Multi-agent cooperative microgrid cluster optimization method and device

Abstract

The application relates to the technical field of smart grid regulation, in particular to a micro-grid cluster optimization method and device based on multi-agent cooperation, wherein the method comprises the following steps: constructing a power coordination control strategy based on distributed consistency based on the system architecture of the micro-grid cluster, so as to determine a distribution ratio; while executing the power coordination control strategy, designing a hierarchical control system, so as to generate corresponding economic optimization scheduling instructions according to a current frequency modulation stage; taking the minimum system operation cost and the highest renewable energy consumption rate as targets, generating optimal power instructions, and utilizing a preset event triggering mechanism to communicate, so as to realize the optimization purpose of the micro-grid cluster based on multi-agent cooperation. Therefore, the problems that in the related art, a fixed cycle communication mechanism is usually adopted to realize the interaction between micro-grids, the adaptive adjustment capability for network topology changes and communication conditions is poor, and it is difficult to simultaneously consider the communication efficiency and control performance under complex conditions are solved.

Description

Technical Field

[0001] This application relates to the field of smart grid control technology, and in particular to a method and apparatus for optimizing microgrid clusters through multi-agent cooperation. Background Technology

[0002] In related technologies, microgrid clusters are cluster systems composed of multiple distributed microgrids. Due to differences in geographical location, load characteristics, and renewable energy integration among the microgrids, and the need to achieve global power balance and economical operation while ensuring the autonomy of each microgrid, distributed optimization and coordination methods are typically employed for power control. These methods can improve dispatch efficiency, enhance system robustness, and, to some extent, protect data privacy.

[0003] However, related technologies and distributed power control methods often use fixed-period communication mechanisms for interaction between microgrids, which lack the ability to adaptively adjust to changes in network topology and communication conditions. This makes it difficult to simultaneously balance communication efficiency and control performance under complex conditions such as limited communication resources or dynamic changes in network topology, resulting in a decline in optimization performance, which urgently needs to be solved. Summary of the Invention

[0004] This application provides a method and apparatus for optimizing microgrid clusters through multi-agent cooperation, in order to solve the problem that in related technologies, distributed power control methods often use fixed-period communication mechanisms for interaction between microgrids, which lack the ability to adaptively adjust to changes in network topology and communication conditions. This makes it difficult to simultaneously consider communication efficiency and control performance under complex conditions, resulting in a decline in optimization performance, and this problem urgently needs to be solved.

[0005] The first aspect of this application provides a method for optimizing a microgrid cluster through multi-agent cooperation, comprising the following steps: establishing a system architecture for the microgrid cluster; constructing a power coordination control strategy based on distributed consensus based on the system architecture, so as to use the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus; while executing the power coordination control strategy, designing a hierarchical control system to generate corresponding economically optimized scheduling instructions according to the current frequency regulation stage, and responding to the economically optimized scheduling instructions to generate optimal power instructions with the goal of minimizing system operating costs and maximizing renewable energy absorption rate, and communicating using a preset event triggering mechanism to achieve the optimization objective of the microgrid cluster through multi-agent cooperation.

[0006] Through the above technical means, the embodiments of this application adopt a collaborative architecture of system architecture modeling, distributed consistency coordination, hierarchical control system, model prediction rolling optimization and event-triggered communication, which can effectively improve the operating performance of microgrid clusters with a high proportion of renewable energy, thereby coping with the uncertainty of distributed power output and the randomness of load demand, and enhancing the stability and economy of the system.

[0007] Optionally, in one embodiment of this application, the construction of a power coordination control strategy based on distributed consensus, so as to use the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus, includes: after determining the power imbalance, obtaining the power imbalance of each sub-microgrid in the system architecture to estimate the global imbalance, and calculating the power allocation ratio; generating power adjustment instructions for each sub-microgrid based on the global imbalance and the power allocation ratio, wherein distributed convergence detection based on Lyapunov is performed until a preset convergence condition is met, and iteration is stopped, so that after each sub-microgrid performs collaborative optimization using a preset value function, the collaborative allocation of power deficit or surplus is achieved.

[0008] Through the above technical means, the embodiments of this application can generate power adjustment instructions for each sub-microgrid according to the global imbalance and power allocation ratio, realize the coordinated allocation of power deficit or surplus, maintain the global power balance of the microgrid cluster while ensuring the autonomy of each sub-microgrid, improve the system's adaptability to the fluctuation of renewable energy output and the randomness of load demand, thereby enhancing the system's stability and robustness.

[0009] Optionally, in one embodiment of this application, the power adjustment amount of the power adjustment command is calculated using the following formula: , in, Indicates the first The amount of power regulation that each microgrid needs to handle; The differential gain coefficient; Indicates the first The estimated global power imbalance of each sub-microgrid obtained through a consensus algorithm; This represents the power allocation ratio of the i-th sub-microgrid; The preset convergence condition is:

[0010] in, The threshold for the change in the Lyapunov function; The maximum consistency deviation threshold; This represents the total number of sub-microgrids in a microgrid cluster; The formula for generating the power adjustment command is: , in, Indicates the first Policy network of individual agents Indicates parameters; Indicates the first Local state information of each agent; The collaborative weighting coefficients among neighboring intelligent agents; For the first A set of neighbors for an agent.

[0011] Through the above technical means, the embodiments of this application quantify the power adjustment amount of the power adjustment command, which can determine the power increase or decrease of each sub-microgrid in each adjustment cycle; based on the power adjustment command generation formula, combined with the global imbalance and power allocation ratio, the power adjustment command of each sub-microgrid can be dynamically generated; at the same time, corresponding convergence conditions are set, which can be used to determine whether the power adjustment iteration meets the system balance and performance optimization requirements, thereby ensuring that the microgrid cluster achieves stable and efficient global power regulation and optimization under the cooperation of multiple agents.

[0012] Optionally, in one embodiment of this application, the design of the hierarchical control system to generate corresponding economically optimized scheduling instructions according to the current frequency regulation stage includes: using improved active power-frequency droop characteristics for primary power allocation to achieve droop control layer design for the primary frequency regulation stage; using an adaptive PI (Proportional-Integral Controller) controller with anti-saturation integral to eliminate steady-state frequency and voltage deviations to achieve voltage / frequency recovery control design for the secondary frequency regulation stage; and establishing an optimization problem with the goal of minimizing operating costs to achieve economically optimized scheduling layer design for the tertiary frequency regulation stage.

