Virtual power plant distributed collaborative optimization method and system for communication interruption

Abstract

The invention discloses a virtual power plant distributed collaborative optimization method and system for communication interruption, and the method comprises the steps: building a scheduling control model of a virtual power plant based on a communication network topology structure of distributed power generation nodes in a power grid system, and generating a cost optimization function according to the active output power of each distributed power generation node, generating an initial virtual reference signal based on the cost optimization function, smoothing the initial virtual reference signal, generating a smooth reference signal, generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal, and optimizing the scheduling control model based on the decentralized controller. According to the method, the distributed cooperative scheduling of the virtual power plant in a communication interruption scene is optimized, so that the dependence on continuous communication is effectively reduced while the economic scheduling precision is guaranteed, the scheduling continuity and the control response capability of the virtual power plant are improved, and the economic efficiency of the virtual power plant is improved. Coordinated control and stable operation of the virtual power plant under an unstable communication condition are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of distributed power distribution communications, and in particular to a distributed collaborative optimization method and system for a virtual power plant targeting communication interruptions. Background Art

[0002] In recent years, with the continuous advancement of energy diversity, distributed generation (DG), characterized by clean and renewable characteristics, has been gradually integrated into power systems. Currently, most distributed generation (DG) is connected to the distribution network, improving system efficiency by reducing active power losses and improving power quality. However, due to the small capacity and geographical dispersion of DG nodes, power system operators face difficulties in implementing refined management. To overcome this limitation, virtual power plants (VPPs) have emerged as an effective integration approach, aggregating multiple DGs into a single entity for unified dispatch and control. Economic dispatch is a core issue in ensuring efficient operation in VPPs. Current centralized control approaches for VPPs typically rely on a central controller to uniformly dispatch all distributed units. This approach requires global information, resulting in high management costs, limited flexibility, and poor reconfigurability when dealing with geographically dispersed DGs. In contrast, distributed control strategies based on local communication are more suitable for solving economic dispatch problems in distributed environments due to their low implementation cost, strong scalability, and excellent robustness. For this reason, distributed control methods for VPPs based on cyber-physical systems have garnered widespread attention in recent years. However, such methods still face problems such as high communication overhead, sensitivity to single point failures, and insufficient data security.

[0003] It's worth noting that the implementation of distributed control methods relies heavily on stable and reliable communication networks. However, these networks are vulnerable to factors such as natural disasters (such as lightning strikes, storms, and earthquakes), equipment failures, power supply anomalies, and malicious attacks (such as denial-of-service attacks and signal interference), which can lead to communication node failures or even complete network disruptions. Furthermore, in remote areas with weak infrastructure, unstable or interrupted communications are even more prominent. Such communication disruptions can severely impair the coordination and scheduling capabilities of virtual power plants. Because VPPs rely on communication systems for real-time monitoring, data exchange, and command issuance of various distributed resources, communication disruptions can easily lead to information transmission delays or loss, causing power allocation imbalances, delayed dispatch response, and reduced system dynamic performance, potentially impacting local grid stability and operational safety. Existing distributed control technologies for virtual power plants rely on stable, low-latency communication networks. Communication disruptions, packet loss, or excessive latency can lead to information exchange failures and scheduling delays, severely impacting power balance and system stability. Currently, when communications are intermittent or unstable, the operational stability of the virtual power plant cannot be guaranteed, resulting in imbalance in the economic dispatch of the virtual power plant, delayed dispatch response, and decreased dynamic performance of the system. Summary of the Invention

[0004] The main purpose of the present invention is to provide a distributed collaborative optimization method and system for virtual power plants in response to communication interruptions, aiming to solve the technical problem that the existing technology cannot guarantee the operational stability of virtual power plants in the case of intermittent or unstable communication, resulting in imbalance in the economic dispatch of virtual power plants, delayed dispatch response, and decreased dynamic performance of the system.

[0005] To achieve the above objectives, the present invention provides a distributed collaborative optimization method for a virtual power plant in response to communication interruption, the method comprising the following steps: Construct a dispatching and control model for a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system; generating a cost optimization function according to the active output power of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function; Smoothing the initial virtual reference signal to generate a smoothed reference signal; generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal; The dispatch control model is optimized based on the decentralized controller to optimize the distributed collaborative dispatch of the virtual power plant in a communication interruption scenario.

[0006] Optionally, generating a cost optimization function according to the active output power of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function includes: The power cost function is generated according to the active output power of each distributed generation node: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; Optimization constraints are generated based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node. The optimization constraints include: in, represents the actual output power of the virtual power plant, represents the expected output power of the virtual power plant, and Represent distributed generation nodes The minimum and maximum power output; A cost optimization function is generated based on the power cost function and the optimization constraint, the cost optimization function comprising: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents a set of distributed generation nodes; An initial virtual reference signal is generated based on the cost optimization function.

[0007] Optionally, generating an initial virtual reference signal based on the cost optimization function includes: The incremental cost of each distributed generation node is calculated based on the power cost function. The incremental cost is calculated based on the following formula: in, represents the incremental cost; A local objective function is generated according to the power cost function, and a gradient of the local objective function is determined based on the incremental cost: in, represents the local objective function, Represents the gradient of the local objective function; A virtual reference signal dynamic equation is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the virtual reference signal dynamic equation and the cost optimization function. The virtual reference signal dynamic equation refers to the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation, Represents intermediate variables The time derivative of Indicates time.

