A micro-grid energy coordination control method considering dynamic change of communication topology

Abstract

The application discloses a kind of microgrid energy coordination control method considering communication topology dynamic change, belong to electric energy control and management technical field, it includes monitoring the communication connection state between control node, generates topology change event;Assess energy supply capacity and communication reliability and adjust the influence in energy coordination to generate adaptive weight;Combining real-time sensor information and historical operation data, generate short-term energy prediction data and combine adaptive weight, reach global energy distribution consensus, and then generate energy distribution decision;In execution process, monitor the deviation of topology change event and short-term energy prediction data and real-time energy data, if exceed deviation threshold, then trigger event signal, drive control strategy update.The application generates adaptive weight by monitoring communication topology and energy state, combined with short-term prediction, carries out distributed negotiation and event triggered update, improves the energy coordination robustness and operating stability of microgrid in dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of power control and management technology, and in particular to a microgrid energy coordination control method that takes into account dynamic changes in communication topology. Background Technology

[0002] Microgrids, as small-scale power generation and distribution systems integrating distributed power sources, energy storage devices, energy conversion devices, load monitoring and protection devices, are an important component of modern smart grids. To achieve coordinated operation of the various units within a microgrid, ensure power quality and reliability, and improve energy efficiency, energy coordination control methods are a core technology. These methods are primarily responsible for the rational scheduling and allocation of energy flow within the microgrid, under various constraints, to achieve supply-demand balance or specific optimization goals.

[0003] In existing technologies, energy coordination and control methods for microgrids are mainly divided into centralized control and distributed control. Centralized control typically relies on a central controller to collect information from the entire network, make unified decisions, and then issue instructions to each unit. Distributed control, on the other hand, does not require a central controller; each control node only interacts with its neighboring nodes, achieving the global control objective through local iterative calculations and negotiations. Distributed control has attracted widespread attention in the field of microgrid energy management due to its flexibility, scalability, and robustness to single-point failures.

[0004] However, existing distributed energy coordination and control methods still have some shortcomings in practical applications. On the one hand, many methods are designed under the assumption that the communication network between control nodes is ideal, i.e., the communication links are reliable and fixed. But in real microgrid environments, especially when wireless communication is used, the communication topology may change dynamically due to node movement, signal interference, or equipment failure, leading to link interruptions or node disconnection. On the other hand, existing distributed negotiation algorithms usually treat each control node equally or use static weighting coefficients, failing to fully consider the differences in the actual energy supply capacity and communication status of each node at different times. This may result in nodes with high energy potential having insufficient say, or nodes with unstable communication interfering with global decision-making. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a microgrid energy coordination control method that takes into account dynamic changes in communication topology. It employs a control strategy that uses real-time monitoring of communication topology and energy status to generate adaptive weights, combined with short-term forecasts for distributed iterative negotiation and event-triggered updates. This approach effectively improves the robustness and operational stability of microgrid energy coordination under dynamic and uncertain environments.

[0006] The above objectives can be achieved through the following approach:

[0007] A microgrid energy coordination control method considering dynamic changes in communication topology includes: real-time monitoring of the communication connection status between all control nodes in the microgrid to generate topology change events; real-time energy supply monitoring and communication reliability assessment based on the control nodes to obtain real-time energy supply capacity and communication reliability parameters, and dynamically adjusting the influence of the control nodes in energy coordination in conjunction with the topology change events to generate adaptive weights; acquiring real-time sensor information and historical operating data and performing predictive processing to generate short-term energy prediction data; fusing the adaptive weights and the short-term energy prediction data to reach a global energy allocation consensus through distributed iterative negotiation; generating power commands based on the global energy allocation consensus to generate energy allocation decisions; acquiring real-time energy data from the real-time sensor information, and while executing the energy allocation decisions, determining whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold to generate an event trigger signal; and updating the control strategy based on the event trigger signal.

[0008] Optionally, generating a topology change event includes: sending a check signal to the neighboring nodes of the control node through a preset heartbeat mechanism and receiving the heartbeat response signal returned by them; determining the link connection status between the control nodes based on the reception status of the heartbeat response signal and generating connection status data; performing time-series difference analysis based on the connection status data to identify nodes joining, leaving, or link interruption in the network and generating a topology change event.