[0013] Through the above technical means, the embodiments of this application can perform hierarchical regulation of the power output of microgrids or power systems based on the droop control of primary frequency regulation, the voltage / frequency recovery of secondary frequency regulation, and the economic optimization scheduling of tertiary frequency regulation. This enables rapid response to load fluctuations, restoration of system voltage and frequency stability, and optimization of economic operation, thereby improving the stability, reliability, and operating efficiency of the system.

[0014] Optionally, in one embodiment of this application, generating the optimal power command with the goal of minimizing system operating costs and maximizing renewable energy absorption rate includes: establishing a multi-objective optimization function and defining system operating costs and renewable energy absorption rate to establish a prediction model and construct a rolling optimization framework; and generating the optimal power command based on the prediction model and the rolling optimization framework.

[0015] Through the above technical means, the embodiments of this application construct a rolling optimization framework for system operating costs and renewable energy absorption rate, and then generate the optimal power command. Under the premise of ensuring the autonomy of each sub-microgrid, the global power balance and economical and efficient operation of the microgrid cluster can be achieved, while improving the renewable energy absorption rate, reducing system operating costs, and enhancing the system's adaptability to load fluctuations and uncertainties in distributed power output.

[0016] Optionally, in one embodiment of this application, the step of using a preset event triggering mechanism for communication to achieve the optimization of a microgrid cluster with multi-agent cooperation includes: defining a communication topology and designing event triggering conditions to establish the preset event triggering mechanism; based on the preset event triggering mechanism, defining communication quality indicators, designing an adaptive triggering threshold based on reinforcement learning, and establishing a communication reliability model; designing a fault-tolerant communication protocol to statistically analyze communication traffic, and using the communication reliability model to evaluate the reliability indicators of communication.

[0017] Through the above technical means, the embodiments of this application establish a preset event triggering mechanism and design a fault-tolerant communication protocol, which can realize efficient and reliable information interaction in the microgrid cluster, enabling each sub-microgrid to respond quickly when a sudden event or communication anomaly occurs, ensuring the timely transmission and execution of power adjustment commands, thereby improving the system's stability, robustness and adaptability to fluctuations in renewable energy output.

[0018] A second aspect of this application provides a microgrid cluster optimization device for multi-agent cooperation, comprising: an establishment module for establishing a system architecture for the microgrid cluster; a determination module for constructing a power coordination control strategy based on the system architecture, so as to use the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus; and an optimization module for designing a hierarchical control system while executing the power coordination control strategy, so as to generate corresponding economic optimization scheduling instructions according to the current frequency regulation stage, and in response to the economic optimization scheduling instructions, generate optimal power instructions with the goal of minimizing system operating costs and maximizing renewable energy absorption rate, and communicate using a preset event triggering mechanism to achieve the optimization objective of the microgrid cluster for multi-agent cooperation.

[0019] Through the above technical means, the embodiments of this application adopt a collaborative architecture of system architecture modeling, distributed consistency coordination, hierarchical control system, model prediction rolling optimization and event-triggered communication, which can effectively improve the operating performance of microgrid clusters with a high proportion of renewable energy, thereby coping with the uncertainty of distributed power output and the randomness of load demand, and enhancing the stability and economy of the system.

[0020] Optionally, in one embodiment of this application, the determining module includes: a calculation unit, configured to, after determining the power imbalance, obtain an estimate of the global imbalance of each sub-microgrid in the system architecture and calculate the power allocation ratio; and a first generation unit, configured to generate power adjustment instructions for each sub-microgrid based on the global imbalance and the power allocation ratio, wherein distributed convergence detection based on Lyapunov is performed until a preset convergence condition is met, and iteration is stopped, so that after each sub-microgrid performs collaborative optimization using a preset value function, collaborative allocation of power deficit or surplus is achieved.

[0021] Through the above technical means, the embodiments of this application can generate power adjustment instructions for each sub-microgrid according to the global imbalance and power allocation ratio, realize the coordinated allocation of power deficit or surplus, maintain the global power balance of the microgrid cluster while ensuring the autonomy of each sub-microgrid, improve the system's adaptability to the fluctuation of renewable energy output and the randomness of load demand, thereby enhancing the system's stability and robustness.

[0022] Optionally, in one embodiment of this application, the power adjustment amount of the power adjustment command is calculated using the following formula: , in, Indicates the first The amount of power regulation that each microgrid needs to handle; The differential gain coefficient; Indicates the first The estimated global power imbalance of each sub-microgrid obtained through a consensus algorithm; This represents the power allocation ratio of the i-th sub-microgrid; The preset convergence condition is:

[0023] in, The threshold for the change in the Lyapunov function; The maximum consistency deviation threshold; This represents the total number of sub-microgrids in a microgrid cluster; The formula for generating the power adjustment command is: , in, Indicates the first Policy network of individual agents Indicates parameters; Indicates the first Local state information of each agent; The collaborative weighting coefficients among neighboring intelligent agents; For the first A set of neighbors for an agent.

[0024] Through the above technical means, the embodiments of this application quantify the power adjustment amount of the power adjustment command, which can determine the power increase or decrease of each sub-microgrid in each adjustment cycle; based on the power adjustment command generation formula, combined with the global imbalance and power allocation ratio, the power adjustment command of each sub-microgrid can be dynamically generated; at the same time, corresponding convergence conditions are set, which can be used to determine whether the power adjustment iteration meets the system balance and performance optimization requirements, thereby ensuring that the microgrid cluster achieves stable and efficient global power regulation and optimization under the cooperation of multiple agents.

[0025] Optionally, in one embodiment of this application, the optimization module includes: a first design unit for performing primary power allocation using improved active power-frequency droop characteristics to achieve droop control layer design in the primary frequency regulation stage; a second design unit for eliminating steady-state frequency and voltage deviations using an adaptive PI controller with anti-saturation integral to achieve voltage / frequency recovery control design in the secondary frequency regulation stage; and a third design unit for establishing an optimization problem with the goal of minimizing operating costs to achieve economic optimization scheduling layer design in the tertiary frequency regulation stage.

[0026] Through the above technical means, the embodiments of this application can perform hierarchical regulation of the power output of microgrids or power systems based on the droop control of primary frequency regulation, the voltage / frequency recovery of secondary frequency regulation, and the economic optimization scheduling of tertiary frequency regulation. This enables rapid response to load fluctuations, restoration of system voltage and frequency stability, and optimization of economic operation, thereby improving the stability, reliability, and operating efficiency of the system.

[0027] Optionally, in one embodiment of this application, the optimization module includes: a construction unit, used to establish a multi-objective optimization function and define system operating cost and renewable energy absorption rate to establish a prediction model and construct a rolling optimization framework; and a second generation unit, used to generate the optimal power command based on the prediction model and the rolling optimization framework.