[0008] Optionally, the smoothing the initial virtual reference signal to generate a smoothed reference signal includes: The initial virtual reference signal is smoothed using a Hermite interpolation strategy to generate a smoothed reference signal: in, represents the smoothed reference signal, represents the order of the interpolation polynomial, represents the Hermitian interpolation matrix, represents the time node of interpolation, is a positive constant, represents the Hermitian interpolation matrix OK.

[0009] Optionally, generating a decentralized controller corresponding to each distributed power generation node according to the smoothed reference signal includes: Determining a power output error variable of each distributed generation node and a controller model output error variable according to the smooth reference signal; Determine a target error variable based on the rate output error variable and the controller model output error variable: in, represents the target error variable, represents the power output error variable, represents the controller model output error variable, For adjustment A positive constant of the weight; The second dummy variable is determined according to the target error variable and the controller model output error variable: in, represents the second dummy variable, For adjustment A positive constant with a weight of For adjustment A positive constant with a weight of represents the output signal after the gain of the decentralized controller, represents the initial power estimate, represents the first derivative of the smoothed reference signal, represents the second derivative of the smoothed reference signal; A controller gain coefficient is determined based on the second virtual variable and the controller model output error variable. The controller gain coefficient is calculated based on the following formula: in, represents the controller gain coefficient, Indicates the gain coefficient update rate adjustment coefficient, represents the controller gain prediction value, represents the derivative of the controller gain prediction value; A decentralized controller corresponding to each distributed power generation node is generated based on the controller gain coefficient and the second virtual variable, and the decentralized controller includes: in, Represents a decentralized controller.

[0010] Optionally, determining the power output error variable of each distributed generation node and the controller model output error variable according to the smooth reference signal includes: The power output error variable is determined according to the smooth reference signal and the active output power of each distributed generation node: Determine a first dummy variable based on the power output error variable: in, represents the first dummy variable; Determine the controller model output error variable according to the first virtual variable: in, represents the controller model output error variable, represents the output signal after the gain of the decentralized controller, Represents the ungained output signal of the decentralized controller.

[0011] Optionally, the scheduling control model includes: in, represents the output signal of the scheduling control model, Represents a distributed generation node The initial power, represents the output signal after the gain of the decentralized controller, represents the un-gained output signal of the decentralized controller, Represents the controller gain coefficient.

[0012] In addition, to achieve the above-mentioned purpose, the present invention further proposes a distributed collaborative optimization system for a virtual power plant in response to communication interruption, the distributed collaborative optimization system for a virtual power plant in response to communication interruption comprising: A virtual power plant model building module is used to build a dispatching and control model of a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system; a virtual reference signal generating module, configured to generate a cost optimization function according to the active output power of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function; a signal smoothing processing module, configured to smooth the initial virtual reference signal to generate a smoothed reference signal; A decentralized controller building module, configured to generate a decentralized controller corresponding to each distributed power generation node according to the smoothed reference signal; A distributed collaborative optimization module is used to optimize the scheduling control model based on the decentralized controller to optimize the distributed collaborative scheduling of the virtual power plant in a communication interruption scenario.

[0013] Optionally, the virtual reference signal generating module is further configured to generate a power cost function according to the active output power of each distributed generation node: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; Optimization constraints are generated based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node. The optimization constraints include: in, represents the actual output power of the virtual power plant, represents the expected output power of the virtual power plant, and Represent distributed generation nodes The minimum and maximum power output; A cost optimization function is generated based on the power cost function and the optimization constraint, the cost optimization function comprising: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents a set of distributed generation nodes; An initial virtual reference signal is generated based on the cost optimization function.

[0014] Optionally, the virtual reference signal generation module is further configured to calculate the incremental cost of each distributed generation node based on a power cost function, where the incremental cost is calculated based on the following formula: in, represents the incremental cost; A local objective function is generated according to the power cost function, and a gradient of the local objective function is determined based on the incremental cost: in, represents the local objective function, Represents the gradient of the local objective function; A virtual reference signal dynamic equation is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the virtual reference signal dynamic equation and the cost optimization function. The virtual reference signal dynamic equation refers to the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation, Represents intermediate variables The time derivative of Indicates time.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a distributed collaborative optimization device for a virtual power plant for communication interruption, the device including: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the distributed collaborative optimization method for a virtual power plant for communication interruption as described above.

[0016] In addition, to achieve the above-mentioned purpose, the present application also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the distributed collaborative optimization method of a virtual power plant for communication interruption as described above are implemented.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the distributed collaborative optimization method of the virtual power plant for communication interruption as described above.

