Distributed Cooperative Optimization Method and System for Virtual Power Plants to Address Communication Interruptions

Abstract

This invention discloses a distributed collaborative optimization method and system for virtual power plants under communication interruption. The method includes: constructing a scheduling and control model of the virtual power plant based on the communication network topology of distributed generation nodes in the power grid system; generating a cost optimization function based on the active power output of each distributed generation node; 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 distributed controller corresponding to each distributed generation node based on the smoothed reference signal; and optimizing the scheduling and control model based on the distributed controller. This optimizes the distributed collaborative scheduling of the virtual power plant under communication interruption scenarios, thereby effectively reducing the dependence on continuous communication while ensuring the accuracy of economic scheduling, improving the scheduling continuity and control response capability of the virtual power plant, and effectively ensuring the coordinated control and stable operation of the virtual power plant under unstable communication conditions.

Description

Technical Field

[0001] This invention relates to the field of distributed power distribution communication, and in particular to a distributed collaborative optimization method and system for virtual power plants in response to communication interruptions. Background Technology

[0002] In recent years, with the continuous advancement of energy diversification, distributed generation, characterized by its cleanliness and renewability, has gradually been integrated into the power system. Currently, most distributed generation (DG) is connected to the distribution network, improving system operating efficiency by reducing active power losses and enhancing power quality. However, due to the small capacity and geographically dispersed nature of distributed generation nodes, power system operators find it difficult to implement refined management. To overcome this limitation, Virtual Power Plants (VPPs) serve as an effective integration method, aggregating multiple DGs into a single entity to achieve unified scheduling and control. In VPPs, economic dispatch is the core issue ensuring efficient operation. Currently, centralized control methods in VPPs typically rely on a central controller to uniformly schedule all distributed units. This method requires global information, resulting in high management costs, poor flexibility, and weak reconfigurability when dealing with geographically widespread 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 robustness. Therefore, VPP distributed control methods based on network physical systems have received widespread attention in recent years. However, such methods still face problems such as high communication overhead, sensitivity to single points of failure, and insufficient data security.

[0003] It is worth noting that the implementation of distributed control methods heavily relies on stable and reliable communication networks. However, these networks are susceptible to 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), leading to communication node failures and even network paralysis. Furthermore, in remote areas with weak infrastructure, communication instability or interruptions are even more pronounced. Such communication outages severely weaken the coordination and scheduling capabilities of virtual power plants. Since VPPs rely on communication systems for real-time monitoring, data exchange, and command issuance of various distributed resources, communication disruptions can easily cause information transmission delays or losses, leading to power distribution imbalances, delayed scheduling responses, and degraded system dynamic performance, even affecting the local stability and operational safety of the power grid. Existing distributed control technologies for virtual power plants rely on stable, low-latency communication networks; once communication interruptions, data packet loss, or excessive delays occur, information exchange will fail, scheduling will lag, and power balance and system stability will be severely affected. Currently, under conditions of intermittent or unstable communication, the operational stability of virtual power plants cannot be guaranteed, leading to imbalances in the economic dispatch of virtual power plants, delayed dispatch response, and a decline in the dynamic performance of the system. Summary of the Invention

[0004] The main objective of this invention is to provide a distributed collaborative optimization method and system for virtual power plants in the event of communication interruption. This aims to solve the technical problem that existing technologies cannot guarantee the operational stability of virtual power plants under intermittent or unstable communication conditions, leading to imbalances in the economic dispatch of virtual power plants, delayed dispatch response, and a decline in the dynamic performance of the system.

[0005] To achieve the above objectives, the present invention provides a distributed collaborative optimization method for virtual power plants in the event of communication interruption, the method comprising the following steps:

[0006] A scheduling and control model for a virtual power plant is constructed based on the communication network topology of distributed generation nodes in a power grid system.

[0007] A cost optimization function is generated based on the active power output of each distributed generation node, and an initial virtual reference signal is generated based on the cost optimization function.

[0008] The initial virtual reference signal is smoothed to generate a smoothed reference signal;

[0009] The distributed controllers corresponding to each distributed generation node are generated based on the smoothed reference signal.

[0010] The scheduling control model is optimized based on the distributed controller to optimize the distributed collaborative scheduling of the virtual power plant in the event of communication interruption.

[0011] Optionally, the step of generating a cost optimization function based on the active power output of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function, includes:

[0012] A power cost function is generated based on the active power output of each distributed generation node:

[0013]

[0014] in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation;

[0015] Based on the scheduling information of the virtual power plant and the power output range of each distributed generation node, optimization constraints are generated, including:

[0016]

[0017]

[0018] in, This represents the actual output power of the virtual power plant. This represents the expected output power of the virtual power plant. and Representing distributed generation nodes The minimum and maximum values ​​of the power output;

[0019] A cost optimization function is generated based on the power cost function and the optimization constraints. The cost optimization function includes:

[0020]

[0021] in, This represents the total power generation cost of the virtual power plant. Represents the cost optimization function. Represents a set of distributed generation nodes;

[0022] An initial virtual reference signal is generated based on the cost optimization function.

[0023] Optionally, generating the initial virtual reference signal based on the cost optimization function includes:

[0024] The incremental cost of each distributed generation node is calculated based on the power cost function, and the incremental cost is calculated based on the following formula:

[0025]

[0026] in, Indicates incremental cost;

[0027] A local objective function is generated based on the power cost function, and the gradient of the local objective function is determined based on the incremental cost:

[0028]

[0029] in, Represents the local objective function. This represents the gradient of the local objective function;

[0030] The dynamic equation for generating a virtual reference signal is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the dynamic equation for the virtual reference signal and the cost optimization function. The dynamic equation for the virtual reference signal is given by the following formula:

[0031]

[0032]

[0033] in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation, Indicate intermediate variables Time derivative, Indicates time.

