Multi - microgrid Load Frequency Control Method and Device Based on Dynamic Event Triggering

Abstract

The present invention provides a multi-microgrid load frequency control method based on dynamic event triggering, which can be applied to the field of intelligent microgrid technology. The method includes: obtaining the local sampling error of the microgrid system, where the local sampling error is obtained based on the global neighborhood error and the local neighborhood error of the microgrid system, and the local neighborhood error is used to represent the state difference between the microgrid system and its neighbor microgrid systems at the sampling moment; when the local sampling error satisfies the dynamic event triggering rule, updating the initial weight matrix of the action-evaluation neural network based on the local sampling error to obtain the target action-evaluation neural network; determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network. The present invention also provides a multi-microgrid load frequency control device based on dynamic event triggering.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent microgrids, and more specifically, to a multi-microgrid load frequency control method and device based on dynamic event triggering. Background Art

[0002] The multi-microgrid system is a typical mode of a new power system, which is a complex power system composed of multiple interconnected and coordinated microgrids. In the multi-microgrid system, load frequency control is to adjust the output power of generators to match the load changes, so as to achieve the adaptive adjustment of power loads. Therefore, load frequency control plays a key role in maintaining the steady-state frequency at the nominal value during load disturbances.

[0003] In view of the complex dynamics and nonlinearity of the multi-microgrid system, traditional methods based on linearized models can only be controlled at specific nominal operating points, and it is difficult to cope with various changes and uncertainties that the system may encounter during actual operation. In addition, traditional methods usually require certain system model information or structural knowledge to construct performance functions and optimization problems. Summary of the Invention

[0004] In view of this, the present invention provides a multi-microgrid load frequency control method and device based on dynamic event triggering.

[0005] One aspect of the present invention provides a multi-microgrid load frequency control method based on dynamic event triggering. The control method includes: obtaining the local sampling error of the microgrid system, where the local sampling error is obtained based on the global neighborhood error and local neighborhood error of the microgrid system; when the local sampling error satisfies the dynamic event triggering rule, updating the initial weight matrix of the action-evaluation neural network based on the local sampling error to obtain a target action-evaluation neural network; determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network.

[0006] According to an embodiment of the present invention, the control method further includes: determining the load frequency control model of the microgrid system based on the control parameters of the governor, turbine, power system, and controller of the microgrid system; obtaining the local cost function of the microgrid system based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system, where the local neighborhood error is determined based on the connection relationship between the microgrid system and its neighbor microgrid systems.

[0007] According to an embodiment of the present invention, obtaining the local cost function of the microgrid system based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system includes: performing differential transformation on the load frequency control model of the microgrid system to obtain the state space model of the microgrid system; determining the global neighborhood error based on the system matrix and control matrix of the state space model of the microgrid system and the local neighborhood error of the microgrid system; and obtaining the local cost function of the microgrid system based on the global neighborhood error and the differential game revenue function of the microgrid system.

[0008] According to an embodiment of the present invention, a local event trigger is included in the microgrid system, and the control method further includes: determining a static event trigger rule for the local event trigger based on the local cost function of the microgrid system; and updating the static event trigger rule by using distributed dynamic variables to obtain the dynamic event trigger rule, where the distributed dynamic variables are used to represent the influence degree of the historical event trigger state on the event trigger state at the event trigger moment.

[0009] According to an embodiment of the present invention, when the local sampling error satisfies the dynamic event trigger rule, updating the initial weight matrix of the action-neural evaluation network based on the local sampling error to obtain a target action-evaluation neural network includes: determining a dynamic trigger threshold based on the dynamic event trigger rule; obtaining a sampling signal at the event trigger moment when the local sampling error exceeds the dynamic trigger threshold; determining a reward signal of the action-evaluation neural network based on the sampling signal at the event trigger moment and the Bellman residual of the microgrid system; and updating the initial weight matrix of the action-evaluation neural network based on the reward signal of the action-evaluation neural network to obtain the target action-evaluation neural network.

[0010] According to an embodiment of the present invention, updating the initial weight matrix of the action-evaluation neural network based on the reward signal of the action-evaluation neural network to obtain the target action-evaluation neural network includes: processing the reward signal of the action-evaluation neural network by using the least squares method to obtain a weight update rule; updating the initial weight matrix based on the weight update rule to obtain an updated weight matrix; and determining the target action-evaluation neural network according to the updated weight matrix.

[0011] According to an embodiment of the present invention, updating the initial weight matrix based on the above weight update rule to obtain an updated weight matrix includes: calculating the weight matrix of the current iteration round using the gradient descent method based on the above weight update rule; and obtaining the updated weight matrix when the weight matrix of the current iteration round meets the iteration condition.

[0012] According to an embodiment of the present invention, determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system and the target action-evaluation neural network at the above sampling moment includes: obtaining a control strategy based on event triggering based on the activation function of the above target action-evaluation neural network, the above updated weight matrix, and the sampling signal at the above event triggering moment; and determining the load frequency control strategy of the microgrid system according to the control strategy based on event triggering.

[0013] Another aspect of the present invention provides a multi-microgrid load frequency control device based on dynamic event triggering. The control device includes: an acquisition module for acquiring the local sampling error of the microgrid system, where the local sampling error is obtained based on the global neighborhood error and the local neighborhood error of the microgrid system; an update module for updating the initial weight matrix of the action-neural evaluation network based on the local sampling error to obtain a target action-evaluation neural network when the local sampling error meets the dynamic event triggering rule; and a determination module for determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system and the target action-evaluation neural network at the above sampling moment.

[0014] Another aspect of the present invention provides an electronic device, including: one or more processors; a memory for storing one or more computer programs, where the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] Another aspect of the present invention provides a computer-readable storage medium having a computer program or instruction stored thereon, where the computer program or instruction implements the steps of the above method when executed by a processor.

[0016] Another aspect of the present invention provides a computer program product including a computer program or instruction, where the computer program or instruction implements the steps of the above method when executed by a processor.