[0018] The present invention constructs a dispatching control model of a virtual power plant based on the communication network topology of distributed power generation nodes in a power grid system, generates a cost optimization function according to the active output power of each distributed power generation node, generates an initial virtual reference signal based on the cost optimization function, smoothes the initial virtual reference signal to generate a smoothed reference signal, generates a decentralized controller corresponding to each distributed power generation node according to the smoothed reference signal, and optimizes the dispatching control model based on the decentralized controller to optimize the distributed collaborative dispatching of the virtual power plant under the communication interruption scenario; since the present invention generates an initial virtual reference signal by solving the cost optimization function, thereby realizing the economic optimization under the communication interruption scenario The economic dispatch problem is solved and the optimal virtual reference signal is generated. By constructing a decentralized controller corresponding to each distributed power generation node, it is ensured that each distributed power source can track the reference signal in a timely manner and quickly respond to the dispatch instruction when communication is limited, thereby effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions. While ensuring the accuracy of economic dispatch, it effectively reduces the dependence on continuous communication, significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication anomalies, takes into account economy, scalability and communication robustness, is suitable for the actual deployment of virtual power plants in large-scale distributed energy systems, and effectively guarantees the coordinated control and stable operation of virtual power plants under unstable communication conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 It is a structural diagram of a distributed collaborative optimization device for a virtual power plant in response to communication interruption in a hardware operating environment involved in an embodiment of the present invention; Figure 2 This is a flow chart of an embodiment of a distributed collaborative optimization method for a virtual power plant in response to communication interruption according to the present invention; Figure 3 Schematic diagram of a communication network topology structure of distributed power generation nodes in a power grid system according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a process of cost optimization and virtual reference signal generation in an embodiment of the distributed collaborative optimization method of a virtual power plant for communication interruption according to the present invention; FIG5( a ) is a schematic diagram of a normal structure of a power grid communication network in one embodiment; Figure 5(b) is a schematic diagram of a communication network with some communication links of distributed generation nodes interrupted; Figure 5 (c) is a schematic diagram of a communication network in which all distributed generation node communication networks are disconnected and isolated; Figure 6 This is a structural block diagram of an embodiment of the distributed collaborative optimization system of a virtual power plant for communication interruption according to the present invention.

[0021] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0023] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a distributed collaborative optimization device for a virtual power plant against communication interruption in the hardware operating environment involved in an embodiment of the present invention.

[0024] like Figure 1As shown, the distributed collaborative optimization device for a virtual power plant with communication interruptions may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display and an input unit, such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may be a storage system independent of the processor 1001.

[0025] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the distributed collaborative optimization device of the virtual power plant for communication interruption, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0026] like Figure 1 As shown, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, a user interface module, and a virtual power plant distributed collaborative optimization program for communication interruptions.

[0027] exist Figure 1 In the distributed collaborative optimization device for a virtual power plant for communication interruption shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the distributed collaborative optimization device for a virtual power plant for communication interruption of the present invention can be set in the distributed collaborative optimization device for a virtual power plant for communication interruption, and the distributed collaborative optimization device for a virtual power plant for communication interruption calls the distributed collaborative optimization program for a virtual power plant for communication interruption stored in the memory 1005 through the processor 1001, and executes the distributed collaborative optimization method for a virtual power plant for communication interruption provided in an embodiment of the present invention.

[0028] The embodiment of the present invention provides a distributed collaborative optimization method for a virtual power plant for communication interruption, referring to Figure 2 , Figure 2This is a flow chart of an embodiment of a distributed collaborative optimization method for a virtual power plant with communication interruption according to the present invention.

[0029] In this embodiment, the distributed collaborative optimization method for a virtual power plant in response to communication interruption includes the following steps: Step S10: Constructing a dispatching control model of a virtual power plant based on the communication network topology of distributed power generation nodes in the power grid system.

[0030] It should be noted that this embodiment is used to ensure the normal operation and convergence of the economic dispatch of distributed power generation nodes by the virtual power plant in the case of intermittent or unstable communication, and to ensure the timeliness and stability of the dispatch.

[0031] This embodiment designs a distributed optimization algorithm that can still converge stably under intermittent communication interruptions to solve the economic dispatch problem and generate an optimal virtual reference signal. Subsequently, a decentralized adaptive controller is proposed to ensure that each distributed power generation node can track the reference signal in a timely manner during communication-restricted periods and quickly respond to dispatch instructions, thereby achieving efficient dispatch and stable operation of the virtual power plant system even when communication conditions are not fully available.

[0032] It should be understood that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, or mobile phone, or a terminal electronic device capable of performing the aforementioned functions. This embodiment and the following embodiments will be described below using a distributed collaborative optimization device for a virtual power plant (hereinafter referred to as the optimization device) for communication interruption as an example.

[0033] It should be noted that a virtual power plant (VPP) can be a decentralized network of distributed energy resources, such as solar panels, wind turbines, battery storage systems, electric vehicles, and controllable loads, which are integrated and managed by a central control system using advanced software and communication technologies.

[0034] It should be noted that Distributed Generation (DG) refers to distributed generation (or distributed energy). Distributed generation refers to small power generation equipment located near the load, usually distributed across multiple nodes in the power grid system.

[0035] In a specific implementation, the optimization device can construct a directed communication network topology based on the communication network information of the distributed generation nodes in the power grid system, referring to Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a communication network topology structure of distributed power generation nodes in a power grid system according to an embodiment of the present invention. Figure 3The communication network topology of a 33-node IEEE standard power network model with 10 units (10 distributed generation nodes, including DG1, DG2, ..., DG10) is presented to simulate a virtual power plant. The proposed control is applied to each unit, and a dispatch control model of the virtual power plant is constructed based on the communication network topology, in which a feeder consisting of multiple dispatchable distributed generation nodes can be controlled as a virtual power plant.

[0036] Furthermore, in order to improve the control efficiency of distributed generation nodes, in one embodiment, the scheduling control model includes: in, represents the output signal of the scheduling control model, Represents a distributed generation node The initial power, represents the output signal after the gain of the decentralized controller, represents the un-gained output signal of the decentralized controller, Represents the controller gain coefficient.

[0037] It should be noted that the dispatching control model can be an active power control model of a virtual power plant for distributed power generation nodes.