[0034] Optionally, the step of smoothing the initial virtual reference signal to generate a smoothed reference signal includes:

[0035] The initial virtual reference signal is smoothed using a Hermitian interpolation strategy to generate a smoothed reference signal.

[0036]

[0037] in, Indicates a smoothed reference signal. This indicates the order of the interpolation polynomial. Represents the Hermitian interpolation matrix. Indicates the time point for interpolation. For positive integers, Denotes the first Hermitian interpolation matrix. OK.

[0038] Optionally, generating the distributed controller corresponding to each distributed generation node based on the smoothed reference signal includes:

[0039] The power output error variable and controller model output error variable of each distributed generation node are determined based on the smoothed reference signal.

[0040] The target error variable is determined based on the rate output error variable and the controller model output error variable:

[0041]

[0042] in, Represents the target error variable. Indicates the power output error variable. This represents the output error variable of the controller model. For adjustment The positive constants of the weights;

[0043] The second dummy variable is determined based on the target error variable and the controller model output error variable:

[0044]

[0045] in, Represents the second dummy variable. For adjustment The positive constants of the weights, For adjustment The positive constants of the weights, The output signal after representing the gain of the distributed controller, This represents the initial power estimate. The first derivative of the smoothed reference signal is represented. The second derivative of the smoothed reference signal;

[0046] The controller gain coefficient is determined based on the second dummy variable and the controller model output error variable, and the controller gain coefficient is calculated based on the following formula:

[0047]

[0048] in, This represents the controller gain coefficient. This represents the gain coefficient update rate adjustment coefficient. This represents the predicted controller gain. The derivative of the predicted controller gain;

[0049] Based on the controller gain coefficient and the second dummy variable, a distributed controller is generated for each distributed generation node. The distributed controller includes:

[0050]

[0051] in, This indicates a distributed controller.

[0052] Optionally, determining the power output error variable and controller model output error variable of each distributed generation node based on the smoothed reference signal includes:

[0053] The power output error variable is determined based on the smoothed reference signal and the active power output of each distributed generation node:

[0054]

[0055] The first dummy variable is determined based on the power output error variable:

[0056]

[0057] in, Indicates the first dummy variable;

[0058] Determine the controller model output error variable based on the first dummy variable:

[0059]

[0060]

[0061] in, This represents the output error variable of the controller model. The output signal after representing the gain of the distributed controller, This indicates the ungained output signal of the distributed controller.

[0062] Optionally, the scheduling control model includes:

[0063]

[0064]

[0065] in, This represents the output signal of the scheduling control model. Represents distributed generation nodes initial power, The output signal after representing the gain of the distributed controller, This represents the ungained output signal of the distributed controller. This represents the controller gain coefficient.

[0066] Furthermore, to achieve the above objectives, the present invention also proposes a distributed collaborative optimization system for virtual power plants in response to communication interruptions, the distributed collaborative optimization system for virtual power plants in response to communication interruptions comprising:

[0067] The virtual power plant model building module is used to build a scheduling and control model for a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system.

[0068] The virtual reference signal generation module is used to generate a cost optimization function based on the active power output of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function.

[0069] A signal smoothing processing module is used to smooth the initial virtual reference signal to generate a smoothed reference signal;

[0070] A distributed controller construction module is used to generate a distributed controller for each distributed generation node based on the smoothed reference signal.

[0071] The distributed collaborative optimization module is used to optimize the scheduling control model based on the distributed controller, so as to optimize the distributed collaborative scheduling of the virtual power plant under the scenario of communication interruption.

[0072] Optionally, the virtual reference signal generation module is further configured to generate a power cost function based on the active power output of each distributed generation node:

[0073]

[0074] in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation;

[0075] Based on the scheduling information of the virtual power plant and the power output range of each distributed generation node, optimization constraints are generated, including:

[0076]

[0077]

[0078] in, This represents the actual output power of the virtual power plant. This represents the expected output power of the virtual power plant. and Representing distributed generation nodes The minimum and maximum values ​​of the power output;

[0079] A cost optimization function is generated based on the power cost function and the optimization constraints. The cost optimization function includes:

[0080]

[0081] in, This represents the total power generation cost of the virtual power plant. Represents the cost optimization function. Represents a set of distributed generation nodes;

[0082] An initial virtual reference signal is generated based on the cost optimization function.

[0083] 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, wherein the incremental cost is calculated based on the following formula:

[0084]

[0085] in, Indicates incremental cost;

[0086] A local objective function is generated based on the power cost function, and the gradient of the local objective function is determined based on the incremental cost:

[0087]

[0088] in, Represents the local objective function. This represents the gradient of the local objective function;

[0089] The dynamic equation for generating a virtual reference signal is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the dynamic equation for the virtual reference signal and the cost optimization function. The dynamic equation for the virtual reference signal is given by the following formula:

[0090]

[0091]

[0092] in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation, Indicate intermediate variables Time derivative, Indicates time.

[0093] Furthermore, to achieve the above objectives, this application also proposes a distributed collaborative optimization device for virtual power plants in response to communication interruptions. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the distributed collaborative optimization method for virtual power plants in response to communication interruptions as described above.