[0017] According to an embodiment of the present invention, a multi - microgrid system is regarded as a multi - agent system. Each agent determines whether to trigger an event and perform sampling based on its own local sampling error and global neighborhood error. Through a dynamic triggering mechanism, the agent can dynamically adjust the triggering rule according to the real - time state of the system, thereby reducing unnecessary data transmission and control updates to improve the overall performance and reliability of the system. In addition, based on the connection relationships among multiple agents obtained by sampling, a reinforcement learning method is used to learn and train an optimal control strategy by observing the state and reward of the multi - agent system to avoid model dependence. This method is applicable to various complex multi - microgrid systems and can improve the flexibility and control efficiency of the control method. Brief Description of the Drawings

[0018] Through the following description of the embodiments of the present invention with reference to the drawings, the above - mentioned and other objects, features, and advantages of the present invention will become clearer.

[0019] Figure 1 Fig. shows an exemplary system architecture of applying a multi - microgrid load frequency control method and device based on dynamic event triggering according to an embodiment of the present invention.

[0020] Figure 2 Fig. shows a flowchart of a multi - microgrid load frequency control method based on dynamic event triggering according to an embodiment of the present invention.

[0021] Figure 3 Fig. shows a schematic diagram of the communication structure of a multi - microgrid system including three microgrids according to a specific embodiment of the present invention.

[0022] Figure 4 Fig. shows a state diagram of the neighborhood error of a multi - microgrid system according to a specific embodiment of the present invention.

[0023] Figure 5 Fig. shows a schematic diagram of the event triggering result of a multi - microgrid system based on a dynamic event triggering mechanism according to a specific embodiment of the present invention.

[0024] Figure 6 Fig. shows a block diagram of a multi - microgrid load frequency control device based on dynamic event triggering according to an embodiment of the present invention.

[0025] Figure 7 Fig. shows a block diagram of an electronic device suitable for implementing a multi - microgrid load frequency control method based on dynamic event triggering according to an embodiment of the present invention. Detailed Embodiment

[0026] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, numerous specific details are set forth to provide a comprehensive understanding of the embodiments of the present invention. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0027] The terms used herein are merely for describing specific embodiments and are not intended to limit the present invention. The terms "comprising", "including", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] In the case of using expressions such as "at least one of A, B, and C, etc.", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0030] The multi - microgrid (hereinafter referred to as multi - microgrid) system is a typical mode of a new - type power system. The multi - microgrid system is a complex power system composed of multiple microgrids interconnected and operating in coordination. In the multi - microgrid system, load - frequency control is achieved by adjusting the output power of generators to match the change of load, so as to realize the adaptive regulation of power load. Therefore, load - frequency control plays a key role in maintaining the steady - state frequency at the nominal value during load disturbances.

[0031] In view of the complex dynamics and nonlinearity of the multi - microgrid system, traditional methods based on linearized models can only be controlled at specific nominal operating points and are difficult to cope with various changes and uncertainties that the system may encounter during actual operation. For example, parameters such as load demand and generator output power may change over time, and measurement data may also be affected by noise, missing, or delay, resulting in problems of low accuracy or stability in actual control strategies. In addition, traditional methods usually require certain system model information or structural knowledge to construct performance functions and optimization problems.

[0032] In view of this, an embodiment of the present invention proposes a model-free multi-microgrid load frequency control method triggered by dynamic events. The multi-microgrid system is regarded as a multi-agent system, and each agent determines whether to trigger an event and perform sampling based on its own local sampling error and global neighborhood error. Through the dynamic trigger mechanism, the agent can dynamically adjust the trigger rule according to the real-time state of the system, thereby reducing unnecessary data transmission and control updates to improve the overall performance and reliability of the system. In addition, based on the connection relationships between multiple agents obtained by sampling, a reinforcement learning method is used to learn and train the optimal control strategy by observing the state and reward of the multi-agent system to avoid model dependence. This method is applicable to various complex multi-microgrid systems and can improve the flexibility and control efficiency of the control method.

[0033] Specifically, an embodiment of the present invention provides a multi-microgrid load frequency control method based on dynamic event triggering, including: obtaining the local sampling error of the microgrid system; when the local sampling error satisfies the dynamic event trigger rule, updating the initial weight matrix of the action-neural evaluation network based on the local sampling error to obtain the target action-evaluation neural network; determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network.

[0034] It should be noted that the multi-microgrid load frequency control method and device determined by the embodiments of the present invention can be used in the field of smart microgrid technology. The multi-microgrid load frequency control method and device determined by the embodiments of the present invention can also be used in any field other than the field of smart microgrid technology, such as artificial intelligence, reinforcement learning, etc. The application field of the multi-microgrid load frequency control method and device determined by the embodiments of the present invention is not limited.

[0035] In the embodiments of the present invention, in terms of the collection, update, analysis, processing, use, transmission, provision, invention, storage, etc. of the data involved (for example, including but not limited to user personal information), they all comply with the provisions of relevant laws and regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and to maintain the security of user personal information, network security, and national security.

[0036] In the embodiments of the present invention, the authorization or consent of the user is obtained before obtaining or collecting user personal information.

[0037] Figure 1 An exemplary system architecture applying the multi-microgrid load frequency control method and device based on dynamic event triggering according to an embodiment of the present invention is shown. It should be noted that Figure 1The illustration below is only an example of the system architecture to which the embodiments of the present invention can be applied, to help those skilled in the art understand the technical content of the present invention. However, it does not mean that the embodiments of the present invention cannot be used in other devices, systems, environments or scenarios.

[0038] As Figure 1 shown, the system architecture according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0039] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only as examples).

[0040] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices with a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers, etc.

[0041] The server 105 may be a server providing various services, such as a background management server that supports the websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (only as an example). The background management server may analyze and process data such as received user requests, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0042] It should be noted that the method for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can generally be executed by the server 105. Correspondingly, the device for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can generally be set in the server 105. The method for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the device for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can also be set in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Or, the method for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can also be executed by the first terminal device 101, the second terminal device 102, the third terminal device 103, or can also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102, the third terminal device 103. Correspondingly, the device for multi - microgrid load frequency control based on dynamic event triggering provided by the embodiments of the present invention can also be set in the terminal devices 101, 102, or 103, or can be set in other terminal devices different from the first terminal device 101, the second terminal device 102, the third terminal device 103.

[0043] It should be understood that Figure 1 the numbers of terminal devices, networks, and servers in

[0044] Figure 2 shows a flowchart of the method for multi - microgrid load frequency control based on dynamic event triggering according to an embodiment of the present invention.

[0045] As Figure 2 shown, the method includes operations S210 - S230.

[0046] In operation S210, obtain the local sampling error of the microgrid system.