[0038] In some embodiments, the optimization device may construct a Directed communication network described G ,in is the index set of nodes, is a set of edges, for Means node i Can j Send information. At the same time, it is assumed that each node (i.e. ) have self-loops. i The inner neighbor set and outer neighbor set of and A graph with a globally reachable node is called connected, and that globally reachable node is considered the leader, while all other nodes are followers of the leader. = [ a ij ]∈ R n×n express G The weighted adjacency matrix of a ij yes j and i The weight of the edge between them.

[0039] Step S20: generating a cost optimization function according to the active output power of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function.

[0040] It should be noted that a feeder consisting of multiple dispatchable distributed generation nodes can be controlled as a virtual power plant, whose output power is equal to the total active power output of the distributed generation nodes minus all active loads. The cost function of the distributed generation nodes in the virtual power plant can be approximated as: It should be noted that the goal of economic dispatch of virtual power plants is to minimize the total cost of power generation while the total active power of the virtual power plant meets certain demands.

[0041] It is understandable that in order to solve the problems of power distribution imbalance, scheduling response delay, and system dynamic performance degradation caused by the interruption of existing communication networks to virtual power plants, this embodiment designs a distributed optimization algorithm that can minimize the cost optimization function under the influence of intermittent communication interruptions. , and generate the initial virtual reference signal .

[0042] Furthermore, in order to ensure the timeliness and stability of scheduling and optimize scheduling costs in an environment with communication interruption, refer to Figure 4 , Figure 4 FIG. 5 is a flow chart of cost optimization and virtual reference signal generation in an embodiment. Step S20 may include: Step S201: generating a power cost function according to the active output power of each distributed generation node; Step S202: generating optimization constraints based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node; Step S203: generating a cost optimization function based on the power cost function and the optimization constraint conditions; Step S204: generating an initial virtual reference signal based on the cost optimization function.

[0043] It should be noted that in a virtual power plant, the power cost function of each distributed generation node is as follows: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; It should be noted that the goal of economic dispatch of virtual power plants is to minimize the total cost of power generation when the total active power of the virtual power plant meets certain demand. The cost optimization formula is as follows: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents the set of distributed generation nodes, represents the actual output power of the virtual power plant (the total power generation of DG minus the load), Indicates the expected output power of the virtual power plant (the dispatch command of the energy management system to the virtual power plant), and Represent distributed generation nodes The minimum and maximum values ​​of the power output.

[0044] Furthermore, in order to achieve optimal power generation cost and generate a better virtual reference signal, thereby improving scheduling efficiency, the above step S204 may include: Step S2041: Calculating the incremental cost of each distributed generation node based on the power cost function; Step S2042: generating a local objective function according to the power cost function, and determining a gradient of the local objective function based on the incremental cost; Step S2043: generating a virtual reference signal dynamic equation based on the local objective function and the gradient, and generating an initial virtual reference signal based on the virtual reference signal dynamic equation and the cost optimization function.

[0045] It should be noted that the incremental cost is calculated based on the following formula: in, represents the incremental cost.

[0046] It should be noted that in order to achieve optimal power generation costs, the incremental cost of each distributed generation node must remain consistent. That is, the incremental cost constraint is based on the following formula: Among them, the first constraint ensures long-term convergence. As time goes by, any two distributed generation nodes i and j The difference between the incremental costs of and tends to zero. The second constraint ensures short-term stability. There is a point in time T, after which the incremental cost difference between any two DG units is always zero.

[0047] It is understandable that this embodiment designs a distributed optimization algorithm that can minimize the optimization function under the influence of intermittent communication interruptions. , and generate the optimal virtual reference signal. i DG nodes ( i =1,..., N ), we introduce a virtual reference signal , and is updated by the following distributed optimization algorithm: The intermediate variables are calculated according to the following formula: The function gradient is calculated based on the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation, Represents intermediate variables The time derivative of Indicates time, represents the local objective function, Represents the gradient of the local objective function, the time-varying weight used here It is caused by a communication network interruption.

[0048] Step S30: Smoothing the initial virtual reference signal to generate a smoothed reference signal.

[0049] It is understandable that due to the influence of intermittent communication interruption, the initial virtual reference signal It is a discontinuous and intermittent signal. In order to ensure the continuity of the reference signal, this embodiment can smooth the initial virtual reference signal to generate a continuous smoothed reference signal.

[0050] Furthermore, in order to ensure the continuity of the virtual reference signal and improve signal stability, the above step S30 may include: Step S301: smoothing the initial virtual reference signal using a Hermite interpolation strategy to generate a smoothed reference signal.

[0051] It should be noted that due to intermittent communication interruption, the initial virtual reference signal It is a discontinuous and intermittent signal. In order to ensure the continuity of the reference signal, this embodiment adopts the Hermite interpolation method. The signal is processed to obtain a new smooth reference signal , ( i=1,...,N ), the specific design is as follows: in, represents the smoothed reference signal, represents the order of the interpolation polynomial, represents the Hermitian interpolation matrix, represents the time node of interpolation, is a positive constant, represents the Hermitian interpolation matrix OK.

[0052] Where, , T is a small positive constant; when hour, ; yes No. q OK, , , .

[0053] for k =2,…, m +1, the calculation process of the intermediate variables in the above formula is as follows: Step S40: generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal.

[0054] It should be noted that in order to ensure that each distributed power source can track the reference signal in time and respond quickly to the dispatch instruction during the communication restriction period, the optimization device of this embodiment designs a decentralized controller for each distributed power generation node. , which can adaptively adjust controller parameters according to the communication status, thereby effectively ensuring the coordinated control and stable operation of the virtual power plant under unstable communication conditions.