[0094] In addition, to achieve the above objectives, this application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the virtual power plant distributed collaborative optimization method for communication interruption described above.

[0095] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the virtual power plant distributed collaborative optimization method for communication interruption as described above.

[0096] This invention constructs a scheduling and control model for a virtual power plant based on the communication network topology of distributed generation nodes in a power grid system. It generates a cost optimization function based on the active power output of each distributed generation node, and generates an initial virtual reference signal based on this function. The initial virtual reference signal is then smoothed to generate a smoothed reference signal. Based on the smoothed reference signal, a distributed controller corresponding to each distributed generation node is generated. The scheduling and control model is then optimized based on the distributed controllers to achieve optimized distributed collaborative scheduling of the virtual power plant under communication interruption scenarios. Because this invention generates the initial virtual reference signal by solving the cost optimization function, it achieves optimized scheduling under communication interruption scenarios. This method solves the economic dispatch problem by generating an optimal virtual reference signal. By constructing distributed controllers corresponding to each distributed generation node, it ensures that each distributed power source can track the reference signal in a timely manner and respond quickly to dispatch commands even when communication is limited. This effectively guarantees 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 and significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication 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, effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions. Attached Figure Description

[0097] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0098] Figure 1 This is a schematic diagram of the structure of a distributed collaborative optimization device for a virtual power plant in response to communication interruptions in the hardware operating environment involved in the embodiments of the present invention;

[0099] Figure 2 This is a flowchart illustrating an embodiment of the distributed collaborative optimization method for virtual power plants in response to communication interruptions according to the present invention.

[0100] Figure 3 This is a schematic diagram of the communication network topology of distributed generation nodes in a power grid system according to an embodiment of the present invention;

[0101] Figure 4 This is a schematic diagram of the cost optimization and virtual reference signal generation process in one embodiment of the distributed collaborative optimization method for virtual power plants in response to communication interruptions according to the present invention.

[0102] Figure 5(a) is a schematic diagram of the normal structure of the power grid communication network in one embodiment;

[0103] Figure 5(b) is a schematic diagram of the communication network when some communication links of the distributed generation nodes are interrupted;

[0104] Figure 5(c) is a schematic diagram of the communication network when all communication networks of the distributed generation nodes are interrupted and isolated;

[0105] Figure 6 This is a structural block diagram of an embodiment of the distributed collaborative optimization system for virtual power plants in response to communication interruptions according to the present invention.

[0106] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0107] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0108] Reference Figure 1 , Figure 1 This is a schematic diagram of the distributed collaborative optimization device structure of a virtual power plant for communication interruption in the hardware operating environment involved in the embodiments of the present invention.

[0109] like Figure 1As shown, the distributed collaborative optimization device for virtual power plants in response to 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 screen or 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 high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage system independent of the aforementioned processor 1001.

[0110] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the distributed collaborative optimization device for virtual power plants in response to communication interruptions, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0111] like Figure 1 As shown, the memory 1005, which is 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.

[0112] exist Figure 1 In the virtual power plant distributed collaborative optimization device for communication interruption shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the virtual power plant distributed collaborative optimization device for communication interruption can be set in the device, and the virtual power plant distributed collaborative optimization device for communication interruption calls the virtual power plant distributed collaborative optimization program for communication interruption stored in the memory 1005 through the processor 1001, and executes the virtual power plant distributed collaborative optimization method for communication interruption provided in the embodiment of the present invention.

[0113] This invention provides a distributed collaborative optimization method for virtual power plants in the event of communication interruption, referring to... Figure 2 , Figure 2This is a flowchart illustrating an embodiment of the distributed collaborative optimization method for virtual power plants in response to communication interruptions according to the present invention.

[0114] In this embodiment, the distributed collaborative optimization method for virtual power plants in response to communication interruptions includes the following steps:

[0115] Step S10: Construct a scheduling and control model for a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system.

[0116] It should be noted that this embodiment is applied to ensure the normal operation and convergence of the economic dispatch of the virtual power plant to the distributed generation nodes in the event of intermittent or unstable communication, and to ensure the timeliness and stability of the dispatch.

[0117] This embodiment designs a distributed optimization algorithm that can still converge stably under intermittent communication interruption conditions to solve the economic dispatch problem and generate the optimal virtual reference signal. Subsequently, a distributed adaptive controller is proposed to ensure that each distributed generation node can track the reference signal in a timely manner during communication-limited periods and respond quickly to dispatch instructions, thereby achieving efficient dispatch and stable operation of the virtual power plant system under conditions where communication is not fully available.

[0118] It should be understood that the executing entity of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or a terminal electronic device capable of performing the above functions. The following description uses a distributed collaborative optimization device for virtual power plants (hereinafter referred to as the optimization device) for communication interruption as an example to illustrate this embodiment and the following embodiments.

[0119] It should be noted that a virtual power plant (VPP) can be a distributed network of 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.

[0120] It should be noted that Distributed Generation (DG) nodes refer to distributed generation (or distributed energy). Distributed generation refers to small generating devices located near the load, typically distributed across multiple nodes in the power grid system.

[0121] In practical implementation, the optimization equipment can construct a directed communication network topology based on the communication network information of distributed generation nodes in the power grid system, referring to... Figure 3 , Figure 3 This is a schematic diagram of the communication network topology of distributed generation nodes in a power grid system in one embodiment. Figure 3 The communication network topology of a 10-unit (10 distributed generation nodes, including DG1, DG2, ..., DG10) 33-node IEEE standard power network model is presented to simulate a virtual power plant. The proposed control is applied to each unit, and a scheduling and control model of the virtual power plant is constructed based on the communication network topology. In this model, a feeder consisting of multiple schedulable distributed generation nodes can be controlled as a virtual power plant.