[0028] Through the above technical means, the embodiments of this application construct a rolling optimization framework for system operating costs and renewable energy absorption rate, and then generate the optimal power command. Under the premise of ensuring the autonomy of each sub-microgrid, the global power balance and economical and efficient operation of the microgrid cluster can be achieved, while improving the renewable energy absorption rate, reducing system operating costs, and enhancing the system's adaptability to load fluctuations and uncertainties in distributed power output.

[0029] Optionally, in one embodiment of this application, the optimization module includes: a first establishment unit, configured to define a communication topology and design event triggering conditions to establish the preset event triggering mechanism; a second establishment unit, configured to define communication quality indicators based on the preset event triggering mechanism, design an adaptive triggering threshold based on reinforcement learning, and establish a communication reliability model; and an evaluation unit, configured to design a fault-tolerant communication protocol to statistically analyze communication traffic and evaluate the communication reliability indicators using the communication reliability model.

[0030] Through the above technical means, the embodiments of this application establish a preset event triggering mechanism and design a fault-tolerant communication protocol, which can realize efficient and reliable information interaction in the microgrid cluster, enabling each sub-microgrid to respond quickly when a sudden event or communication anomaly occurs, ensuring the timely transmission and execution of power adjustment commands, thereby improving the system's stability, robustness and adaptability to fluctuations in renewable energy output.

[0031] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-agent cooperative microgrid cluster optimization method as described in the above embodiments.

[0032] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-agent cooperative microgrid cluster optimization method.

[0033] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described multi-agent cooperative microgrid cluster optimization method.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a multi-agent cooperative microgrid cluster optimization method provided according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating the process of optimizing a microgrid cluster through multi-agent cooperation, according to one embodiment of this application. Figure 3 This is a block diagram of a multi-agent cooperative microgrid cluster optimization device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application.

[0036] Figure label: 10 - Microgrid cluster optimization device for multi-agent collaboration; 100 - Establishment module, 200 - Determination module, 300 - Optimization module; 401 - Memory, 402 - Processor, 403 - Communication interface. Detailed Implementation

[0037] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0038] The following description, with reference to the accompanying drawings, illustrates a multi-agent cooperative microgrid cluster optimization method and apparatus according to embodiments of this application. Addressing the technical problem mentioned in the background art, where distributed power control methods often employ fixed-period communication mechanisms for microgrid interaction, lacking adaptive adjustment capabilities to network topology changes and communication conditions, making it difficult to simultaneously balance communication efficiency and control performance under complex conditions, thus resulting in decreased optimization performance, this application provides a multi-agent cooperative microgrid cluster optimization method. In this method, a microgrid cluster architecture is first constructed. Then, a collaborative architecture combining distributed consensus coordination, hierarchical control systems, model prediction rolling optimization, and event-triggered communication is used to generate optimal power commands with the goals of minimizing system operating costs and maximizing renewable energy absorption, thereby achieving multi-agent... Collaborative microgrid cluster optimization can effectively address the uncertainty of distributed power output and the randomness of load demand. It coordinates the automatic proportional allocation of power deficits or surpluses among sub-microgrids, and simultaneously suppresses power disturbances, restores voltage and frequency, and coordinates economic dispatch to ensure system stability and high response speed through a hierarchical control system. Furthermore, the event-triggered communication mechanism can dynamically adjust trigger thresholds and intervals based on communication quality, reducing redundant communication traffic and improving system reliability and convergence. This provides an efficient, reliable, and economical power control solution for microgrid clusters with a high proportion of renewable energy, achieving stable system operation, efficient energy utilization, and multi-agent collaborative optimization. This solves the problem that related technologies often use fixed-period communication mechanisms for microgrid interaction, lacking adaptive adjustment capabilities to network topology changes and communication conditions, making it difficult to simultaneously achieve communication efficiency and control performance in complex situations.

[0039] Specifically, Figure 1This is a flowchart illustrating a multi-agent cooperative microgrid cluster optimization method provided in an embodiment of this application.

[0040] like Figure 1 As shown, the multi-agent cooperative microgrid cluster optimization method includes the following steps: In step S101, the system architecture of the microgrid cluster is established.

[0041] It can be explained that a microgrid cluster consists of multiple sub-microgrids, each of which may include, but is not limited to, distributed power sources, energy storage units, and loads. Distributed power sources can provide controllable active and reactive power to meet local load demands, energy storage units can regulate power balance and store energy through charging and discharging, and loads can consume power as time fluctuates. While operating autonomously, each sub-microgrid can achieve economic operation and stable regulation of the cluster as a whole through power exchange.

[0042] Specifically, embodiments of this application may include, but are not limited to, the following steps: (1) Define the microgrid cluster topology.

[0043] This application embodiment can be configured to include microgrid clusters. Individual microgrids form an undirected connected graph. ,in For a set of nodes, Let edge set represent the power interaction links between sub-microgrids; define the adjacency matrix. ,in Represents a node and Power exchange is possible, otherwise .

[0044] (2) Establish a distributed power source model.

[0045] In the embodiments of this application, the first The output model of distributed power sources (such as photovoltaics and wind turbines) in a microgrid can be expressed as: , in, To contribute to distributed power sources To predict the output, The prediction error is represented by a Gaussian distribution with a mean of zero. The climbing power factor. This is the power compensation term for hill climbing.

[0046] (3) Establish an energy storage system model As a concrete example, the first The charging and discharging power constraints of the energy storage system in a microgrid can be expressed as: , , in, This refers to the charging and discharging power. and These are the minimum charge / discharge power and the maximum charge / discharge power, respectively. For the energy storage capacity in the next moment; For the current energy storage capacity, For time intervals, , These are the charging and discharging efficiencies, respectively. This is the rated capacity.

[0047] (4) Establish a load model.

[0048] In the embodiments of this application, the first The load power of a microgrid can be expressed as: , in, It is a rigid load; For adjustable loads, the following can be satisfied:

[0049] in, Indicates the first The total adjustable energy of a microgrid's adjustable loads within a dispatch cycle.

[0050] (5) Define power interaction constraints.

[0051] It can be noted that, in the embodiments of this application, the sub-microgrid and Interaction power between It can satisfy: , , in, For sub-microgrids The set of adjacent nodes; Indicates the first Each sub-microgrid and all neighboring sub-microgrids The power commutative algebraic sum; This represents the line power transmission limit; Represent the adjacency matrix, if A value of 1 indicates that nodes i and j can exchange power; a value of 0 indicates that they cannot exchange power.

[0052] In step S102, based on the system architecture, a power coordination control strategy based on distributed consistency is constructed to utilize the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus.