[0055] Furthermore, in order to improve the scheduling control efficiency and the scheduling response speed, the above step S40 may include: Step S401: determining the power output error variable of each distributed generation node and the controller model output error variable according to the smooth reference signal.

[0056] It should be noted that the power output error variable represents the difference between the actual power of the distributed generation node and the reference power. The controller model output error variable represents the difference between the power output predicted by the controller model and the test drive power output.

[0057] Furthermore, in order to accurately quantify the system error variable, the above step S401 may include: Step S4011: Determine a power output error variable according to the smoothed reference signal and the active output power of each distributed generation node: Step S4012: Determine a first virtual variable based on the power output error variable.

[0058] It should be noted that in order to effectively compensate for the power output error variable and the controller model output error variable, this embodiment designs a first virtual variable through the feedback term and the reference signal change rate to reduce the impact of the error on the system. The controller output is dynamically adjusted based on the current error, error change rate, and reference signal change rate, so that the system adapts to real-time state changes (such as load fluctuations and environmental disturbances). The first virtual variable is calculated according to the following formula: in, represents the first dummy variable, represents the initial power estimate, is a positive constant; Step S4013: Determine the controller model output error variable according to the first virtual variable: in, represents the controller model output error variable, represents the output signal after the gain of the decentralized controller, Represents the ungained output signal of the decentralized controller.

[0059] Step S402: determining a target error variable based on the rate output error variable and the controller model output error variable.

[0060] It should be noted that in order to quantify the errors of the virtual power plant and distributed generation nodes, this embodiment defines a power output error variable and a controller model output error variable. Based on the power output error variable and the controller model output error variable, the overall target error variable is quantified. The target error variable is calculated according to the following formula: in, represents the target error variable, represents the power output error variable, represents the controller model output error variable, For adjustment A positive constant with weights to balance the relative importance of power output error and model error.

[0061] Step S403: determining a second virtual variable according to the target error variable and the controller model output error variable.

[0062] It should be noted that in order to accurately calculate the relevant design parameters of the decentralized controller, this embodiment designs dummy variables. The design goal of the dummy variables is to improve the control performance of distributed generation nodes (DGs) during communication-restricted periods by dynamically adjusting the controller behavior. Based on the first dummy variable, an integral term (to eliminate steady-state errors) and a second-order derivative of the reference signal (to predict accelerated changes in the reference signal) are introduced to more precisely process errors, improve the system's robustness to complex dynamic environments (such as nonlinear loads and high-frequency disturbances), accelerate response speed, and improve stability. The second dummy variable is calculated according to the following formula: in, represents the second dummy variable, For adjustment A positive constant with a weight of For adjustment A positive constant with a weight of represents the output signal after the gain of the decentralized controller, represents the initial power estimate, represents the first derivative of the smoothed reference signal, Represents the second derivative of the smoothed reference signal.

[0063] Step S404: determining a controller gain coefficient based on the second virtual variable and the controller model output error variable.

[0064] It should be noted that in order to enhance the decentralized controller's ability to track changes in the reference signal, a controller gain coefficient is designed in this embodiment. The controller gain coefficient can be used to adjust the system response speed, eliminate steady-state errors, improve robustness, and achieve target tracking, ensuring that the controller operates efficiently in a communication-restricted environment. The controller gain coefficient is calculated based on the following formula: in, represents the controller gain coefficient, Indicates the gain coefficient update rate adjustment coefficient, represents the controller gain prediction value, Represents the derivative of the controller gain prediction.

[0065] Step S405: generating a decentralized controller corresponding to each distributed power generation node based on the controller gain coefficient and the second virtual variable.

[0066] It should be noted that this embodiment designs a distributed adaptive controller that can adaptively adjust controller parameters according to the communication status, thereby effectively ensuring the coordinated control and stable operation of the virtual power plant under unstable communication conditions. The distributed controller includes: in, Represents a decentralized controller.

[0067] Step S50: Optimizing the dispatch control model based on the decentralized controller to optimize the distributed collaborative dispatching of the virtual power plant in a communication interruption scenario.

[0068] It is understandable that the distributed controller designed in this embodiment Applied to the dispatching control model of virtual power plants, it optimizes the distributed collaborative dispatching performance of virtual power plants in communication interruption scenarios.

[0069] It should be understood that this embodiment is applicable to scenarios where distributed generation nodes in a power grid system experience communication anomalies. Referring to Figures 5(a), 5(b), and 5(c), Figures 5(a), 5(b), and 5(c) illustrate changes in the communication topology of ten distributed generators when communication is interrupted due to extreme weather or other factors. Figure 5(a) is a schematic diagram of the normal structure of the power grid communication network in one embodiment, Figure 5(b) is a schematic diagram of the communication network in which some communication links of distributed generation node DG6 are interrupted, and Figure 5(c) is a schematic diagram of the communication network in which distributed generation node DG6 is isolated due to a complete communication network outage. This embodiment employs a two-stage distributed collaborative optimization strategy for scenarios where distributed generation node communication anomalies occur. In the first stage, to achieve the VPP's economic dispatch goal of minimizing the total power generation cost while meeting the total active power demand, a cost optimization algorithm for distributed generation nodes is designed that can maintain stable convergence under intermittent communication interruptions. This algorithm is used to solve the economic dispatch problem and generate the optimal virtual reference signal. In the second phase, to ensure that each distributed power source can track the reference signal and quickly respond to dispatch instructions even when communication is limited, a distributed adaptive controller was proposed. A corresponding distributed controller was designed for each distributed power generation node, which can adaptively adjust controller parameters based on the communication status, effectively ensuring the coordinated control and stable operation of the virtual power plant under unstable communication conditions. While ensuring economic dispatch accuracy, this method effectively reduces the reliance on continuous communication, significantly improving the system's dispatch continuity and control responsiveness under network anomalies. It takes into account economy, scalability, and communication robustness, and is suitable for the actual deployment of virtual power plants in large-scale distributed energy systems.