[0122] Furthermore, to improve the control efficiency of distributed generation nodes, in one embodiment, the scheduling control model includes:

[0123]

[0124]

[0125] in, This represents the output signal of the scheduling control model. Represents distributed generation nodes initial power, The output signal after representing the gain of the distributed controller, This represents the ungained output signal of the distributed controller. This represents the controller gain coefficient.

[0126] It should be noted that the dispatch control model can be a virtual power plant's active power control model for distributed generation nodes.

[0127] In some embodiments, the optimization device may construct a system composed of Described directed communication network G ,in It is a set of node indexes. It is the set of edges, for Meaning node i Can to j Send information. It is also assumed that each node (i.e., ...) sends a message. All of these contain self-loops. Nodes in the control subnet... i The inner neighbor set and outer neighbor set are defined as follows: 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, a ij yes jand i The weight of the edges between them.

[0128] Step S20: Generate a cost optimization function based on the active power output of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function.

[0129] It should be noted that a feeder consisting of multiple schedulable distributed generation nodes can be controlled as a virtual power plant, whose output power equals the total active power output of the distributed generation nodes minus all active power loads. The cost function of the distributed generation nodes in the virtual power plant can be approximated as:

[0130]

[0131] It should be noted that the goal of economic dispatch of virtual power plants is to minimize the total cost of power generation while ensuring that the total active power of the virtual power plants meets certain demands.

[0132] Understandably, to address the problems of power allocation imbalance, delayed dispatch response, and degraded system dynamic performance caused by existing communication network interruptions in virtual power plants, this embodiment designs a distributed optimization algorithm. This algorithm can minimize the cost optimization function under the influence of intermittent communication interruptions. And generate an initial virtual reference signal. .

[0133] Furthermore, in order to ensure scheduling timeliness and stability and optimize scheduling costs in the event of communication interruption, refer to Figure 4 , Figure 4 As shown in one embodiment, the process of cost optimization and virtual reference signal generation is illustrated. Step S20 may include:

[0134] Step S201: Generate a power cost function based on the active power output of each distributed generation node;

[0135] Step S202: Generate optimized constraints based on the scheduling information of the virtual power plant and the power output range of each distributed generation node;

[0136] Step S203: Generate a cost optimization function based on the power cost function and the optimization constraints;

[0137] Step S204: Generate an initial virtual reference signal based on the cost optimization function.

[0138] It should be noted that the power cost function for each distributed generation node in the virtual power plant is as follows:

[0139]

[0140] in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation;

[0141] It should be noted that the objective of economic dispatch of virtual power plants is to minimize the total generation cost while ensuring that the total active power of the virtual power plants meets a certain demand. The cost optimization formula is as follows:

[0142]

[0143]

[0144]

[0145] in, This represents the total power generation cost of the virtual power plant. Represents the cost optimization function. Represents a set of distributed generation nodes. This represents the actual output power of the virtual power plant (total DG power generation minus load). This represents the expected output power of the virtual power plant (as a dispatch command from the energy management system to the virtual power plant). and Representing distributed generation nodes The minimum and maximum power output values.

[0146] Furthermore, in order to achieve optimal power generation costs and generate a better virtual reference signal, thereby improving dispatch efficiency, step S204 above may include:

[0147] Step S2041: Calculate the incremental cost of each distributed generation node based on the power cost function;

[0148] Step S2042: Generate a local objective function based on the power cost function, and determine the gradient of the local objective function based on the incremental cost;

[0149] Step S2043: Generate a dynamic equation for the virtual reference signal based on the local objective function and the gradient, and generate an initial virtual reference signal based on the dynamic equation for the virtual reference signal and the cost optimization function.

[0150] It should be noted that the incremental cost is calculated based on the following formula:

[0151]

[0152] in, This represents incremental cost.

[0153] 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:

[0154]

[0155] The first constraint ensures long-term convergence; as time increases, any two distributed generation nodes... i and j The difference between incremental costs tends to zero. The second constraint ensures short-term stability, with a point in time where... T After this point, the incremental cost difference between any two DG units remains zero.

[0156] It is understood 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. For the first... i DG nodes ( i =1,..., N We introduce a virtual reference signal. And updated by the following distributed optimization algorithm:

[0157]

[0158] The intermediate variables are calculated using the following formula:

[0159]

[0160] The gradient of a function is calculated based on the following formula:

[0161]

[0162] in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation, Indicate intermediate variables Time derivative, Indicates time, Represents the local objective function. The gradient of the local objective function is represented by time-varying weights. It was caused by a communication network outage.

[0163] Step S30: Smooth the initial virtual reference signal to generate a smoothed reference signal.

[0164] Understandably, due to the intermittent communication interruptions, the initial virtual reference signal... Since the signal is discontinuous and intermittent, in order to ensure the continuity of the reference signal, this embodiment can smooth the initial virtual reference signal to generate a continuous and smooth reference signal.

[0165] Furthermore, to ensure the continuity of the virtual reference signal and improve signal stability, step S30 may include:

[0166] Step S301: The initial virtual reference signal is smoothed using a Hermitian interpolation strategy to generate a smoothed reference signal.