[0053] It can be explained that in a microgrid cluster, in order to ensure the power balance of the entire cluster, when a sub-microgrid experiences a power deficit (supply falling short of demand) or a power surplus (excess energy), these power gaps or excess power need to be rationally allocated among the microgrids. A power coordination control strategy based on distributed consensus can, through local communication between the sub-microgrids, enable each microgrid to automatically adjust its output or energy storage behavior according to a certain proportion (usually related to its capacity or preset weight), thereby achieving gradual power consistency and balance for the entire cluster without relying on a centralized control center.

[0054] Optionally, in one embodiment of this application, a power coordination control strategy based on distributed consensus is constructed to enable the allocation ratio within a microgrid cluster to be determined based on power deficit or surplus. This includes: after determining the power imbalance, obtaining the power imbalance of each sub-microgrid in the system architecture to estimate the global imbalance and calculating the power allocation ratio; generating power adjustment instructions for each sub-microgrid based on the global imbalance and the power allocation ratio, wherein distributed convergence detection based on Lyapunov is performed until a preset convergence condition is met, and iteration is stopped, so that after each sub-microgrid performs collaborative optimization using a preset value function, the collaborative allocation of power deficit or surplus is achieved.

[0055] Among them, the Lyapunov method can measure the degree of deviation of the system state from equilibrium by constructing a positive definite function and analyzing its changing trend over time. In the distributed power coordination of microgrids, this function can be used to represent the power imbalance of each sub-microgrid, and a local control law can be designed to make the Lyapunov function monotonically decrease, thereby ensuring that the entire cluster gradually converges under the condition of relying only on neighbor communication, achieving power consistency and stable system operation.

[0056] It can be explained that the preset convergence condition can be that the derivative of the Lyapunov function is less than zero, i.e., the system state decreases monotonically with time error; or it can be that the absolute value of the power imbalance of each sub-microgrid is less than a certain threshold, i.e., when the power difference is within the allowable range, the system is considered to have reached an approximately convergent state. The preset value function can be used to measure the quality of the system state or control strategy. It can be a power balance-related indicator, such as the power imbalance of the entire cluster or the sum of squares of the power deviations of each sub-microgrid, used to minimize the supply and demand difference; or it can be an economic operation indicator, such as the total generation cost, energy storage usage cost, or renewable energy utilization rate, used to optimize operating costs and energy efficiency while ensuring stability. It can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0057] As one possible implementation, embodiments of this application may include the following steps: (1) Define the power imbalance.

[0058] The embodiments of this application are for the first Local power imbalance in individual microgrids It can be defined as: , in, Indicates the first Individual microgrids at any time Power deficit or power surplus.

[0059] (2) Construct a consistency coordination algorithm.

[0060] In the embodiments of this application, each sub-microgrid exchanges power imbalance information through a distributed communication network. The global imbalance can be estimated using a consensus update protocol with a forgetting factor, which can be expressed as: , in, Indicates the first The sub-microgrid in the first The estimated global average power imbalance at the next iteration; Indicates the first The sub-microgrid in the first The estimated global average power imbalance at the next iteration; As a forgetting factor, it can be used to balance historical information with new information; For time-varying communication weights; For local feedback gain coefficient; Indicates the relationship with the first A set of adjacent sub-microgrids.

[0061] (3) Calculate the power distribution ratio.

[0062] Furthermore, each sub-microgrid can determine its power allocation ratio based on its adjustable capacity: , in, Total adjustable capacity; Reputation weights are based on historical response performance. To adjust the sensitivity index; This represents the total number of sub-microgrids in a microgrid cluster.

[0063] (4) Generate power adjustment command.

[0064] As a concrete example, the first The power regulation of the sub-microgrid root can be calculated as follows: , in, Indicates the first The amount of power regulation that each microgrid needs to handle; This is the differential gain coefficient, used to suppress overshoot; Indicates the first The estimated global power imbalance of each microgrid obtained through a consensus algorithm.

[0065] (5) Implement distributed convergence detection based on Lyapunov.

[0066] In the embodiments of this application, iteration can be stopped when the following convergence condition is met:

[0067] in, The threshold for the change in the Lyapunov function; This is the threshold for the maximum consistency deviation.

[0068] (6) Perform power redistribution.

[0069] This application's embodiments can employ a multi-agent reinforcement learning framework to achieve collaborative allocation of power deficits or surpluses. Specifically, in this application's embodiments, each sub-microgrid can act as an agent, generating power adjustment commands through a distributed policy network: , in: Indicates the first A policy network for each agent, with parameters as follows: ; Indicates the first Local state information of an agent, including but not limited to distributed power output, energy storage state of charge, load power, local power imbalance, voltage amplitude and system frequency; The collaborative weighting coefficients among neighboring intelligent agents; For the first The set of neighbors of an agent includes all neighboring sub-microgrids with which it has a physical connection and reliable communication quality.

[0070] Furthermore, the agents can perform collaborative optimization through the following value function: , in, As a discount factor, Let be the instantaneous reward function for the i-th agent. For expectation operators; For the joint action of the system at time t; Let t be the system state at time t; The calculation of the value function is based on the premise that the system starts from a specific initial state s.

[0071] In step S103, while executing the power coordination control strategy, a hierarchical control system is designed to generate corresponding economic optimization dispatch instructions based on the current frequency regulation stage. In response to the economic optimization dispatch instructions, with the goal of minimizing system operating costs and maximizing renewable energy consumption, the optimal power instruction is generated. Furthermore, a preset event triggering mechanism is used for communication to achieve the microgrid cluster optimization objective of multi-agent collaboration.

[0072] It can be explained that the hierarchical control system may include, but is not limited to: droop control of primary frequency regulation, which can be used to quickly respond to load fluctuations and achieve local power balance; voltage / frequency recovery of secondary frequency regulation, which can bring the system voltage and frequency back to the rated values ​​by adjusting energy storage or controllable power sources; and economic optimization scheduling of tertiary frequency regulation, which can minimize operating costs and maximize the utilization of renewable energy through power optimization and energy storage scheduling while ensuring system stability, thereby forming a complete control closed loop from rapid response to long-term optimization.

[0073] The preset event triggering mechanism can trigger control actions based on power imbalance, frequency deviation, voltage deviation, or energy storage status, etc. It can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0074] Optionally, in one embodiment of this application, a hierarchical control system is designed to generate corresponding economically optimized scheduling instructions based on the current frequency regulation stage. This includes: using improved active power-frequency droop characteristics for primary power allocation to achieve droop control layer design for the primary frequency regulation stage; using an adaptive PI controller with anti-saturation integral to eliminate steady-state frequency and voltage deviations to achieve voltage / frequency recovery control design for the secondary frequency regulation stage; and establishing an optimization problem with the goal of minimizing operating costs to achieve economically optimized scheduling layer design for the tertiary frequency regulation stage.