[0070] This embodiment builds a dispatching control model of a virtual power plant based on the communication network topology of distributed power generation nodes in the power grid system, generates a cost optimization function according to the active output power of each distributed power generation node, generates an initial virtual reference signal based on the cost optimization function, smoothes the initial virtual reference signal to generate a smoothed reference signal, generates a distributed controller corresponding to each distributed power generation node based on the smoothed reference signal, and optimizes the dispatching control model based on the distributed controller to optimize the distributed collaborative dispatching of the virtual power plant in the communication interruption scenario; since the present invention generates an initial virtual reference signal by solving the cost optimization function, thereby achieving the optimization of the distributed collaborative dispatching of the virtual power plant in the communication interruption scenario The economic dispatch problem is solved and the optimal virtual reference signal is generated. By constructing a decentralized controller corresponding to each distributed power generation node, it is ensured that each distributed power source can track the reference signal in a timely manner and quickly respond to the dispatch instruction when communication is limited, thereby effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions. While ensuring the accuracy of economic dispatch, it effectively reduces the dependence on continuous communication, significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication anomalies, and takes into account economy, scalability and communication robustness. It is suitable for the actual deployment of virtual power plants in large-scale distributed energy systems, and effectively guarantees the coordinated control and stable operation of virtual power plants under unstable communication conditions.

[0071] In addition, an embodiment of the present invention also proposes a computer-readable storage medium, on which a distributed collaborative optimization program for a virtual power plant for communication interruptions is stored. When the distributed collaborative optimization program for a virtual power plant for communication interruptions is executed by a processor, the steps of the distributed collaborative optimization method for a virtual power plant for communication interruptions as described above are implemented.

[0072] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0073] The above-mentioned computer-readable storage medium may be included in the distributed collaborative optimization device for virtual power plants against communication interruptions; or it may exist independently without being assembled into the distributed collaborative optimization device for virtual power plants against communication interruptions.

[0074] In addition, an embodiment of the present invention also proposes a computer program product, including a distributed collaborative optimization program for a virtual power plant for communication interruptions. When the distributed collaborative optimization program for a virtual power plant for communication interruptions is executed by a processor, the steps of the distributed collaborative optimization method for a virtual power plant for communication interruptions as described above are implemented.

[0075] The specific implementation of the computer program product of the present invention is basically the same as the above-mentioned embodiments of the distributed collaborative optimization method of the virtual power plant for communication interruption, and will not be repeated here.

[0076] Reference Figure 6 , Figure 6 This is a structural block diagram of an embodiment of the distributed collaborative optimization system of a virtual power plant for communication interruption according to the present invention.

[0077] like Figure 6 As shown, the distributed collaborative optimization system for a virtual power plant against communication interruption proposed in an embodiment of the present invention includes: A virtual power plant model construction module 10 is used to construct a dispatching control model of a virtual power plant based on the communication network topology of distributed power generation nodes in the power grid system; a virtual reference signal generating module 20, configured to generate a cost optimization function according to the active output power of each distributed generation node, and to generate an initial virtual reference signal based on the cost optimization function; a signal smoothing processing module 30, configured to smooth the initial virtual reference signal to generate a smoothed reference signal; A decentralized controller construction module 40 is configured to generate a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal; The distributed collaborative optimization module 50 is used to optimize the scheduling control model based on the decentralized controller to optimize the distributed collaborative scheduling of the virtual power plant in a communication interruption scenario.

[0078] Furthermore, the virtual reference signal generation module is further configured to generate a power cost function according to the active output power of each distributed generation node: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; Optimization constraints are generated based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node. The optimization constraints include: in, represents the actual output power of the virtual power plant, represents the expected output power of the virtual power plant, and Represent distributed generation nodes The minimum and maximum power output; A cost optimization function is generated based on the power cost function and the optimization constraint, the cost optimization function comprising: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents a set of distributed generation nodes; An initial virtual reference signal is generated based on the cost optimization function.

[0079] Furthermore, the virtual reference signal generation module is further configured to calculate the incremental cost of each distributed generation node based on the power cost function, wherein the incremental cost is calculated based on the following formula: in, represents the incremental cost; A local objective function is generated according to the power cost function, and a gradient of the local objective function is determined based on the incremental cost: in, represents the local objective function, Represents the gradient of the local objective function; A virtual reference signal dynamic equation is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the virtual reference signal dynamic equation and the cost optimization function. The virtual reference signal dynamic equation refers to the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation, Represents intermediate variables The time derivative of Indicates time.

[0080] Furthermore, the signal smoothing processing module 30 is further configured to smooth the initial virtual reference signal using a Hermitian interpolation strategy to generate a smoothed reference signal: in, represents the smoothed reference signal, represents the order of the interpolation polynomial, represents the Hermitian interpolation matrix, represents the time node of interpolation, is a positive constant, represents the Hermitian interpolation matrix OK.