[0047] In operation S220, when the local sampling error satisfies the dynamic event triggering rule, update the initial weight matrix of the action - neural evaluation network based on the local sampling error to obtain the target action - evaluation neural network.

[0048] In operation S230, determine the load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action - evaluation neural network.

[0049] According to an embodiment of the present invention, for a distributed microgrid system composed of multiple microgrids, each microgrid system can be regarded as an independent agent to sense its own state, interact with other agents, and make decisions and take actions based on this information.

[0050] According to an embodiment of the present invention, the local neighborhood error is used to represent the state difference between any microgrid system and its neighboring microgrid systems at a specific sampling moment. Among them, the local neighborhood error can be used to reflect the degree of consistency in state between any microgrid system and its neighboring microgrid at the sampling moment. The local sampling state can be used to represent the local state of any microgrid system in the system at the sampling moment.

[0051] According to an embodiment of the present invention, the global neighborhood error is used to represent the state difference between a microgrid system and its neighboring microgrid systems at any moment within a time window related to the sampling moment. The global system state can be used to characterize the state of the entire microgrid system sensed or measured by the microgrid system at any moment within a time window related to the sampling moment. Among them, the global system state can include the overall frequency of the system, the total output power, the load condition, etc. The global system state can change in real time, reflecting the dynamic behavior of the system at the current moment.

[0052] According to an embodiment of the present invention, under the distributed trigger setting, each local event trigger can calculate the local sampling error based on the global neighborhood error in the global system state and the local neighborhood error in the local sampling state.

[0053] According to an embodiment of the present invention, the local sampling error can be used to determine the sampling trigger moment of each local event trigger. Each local event trigger has an adaptive trigger threshold corresponding to its event trigger rule. When the local sampling error exceeds the trigger threshold of the event trigger rule, the local event trigger will generate an event trigger signal, and the local event trigger will obtain new sampling information and calculate a new local sampling error to form an event trigger loop. At non-trigger moments, the current value will be kept unchanged.

[0054] According to an embodiment of the present invention, in a distributed multi-microgrid system, in order to improve the efficiency and flexibility of event triggering, a dynamic event triggering mechanism can be introduced on the basis of the static event trigger rule. This mechanism not only depends on static trigger conditions but can also dynamically adjust the trigger rule according to the real-time state of the system.

[0055] According to an embodiment of the present invention, when the local sampling error satisfies the dynamic event-triggering rule, the system enters the reinforcement learning stage. Based on the new sampling information obtained by the local event trigger, it is used as the current state of the microgrid system and input into the action-evaluation neural network. The initial weight matrix of the action-evaluation neural network is updated through reinforcement learning or other means until the loss function of the action-evaluation neural network converges or reaches a preset number of iterations, and the updated target action-evaluation neural network can be obtained. Based on the updated target action-evaluation neural network, a load frequency control strategy based on dynamic event triggering for the microgrid system is determined.

[0056] Based on this, an embodiment of the present invention proposes a model-free multi-microgrid load frequency control method based on dynamic event triggering. The multi-microgrid system is regarded as a multi-agent system, and each agent decides whether to trigger an event and perform sampling according to its own local sampling error and global neighborhood error. Through the dynamic triggering mechanism, the agent can dynamically adjust the triggering rule according to the real-time state of the system, thereby reducing unnecessary data transmission and control updates to improve the overall performance and reliability of the system. In addition, based on the connection relationships among the multiple agents obtained by sampling, a reinforcement learning method is used to learn and train the optimal control strategy by observing the state and reward of the multi-agent system to avoid model dependence. This method is applicable to various complex multi-microgrid systems and can improve the flexibility and control efficiency of the control method.

[0057] According to an embodiment of the present invention, the control method further includes: determining a load frequency control model of the microgrid system based on the control parameters of the governor, turbine, power system, and controller of the microgrid system; and obtaining a local cost function of the microgrid system based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system.

[0058] According to an embodiment of the present invention, since the load fluctuation of the power system is small during normal operation and its dynamic behavior can be approximated by a linear model around the operating point, a load frequency control model of the microgrid system can be constructed based on the state variables of the governor, turbine, power system, and controller respectively. In one embodiment, the load frequency control model of each agent mainly includes four state variables: frequency deviation, increment of generator output power, increment of governor valve position, and increment of integral control. Specifically, the load frequency control model of the microgrid system is as follows:

[0059] (1);

[0060] In the formula, represents the control signal; represents the frequency deviation, represents the increment of generator output power, represents the increment of the governor valve position, represents the increment of integral control, represents the load change, which is regarded as a disturbance signal. represents the time constant of the governor, represents the time constant of the turbine, represents the time constant of the generator, represents the gain, represents the speed regulation coefficient caused by the governor action, represents the integral control gain.

[0061] According to an embodiment of the present invention, the local neighborhood error of each microgrid system can be determined based on the connection relationship between the microgrid system and its neighboring microgrid systems. The local neighborhood error can be used to represent the weighted sum of the state differences between the i-th microgrid system and its neighboring microgrid systems, plus the weighted state difference between it and the virtual leader. This definition helps to capture the interactions between microgrid systems and their associations with the virtual leader. Specifically, the local neighborhood error can be expressed as:

[0062] (2);

[0063] In the formula, represents the local neighborhood error of the i-th microgrid system, represents the connection weight in the adjacency matrix A, representing the connection relationship between the i-th microgrid system and the j-th microgrid system, x i and x j represent the state variables of the i-th microgrid system and the j-th microgrid system respectively, and x0 represents the state variable of the virtual leader; N i represents the neighbor set of the i-th microgrid system, represents the pinning gain, used to represent the connection strength between the i-th microgrid system and the j-th microgrid system. If the agent can obtain information from the leader, then ; otherwise, .

[0064] According to an embodiment of the present invention, the convergence of the local neighborhood error is the same as that of the consensus error . By making the neighborhood error asymptotically stable, that is , the goal of optimal state synchronization of each agent can be achieved.

[0065] According to an embodiment of the present invention, based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system, a local cost function of the microgrid system is obtained, including: performing differential transformation on the load frequency control model of the microgrid system to obtain the state space model of the microgrid system; determining the global neighborhood error based on the system matrix, control matrix of the state space model of the microgrid system, and the local neighborhood error of the microgrid system; and obtaining the local cost function of the microgrid system based on the global neighborhood error and the differential game revenue function of the microgrid system.