[0167] It should be noted that due to intermittent communication interruptions, the initial virtual reference signal... Since the signal is discontinuous and intermittent, this embodiment employs Hermite interpolation to ensure the continuity of the reference signal. The signal was processed to obtain a new smoothed reference signal. , ( i=1,...,N The specific design is as follows:

[0168]

[0169] in, Indicates a smoothed reference signal. This indicates the order of the interpolation polynomial. Represents the Hermitian interpolation matrix. Indicates the time point for interpolation. For positive integers, Denotes the first Hermitian interpolation matrix. OK.

[0170] In the formula, , T It is a small positive number; when hour, ; yes The qOK, , , .

[0171] for k =2,…, m +1, the calculation process for the intermediate variables in the above formula is as follows:

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179] Step S40: Generate a distributed controller corresponding to each distributed generation node based on the smoothed reference signal.

[0180] It should be noted that, in order to ensure that each distributed power source can track the reference signal in a timely manner and respond quickly to dispatch commands during periods of communication constraints, this embodiment optimizes the device by designing a distributed controller for each distributed generation node. It 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.

[0181] Furthermore, in order to improve scheduling control efficiency and scheduling response speed, step S40 above may include:

[0182] Step S401: Determine the power output error variable and controller model output error variable of each distributed generation node based on the smoothed reference signal.

[0183] It should be noted that the power output error variable represents the difference between the actual power and the reference power of the distributed generation node. The controller model output error variable represents the difference between the controller model's predicted power output and the test power output.

[0184] Furthermore, in order to accurately quantify the systematic error variable, step S401 above may include:

[0185] Step S4011: Determine the power output error variable based on the smoothed reference signal and the active power output of each distributed generation node:

[0186]

[0187] Step S4012: Determine the first dummy variable based on the power output error variable.

[0188] 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 dummy variable through feedback terms and the rate of change of the reference signal to reduce the impact of errors on the system. By combining the current error, the rate of change of the error, and the rate of change of the reference signal, the controller output is dynamically adjusted to enable the system to adapt to real-time state changes (such as load fluctuations and environmental disturbances). The first dummy variable is calculated with reference to the following formula:

[0189]

[0190]

[0191] in, Represents the first dummy variable. This represents the initial power estimate. It is a positive number;

[0192] Step S4013: Determine the controller model output error variable based on the first dummy variable:

[0193]

[0194]

[0195] in, This represents the output error variable of the controller model. The output signal after representing the gain of the distributed controller, This indicates the ungained output signal of the distributed controller.

[0196] Step S402: Determine the target error variable based on the rate output error variable and the controller model output error variable.

[0197] 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, and quantifies the overall target error variable based on the power output error variable and the controller model output error variable. The target error variable is calculated with reference to the following formula:

[0198]

[0199] in, Represents the target error variable. Indicates the power output error variable. This represents the output error variable of the controller model. For adjustment The weights are positive constants to balance the relative importance of power output error and model error.

[0200] Step S403: Determine the second dummy variable based on the target error variable and the controller model output error variable.

[0201] It should be noted that, in order to accurately calculate the relevant design parameters of the distributed 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-constrained periods by dynamically adjusting the controller behavior. Based on the first dummy variable, an integral term (to eliminate steady-state error) and the second derivative of the reference signal (to predict the accelerated change of the reference signal) are introduced to handle errors more finely, 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 with reference to the following formula:

[0202]

[0203] in, Represents the second dummy variable. For adjustment The positive constants of the weights, For adjustment The positive constants of the weights, The output signal after representing the gain of the distributed controller, This represents the initial power estimate. The first derivative of the smoothed reference signal is represented. It represents the second derivative of the smoothed reference signal.

[0204] Step S404: Determine the controller gain coefficient based on the second dummy variable and the controller model output error variable.

[0205] It should be noted that, in order to enhance the distributed controller's ability to track changes in the reference signal, this embodiment designs a controller gain coefficient. 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 communication-constrained environments. The controller gain coefficient is calculated based on the following formula:

[0206]

[0207] in, This represents the controller gain coefficient. This represents the gain coefficient update rate adjustment coefficient. This represents the predicted controller gain. This represents the derivative of the predicted controller gain.

[0208] Step S405: Generate distributed controllers corresponding to each distributed generation node based on the controller gain coefficient and the second virtual variable.

[0209] It should be noted that this embodiment designs a distributed adaptive controller, which can adaptively adjust the 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:

[0210]

[0211] in, This indicates a distributed controller.

[0212] Step S50: Optimize the scheduling control model based on the distributed controller to optimize the distributed collaborative scheduling of the virtual power plant under communication interruption scenarios.

[0213] It is understood that this embodiment will design a distributed controller. It is applied to the scheduling and control model of virtual power plants to optimize the distributed collaborative scheduling performance of virtual power plants under communication interruption scenarios.

[0214] It should be understood that this embodiment is applied to the scenario of abnormal communication between distributed generation nodes in a power grid system. Referring to Figures 5(a), 5(b), and 5(c), Figures 5(a), 5(b), and 5(c) show the changes in the communication topology when communication is interrupted due to extreme weather or other factors for 10 distributed generation units. 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 when part of the communication link of distributed generation node DG6 is interrupted; and Figure 5(c) is a schematic diagram of the communication network when distributed generation node DG6 is isolated due to the complete interruption of the communication network. This embodiment adopts a two-stage distributed collaborative optimization strategy for the scenario of abnormal communication between distributed generation nodes: In the first stage, in order to achieve the economic dispatch objective of VPP, that is, to minimize the total generation cost while meeting the total active power demand, a cost optimization algorithm for distributed generation nodes that can still maintain stable convergence under intermittent communication interruption conditions is designed to solve the economic dispatch problem and generate the optimal virtual reference signal. In the second stage, to ensure that each distributed power source can track the reference signal in a timely manner and respond quickly to dispatch commands under communication constraints, a distributed adaptive controller is proposed. A corresponding distributed controller is designed for each distributed 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. This method effectively reduces the dependence on continuous communication while ensuring economic dispatch accuracy, significantly improving the system's dispatch continuity and control response capability under network anomalies. It balances economy, scalability, and communication robustness, making it suitable for the practical deployment of virtual power plants in large-scale distributed energy systems.