[0081] Furthermore, the decentralized controller construction module 40 is further configured to determine a power output error variable and a controller model output error variable of each distributed generation node according to the smoothed reference signal; and determine a target error variable based on the power output error variable and the controller model output error variable: in, represents the target error variable, represents the power output error variable, represents the controller model output error variable, For adjustment A positive constant of the weight; The second dummy variable is determined according to the target error variable and the controller model output error variable: in, represents the second dummy variable, For adjustment A positive constant with a weight of For adjustment A positive constant with a weight of represents the output signal after the gain of the decentralized controller, represents the initial power estimate, represents the first derivative of the smoothed reference signal, represents the second derivative of the smoothed reference signal; A controller gain coefficient is determined based on the second virtual variable and the controller model output error variable. The controller gain coefficient is calculated based on the following formula: in, represents the controller gain coefficient, Indicates the gain coefficient update rate adjustment coefficient, represents the controller gain prediction value, represents the derivative of the controller gain prediction value; A decentralized controller corresponding to each distributed power generation node is generated based on the controller gain coefficient and the second virtual variable, and the decentralized controller includes: in, Represents a decentralized controller.

[0082] Furthermore, the decentralized controller construction module 40 is further configured to determine a power output error variable according to the smoothed reference signal and the active output power of each distributed generation node: Determine a first dummy variable based on the power output error variable: in, represents the first dummy variable; Determine the controller model output error variable according to the first virtual variable: in, represents the controller model output error variable, represents the output signal after the gain of the decentralized controller, Represents the ungained output signal of the decentralized controller.

[0083] Furthermore, the scheduling control model includes: in, represents the output signal of the scheduling control model, Represents a distributed generation node The initial power, represents the output signal after the gain of the decentralized controller, represents the un-gained output signal of the decentralized controller, Represents the controller gain coefficient.

[0084] This embodiment builds a dispatching control model of a virtual power plant based on the communication network topology of distributed power generation nodes in the power grid system, generates a cost optimization function according to the active output power of each distributed power generation node, generates an initial virtual reference signal based on the cost optimization function, smoothes the initial virtual reference signal to generate a smoothed reference signal, generates a distributed controller corresponding to each distributed power generation node based on the smoothed reference signal, and optimizes the dispatching control model based on the distributed controller to optimize the distributed collaborative dispatching of the virtual power plant in the communication interruption scenario; since the present invention generates an initial virtual reference signal by solving the cost optimization function, thereby achieving the optimization of the distributed collaborative dispatching of the virtual power plant in the communication interruption scenario The economic dispatch problem is solved and the optimal virtual reference signal is generated. By constructing a decentralized controller corresponding to each distributed power generation node, it is ensured that each distributed power source can track the reference signal in a timely manner and quickly respond to the dispatch instruction when communication is limited, thereby effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions. While ensuring the accuracy of economic dispatch, it effectively reduces the dependence on continuous communication, significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication anomalies, and takes into account economy, scalability and communication robustness. It is suitable for the actual deployment of virtual power plants in large-scale distributed energy systems, and effectively guarantees the coordinated control and stable operation of virtual power plants under unstable communication conditions.

[0085] The distributed collaborative optimization system for virtual power plants for communication interruptions provided by this application adopts the distributed collaborative optimization method for virtual power plants for communication interruptions in the above-mentioned embodiment, which can solve the technical problems of distributed collaborative optimization of virtual power plants for communication interruptions. Compared with the prior art, the beneficial effects of the distributed collaborative optimization system for virtual power plants for communication interruptions provided by this application are the same as the beneficial effects of the distributed collaborative optimization method for virtual power plants for communication interruptions provided by the above-mentioned embodiment, and the other technical features of the distributed collaborative optimization system for virtual power plants for communication interruptions are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0086] It should be understood that the above is only an example and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any limitation on this.

[0087] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of it according to actual needs to achieve the purpose of the embodiment scheme, and no limitation is made here.

[0088] In addition, for technical details not fully described in this embodiment, please refer to the distributed collaborative optimization method of virtual power plants for communication interruptions provided in any embodiment of the present invention, and will not be repeated here.

[0089] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0090] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0091] Through the above description of the embodiments, those skilled in the art will clearly understand that the above-mentioned embodiments and methods can be implemented by means of software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, a magnetic disk, or an optical disk) and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0092] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distributed collaborative optimization method for virtual power plants against communication interruptions, characterized in that: The method comprises: Construct a dispatching and control model for a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system; generating a cost optimization function according to the active output power of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function; Smoothing the initial virtual reference signal to generate a smoothed reference signal; generating a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal; The dispatch control model is optimized based on the decentralized controller to optimize the distributed collaborative dispatch of the virtual power plant in a communication interruption scenario.

2. The distributed collaborative optimization method for virtual power plants against communication interruption according to claim 1, characterized in that: Generating a cost optimization function according to the active output power of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function, includes: The power cost function is generated according to the active output power of each distributed generation node: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; Optimization constraints are generated based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node. The optimization constraints include: in, represents the actual output power of the virtual power plant, represents the expected output power of the virtual power plant, and Represent distributed generation nodes The minimum and maximum power output; A cost optimization function is generated based on the power cost function and the optimization constraint, the cost optimization function comprising: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents a set of distributed generation nodes; An initial virtual reference signal is generated based on the cost optimization function.

3. The distributed collaborative optimization method for virtual power plants against communication interruption according to claim 2, characterized in that: Generating an initial virtual reference signal based on the cost optimization function includes: The incremental cost of each distributed generation node is calculated based on the power cost function. The incremental cost is calculated based on the following formula: in, represents the incremental cost; A local objective function is generated according to the power cost function, and a gradient of the local objective function is determined based on the incremental cost: in, represents the local objective function, Represents the gradient of the local objective function; A virtual reference signal dynamic equation is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the virtual reference signal dynamic equation and the cost optimization function. The virtual reference signal dynamic equation refers to the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation between adjacent nodes, Represents intermediate variables The time derivative of Indicates time.