[0066] According to an embodiment of the present invention, the idea of non-zero-sum differential game can be adopted, and the strategy interaction between all game agents is modeled by a communication network to describe which objects can obtain the decision-making information of the game agents. Specifically, by regarding the control input of each microgrid, that is, the agent, as the strategy of game decision-making, a multi-agent collaborative load frequency control model based on graphical differential game can be obtained. For example, by performing differential transformation on the load frequency control model of the microgrid system, the state space model of this control model can be obtained as follows:

[0067] (3);

[0068] In the formula, represents the state space expression form of the load frequency control model of the microgrid system, x i (t) represents the state vector of the i-th microgrid system, u i (t) represents the control input vector of the i-th microgrid system, represents the system matrix, represents the control matrix.

[0069] According to an embodiment of the present invention, based on the system matrix, control matrix of the state space model of the microgrid system, and the local neighborhood error of the microgrid system, the global neighborhood error is determined. By stabilizing the global neighborhood error of the system to zero, the collaborative control of the load frequency of multiple microgrids can be achieved. Specifically, the representation of the global neighborhood error of the system is as follows:

[0070] (4);

[0071] In the formula, represents the global neighborhood error of the system, represents the in-degree of the i-th microgrid system, and this in-degree is the diagonal element of the in-degree matrix , u i and u j respectively represent the control input vectors of the i-th microgrid system and the j-th microgrid system.

[0072] In a specific embodiment of the present invention, since the control objective is to seek an optimal control strategy for each game agent, making the global neighborhood error of the system asymptotically stable, and it is necessary to optimize the local cost function of each game agent. To achieve this goal, based on the global neighborhood error of the system and the differential game revenue function of the microgrid system, the local cost function of the microgrid system can be constructed. Specifically, the expression of the local cost function of the microgrid system is as follows:

[0073] (5);

[0074] In the formula, represents the local cost function of the i-th microgrid system, Q i and R i are positive definite weight matrices, represents the control input vector of other microgrid systems except the i-th microgrid system.

[0075] In this specific embodiment, by optimizing the local cost function shown in formula (5), the optimal cooperative control strategy of each agent can be obtained, and the expression of the optimal cooperative control strategy of each microgrid system is as follows:

[0076] (6);

[0077] In the formula, represents the optimal cooperative control strategy of the i-th microgrid system, represents the optimal value function of the local cost function.

[0078] Based on this, the embodiment of the present invention constructs a load frequency control model of the microgrid system, uses the local neighborhood error to capture the interaction between microgrid systems and the association with the virtual leader, and uses the local neighborhood error to derive the local cost function, thereby realizing the cooperative load frequency control of multi-microgrid systems. Therefore, this method provides new ideas and methods for the cooperative control of microgrid systems. At the same time, by treating the control input of each microgrid system as a strategy for game decision-making and using the graphical differential game theory for cooperative control, the overall stability of the microgrid system is effectively improved.

[0079] In addition, since the multi-agent cooperative control scheme does not need to rely on the accurate model of the system, each agent can adaptively respond to local load demands, and at the same time cooperate with neighboring agents to achieve the global regulation goal. Therefore, the embodiment of the present invention formulates an optimal strategy for each agent through information exchange between neighbors, enabling each agent to gradually reach the desired state, thereby realizing the coordination of the entire system.

[0080] According to an embodiment of the present invention, the control method further includes: determining a static event triggering rule of a local event trigger based on a local cost function of the microgrid system; updating the static event triggering rule by using a distributed dynamic variable to obtain a dynamic event triggering rule, where the distributed dynamic variable is used to represent the influence degree of the historical event triggering state on the event triggering state at the event triggering moment.

[0081] According to an embodiment of the present invention, before determining the event triggering rule of the local event trigger, initial values of the local neighborhood error and the global neighborhood error are determined, and the local sampling error can be calculated within the triggering interval. Specifically, the calculation formula of the local sampling error is as follows:

[0082] (7);

[0083] In the formula, represents the local sampling error at a certain moment t, represents the k-th triggering moment, represents the (k + 1)-th triggering moment, represents the local neighborhood error at the k-th triggering moment.

[0084] According to an embodiment of the present invention, at the event triggering moment , the triggering error can be reset to zero, and the control signal u i is updated. In order to convert the discrete control signal into a continuous control signal, a zero-order hold is used within the triggering interval to keep the control signal unchanged until the next triggering moment arrives. In this way, although the control signal is discrete, the system can approximately consider that the control signal is continuous within the triggering interval.

[0085] According to an embodiment of the present invention, after obtaining the local sampling error, a distributed static event triggering rule can be determined according to the local cost function of each game agent. In a specific embodiment, considering the conservative design and the stability design, a static event triggering rule can be designed according to the local cost function and the conservative parameter, and the static event triggering rule is as follows:

[0086] (8);

[0087] In the formula, represents the conservative parameter, and are positive real numbers.

[0088] According to an embodiment of the present invention, it should be noted that this rule actually corresponds to an adaptive static triggering threshold. If the local sampling error exceeds the static triggering threshold, a new event will be triggered. The corresponding triggering condition can be obtained, and the triggering condition can be as follows:

[0089] (9);

[0090] Wherein, represents the static trigger threshold.

[0091] According to an embodiment of the present invention, based on the static event trigger rule, in order to improve the trigger efficiency, a suitable filtering coefficient can also be selected for each local event trigger. Specifically, a dynamic event trigger rule can be constructed using the filtering coefficient and the distributed internal dynamic variable. Among them, the filtering coefficient can be used to measure the influence degree of the trigger information on the dynamic variable, and the distributed internal dynamic variable can be used to track the current state of the microgrid system and update these states according to past events or trigger data. For example, in a distributed microgrid system, each microgrid system may need to maintain an internal variable representing its data synchronization state, and this variable will be dynamically adjusted according to the data update events of other microgrid systems. Among them, the expression of the distributed internal dynamic variable is as follows:

[0092] (10);

[0093] Wherein, represents the distributed internal dynamic variable, represents the filtering coefficient, represents the first-order form of the distributed internal dynamic variable. Among them, the initial value of the distributed internal dynamic variable .

[0094] According to an embodiment of the present invention, based on the static event trigger rule shown in formula (8) and the distributed internal dynamic variable shown in formula (10), a bridging parameter can be used to establish a connection between the static trigger rule and the dynamic trigger rule, so as to obtain the dynamic event trigger rule. Specifically, the expression of this dynamic event trigger rule is as follows:

[0095] (11);

[0096] Wherein, represents the dynamic event trigger rule, represents the bridging parameter, wherein, .