[0215] This embodiment constructs a virtual power plant scheduling and control model based on the communication network topology of distributed generation nodes in a power grid system. A cost optimization function is generated based on the active power output of each distributed generation node, and an initial virtual reference signal is generated based on this function. The initial virtual reference signal is then smoothed to generate a smoothed reference signal. A distributed controller corresponding to each distributed generation node is generated based on the smoothed reference signal, and the scheduling and control model is optimized based on the distributed controllers to achieve optimized distributed collaborative scheduling of the virtual power plant under communication interruption scenarios. Because this invention generates the initial virtual reference signal by solving the cost optimization function, it achieves optimization under communication interruption scenarios. This method solves the economic dispatch problem by generating an optimal virtual reference signal. By constructing distributed controllers for each distributed generation node, it ensures that each distributed power source can track the reference signal in a timely manner and respond quickly to dispatch commands even when communication is limited. This effectively guarantees 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 and significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication 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, effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions.

[0216] Furthermore, embodiments of the present invention also propose a computer-readable storage medium storing a distributed collaborative optimization program for virtual power plants in response to communication interruptions. When executed by a processor, the distributed collaborative optimization program for virtual power plants in response to communication interruptions implements the steps of the distributed collaborative optimization method for virtual power plants in response to communication interruptions as described above.

[0217] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing 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.

[0218] The aforementioned computer-readable storage medium may be included in a virtual power plant distributed collaborative optimization device for communication interruption; or it may exist independently and not assembled into a virtual power plant distributed collaborative optimization device for communication interruption.

[0219] Furthermore, this invention also proposes a computer program product, including a distributed collaborative optimization program for virtual power plants in response to communication interruptions. When the distributed collaborative optimization program for virtual power plants in response to communication interruptions is executed by a processor, it implements the steps of the distributed collaborative optimization method for virtual power plants in response to communication interruptions as described above.

[0220] The specific implementation of the computer program product of the present invention is basically the same as the embodiments of the above-described distributed collaborative optimization method for virtual power plants in response to communication interruption, and will not be repeated here.

[0221] Reference Figure 6 , Figure 6 This is a structural block diagram of an embodiment of the distributed collaborative optimization system for virtual power plants in response to communication interruptions according to the present invention.

[0222] like Figure 6 As shown, the distributed collaborative optimization system for virtual power plants in response to communication interruptions proposed in this embodiment of the invention includes:

[0223] The virtual power plant model building module 10 is used to build a scheduling and control model of a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system.

[0224] The virtual reference signal generation module 20 is used to generate a cost optimization function based on the active power output of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function.

[0225] The signal smoothing processing module 30 is used to smooth the initial virtual reference signal to generate a smoothed reference signal.

[0226] The distributed controller construction module 40 is used to generate a distributed controller for each distributed generation node based on the smoothed reference signal.

[0227] The distributed collaborative optimization module 50 is used to optimize the scheduling control model based on the distributed controller, so as to optimize the distributed collaborative scheduling of the virtual power plant in the scenario of communication interruption.

[0228] Furthermore, the virtual reference signal generation module is also used to generate a power cost function based on the active power output of each distributed generation node:

[0229]

[0230] in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation;

[0231] Based on the scheduling information of the virtual power plant and the power output range of each distributed generation node, optimization constraints are generated, including:

[0232]

[0233]

[0234] in, This represents the actual output power of the virtual power plant. This represents the expected output power of the virtual power plant. and Representing distributed generation nodes The minimum and maximum values ​​of the power output;

[0235] A cost optimization function is generated based on the power cost function and the optimization constraints. The cost optimization function includes:

[0236]

[0237] in, This represents the total power generation cost of the virtual power plant. Represents the cost optimization function. Represents a set of distributed generation nodes;

[0238] An initial virtual reference signal is generated based on the cost optimization function.

[0239] Furthermore, the virtual reference signal generation module is also used 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:

[0240]

[0241] in, Indicates incremental cost;

[0242] A local objective function is generated based on the power cost function, and the gradient of the local objective function is determined based on the incremental cost:

[0243]

[0244] in, Represents the local objective function. This represents the gradient of the local objective function;

[0245] The dynamic equation for generating a virtual reference signal is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the dynamic equation for the virtual reference signal and the cost optimization function. The dynamic equation for the virtual reference signal is given by the following formula:

[0246]

[0247]

[0248] in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation, Indicate intermediate variables Time derivative, Indicates time.

[0249] Furthermore, the signal smoothing processing module 30 is also used to smooth the initial virtual reference signal using a Hermitian interpolation strategy to generate a smoothed reference signal:

[0250]

[0251] in, Indicates a smoothed reference signal. This indicates the order of the interpolation polynomial. Represents the Hermitian interpolation matrix. Indicates the time point for interpolation. For positive integers, Denotes the first Hermitian interpolation matrix. OK.