4. The distributed collaborative optimization method for virtual power plants against communication interruption according to claim 3, characterized in that: The smoothing process on the initial virtual reference signal to generate a smoothed reference signal includes: The initial virtual reference signal is smoothed using a Hermite interpolation strategy to generate a smoothed reference signal: in, represents the smoothed reference signal, represents the order of the interpolation polynomial, represents the Hermitian interpolation matrix, represents the time node of interpolation, is a positive constant, represents the Hermitian interpolation matrix OK.

5. The distributed collaborative optimization method for virtual power plants against communication interruption according to claim 4, characterized in that: The generating of a decentralized controller corresponding to each distributed power generation node according to the smooth reference signal includes: Determining a power output error variable of each distributed generation node and a controller model output error variable according to the smooth reference signal; Determine a target error variable based on the rate output error variable and the controller model output error variable: in, represents the target error variable, represents the power output error variable, represents the controller model output error variable, For adjustment A positive constant of the weight; The second dummy variable is determined according to the target error variable and the controller model output error variable: in, represents the second dummy variable, For adjustment A positive constant with a weight of For adjustment A positive constant with a weight of represents the output signal after the gain of the decentralized controller, represents the initial power estimate, represents the first derivative of the smoothed reference signal, represents the second derivative of the smoothed reference signal; A controller gain coefficient is determined based on the second virtual variable and the controller model output error variable. The controller gain coefficient is calculated based on the following formula: in, represents the controller gain coefficient, Indicates the gain coefficient update rate adjustment coefficient, represents the controller gain prediction value, represents the derivative of the controller gain prediction value; A decentralized controller corresponding to each distributed power generation node is generated based on the controller gain coefficient and the second virtual variable, and the decentralized controller includes: in, Represents a decentralized controller.

6. The distributed collaborative optimization method for virtual power plants against communication interruption according to claim 5, characterized in that: The determining of the power output error variable of each distributed power generation node and the controller model output error variable according to the smooth reference signal includes: The power output error variable is determined according to the smooth reference signal and the active output power of each distributed generation node: Determine a first dummy variable based on the power output error variable: in, represents the first dummy variable; Determine the controller model output error variable according to the first virtual variable: in, represents the controller model output error variable, represents the output signal after the gain of the decentralized controller, Represents the ungained output signal of the decentralized controller.

7. The distributed collaborative optimization method for a virtual power plant against communication interruption according to any one of claims 1 to 6, characterized in that: The scheduling control model includes: in, represents the output signal of the scheduling control model, Represents a distributed generation node The initial power, represents the output signal after the gain of the decentralized controller, represents the un-gained output signal of the decentralized controller, Represents the controller gain coefficient.

8. A distributed collaborative optimization system for virtual power plants against communication interruptions, characterized in that: The virtual power plant distributed collaborative optimization system for communication interruption includes: A virtual power plant model building module is used to build a dispatching and control model of a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system; a virtual reference signal generating module, configured to generate a cost optimization function according to the active output power of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function; a signal smoothing processing module, configured to smooth the initial virtual reference signal to generate a smoothed reference signal; A decentralized controller building module, configured to generate a decentralized controller corresponding to each distributed power generation node according to the smoothed reference signal; A distributed collaborative optimization module is used to optimize the scheduling control model based on the decentralized controller to optimize the distributed collaborative scheduling of the virtual power plant in a communication interruption scenario.

9. The distributed collaborative optimization system for virtual power plants against communication interruption according to claim 8, characterized in that: The virtual reference signal generation module is further configured to generate a power cost function according to the active output power of each distributed generation node: in, 、 、 Represent the cost coefficients, Represents a distributed generation node The active output power, Represents a distributed generation node the cost of electricity generation; Optimization constraints are generated based on the scheduling information of the virtual power plant and the power output range of each distributed power generation node. The optimization constraints include: in, represents the actual output power of the virtual power plant, represents the expected output power of the virtual power plant, and Represent distributed generation nodes The minimum and maximum power output; A cost optimization function is generated based on the power cost function and the optimization constraint, the cost optimization function comprising: in, represents the total power generation cost of the virtual power plant, represents the cost optimization function, represents a set of distributed generation nodes; An initial virtual reference signal is generated based on the cost optimization function.

10. The distributed collaborative optimization system for virtual power plants against communication interruption according to claim 9, characterized in that: The virtual reference signal generation module is further configured to calculate the incremental cost of each distributed generation node based on the power cost function, wherein the incremental cost is calculated based on the following formula: in, represents the incremental cost; A local objective function is generated according to the power cost function, and a gradient of the local objective function is determined based on the incremental cost: in, represents the local objective function, Represents the gradient of the local objective function; A virtual reference signal dynamic equation is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the virtual reference signal dynamic equation and the cost optimization function. The virtual reference signal dynamic equation refers to the following formula: in, represents the time derivative of the virtual reference signal, Represents a distributed generation node The virtual reference signal, Represents a distributed generation node Neighbor nodes The virtual reference signal, Represents the time-varying weight, which is used to reflect the node under the interruption of the communication network With node The connection strength between and They represent the control gain, is an intermediate variable, representing the distributed generation node The state deviation, Represents intermediate variables The time derivative of Indicates time.

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