[0097] Therefore, the dynamic trigger condition of each game agent can be expressed as:

[0098] (12);

[0099] Wherein, represents the dynamic trigger threshold.

[0100] According to an embodiment of the present invention, the control method further includes: determining a dynamic trigger threshold based on a dynamic event trigger rule; and obtaining a sampling signal at the event trigger moment when the local sampling error exceeds the dynamic trigger threshold.

[0101] According to an embodiment of the present invention, by continuously monitoring the local sampling error, comparing the local sampling error with a dynamic trigger condition, once the local sampling error exceeds the dynamic trigger threshold, a new event is triggered, and an event trigger signal is generated at the trigger moment. At this time, the event trigger obtains a new local neighborhood error at the trigger moment, and calculates a new local sampling error using the newly sampled local neighborhood error. Utilizing the local sampling state at the sampling moment Update the control strategy as shown in formula (6) . At non-trigger moments, keep the current control strategy unchanged. The updated control strategy is as follows:

[0102] (13);

[0103] In the formula, represents the updated control strategy.

[0104] Based on this, the above steps constitute a complete event-triggered control loop, and each agent independently updates its strategy, thereby realizing the distributed control of the load frequency of multiple microgrids. This improves the scalability and flexibility of the system, enabling the system to more easily adapt to the requirements of large-scale microgrid systems. Among them, the static event trigger rule provides the basic trigger condition, ensuring the stability and conservativeness of the system, while the introduction of distributed dynamic variables and dynamic event trigger rules can improve the trigger efficiency, enabling the system to automatically adjust the trigger condition according to the current state and historical data, and thus more flexibly respond to changes in local and global states.

[0105] According to an embodiment of the present invention, when the local sampling error satisfies the dynamic event trigger rule, based on the local sampling error, update the initial weight matrix of the action-neural evaluation network to obtain a target action-evaluation neural network, including: determining a reward signal of the action-evaluation neural network based on the sampling signal at the event trigger moment and the Bellman residual of the microgrid system; and updating the initial weight matrix of the action-evaluation neural network based on the reward signal of the action-evaluation neural network to obtain a target action-evaluation neural network.

[0106] According to an embodiment of the present invention, in order to approximately solve the differential game problem, a multi-agent collaborative load frequency control model based on graphical differential game as shown in formula (3) can be utilized to construct an action-evaluation neural network. Specifically, a three-layer feedforward neural network including an input layer, a hidden layer, and an output layer can be adopted to construct the action-evaluation neural network. Among them, the input layer receives state information, the hidden layer performs a non-linear transformation, and the output layer outputs an approximation of the value function or the control strategy.

[0107] According to an embodiment of the present invention, the action-evaluation neural network can include two parts: an evaluation network and an action network. Among them, the evaluation network can be used to approximate the value function, that is, the long-term reward under a given state and strategy. The action network can be used to approximate the control strategy, that is, the optimal control input under a given state. Its output can be expressed as:

[0108] (14);

[0109] (15);

[0110] In the formula, represents the approximated evaluation network, represents the approximated action network, where and represent the basis function vectors, and respectively represent the initial weight matrices of the evaluation network and the action network, and represent the number of neurons in the hidden layer.

[0111] According to an embodiment of the present invention, for each game agent, considering the system operation cost and the game player control cost, a off-policy integral reinforcement learning algorithm can be adopted to train the action-evaluation neural network to seek an approximate optimal solution.

[0112] Specifically, the process of implementing the off-policy integral reinforcement learning algorithm by using the action-evaluation neural network includes two stages: an online measurement stage and an offline learning stage. Among them, the online measurement stage is used to collect system data. In this stage, the agent collects system data under the specified control input, and these system data will be used in the subsequent offline learning stage. The offline learning stage then uses the collected system data and the iterative process to update the weight matrix and converge to the optimal solution. Among them, the approximation degree of the current value function can be evaluated by constructing the Bellman residual, and the reward signal can be calculated according to the sampling signal at the event trigger moment and the Bellman residual of the microgrid system. Since the off-policy Bellman equation does not explicitly include the dynamics represented by A and B. Instead, the knowledge of the system model is implicitly reflected through the available measurement data, especially and Therefore, a prerequisite for iteration is the online measurement phase to collect data.

[0113] Furthermore, by evaluating the value function using system data generated by any control strategy, the exploration ability of the learning process can be enhanced. Each game agent calculates the reward signal for reinforcement learning based on the system operation cost and control cost.

[0114] In some specific embodiments, the constructed Bellman residual is as follows:

[0115] (16);

[0116] (17);

[0117] (18);

[0118] (19);

[0119] (20);

[0120] Wherein, represents the Bellman residual, represents the weight matrix of the agent, represents the weight matrix of the evaluation network of the agent, represents the weight matrix of the action network of the agent, and the vector , and represent the reinforcement learning signals to be calculated, where and respectively represent the basis function vectors of the evaluation network and the action network, , are weight parameters.

[0121] According to the Bellman residual as in formula (16), the reward signals of the evaluation network and the action network can be determined. This reward signal is the cumulative reinforcement over a fixed integration interval, and the expression is as follows:

[0122] (21);

[0123] (22);

[0124] Where: q represents the number of groups of data collected, and are matrices respectively composed of multiple groups of cumulative reinforcement signals and .

[0125] According to an embodiment of the present invention, updating the initial weight matrix of the action-evaluation neural network based on the reward signal of the action-evaluation neural network to obtain the target action-evaluation neural network includes: processing the reward signal of the action-evaluation neural network using the least squares method to obtain a weight update rule; updating the initial weight matrix based on the weight update rule to obtain an updated weight matrix; and determining the target action-evaluation neural network according to the updated weight matrix.

[0126] According to an embodiment of the present invention, processing the reward signal of the action-evaluation neural network using the least squares method can construct a network weight update rule, where the network weight update rule can be as follows:

[0127] (23);

[0128] In the formula, represents the weight matrix of the agent.

[0129] According to an embodiment of the present invention, based on formula (23), the initial weight matrices of the evaluation network and the action network in formula (14) and formula (15) can be updated to determine the target action-evaluation neural network. The agent will determine an event-triggered control strategy using the weights of the updated target action-evaluation neural network.