[0252] Furthermore, the distributed controller construction module 40 is also used to determine the power output error variable and the controller model output error variable of each distributed generation node based on the smoothing reference signal; and to determine the target error variable based on the power output error variable and the controller model output error variable.

[0253]

[0254] in, Represents the target error variable. Indicates the power output error variable. This represents the output error variable of the controller model. For adjustment The positive constants of the weights;

[0255] The second dummy variable is determined based on the target error variable and the controller model output error variable:

[0256]

[0257] in, Represents the second dummy variable. For adjustment The positive constants of the weights, For adjustment The positive constants of the weights, The output signal after representing the gain of the distributed controller, This represents the initial power estimate. The first derivative of the smoothed reference signal is represented. The second derivative of the smoothed reference signal;

[0258] The controller gain coefficient is determined based on the second dummy variable and the controller model output error variable, and the controller gain coefficient is calculated based on the following formula:

[0259]

[0260] in, This represents the controller gain coefficient. This represents the gain coefficient update rate adjustment coefficient. This represents the predicted controller gain. The derivative of the predicted controller gain;

[0261] Based on the controller gain coefficient and the second dummy variable, a distributed controller is generated for each distributed generation node. The distributed controller includes:

[0262]

[0263] in, This indicates a distributed controller.

[0264] Furthermore, the distributed controller construction module 40 is also used to determine the power output error variable based on the smoothing reference signal and the active power output of each distributed generation node:

[0265]

[0266] The first dummy variable is determined based on the power output error variable:

[0267]

[0268] in, Indicates the first dummy variable;

[0269] Determine the controller model output error variable based on the first dummy variable:

[0270]

[0271]

[0272] in, This represents the output error variable of the controller model. The output signal after representing the gain of the distributed controller, This indicates the ungained output signal of the distributed controller.

[0273] Furthermore, the scheduling control model includes:

[0274]

[0275]

[0276] in, This represents the output signal of the scheduling control model. Represents distributed generation nodes initial power, The output signal after representing the gain of the distributed controller, This represents the ungained output signal of the distributed controller. This represents the controller gain coefficient.

[0277] This embodiment constructs a virtual power plant scheduling and control model based on the communication network topology of distributed generation nodes in a power grid system. A cost optimization function is generated based on the active power output of each distributed generation node, and an initial virtual reference signal is generated based on this function. The initial virtual reference signal is then smoothed to generate a smoothed reference signal. A distributed controller corresponding to each distributed generation node is generated based on the smoothed reference signal, and the scheduling and control model is optimized based on the distributed controllers to achieve optimized distributed collaborative scheduling of the virtual power plant under communication interruption scenarios. Because this invention generates the initial virtual reference signal by solving the cost optimization function, it achieves optimization under communication interruption scenarios. This method solves the economic dispatch problem by generating an optimal virtual reference signal. By constructing distributed controllers for each distributed generation node, it ensures that each distributed power source can track the reference signal in a timely manner and respond quickly to dispatch commands even when communication is limited. This effectively guarantees 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 and significantly improves the dispatch continuity and control response capability of the virtual power plant under network communication 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, effectively guaranteeing the coordinated control and stable operation of the virtual power plant under unstable communication conditions.

[0278] The distributed collaborative optimization system for virtual power plants under communication interruption provided in this application employs the distributed collaborative optimization method for virtual power plants under communication interruption described in the above embodiments, and can solve the technical problem of distributed collaborative optimization of virtual power plants under communication interruption. Compared with the prior art, the beneficial effects of the distributed collaborative optimization system for virtual power plants under communication interruption provided in this application are the same as the beneficial effects of the distributed collaborative optimization method for virtual power plants under communication interruption provided in the above embodiments, and other technical features of the distributed collaborative optimization system for virtual power plants under communication interruption are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0279] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions 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 restrictions on this.

[0280] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0281] In addition, for technical details not described in detail in this embodiment, please refer to the distributed collaborative optimization method for virtual power plants in response to communication interruptions provided in any embodiment of the present invention, which will not be repeated here.

[0282] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0283] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0284] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part 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, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may 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.

[0285] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A distributed collaborative optimization method for virtual power plants in response to communication interruptions, characterized in that, The method includes: A scheduling and control model for a virtual power plant is constructed based on the communication network topology of distributed generation nodes in a power grid system. A cost optimization function is generated based on the active power output of each distributed generation node, and an initial virtual reference signal is generated based on the cost optimization function. The initial virtual reference signal is smoothed to generate a smoothed reference signal; The distributed controllers corresponding to each distributed generation node are generated based on the smoothed reference signal. The scheduling control model is optimized based on the distributed controller to optimize the distributed collaborative scheduling of the virtual power plant under communication interruption scenarios. The step of generating distributed controllers for each distributed generation node based on the smoothed reference signal includes: The power output error variable and controller model output error variable of each distributed generation node are determined based on the smoothed reference signal. The target error variable is determined based on the rate output error variable and the controller model output error variable: in, Represents the target error variable. Indicates the power output error variable. This represents the output error variable of the controller model. For adjustment The positive constants of the weights; The second dummy variable is determined based on the target error variable and the controller model output error variable: in, Represents the second dummy variable. For adjustment The positive constants of the weights, For adjustment The positive constants of the weights, The output signal after representing the gain of the distributed controller, This represents the initial power estimate. The first derivative of the smoothed reference signal is represented. The second derivative of the smoothed reference signal; The controller gain coefficient is determined based on the second dummy variable and the controller model output error variable, and the controller gain coefficient is calculated based on the following formula: in, This represents the controller gain coefficient. This represents the gain coefficient update rate adjustment coefficient. This represents the predicted controller gain. The derivative of the predicted controller gain; Based on the controller gain coefficient and the second dummy variable, a distributed controller is generated for each distributed generation node. The distributed controller includes: in, Indicates a distributed controller; The step of determining the power output error variable and controller model output error variable of each distributed generation node based on the smoothed reference signal includes: The power output error variable is determined based on the smoothed reference signal and the active power output of each distributed generation node: The first dummy variable is determined based on the power output error variable: in, Indicates the first dummy variable; Determine the controller model output error variable based on the first dummy variable: in, This represents the output error variable of the controller model. The output signal after representing the gain of the distributed controller, This indicates the ungained output signal of the distributed controller.