[0075] The following examples illustrate the hierarchical control system of this application. The embodiments of this application may include the following steps: (1) Design of the droop control layer for primary frequency modulation.

[0076] The embodiments of this application can achieve primary power distribution by using improved active power-frequency (Pf) droop characteristics: , in, For the first A distributed power source at time ; output angular frequency; This is the system's rated angular frequency; Active power-frequency droop factor; The active power differential damping coefficient; For the i-th distributed power source at time... The actual output active power; This is the reference value for the active power of the i-th distributed power source.

[0077] Furthermore, its reactive power-voltage droop characteristic can be expressed as: , in, Let be the output voltage amplitude of the i-th distributed power source at time t; This is the system's rated voltage; Reactive power minus voltage droop factor; The reactive power differential damping coefficient; For the first A distributed power source at time The actual output reactive power; For the first Reference value for reactive power of a distributed power source.

[0078] (2) Design of voltage / frequency recovery control for secondary frequency modulation.

[0079] The embodiments of this application can use an adaptive PI controller with anti-saturation integral to eliminate steady-state frequency and voltage deviation.

[0080] Specifically, in the embodiments of this application, frequency recovery control can be expressed as: , in, For the first A distributed power source at time Frequency correction amount; This is for frequency deviation; This is the frequency proportional control coefficient; For frequency integral control coefficients; This is the anti-saturation adjustment coefficient.

[0081] Meanwhile, voltage recovery control can be expressed as: , in, For the first A distributed power source at time Voltage correction amount; For voltage deviation; , This is the voltage proportional control coefficient; This is the anti-saturation adjustment coefficient.

[0082] (3) Design of economically optimized scheduling layer for three-frequency modulation.

[0083] The embodiments of this application can establish an optimization problem with the objective of minimizing operating costs, which can be expressed as: , in, Indicates the first The generation cost function of a distributed power source; Indicates the first The operating cost function of an energy storage system; Indicates the first The cost function of curtailment penalty for a distributed power source; Indicates the first The amount of power abandoned by a distributed power source.

[0084] (4) Embodiments of this application may also include power balance constraints: , in, This represents the total active power loss of the system.

[0085] Operational safety constraints: , in, , They represent the first The lower and upper limits of active power output of a distributed power source; , They represent the first The lower and upper limits of reactive power output of a distributed power source; , They represent the first The lower and upper limits of the allowable voltage for each node; Indicates the line The active power flow; Indicates the line The active power transmission limit.

[0086] Optionally, in one embodiment of this application, the optimal power command is generated with the goal of minimizing system operating costs and maximizing renewable energy absorption rate. This includes: establishing a multi-objective optimization function and defining system operating costs and renewable energy absorption rate to establish a prediction model and construct a rolling optimization framework; and generating the optimal power command based on the prediction model and the rolling optimization framework.

[0087] As one possible implementation, embodiments of this application can generate optimal power commands in real time based on a rolling optimization algorithm based on model predictive control, with the goal of minimizing system operating costs and maximizing renewable energy absorption. Embodiments of this application may include the following steps: (1) Establish a multi-objective optimization function.

[0088] First, within each prediction time domain, the embodiments of this application can construct the following multi-objective optimization function: , in, This indicates that the first sub-microgrid is at time [time]. The local objective function; To predict the length of the time domain; Indicates the first The operating cost of a sub-microgrid at time t; Indicates the first Individual microgrids at any time The renewable energy consumption rate; Indicates the first Individual microgrids at any time The amount of power adjustment; This indicates that the first sub-microgrid is at time [time]. The control input vector; These are the weighting coefficients for operating costs, renewable energy consumption, power smoothing, and changes in control input, respectively. To control the input change penalty matrix; For the terminal cost function; This is the terminal cost weighting coefficient.

[0089] (2) Define the system operating cost.

[0090] Secondly, the system operating cost can be defined as follows in the embodiments of this application: , in, Indicates the first The distributed power source is in the first The cost of generating electricity at any given moment; Indicates the first The energy storage system in the first The operating cost at any given moment; Indicates the first The distributed power source is in the first The cost of energy abandonment at any moment; Indicates the first The distributed power source is in the first The amount of power discarded at any given moment.

[0091] (3) Define the renewable energy consumption rate.

[0092] Next, the renewable energy integration rate can be defined in this embodiment as: , in, Indicates the first The renewable energy in the first Actual power utilization at any given time; Indicates the first The renewable energy in the first Available power at any given time; To ensure the fraction is a very small number, avoid having a denominator of 0.

[0093] (4) Establish a prediction model.

[0094] Furthermore, this application can employ an autoregressive integral moving average model for power prediction: , in, Indicates the first The renewable energy in the first Predicted power at time; Indicates the order of autoregression; Indicates the order of the moving average; Indicates the first One autoregressive coefficient; Indicates the first One moving average coefficient; It is a dimensionless number; This represents a white noise sequence.

[0095] (5) Construct a rolling optimization framework.

[0096] As one possible approach, embodiments of this application can generate an optimal control sequence by solving a finite-time optimization problem at each sampling time, and implement only the first control variable, thereby enabling the prediction and optimization of the future behavior of the system while handling various operational constraints.

[0097] Specifically, in the embodiments of this application, the finite-time optimization problem to be solved can be expressed as: , Meanwhile, the embodiments of this application need to meet the following constraints: , in, To control the control input sequence in the time domain. ; The control input vector at any given time includes, but is not limited to, the power commands of each distributed power source and the energy storage charging and discharging power; for The system state vector at any given time includes, but is not limited to, the voltage, frequency, and energy storage state of each node. for The disturbance vector at any given time includes load changes, renewable energy fluctuations, etc. This is the weighting coefficient for the operating cost item, and it is a positive constant. The weighting coefficient for the energy forfeiture penalty term is a positive constant. The weighting coefficient for the terminal cost term is a positive constant. The system parameters for predicting the time domain length represent the number of future time steps considered in the optimization problem; For system parameters controlling the time domain length, this represents the future time steps of the optimized control input; This is the coefficient matrix of the system's state-space model; The coefficient matrix and boundary vectors of the inequality constraints; The coefficient matrix and boundary vectors of the equality constraints; These are the lower and upper bound vectors for controlling the input, respectively; These are the lower and upper bound vectors of the state variables, respectively; System operating cost function; The power vector abandoned at time t; The terminal cost function can be used to ensure the stability of the optimization problem.

[0098] (6) Implement feedback correction.

