, the system stability considering cable resistance and communication delay is analyzed, and the stability condition is given based on the current communication situation of the microgrid. The optimal scheduling algorithm in the embodiment of the present invention does not depend on communication, and optimal scheduling can be achieved even when there is no communication; the control scheme proposed in the embodiment of the present invention does not require real-time information of loads; if communication is normal, the control scheme proposed in the embodiment of the present invention can achieve both optimal scheduling and frequency recovery.

[0104] The effectiveness of the proposed distributed cooperative control method is verified through simulation below.

[0105] The voltage amplitude of the distributed generator is set as V=200V, and the line impedance is:

[0106]

[0107] The reactive power of the load is zero, and the active power of the load is designed as: when 0 < t ≤ 5s, p load = 1kW; when 5 < t ≤ 10s, p load = 1.5kW; when 10 < t ≤ 15s, p load = 2kW; when t > 15s, p load = 3kW. The parameters of the cost function are a1 = 0.01, b1 = 40, a2 = 0.02, b2 = 40, a3 = 0.01, b3 = 10, a4 = 0.04, b4 = 20, c1 = c2 = c3 = c4 = 0.

[0108] The Laplacian matrix of the communication network is:

[0109] Assuming ε = 0.1, then K = [1.27 1.04 1.27 1.04], d = 5, m = 0.1. Three examples are given below.

[0110] Example 1: Communication is normal and there is no delay.

[0111] Example 2: Communication is normal, τ = 0.09s.

[0112] Example 3: Communication is normal. When t≤5s, τ=0.13s; when t>5s, there is no communication.

[0113] Based on the preceding analysis, if d is small enough, then Example 1 is stable. After calculating d... * =43.5, d=5<43.5, therefore Example 1 is stable. Because Therefore, the system is stable in Example 2. In Example 3, when t ≤ 5s, τ = 0.13 > 0.0785, and the system is unstable. When t > 5s, there is no system communication, and the system will be stable. Optimal scheduling is achieved, and simulation results verify the effectiveness of the method proposed in this invention.

[0114] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0115] Figure 3 This is a schematic diagram of the distributed cooperative control device for optimized scheduling considering line loss provided in an embodiment of the present invention. See also... Figure 3 As shown, the device 30 includes:

[0116] The acquisition module 31 is used to acquire the parameters of the microgrid and establish an optimization model that takes line losses into account based on the parameters.

[0117] The solver module 32 is used to solve the optimality conditions of the microgrid based on the optimization model.

[0118] The regulation module 33 is used to regulate the frequency of distributed power sources in the microgrid according to optimality conditions.

[0119] The determination module 34 is used to determine the stability conditions of the microgrid based on the current communication status of the microgrid.

[0120] As one possible implementation, the optimized model is:

[0121]

[0122]

[0123] 0≤p i ≤p max,i ;

[0124] In the formula, p i Let f be the output power of the i-th distributed power source. i (p i Let be the operating cost function of the i-th distributed power source, and let a be the operating cost function of the i-th distributed power source. i b i c i p is a coefficient load p represents the active power of the load. loss For line loss, p max,i Let be the maximum output power of the i-th distributed power source, and n be the number of distributed power sources in the microgrid.

[0125] As one possible implementation, the optimality condition is:

[0126]

[0127] In the formula, X i ,α i Let be the impedance magnitude and impedance angle of the i-th line, respectively, and ε be the maximum allowable voltage deviation percentage of the node voltage. i Let f be the output power of the i-th distributed power source. i (p i Let be the operating cost function of the i-th distributed power source.

[0128] As one possible implementation, frequency regulation of distributed generation in a microgrid is performed based on optimality conditions, including:

[0129]

[0130] In the formula, m is a preset coefficient, θ is the frequency of the distributed power source, d is the gain coefficient, ω* is the reference frequency, and K=diag(1-βcotα) i ) -1 ,1 n =[1 1 … 1] T L is the Laplace matrix of the communication network.

[0131] As one possible implementation, based on the current communication status of the microgrid, the stability conditions of the microgrid are determined, including:

[0132] When a microgrid contains no communication or has communication with no delay, the stability condition of the microgrid is |θ i -θ L | less than a preset threshold; where |θ i -θ L | represents the phase angle difference in steady state.

[0133] As one possible implementation, based on the current communication status of the microgrid, the stability conditions of the microgrid are determined, including:

[0134] When communication exists in a microgrid and there is a communication delay, the stability condition of the microgrid is: Where τ is the communication delay, d is the gain coefficient, and λ max Let L be the largest eigenvalue of the Laplace matrix L.