2. The distributed collaborative optimization method for virtual power plants in response to communication interruptions as described in claim 1, characterized in that, The step of generating a cost optimization function based on the active power output of each distributed generation node, and generating an initial virtual reference signal based on the cost optimization function, includes: A power cost function is generated based on the active power output of each distributed generation node: in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation; Based on the scheduling information of the virtual power plant and the power output range of each distributed generation node, optimization constraints are generated, including: in, This represents the actual output power of the virtual power plant. This represents the expected output power of the virtual power plant. and Representing distributed generation nodes The minimum and maximum values ​​of the power output; A cost optimization function is generated based on the power cost function and the optimization constraints. The cost optimization function includes: in, This 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 in response to communication interruptions as described in claim 2, characterized in that, The generation of the 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, and the incremental cost is calculated based on the following formula: in, Indicates incremental cost; A local objective function is generated based on the power cost function, and the gradient of the local objective function is determined based on the incremental cost: in, Represents the local objective function. This represents the gradient of the local objective function; The dynamic equation for generating a virtual reference signal is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the dynamic equation for the virtual reference signal and the cost optimization function. The dynamic equation for the virtual reference signal is given by the following formula: in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation from adjacent nodes Indicate intermediate variables Time derivative, Indicates time.

4. The distributed collaborative optimization method for virtual power plants in response to communication interruptions as described in claim 3, characterized in that, The step of smoothing the initial virtual reference signal to generate a smoothed reference signal includes: The initial virtual reference signal is smoothed using a Hermitian interpolation strategy to generate a smoothed reference signal. in, Indicates a smoothed reference signal. This indicates the order of the interpolation polynomial. Represents the Hermitian interpolation matrix. Indicates the time point for interpolation. For positive integers, Denotes the first Hermitian interpolation matrix. OK.

5. The distributed collaborative optimization method for virtual power plants in response to communication interruptions as described in any one of claims 1 to 4, characterized in that, The scheduling control model includes: in, This represents the output signal of the scheduling control model. Represents distributed generation nodes initial power, The output signal after representing the gain of the distributed controller, This represents the ungained output signal of the distributed controller. This represents the controller gain coefficient.

6. A distributed collaborative optimization system for virtual power plants to address communication interruptions, characterized in that, The virtual power plant distributed collaborative optimization system for communication interruption is used to implement the virtual power plant distributed collaborative optimization method for communication interruption as described in any one of claims 1 to 5, the system comprising: The virtual power plant model building module is used to build a scheduling and control model for a virtual power plant based on the communication network topology of distributed generation nodes in the power grid system. The virtual reference signal generation module is used to generate a cost optimization function based on the active power output of each distributed generation node, and generate an initial virtual reference signal based on the cost optimization function. A signal smoothing processing module is used to smooth the initial virtual reference signal to generate a smoothed reference signal; A distributed controller construction module is used to generate a distributed controller for each distributed generation node based on the smoothed reference signal. The distributed collaborative optimization module is used to optimize the scheduling control model based on the distributed controller, so as to optimize the distributed collaborative scheduling of the virtual power plant under the scenario of communication interruption.

7. The distributed collaborative optimization system for virtual power plants in response to communication interruptions as described in claim 6, characterized in that, The virtual reference signal generation module is also used to generate a power cost function based on the active power output of each distributed generation node: in, , , These represent cost coefficients, Represents distributed generation nodes Active output power, Represents distributed generation nodes The cost of electricity generation; Based on the scheduling information of the virtual power plant and the power output range of each distributed generation node, optimization constraints are generated, including: in, This represents the actual output power of the virtual power plant. This represents the expected output power of the virtual power plant. and Representing distributed generation nodes The minimum and maximum values ​​of the power output; A cost optimization function is generated based on the power cost function and the optimization constraints. The cost optimization function includes: in, This 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.

8. The distributed collaborative optimization system for virtual power plants in response to communication interruptions as described in claim 7, characterized in that, The virtual reference signal generation module is also used 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, Indicates incremental cost; A local objective function is generated based on the power cost function, and the gradient of the local objective function is determined based on the incremental cost: in, Represents the local objective function. This represents the gradient of the local objective function; The dynamic equation for generating a virtual reference signal is generated based on the local objective function and the gradient, and an initial virtual reference signal is generated based on the dynamic equation for the virtual reference signal and the cost optimization function. The dynamic equation for the virtual reference signal is given by the following formula: in, The time derivative of the virtual reference signal. Represents distributed generation nodes The virtual reference signal Represents distributed generation nodes neighboring nodes The virtual reference signal Indicates time-varying weights, used to reflect the impact of network outages on nodes. With nodes The strength of the connection between them and These represent the control gain, As an intermediate variable, representing distributed generation nodes State deviation, Indicate intermediate variables Time derivative, Indicates time.

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