[0099] Furthermore, at the end of each control cycle, embodiments of this application can use actual measured values ​​to correct the prediction model: , in, This represents the corrected predicted power; Indicates the correction gain coefficient; Indicates the first The actual power measurement at any given time.

[0100] (7) Generate the optimal power command.

[0101] Finally, the optimal control sequence can be obtained by solving an optimization problem in the embodiments of this application: , It can be noted that the embodiments of this application can make the first control quantity The optimal power command for the current moment is issued to each distributed power source.

[0102] Optionally, in one embodiment of this application, a preset event triggering mechanism is used for communication to achieve the optimization of a microgrid cluster with multi-agent cooperation. This includes: defining a communication topology and designing event triggering conditions to establish a preset event triggering mechanism; defining communication quality indicators based on the preset event triggering mechanism, designing an adaptive triggering threshold based on reinforcement learning, and establishing a communication reliability model; designing a fault-tolerant communication protocol to count communication traffic and using the communication reliability model to evaluate the reliability indicators of communication.

[0103] Specifically, embodiments of this application can employ an event-triggered mechanism based on a communication network model to reduce communication traffic and improve system reliability. Embodiments of this application may include the following steps: (1) Define the communication topology.

[0104] The embodiments of this application can construct a directed graph as the communication network for a microgrid cluster. ,in, This represents a set of communication nodes, with each node corresponding to a sub-microgrid. Represents a set of communication links; a communication adjacency matrix can be defined. ,in Represents a node Can be nodes Send a message, otherwise return 0.

[0105] (2) Design event triggering conditions.

[0106] As one possible way to achieve this, the first The local event triggering conditions of a microgrid can be based on the state error covariance matrix, which can be expressed as: , in, This is the state error vector; The error covariance matrix for time-varying state estimation; This is the trigger sensitivity coefficient; For dynamic trigger threshold, and These are the minimum and maximum values ​​of the trigger threshold, respectively. For threshold adaptation rate, The gradient of the local objective function; The exponential decay coefficient; Indicates the first The first sub-microgrid The next trigger moment.

[0107] (3) Establish a hybrid triggering mechanism.

[0108] Furthermore, embodiments of this application can design a time-varying hybrid triggering strategy to dynamically adjust the triggering interval and event judgment threshold according to the system operating status and communication quality, which can be expressed as: , in, The trigger interval is a fixed time. This refers to the event trigger interval; As an indicator of network congestion; This is the congestion threshold.

[0109] In the embodiments of this application, the event trigger interval calculation can be expressed as: , in, For the Sigmoid function; To adjust the parameters.

[0110] (4) Define communication quality indicators.

[0111] In some cases, embodiments of this application may use PRR (Packet Reception Rate) to evaluate communication quality, which can be expressed as: , in, Indicates from node To the node Packet reception rate; Indicates the node within the statistical time period Successfully received from node The number of data packets; Indicates the node within the statistical time period To the node Total number of data packets sent.

[0112] (5) Design an adaptive trigger threshold based on reinforcement learning.

[0113] Furthermore, embodiments of this application can dynamically adjust the trigger threshold according to communication quality, raising the threshold when communication quality is poor and lowering it when quality is good, thereby ensuring system stability while also considering response speed; embodiments of this application can be represented as follows: , in, Indicates the first Individual microgrids at any time Adaptive trigger threshold; , These represent the minimum and maximum values ​​of the trigger threshold, respectively. Indicates time The average packet reception rate.

[0114] (6) Establish a communication reliability model.

[0115] As one possible implementation, embodiments of this application can use a Markov chain model to characterize the reliability of the communication link, dividing the link state into three modes: normal, congested, and interrupted, and describing its transition characteristics over time through a state transition probability matrix; the Markov chain model used in embodiments of this application to describe communication state transitions can be expressed as follows: , in, Indicates time The communication status; Indicates from state Transition to state The probability is that the communication status is divided into three states: normal, congestion, and interruption.

[0116] (7) Design a fault-tolerant communication protocol.

[0117] In actual implementation, when a communication failure is detected, the embodiments of this application can design a local backup control strategy to ensure that the sub-microgrid can still maintain supply and demand balance and basic stable operation even in the event of loss of external communication; the embodiments of this application can be represented as follows: , in, This represents the backup control command for the i-th sub-microgrid; Represents the local control gain matrix; This indicates the rated control command.

[0118] (8) Implement communication traffic statistics: This application embodiment can also count the amount of communication data in each control cycle. By recording the number and size of data packets sent and received by each node in that cycle, the total amount of communication data is calculated, and its relationship with the control frequency, network topology, and communication protocol is further analyzed to assess the communication load level and provide data support for bandwidth planning, protocol optimization, and distributed control strategy adjustment. This application embodiment can be represented as follows: , in, Indicates the total amount of communication data; Indicates from node To the node The amount of data in a single communication; Indicates from node To the node The communication frequency.

[0119] (9) Assess system reliability.

[0120] Ultimately, the embodiments of this application can use reliability indicators to evaluate the performance of the communication system, quantify the stability and availability of the communication system under different operating conditions, thereby providing a basis for the optimization of distributed control strategies and fault-tolerant design of microgrid clusters; the embodiments of this application can be represented as follows: , in, Indicates the overall reliability of the system; Indicates the first The reliability of each communication node; Indicates communication link Reliability.

[0121] The following is a specific example, such as Figure 2 The flowchart illustrates the multi-agent cooperative microgrid cluster optimization method according to an embodiment of this application. The embodiments of this application may include the following steps: In step S201, a system architecture for a microgrid cluster is established, including distributed power sources, energy storage units, and loads of each sub-microgrid, which are interconnected through power and communication networks to achieve local autonomy and global collaboration.

[0122] In step S202, a power coordination control strategy based on distributed consensus is constructed. Through local communication among the sub-microgrids, the power deficit or surplus is automatically allocated proportionally according to capacity / weight, ensuring that the entire cluster can quickly and reliably achieve power consistency without relying on central control, thereby improving the system's reliability and response speed. In step S203, a hierarchical control system is designed, including droop control of primary frequency regulation to quickly suppress power disturbances, voltage / frequency recovery of secondary frequency regulation to bring the system operating point back to the rated value, and economic optimization scheduling of tertiary frequency regulation to minimize operating costs and maximize the absorption of renewable energy while ensuring system safety and stability, thereby taking into account both real-time performance and economy.

[0123] In step S204, a rolling optimization algorithm based on model predictive control is proposed. With the goal of minimizing system operating costs and maximizing renewable energy absorption rate, the algorithm generates the optimal power command in real time within each control cycle and considers energy storage constraints, power limits, and network constraints to achieve dynamic optimization and global coordination of the system.