[0009] Optionally, generating adaptive weights includes: monitoring the real-time output power or state of charge of the control node to generate real-time energy supply capability; recording the historical link online duration and communication error rate of the control node and performing a comprehensive evaluation to generate communication reliability parameters; updating the communication reliability parameters based on the topology change event to generate updated communication reliability parameters; and combining the real-time energy supply capability and the updated communication reliability parameters to generate adaptive weights through weighted fusion calculation.

[0010] Optionally, generating short-term energy prediction data includes: extracting features based on the real-time sensor information to obtain real-time electrical parameters including voltage, current, power, and frequency, forming the current operating condition; deriving the dominant path and change tendency of internal energy based on the direction and magnitude of active power, network topology connections, and node voltage phase angle differences in the real-time electrical parameters, and identifying energy flow trends; extracting trend features based on the historical operating data, and constructing an energy dynamic evolution sequence by combining environmental parameters and load fluctuation characteristics in the real-time sensor information; performing multi-step forward extrapolation based on the energy dynamic evolution sequence, and forming an energy supply and demand situation forecast for future periods by combining the current operating condition and the energy flow trend; and conducting a confidence assessment based on the energy supply and demand situation forecast to select energy fluctuation ranges for key periods and generate short-term energy prediction data.

[0011] Optionally, the step of fusing the adaptive weights and the short-term energy prediction data to achieve a global energy allocation consensus through distributed iterative negotiation includes: using the adaptive weights as voting coefficients for each control node in energy allocation negotiation, and combining them with the corresponding short-term energy prediction data to form a local energy allocation proposal; conducting multiple rounds of information interaction between adjacent control nodes, dynamically correcting the local energy allocation proposal according to the weight ratio of each control node, and gradually reducing the power allocation difference between nodes; and determining that a global energy allocation consensus has been reached when the local energy allocation proposals of all control nodes satisfy power balance constraints and node operating limits, and the power deviation between adjacent nodes is less than a preset power threshold.

[0012] Optionally, the generation of energy allocation decisions includes: acquiring equipment output characteristics; coordinating and optimizing the power allocation values ​​of each control node in the global energy allocation consensus with the current operating conditions and equipment output characteristics to generate a preliminary power command; performing local feasibility verification based on the preliminary power command to determine whether the node voltage stability range and line transmission capacity limits are met, and obtaining a feasibility judgment result; when the feasibility judgment result does not meet local feasibility, initiating a power recoupling mechanism between adjacent control nodes, and forming a corrected power command through local weight adjustment and power transfer; generating an energy allocation decision based on the corrected power command, and issuing it to each control node for execution.

[0013] Optionally, the acquisition of equipment output characteristics includes: based on the historical operating data, extracting the power response speed, adjustable output range, and efficiency curves of the power generation or energy storage equipment under typical operating conditions at each control node to form basic output characteristics; monitoring the equipment's operating time, environmental conditions, and maintenance status in real time, evaluating its current suitable output capacity and adjustment sensitivity, and generating a dynamic capability status; and combining the basic output characteristics and the dynamic capability status to construct equipment output characteristics that reflect the real-time controllable capability of the equipment.

[0014] Optionally, the generation of the event trigger signal includes: receiving the topology change event and generating a topology anomaly flag when the topology change event occurs; calculating the deviation between the real-time energy data and the short-term energy prediction data to obtain a deviation value; determining whether the deviation value exceeds a preset deviation threshold, and generating a prediction anomaly flag when it does; and outputting an event trigger signal according to the activation state of the topology anomaly flag or the prediction anomaly flag.

[0015] Optionally, sending the check signal to the neighboring nodes of the control node through a preset heartbeat mechanism includes: obtaining the current network communication load status and generating network load parameters; dynamically adjusting the preset basic heartbeat interval based on the network load parameters to generate a dynamic heartbeat interval; sending the check signal within each dynamic heartbeat interval and starting a timer to wait for the heartbeat response signal.