[0130] According to an embodiment of the present invention, updating the initial weight matrix based on the weight update rule to obtain an updated weight matrix includes: calculating the weight matrix of the current iteration round using the gradient descent method based on the weight update rule; and obtaining the updated weight matrix when the weight matrix of the current iteration round satisfies the iteration condition.

[0131] According to an embodiment of the present invention, determining the load frequency control strategy of the microgrid system based on the system state of the microgrid system and the target action-evaluation neural network at the sampling moment includes: obtaining an event-triggered control strategy based on the activation function of the target action-evaluation neural network, the updated weight matrix, and the sampling signal at the event trigger moment; and determining the load frequency control strategy of the microgrid system according to the event-triggered control strategy.

[0132] According to an embodiment of the present invention, once the optimal value is reached, the agent can obtain an event-triggered model-free control strategy based on the target action-evaluation neural network and deploy it in a multi-microgrid system or other practical applications. Specifically, based on the activation function of the target action-evaluation neural network, the updated weight matrix, and the sampling signal at the event trigger moment, the event-triggered control strategy can be determined as follows:

[0133] (24);

[0134] In the formula, represents an event-triggered control strategy, represents the sampled state of the global neighborhood error of the i-th agent at the corresponding k-th trigger moment, represents the activation function of the action network.

[0135] According to an embodiment of the present invention, in order to ensure the convergence of the actor-critic neural network, exploration noise can be introduced into the action policy to satisfy the Persistent Excitation (PE) assumption. Specifically, the control input can be configured as , where is the initial admissible control, and is the exploration noise. Introducing exploration noise can not only enhance the diversity of data, enabling the system to generate more state-action pairs during operation, thereby increasing the diversity of data, but also ensure a sufficient dataset size. By continuously introducing noise, it can ensure that a sufficient number of data points are collected during the training process.

[0136] According to an embodiment of the present invention, when calculating the inverse of the matrix , it is necessary to ensure that the matrix has full column rank. If the matrix does not have full column rank, then its inverse matrix will not exist, which will cause the algorithm to not execute correctly. To meet this condition, the number of data points collected should satisfy , where h ci and h ai are the numbers of neurons in the hidden layers respectively. This condition can ensure the richness and diversity of the dataset, thus contributing to the training of the neural network.

[0137] Next, with reference to Figures 3 to 5 , a specific embodiment will be used to further illustrate the multi-microgrid load frequency control method based on dynamic event triggering.

[0138] Figure 3 shows a schematic diagram of the communication structure of a multi-microgrid system including three microgrids according to a specific embodiment of the present invention.

[0139] Figure 4 shows a schematic diagram of the state of the neighborhood error of the multi-microgrid system according to a specific embodiment of the present invention.

[0140] Figure 5 shows a schematic diagram of the event triggering result of the multi-microgrid system based on the dynamic event triggering mechanism according to a specific embodiment of the present invention.

[0141] In a specific embodiment of the present invention, the parameters of the multi-microgrid system are selected as follows: the time constant of the generator , gain 、Time constant of the turbine 、Time constant of the governor 、Speed regulation coefficient caused by the governor action 。

[0142] As Figure 3 shown, Figure 3 shows the communication structure of a multi - microgrid system including three microgrids. Among them, the positive definite weight matrices in the local cost functions of the three microgrids are respectively configured as 、 、 and 、 、 。

[0143] In this specific embodiment, the distributed internal dynamic variable can be selected in the form of a first - order filter, and the relevant parameters in the dynamic event - triggering rule shown in formula (11) are set as positive real numbers , conservatism parameter , bridging parameter 。

[0144] In this specific embodiment, the basis function vector of the evaluation network can be configured as , the basis function vector of the action network can be configured as , the integration time is , the initial value of the distributed internal dynamic variable is set as , the algorithm iteration threshold is 10 -6 。In the online measurement stage, the initial acceptable strategy with detection noise from to is applied to the multi - microgrid system. In the offline learning stage, weight iteration is performed by repeatedly using the collected data.

[0145] On the basis of the above - mentioned embodiment, as Figure 4 shown, where in neighborhood error 1 、 、 respectively represent the frequency deviation signals of regions 1, 2, and 3, in neighborhood error 2 、 、 respectively represent the incremental deviation signals of the generator output powers of regions 1, 2, and 3, in neighborhood error 3 、 、 respectively represent the incremental deviation signals of the governor valve positions of regions 1, 2, and 3, and in neighborhood error 4 、 、 Increment signals for integral control of regions 1, 2, and 3 respectively. It can be seen that the error is adjusted to zero within about 5 s after data acquisition in the online measurement stage, and the multi-microgrid system realizes cooperative control.

[0146] As Figure 5 shown, the sampling process and data communication process based on dynamic event triggering are aperiodic. Additionally, it is worth noting that due to the dynamic event triggering mechanism, the controllers of each microgrid system only need to perform 82, 81, and 83 control calculations respectively, which greatly reduces the consumption of control calculations and communication resources compared with 300 times in the traditional method.

[0147] It should be noted that unless it is clearly stated that there is a sequential execution order between different operations in the flowcharts shown in the embodiments of the present invention, or there is a sequential execution order between different operations in the technical implementation, otherwise, the execution order between multiple operations can be unordered, and multiple operations can also be executed simultaneously.

[0148] Figure 6 The block diagram of the multi-microgrid load frequency control device based on dynamic event triggering according to an embodiment of the present invention is shown.

[0149] As Figure 6 shown, the multi-microgrid load frequency control device based on dynamic event triggering includes an acquisition module 610, an update module 620, and a determination module 630.

[0150] The acquisition module 610 is used to acquire the local sampling error of the microgrid system, where the local sampling error is obtained based on the global neighborhood error and local neighborhood error of the microgrid system. The local neighborhood error is used to represent the state difference between the microgrid system and its neighbor microgrid systems at the sampling moment, and the global neighborhood error is used to represent the state difference between the microgrid system and its neighbor microgrid systems within a time window related to the sampling moment.

[0151] The update module 620 is used to update the initial weight matrix of the action-neural evaluation network based on the local sampling error when the local sampling error satisfies the dynamic event triggering rule, and obtain the target action-evaluation neural network.

[0152] The determination module 630 is used to determine the load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network.

[0153] According to an embodiment of the present invention, the multi-microgrid load frequency control device based on dynamic event triggering further includes a model determination module and a local cost function determination module.