[0124] In step S205, a communication network model is established and an event triggering mechanism is introduced. The triggering threshold and triggering interval are dynamically adjusted according to communication quality, load fluctuation and power imbalance, so as to reduce redundant communication, reduce bandwidth pressure, and at the same time ensure the convergence of distributed control and system stability, thereby improving the reliability, robustness and communication efficiency of the entire microgrid cluster.

[0125] The microgrid cluster optimization method based on multi-agent cooperation proposed in this application first constructs a microgrid cluster architecture. Then, it combines a collaborative architecture of distributed consistency coordination, hierarchical control system, model prediction rolling optimization, and event-triggered communication. With the goal of minimizing system operating costs and maximizing renewable energy absorption, it generates optimal power commands, thereby achieving multi-agent collaborative microgrid cluster optimization. This effectively addresses the uncertainty of distributed power output and the randomness of load demand, coordinating the automatic proportional allocation of power deficits or surpluses among sub-microgrids. Simultaneously, the hierarchical control system can quickly suppress power disturbances, restore voltage and frequency, and coordinate economic dispatch, ensuring system stability and high response speed. Furthermore, the event-triggered communication mechanism dynamically adjusts the trigger threshold and interval based on communication quality, reducing redundant communication traffic and improving system reliability and convergence. Therefore, it provides an efficient, reliable, and economical power control solution for microgrid clusters with a high proportion of renewable energy, achieving stable system operation, efficient energy utilization, and multi-agent collaborative optimization.

[0126] Next, referring to the accompanying drawings, a microgrid cluster optimization device for multi-agent cooperation proposed according to an embodiment of this application is described.

[0127] Figure 3 This is a block diagram of a microgrid cluster optimization device for multi-agent cooperation according to an embodiment of this application.

[0128] like Figure 3As shown, the multi-agent collaborative microgrid cluster optimization device 10 includes: a setup module 100, a determination module 200, and an optimization module 300.

[0129] Among them, module 100 is used to establish the system architecture of the microgrid cluster.

[0130] The determination module 200 is used to construct a power coordination control strategy based on distributed consensus based on the system architecture, so as to use the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus.

[0131] The optimization module 300 is used to design a hierarchical control system while executing the power coordination control strategy. It generates corresponding economic optimization dispatch instructions based on the current frequency regulation stage, and responds to the economic optimization dispatch instructions to generate the optimal power instructions with the goal of minimizing system operating costs and maximizing renewable energy consumption rate. It also uses a preset event triggering mechanism for communication to achieve the microgrid cluster optimization objective of multi-agent collaboration.

[0132] Optionally, in one embodiment of this application, the determining module 200 includes a calculation unit and a first generation unit.

[0133] The calculation unit is used to obtain the power imbalance of each sub-microgrid in the system architecture after determining the power imbalance, estimate the global imbalance, and calculate the power allocation ratio.

[0134] The first generation unit is used to generate power adjustment instructions for each sub-microgrid based on the global imbalance and power allocation ratio. It performs distributed convergence detection based on Lyapunov until the preset convergence condition is met, at which point it stops iterating. This allows for the coordinated allocation of power deficit or surplus after each sub-microgrid performs collaborative optimization using a preset value function.

[0135] Optionally, in one embodiment of this application, the formula for calculating the power adjustment amount of the power adjustment command is: , in, Indicates the first The amount of power regulation that each microgrid needs to handle; The differential gain coefficient; Indicates the first The estimated global power imbalance of each sub-microgrid obtained through a consensus algorithm; This represents the power allocation ratio of the i-th sub-microgrid.

[0136] The preset convergence condition can be expressed as:

[0137] in, The threshold for the change in the Lyapunov function; The maximum consistency deviation threshold; This represents the total number of sub-microgrids in a microgrid cluster.

[0138] The formula for generating power adjustment commands can be expressed as: , in, Indicates the first Policy network of individual agents Indicates parameters; Indicates the first Local state information of each agent; The collaborative weighting coefficients among neighboring intelligent agents; For the first A set of neighbors for an agent.

[0139] Optionally, in one embodiment of this application, the optimization module 300 includes: a first design unit, a second design unit, and a third design unit.

[0140] The first design unit is used to perform primary power allocation using improved active power-frequency droop characteristics to achieve the droop control layer design in the primary frequency modulation stage.

[0141] The second design unit is used to eliminate steady-state frequency and voltage deviations using an adaptive PI controller with anti-saturation integral, so as to realize the voltage / frequency recovery control design in the secondary frequency modulation stage.

[0142] The third design unit is used to establish an optimization problem with the goal of minimizing operating costs, so as to realize the economic optimization scheduling layer design of the third frequency regulation stage.

[0143] Optionally, in one embodiment of this application, the optimization module 300 includes: a construction unit and a second generation unit.

[0144] The building unit is used to establish a multi-objective optimization function and define the system operating cost and renewable energy absorption rate in order to establish a predictive model and build a rolling optimization framework.

[0145] The second generation unit is used to generate the optimal power command based on the prediction model and the rolling optimization framework.

[0146] Optionally, in one embodiment of this application, the optimization module 300 includes: a first establishment unit, a second establishment unit, and an evaluation unit.

[0147] The first establishment unit is used to define the communication topology and design event triggering conditions to establish a preset event triggering mechanism.

[0148] The second establishment unit is used to define communication quality indicators, design adaptive triggering thresholds based on reinforcement learning, and establish a communication reliability model based on a preset event triggering mechanism.

[0149] The evaluation unit is used to design fault-tolerant communication protocols, collect statistics on communication traffic, and evaluate communication reliability metrics using communication reliability models.

[0150] It should be noted that the foregoing explanation of the microgrid cluster optimization method embodiment for multi-agent cooperation also applies to the microgrid cluster optimization device for multi-agent cooperation in this embodiment, and will not be repeated here.

[0151] According to the multi-agent collaborative microgrid cluster optimization device proposed in this application, a microgrid cluster architecture is first constructed. Then, a collaborative architecture combining distributed consistency coordination, hierarchical control system, model prediction rolling optimization, and event-triggered communication is combined to generate optimal power commands with the goal of minimizing system operating costs and maximizing renewable energy absorption rate. This achieves multi-agent collaborative microgrid cluster optimization, which can effectively cope with the uncertainty of distributed power output and the randomness of load demand. It coordinates the automatic proportional allocation of power deficits or surpluses in each sub-microgrid. At the same time, it can quickly suppress power disturbances, restore voltage and frequency through hierarchical control system, and coordinate economic dispatch to ensure system stability and high response speed.