[0016] Based on the same inventive concept, this invention also provides a microgrid energy coordination control system that considers dynamic changes in communication topology. The system includes: a communication topology monitoring module for real-time monitoring of the communication connection status between all control nodes in the microgrid and generating topology change events; an adaptive weight generation module for real-time energy supply monitoring and communication reliability assessment based on the control nodes, obtaining real-time energy supply capacity and communication reliability parameters, and dynamically adjusting the influence of the control nodes in energy coordination in conjunction with the topology change events to generate adaptive weights; and a short-term energy prediction module for acquiring real-time sensor information and historical operating data and performing prediction processing to generate short-term energy prediction data. The system includes: a distributed energy negotiation module, used to perform fusion processing based on the adaptive weights and the short-term energy prediction data, and to reach a global energy allocation consensus through distributed iterative negotiation; an energy allocation decision module, used to generate power commands based on the global energy allocation consensus, and generate energy allocation decisions; an event trigger judgment module, used to obtain real-time energy data from the real-time sensor information, and while executing the energy allocation decision, to determine whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold, and generate an event trigger signal; and a control strategy update module, used to update the control strategy based on the event trigger signal.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention enhances the robustness and environmental adaptability of the microgrid energy coordination control system by real-time monitoring of the communication topology and dynamic adjustment of the control strategy. When control nodes join or leave, or when the communication link is interrupted, the system can quickly sense the topology change and adjust the influence weight of each node in energy negotiation in real time. This reduces decision-making errors or control failures caused by relying on outdated network information and enhances the stability of the control system in complex communication environments.

[0019] 2. This invention proposes a distributed negotiation mechanism based on adaptive weights, which effectively improves the quality and rationality of energy allocation decisions. This method combines the real-time energy supply capacity of nodes with communication reliability to generate dynamic weights, giving nodes with large energy contributions and stable communication greater say in the negotiation process. This allows the energy coordination results to converge in a direction more conducive to system stability and economic operation, achieving optimal resource allocation.

[0020] 3. This invention establishes a rigorous closed-loop control process from prediction and decision-making to execution verification, improving the safety and executability of energy allocation decisions. By performing local feasibility verification and necessary power re-coordination on the global consensus, the final generated power command will not violate physical constraints such as node voltage or line capacity, effectively reducing grid safety accidents that may be caused by idealized decisions and ensuring the physical operation safety of the microgrid.

[0021] 4. This invention employs an event-triggered control update mechanism, which improves the system's response speed and reduces system overhead. The update of the control strategy no longer depends on a fixed time period, but is driven by key events such as topology changes or prediction deviations. This enables the system to respond immediately when disturbances occur, while avoiding unnecessary computation and communication during stable system operation, achieving a balance between timeliness and economy in control.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a microgrid energy coordination control method that takes into account dynamic changes in communication topology, according to an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram illustrating the dynamic heartbeat interval and topology change event generation in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of the adaptive weight configuration in the initial state of an embodiment of the present invention.

[0027] Figure 4 This is a schematic diagram of adaptive weight dynamic adjustment after communication interruption of node 3 in an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram of the distributed iterative negotiation convergence curve according to an embodiment of the present invention.

[0029] Figure 6This is a schematic diagram of a microgrid energy coordination control system that takes into account dynamic changes in communication topology, according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] Reference Figure 1 One embodiment of the present invention proposes a microgrid energy coordination control method that takes into account dynamic changes in communication topology. It adopts technical means such as real-time monitoring of communication topology and evaluation of node influence, fusion of adaptive weights and energy prediction for distributed negotiation, and event-triggered control strategy updates, which can significantly improve the control robustness, decision rationality and operation safety of microgrids in dynamic communication environments.

[0032] The method described in this embodiment specifically includes:

[0033] Real-time monitoring of the communication connection status between all control nodes in the microgrid, generating topology change events;

[0034] Optionally, the generation of topology change events includes:

[0035] The control node sends a check signal to its neighboring nodes through a preset heartbeat mechanism and receives the heartbeat response signal returned by them.

[0036] Based on the reception status of the heartbeat response signal, the link connection status between the control nodes is determined, and connection status data is generated.

[0037] Based on the connection status data, time-series difference analysis is performed to identify nodes joining, leaving, or links being interrupted in the network, generating topology change events.