[0154] A model determination module, configured to determine a load frequency control model of the microgrid system based on the respective control parameters of the governor, turbine, power system, and controller of the microgrid system.

[0155] A local cost function determination module, configured to obtain a local cost function of the microgrid system based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system, where the local neighborhood error is determined based on the connection relationship between the microgrid system and its neighboring microgrid systems.

[0156] According to an embodiment of the present invention, the local cost function determination module includes a state space model determination sub-module, a global neighborhood error determination sub-module, and a local cost function determination sub-module.

[0157] The state space model determination sub-module is configured to perform a differential transformation on the load frequency control model of the microgrid system to obtain a state space model of the microgrid system.

[0158] The global neighborhood error determination sub-module is configured to determine a global neighborhood error based on the system matrix and control matrix of the state space model of the microgrid system and the local neighborhood error of the microgrid system.

[0159] The local cost function determination sub-module is configured to obtain a local cost function of the microgrid system based on the global neighborhood error and the differential game revenue function of the microgrid system.

[0160] According to an embodiment of the present invention, the control device further includes a static event trigger rule determination module and a dynamic event trigger rule determination module.

[0161] The static event trigger rule determination module is configured to determine a static event trigger rule of the local event trigger based on the local cost function of the microgrid system.

[0162] The dynamic event trigger rule determination module is configured to update the static event trigger rule by using a distributed dynamic variable to obtain a dynamic event trigger rule, where the distributed dynamic variable is used to represent the influence degree of the historical event trigger state on the event trigger state at the event trigger moment.

[0163] According to an embodiment of the present invention, the update module 620 includes a dynamic trigger threshold determination sub-module, a sampling signal determination sub-module, a reward signal determination sub-module, and a target neural network determination sub-module.

[0164] The dynamic trigger threshold determination sub-module is configured to determine a dynamic trigger threshold based on the dynamic event trigger rule.

[0165] The sampling signal determination sub-module is configured to obtain a sampling signal at the event trigger moment when the local sampling error exceeds the dynamic trigger threshold.

[0166] A reward signal determination sub-module, configured to determine a reward signal of the action-evaluation neural network based on the sampling signal at the event trigger moment and the Bellman residual of the microgrid system.

[0167] A target neural network determination sub-module, based on the reward signal of the action-evaluation neural network, updates the initial weight matrix of the action-evaluation neural network to obtain a target action-evaluation neural network.

[0168] According to an embodiment of the present invention, the target neural network determination sub-module includes a processing unit, an updating unit, and a target neural network determination unit.

[0169] The processing unit is configured to process the reward signal of the action-evaluation neural network by using the least squares method to obtain a weight update rule.

[0170] The updating unit is configured to update the initial weight matrix based on the weight update rule to obtain an updated weight matrix.

[0171] The target neural network determination unit is configured to determine a target action-evaluation neural network according to the updated weight matrix.

[0172] According to an embodiment of the present invention, the updating unit includes a calculation sub-unit and a determination sub-unit.

[0173] The calculation sub-unit is configured to calculate the weight matrix of the current iteration round by using the gradient descent method based on the weight update rule.

[0174] The determination sub-unit is configured to obtain the updated weight matrix when the weight matrix of the current iteration round meets the iteration condition.

[0175] According to an embodiment of the present invention, the determination module 630 includes a control strategy determination sub-module and a target control strategy determination sub-module.

[0176] The control strategy determination sub-module is configured to obtain an event-triggered control strategy based on the activation function of the target action-evaluation neural network, the updated weight matrix, and the sampling signal at the event trigger moment.

[0177] The target control strategy determination sub-module is configured to determine a load frequency control strategy of the microgrid system according to the event-triggered control strategy.

[0178] Any number of modules, sub-modules, units, and sub-units according to embodiments of the present invention, or at least some functions of any number of them, can be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention can be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention can be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention can be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0179] For example, any number of the acquisition module 610, the update module 620, and the determination module 630 can be combined and implemented in one module / unit / sub-unit, or any one of the module / unit / sub-unit can be split into multiple modules / units / sub-units. Alternatively, at least some functions of one or more of these modules / units / sub-units can be combined with at least some functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to embodiments of the present invention, at least one of the acquisition module 610, the update module 620, and the determination module 630 can be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or can be implemented by any other reasonable way of integrating or packaging circuits, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the acquisition module 610, the update module 620, and the determination module 630 can be at least partially implemented as a computer program module, and when the computer program module is run, it can execute the corresponding functions.

[0180] It should be noted that the part of the multi-microgrid load frequency control device based on dynamic event triggering in the embodiments of the present invention corresponds to the part of the multi-microgrid load frequency control method based on dynamic event triggering in the embodiments of the present invention. For the description of the part of the multi-microgrid load frequency control device based on dynamic event triggering, please refer to the part of the multi-microgrid load frequency control method based on dynamic event triggering, and details will not be repeated here.

[0181] Figure 7 The block diagram of an electronic device suitable for implementing a multi - microgrid load frequency control method based on dynamic event triggering according to an embodiment of the present invention is shown. Figure 7 The electronic device shown is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0182] As Figure 7 shown, the computer electronic device according to an embodiment of the present invention includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read - only memory ROM 702 or a program loaded from a storage section 708 into a random - access memory RAM 703. The processor 701 can include, for example, a general - purpose microprocessor (such as a CPU), an instruction - set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application - specific integrated circuit (ASIC)), etc. The processor 701 can also include on - board memory for caching purposes. The processor 701 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0183] In the RAM 703, various programs and data required for the operation of the electronic device are stored. The processor 701, ROM 702, and RAM 703 are connected to each other via a bus 704. The processor 701 executes various operations of the method flow according to an embodiment of the present invention by executing the programs in the ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 can also execute various operations of the method flow according to an embodiment of the present invention by executing the programs stored in the one or more memories.

[0184] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 705, and the input / output (I / O) interface 705 is also connected to the bus 704. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto - optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed so that a computer program read from it can be installed into the storage section 708 as needed.

[0185] According to an embodiment of the present invention, the method flow according to the embodiment of the present invention can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the above-described system, device, apparatus, module, unit, etc. can be implemented by computer program modules.

[0186] The present invention also provides a computer-readable storage medium, which can be included in the device / device / system described in the above embodiment; or can exist alone without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0187] According to an embodiment of the present invention, the computer-readable storage medium can be a non-volatile computer-readable storage medium. For example, it can include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or device.