[0135] Figure 4 This is a schematic diagram of the electronic device 40 provided in an embodiment of the present invention. Figure 4 As shown, the electronic device 40 of this embodiment includes: a processor 41, a memory 42, and a computer program 43 stored in the memory 42 and executable on the processor 41, such as a distributed cooperative control program considering line loss optimization scheduling. When the processor 41 executes the computer program 43, it implements the steps in the various embodiments of the distributed cooperative control method considering line loss optimization scheduling described above, for example... Figure 1 The steps S101 to S104 are shown. Alternatively, when the processor 41 executes the computer program 43, it implements the functions of each module in the above-described device embodiments, for example... Figure 3 The functions of modules 31 to 34 are shown.

[0136] For example, computer program 43 may be divided into one or more modules / units, one or more of which are stored in memory 42 and executed by processor 41 to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 43 in electronic device 40.

[0137] Electronic device 40 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Electronic device 40 may include, but is not limited to, a processor 41 and a memory 42. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 40 and does not constitute a limitation on electronic device 40. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 40 may also include input / output devices, network access devices, buses, etc.

[0138] The processor 41 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. A general-purpose processor may be a microprocessor or any conventional processor.

[0139] The memory 42 can be an internal storage unit of the electronic device 40, such as a hard disk or RAM of the electronic device 40. The memory 42 can also be an external storage device of the electronic device 40, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 40. Furthermore, the memory 42 can include both internal and external storage units of the electronic device 40. The memory 42 is used to store computer programs and other programs and data required by the electronic device 40. The memory 42 can also be used to temporarily store data that has been output or will be output.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0141] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0142] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0143] In the embodiments provided by this invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If integrated modules / units are implemented as software functional units and sold or used as independent products, they 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 medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications 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 included within the protection scope of the present invention.

Claims

1. A distributed cooperative control method considering line loss optimization scheduling, characterized in that, include: Obtain the parameters of the microgrid and establish an optimization model that considers line losses based on the parameters; The optimality conditions of the microgrid are solved based on the optimization model. Frequency regulation of distributed power sources in the microgrid is performed based on the aforementioned optimality conditions; Based on the current communication status of the microgrid, determine the stability conditions of the microgrid; Frequency regulation of distributed power sources in a microgrid based on the aforementioned optimality conditions includes: ； In the formula, m is a preset coefficient. For the frequency of distributed power sources, d This is the gain coefficient. For reference frequency, , , Let be the impedance magnitude and impedance angle of the i-th line, respectively. This represents the maximum allowable voltage deviation percentage for the node voltage. n This refers to the number of distributed power sources in a microgrid. L It is the Laplace matrix of the communication network; Based on the current communication status of the microgrid, determine the stability conditions of the microgrid, including: When there is no communication in the microgrid or there is communication in the microgrid and the communication has no delay, the stability condition of the microgrid is that the phase angle difference in steady state is less than a preset threshold.

2. The distributed cooperative control method for optimized scheduling considering line loss as described in claim 1, characterized in that, The optimization model is as follows: ， ， ； In the formula, p i Let i be the output power of the i-th distributed power source. f i ( p i Let be the operating cost function of the i-th distributed power source. a i , b i , c i For coefficients, The active power of the load. For line loss, Let be the maximum output power of the i-th distributed power source.

3. The distributed cooperative control method for optimized scheduling considering line loss as described in claim 1, characterized in that, The optimality condition is: ； In the formula, p i Let i be the output power of the i-th distributed power source. f i ( p i Let be the operating cost function of the i-th distributed power source.

4. The distributed cooperative control method for optimizing scheduling considering line loss as described in claim 1, characterized in that, Based on the current communication status of the microgrid, determine the stability conditions of the microgrid, including: When communication exists in a microgrid and there is a communication delay, the stability condition of the microgrid is: ;in, For communication delay, d This is the gain coefficient. Laplace matrix L The largest eigenvalue.

5. A distributed cooperative control device considering line loss optimization scheduling, characterized in that, Applicable to the method as described in any one of claims 1-4; The device includes: The acquisition module is used to acquire the parameters of the microgrid and establish an optimization model that takes line losses into account based on the parameters; The solution module is used to solve for the optimality conditions of the microgrid based on the optimization model. The regulation module is used to regulate the frequency of distributed power sources in the microgrid according to optimality conditions; The determination module is used to determine the stability conditions of the microgrid based on the current communication status of the microgrid.

6. The distributed cooperative control device for optimized scheduling considering line loss as described in claim 5, characterized in that, The optimality condition is: ； In the formula, p i Let i be the output power of the i-th distributed power source. f i ( p i Let be the operating cost function of the i-th distributed power source.

7. An electronic 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 method as described in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 4.

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