[0152] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0153] When the processor 402 executes the program, it implements the microgrid cluster optimization method for multi-agent cooperation provided in the above embodiments.

[0154] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0155] The memory 401 is used to store computer programs that can run on the processor 402.

[0156] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0157] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0158] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0159] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0160] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-agent cooperative microgrid cluster optimization method.

[0161] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the multi-agent cooperative microgrid cluster optimization method provided in this application.

[0162] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0163] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0164] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0165] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0166] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0168] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0169] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A multi-agent cooperative microgrid cluster optimization method, characterized in that, Includes the following steps: Establish a system architecture for microgrid clusters; Based on the system architecture, a power coordination control strategy based on distributed consistency is constructed to enable the allocation ratio within the microgrid cluster to be determined based on power deficit or surplus using the power coordination control strategy. While executing the power coordination control strategy, a hierarchical control system is designed to generate corresponding economic optimization dispatch instructions based on the current frequency regulation stage, and respond to the economic optimization dispatch instructions to generate the optimal power instructions with the goal of minimizing system operating costs and maximizing renewable energy consumption rate, and to communicate using a preset event triggering mechanism to achieve the microgrid cluster optimization objective of multi-agent collaboration. The step of constructing a power coordination control strategy based on distributed consensus, and using the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus, includes: after determining the power imbalance, obtaining the power imbalance of each sub-microgrid in the system architecture to estimate the global imbalance, and calculating the power allocation ratio; generating power adjustment instructions for each sub-microgrid based on the global imbalance and the power allocation ratio, wherein distributed convergence detection based on Lyapunov is performed until a preset convergence condition is met, and iteration stops, so that after each sub-microgrid performs collaborative optimization using a preset value function, the collaborative allocation of power deficit or surplus is achieved; The formula for calculating the power adjustment amount of the power adjustment command is as follows: ， in, Indicates the first The amount of power regulation that each microgrid needs to handle; The differential gain coefficient; Indicates the first The estimated global power imbalance of each sub-microgrid obtained through a consensus algorithm; This represents the power allocation ratio of the i-th sub-microgrid; The preset convergence condition is: in, The threshold for the change in the Lyapunov function; The maximum consistency deviation threshold; This represents the total number of sub-microgrids in a microgrid cluster; The formula for generating the power adjustment command is: ， in, Indicates the first Policy network of individual agents Indicates parameters; Indicates the first Local state information of each agent; The collaborative weighting coefficients among neighboring intelligent agents; For the first The set of neighbors of an agent; The method of using a preset event triggering mechanism for communication to achieve the goal of optimizing a microgrid cluster through multi-agent collaboration includes: defining a communication topology and designing event triggering conditions to establish the preset event triggering mechanism; defining communication quality indicators based on the preset event triggering mechanism, designing an adaptive triggering threshold based on reinforcement learning, and establishing a communication reliability model; designing a fault-tolerant communication protocol to statistically analyze communication traffic, and using the communication reliability model to evaluate the reliability indicators of communication.

2. The method according to claim 1, characterized in that, The aforementioned hierarchical control system is designed to generate corresponding economically optimized scheduling instructions based on the current frequency regulation phase, including: An improved active power-frequency droop characteristic is used for primary power allocation to realize the droop control layer design in the primary frequency modulation stage. An adaptive PI controller with anti-saturation integral is used to eliminate steady-state frequency and voltage deviations, so as to realize the voltage / frequency recovery control design in the secondary frequency modulation stage; An optimization problem with the goal of minimizing operating costs is established to achieve an economically optimized scheduling layer design for the third frequency regulation stage.

3. The method according to claim 1, characterized in that, The process of generating optimal power commands, with the goal of minimizing system operating costs and maximizing renewable energy absorption, includes: A multi-objective optimization function is established, and the system operating cost and renewable energy absorption rate are defined to build a predictive model and construct a rolling optimization framework. Based on the prediction model and the rolling optimization framework, the optimal power command is generated.

4. A microgrid cluster optimization device for multi-agent cooperation, characterized in that, Includes the following steps: Establish modules for building the system architecture of microgrid clusters; The determination module is used to construct a power coordination control strategy based on the system architecture, so as to use the power coordination control strategy to determine the allocation ratio within the microgrid cluster based on power deficit or surplus. The optimization module is used to design a hierarchical control system while executing the power coordination control strategy, so as to generate corresponding economic optimization dispatch instructions according to the current frequency regulation stage, and respond to the economic optimization dispatch instructions to generate the optimal power instructions with the goal of minimizing system operating costs and maximizing renewable energy consumption rate, and to communicate using a preset event triggering mechanism to achieve the microgrid cluster optimization purpose of multi-agent cooperation. The determining module includes: a calculation unit, used to obtain an estimate of the global imbalance of each sub-microgrid in the system architecture after determining the power imbalance, and to calculate the power allocation ratio; and a first generation unit, used to generate power adjustment instructions for each sub-microgrid based on the global imbalance and the power allocation ratio, wherein distributed convergence detection based on Lyapunov is performed until a preset convergence condition is met, and iteration stops, so that after each sub-microgrid performs collaborative optimization using a preset value function, the collaborative allocation of power deficit or surplus is achieved. The formula for calculating the power adjustment amount of the power adjustment command is as follows: ， in, Indicates the first The amount of power regulation that each microgrid needs to handle; The differential gain coefficient; Indicates the first The estimated global power imbalance of each sub-microgrid obtained through a consensus algorithm; This represents the power allocation ratio of the i-th sub-microgrid; The preset convergence condition is: in, The threshold for the change in the Lyapunov function; The maximum consistency deviation threshold; This represents the total number of sub-microgrids in a microgrid cluster; The formula for generating the power adjustment command is: ， in, Indicates the first Policy network of individual agents Indicates parameters; Indicates the first Local state information of each agent; The collaborative weighting coefficients among neighboring intelligent agents; For the first The set of neighbors of an agent; The optimization module includes: a first establishment unit, used to define the communication topology and design event triggering conditions to establish the preset event triggering mechanism; a second establishment unit, used to define communication quality indicators based on the preset event triggering mechanism, design an adaptive triggering threshold based on reinforcement learning, and establish a communication reliability model; and an evaluation unit, used to design a fault-tolerant communication protocol to count communication traffic and evaluate the communication reliability indicators using the communication reliability model.

5. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, the processor executing the program to implement the multi-agent cooperative microgrid cluster optimization method as described in any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the microgrid cluster optimization method for multi-agent cooperation as described in any one of claims 1-3.

7. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the microgrid cluster optimization method for multi-agent cooperation as described in any one of claims 1-3.

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