[0188] For example, according to an embodiment of the present invention, the computer-readable storage medium can include the above-described ROM 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703.

[0189] An embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program contains program codes for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program codes are used to enable the electronic device to implement the multi-microgrid load frequency control method based on dynamic event triggering provided by the embodiment of the present invention.

[0190] When the computer program is executed by the processor 701, the above functions defined in the system / apparatus of the embodiments of the present invention are executed. According to the embodiments of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0191] In one embodiment, the computer program can rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program can also be transmitted and distributed in the form of signals on a network medium, and be downloaded and installed through the communication part 709, and / or be installed from the removable medium 711. The program code included in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0192] According to the embodiments of the present invention, the program code for executing the computer program provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedures and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include but are not limited to, such as Java, C++, python, the "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0193] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, a block in a flowchart or block diagram can represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that the blocks in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions. Those skilled in the art can understand that the features described in various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0194] The embodiments of the present invention have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present invention.

Claims

1. A multi-microgrid load frequency control method based on dynamic event triggering, characterized in that: The control method comprises: Obtaining a local sampling error of a microgrid system, wherein the local sampling error is obtained based on a global neighborhood error and a local neighborhood error of the microgrid system, wherein the local neighborhood error is used to represent a state difference between the microgrid system and its neighboring microgrid systems at a sampling moment, and the global neighborhood error is used to represent a state difference between the microgrid system and its neighboring microgrid systems within a time window associated with the sampling moment; Based on a load frequency control model of a microgrid system and a local neighborhood error of the microgrid system, a local cost function of the microgrid system is obtained, wherein the local neighborhood error is determined based on a connection relationship between the microgrid system and its neighboring microgrid systems, wherein the microgrid system includes a local event trigger; Based on the local cost function, determining a static event triggering rule of the local event trigger; The static event triggering rule is updated by using distributed dynamic variables to obtain dynamic event triggering rules, wherein the distributed dynamic variables are used to indicate the degree of influence of the historical event triggering state on the event triggering state at the event triggering moment; When the local sampling error satisfies the dynamic event triggering rule, based on the local sampling error, an initial weight matrix of the action-evaluation neural network is updated to obtain a target action-evaluation neural network; Based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network, a load frequency control strategy of the microgrid system is determined.

2. The control method according to claim 1, characterized in that: The control method further comprises: Based on the respective control parameters of the speed governor, turbine, power system and controller of the microgrid system, a load frequency control model of the microgrid system is determined.

3. The control method according to claim 2, characterized in that: The method of obtaining a local cost function of the microgrid system based on the load frequency control model of the microgrid system and the local neighborhood error of the microgrid system includes: Performing differential transformation on the load frequency control model of the microgrid system to obtain a state space model of the microgrid system; Determining the global neighborhood error based on a system matrix of a state space model of the microgrid system, a control matrix, and a local neighborhood error of the microgrid system; Based on the global neighborhood error and the differential game reward function of the microgrid system, a local cost function of the microgrid system is obtained.

4. The control method according to claim 1, characterized in that: When the local sampling error satisfies the dynamic event triggering rule, the initial weight matrix of the action-neural evaluation network is updated based on the local sampling error to obtain the target action-evaluation neural network, including: Based on the dynamic event triggering rule, determining a dynamic triggering threshold; When the local sampling error exceeds the dynamic trigger threshold, acquiring a sampling signal at the event triggering moment; Determining a reward signal of the action-evaluation neural network based on the sampled signal at the event triggering moment and the Bellman residual of the microgrid system; Based on the reward signal of the action-evaluation neural network, the initial weight matrix of the action-evaluation neural network is updated to obtain the target action-evaluation neural network.

5. The control method according to claim 4, characterized in that: The updating of the initial weight matrix of the action-evaluation neural network based on the reward signal of the action-evaluation neural network to obtain the target action-evaluation neural network comprises: The reward signal of the action-evaluation neural network is processed by using the least square method to obtain a weight update rule; Based on the weight update rule, the initial weight matrix is ​​updated to obtain an updated weight matrix; The target action-evaluation neural network is determined according to the updated weight matrix.

6. The control method according to claim 5, characterized in that: The updating of the initial weight matrix based on the weight updating rule to obtain an updated weight matrix includes: Based on the weight update rule, the weight matrix of the current iteration round is calculated using the gradient descent method; When the weight matrix of the current iteration round meets the iteration condition, the updated weight matrix is ​​obtained.

7. The control method according to claim 5, characterized in that: The step of determining a load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network comprises: Based on the activation function of the target action-evaluation neural network, the updated weight matrix and the sampling signal at the event triggering moment, a control strategy based on event triggering is obtained; According to the event-triggered control strategy, a load frequency control strategy of the microgrid system is determined.

8. A multi-microgrid load frequency control device based on dynamic event triggering, characterized in that: The control device comprises: An acquisition module, used for acquiring a local sampling error of a microgrid system, wherein the local sampling error is obtained based on a global neighborhood error and a local neighborhood error of the microgrid system, wherein the local neighborhood error is used to represent a state difference between the microgrid system and its neighboring microgrid systems at a sampling moment, and the global neighborhood error is used to represent a state difference between the microgrid system and its neighboring microgrid systems within a time window related to the sampling moment; A local cost function determination module, used for obtaining a local cost function of the microgrid system based on a load frequency control model of the microgrid system and a local neighborhood error of the microgrid system, wherein the local neighborhood error is determined based on a connection relationship between the microgrid system and its neighboring microgrid systems, wherein the microgrid system includes a local event trigger; A static event triggering rule determination module, used to determine the static event triggering rule of the local event trigger based on the local cost function; A dynamic event trigger rule determination module is used to update the static event trigger rule using distributed dynamic variables to obtain dynamic event trigger rules, wherein the distributed dynamic variables are used to indicate the degree of influence of the historical event trigger state on the event trigger state at the event triggering moment; An updating module, configured to update an initial weight matrix of the action-neural evaluation network based on the local sampling error to obtain a target action-evaluation neural network when the local sampling error satisfies the dynamic event triggering rule; A determination module is used to determine a load frequency control strategy of the microgrid system based on the system state of the microgrid system at the sampling moment and the target action-evaluation neural network.

9. An electronic device, comprising: one or more processors; a storage device for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

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