[0038] Specifically, a pre-defined heartbeat mechanism is first initiated, established through an active probing mechanism designed and deployed at the network communication layer. Each control node periodically sends a structured check signal to all nodes in its neighbor list. This check signal is a lightweight data packet whose primary function is to declare itself online and probe the responsiveness of neighboring nodes. Simultaneously, after sending the check signal, the sending node starts an internal timer to wait for a corresponding heartbeat response signal from the target neighbor node. Then, based on the received heartbeat response signal, the link connection status between control nodes is determined in real time. If the sending node successfully receives the heartbeat response signal from the target neighbor node within a pre-defined timeout period, the bidirectional communication link between that node and its neighbor node is considered to be in a normal connection state. Conversely, if no valid heartbeat response signal is received before the timer expires, the communication link is considered to be interrupted or the target neighbor node is offline. Each determination result, i.e., the "connection" or "interruption" status of each link, is recorded in real time, forming connection status data covering the entire network or a local network. This connectivity data can be constructed into an adjacency matrix or adjacency list, providing structured input for subsequent analysis. Finally, to accurately identify substantial changes in network topology rather than instantaneous fluctuations, a time-series difference analysis step is introduced. The connectivity data generated in the current period is logically compared with the historical connectivity data from the previous period to identify specific network structure evolutions. For example, if the state of a link changes from "connected" to "interrupted," a link interruption event is identified; if all external connections of a node change from "connected" to "interrupted," it can be determined as a node departure event; correspondingly, if a link in the network changes from "interrupted" or non-existent to "connected," it is marked as a node joining or link recovery event. Once any of the above changes is identified, a standardized topology change event is immediately generated. This event contains key information such as the change type, the node numbers involved, and the time of occurrence, and is broadcast to relevant modules that need this information to trigger subsequent control strategy adjustments. Figure 2 As shown, the timeline reveals how changes in network load parameters lead to variations in the dynamic heartbeat interval, clearly marking the occurrence times of key topology change events such as "node joining event," "link interruption event," and "link recovery event." By implementing proactive heartbeat detection, real-time link status determination, and precise time-series difference analysis, the microgrid energy coordination and control system is endowed with dynamic self-awareness of the communication network topology, laying the foundation for subsequent adaptive weight adjustment and reliable achievement of distributed negotiation consensus.

[0039] Based on the control node, real-time energy supply monitoring and communication reliability assessment are performed to obtain real-time energy supply capacity and communication reliability parameters. The influence of the control node in energy coordination is dynamically adjusted in conjunction with the topology change event to generate adaptive weights.

[0040] Optionally, generating adaptive weights includes:

[0041] Monitor the real-time output power or state of charge of the control node to generate real-time energy supply capacity;

[0042] Record the historical link online duration and communication error rate of the control node and perform a comprehensive evaluation to generate communication reliability parameters;

[0043] The communication reliability parameters are updated based on the topology change event to generate updated communication reliability parameters;

[0044] By combining the real-time energy supply capacity and the updated communication reliability parameters, an adaptive weight is generated through weighted fusion calculation.

[0045] Specifically, the energy status of control nodes needs to be continuously monitored first. For control nodes integrating distributed power sources such as photovoltaics or wind turbines, their real-time output power is directly measured by sensors; for control nodes configured with energy storage units, their current state of charge (SOC) is obtained through the battery management system. The obtained real-time output power or SOC is quantified as the node's real-time energy supply capacity, reflecting its current potential to provide or absorb energy to the microgrid. Second, to evaluate the node's communication quality, a communication reliability parameter is generated. The historical link online duration of each control node's communication links with its neighboring nodes is continuously recorded, i.e., the total time the link is in a normal connection state within a statistical period. Simultaneously, the communication error rate on the link is recorded, i.e., the proportion of data packets with transmission failures or data verification errors to the total number of transmitted data packets. By comprehensively evaluating the historical link online duration and communication error rate, an initial communication reliability parameter can be derived, characterizing the stability of the node's historical communication performance. Then, when a topology change event is received, such as a link interruption or node offline, the communication reliability parameter is immediately updated. This event-based update mechanism allows for rapid response to network failures without waiting for historical statistics to change slowly. For example, an outage of a critical link immediately imposes a penalty factor or degrades the communication reliability parameters of relevant nodes, generating updated communication reliability parameters that reflect the current network condition. Finally, through weighted fusion calculations, the energy value and communication value of nodes are combined to generate the final adaptive weights. For computational control nodes... Adaptive weights ,have:

[0046] ;

[0047] For nodes The real-time energy supply capacity after normalization is obtained by linearly mapping the real-time output power or state of charge of the node to the range of 0 to 1 to eliminate the influence of dimensions. For nodes The updated communication reliability parameter is also a normalized metric for evaluating communication quality. and These are the preset energy supply weighting coefficient and communication reliability weighting coefficient, respectively, and their sum is 1. If the microgrid is operating in an energy shortage mode, nodes with strong energy supply capabilities should be given greater say. In this case, the following settings can be configured: ,like If operating in a harsh communication environment with frequent topology changes, communication reliability should be given greater emphasis. In this case, settings can be configured... ,like .like Figure 3 As shown, in the initial state, the energy supply capacity and communication reliability parameters of each node are jointly calculated through weighted fusion to form its adaptive weight; for example, node 1 is a node with high energy supply capacity and high communication reliability, and its adaptive weight is relatively high. Figure 4 As shown, when a topology change event occurs, such as the interruption of the communication link of node 3, its communication reliability parameter is immediately updated. That is, the communication reliability of node 3 in the figure decreases significantly, which in turn leads to its adaptive weight being dynamically adjusted. This mechanism allows nodes with high energy potential and stable communication to have greater influence in energy coordination, improving the rationality of decision-making.

[0048] Acquire real-time sensor information and historical operating data and perform predictive processing to generate short-term energy prediction data;

[0049] Optionally, the generation of short-term energy prediction data includes:

[0050] Based on the real-time sensor information, feature extraction is performed to obtain real-time electrical parameters including voltage, current, power and frequency, thus forming the current operating condition;

[0051] Based on the direction and magnitude of active power, network topology connections, and differences in node voltage phase angles in the real-time electrical parameters, the dominant paths and changing tendencies of internal energy are deduced, and energy flow trends are identified.

[0052] Based on the historical operating data, trend features are extracted, and combined with environmental parameters and load fluctuation features from real-time sensor information, an energy dynamic evolution sequence is constructed.

[0053] Based on the energy dynamic evolution sequence, a multi-step forward extrapolation is performed, and combined with the current operating conditions and the energy flow trend, an energy supply and demand forecast for the future period is formed.

[0054] Based on the aforementioned energy supply and demand situation forecast, a confidence assessment is conducted to identify energy fluctuation ranges during key periods and generate short-term energy forecast data.

[0055] Specifically, real-time sensor information is obtained and features are extracted by real-time sensors deployed at each node, such as phasor measurement units (PMUs) or smart meters, integrating real-time electrical parameters including voltage, current, power, and frequency. These parameters together constitute a high-dimensional state vector, accurately describing the microgrid's operating condition at the current moment, providing a baseline for subsequent dynamic simulations. Next, to understand the distribution and flow of energy within the network, the direction and magnitude of active power in the real-time electrical parameters are used, combined with network topology connections obtained from communication topology monitoring and voltage phase angle differences between nodes, to identify energy flow trends. This step essentially involves a simplified power flow analysis. According to physical laws, active power tends to flow from nodes with leading voltage phase angles to nodes with lagging phase angles. By deriving this relationship, the dominant path and changing trend of energy exchange within the current microgrid can be identified. For example, for approximate calculations from nodes... Flow to Node active power ,have:

[0056] ;

[0057] in, This represents the control node Flow to control node The magnitude and direction of active power, with its positive or negative sign indicating the direction of flow; and These are nodes and nodes The real-time voltage phase angle is provided by electrical parameters in the real-time sensor information; It is a line The reactance between the components is a physical parameter determined by the network topology. This analysis allows us to grasp the spatial distribution trend of energy. We then shift to a time-dimensional analysis, processing historical operating data, such as load curves and power generation records from the past few days or weeks, to extract trend features and construct a dynamic energy evolution sequence. This sequence not only includes historical periodic patterns but also incorporates environmental parameters from real-time sensor information, such as light intensity, wind speed, and ambient temperature, as well as load fluctuation characteristics, such as electricity consumption habits during specific periods, making it adaptable to current conditions. Essentially, this constructs a dynamic model that reflects changes in energy supply and demand over time and in the environment. Based on this dynamic model of the energy evolution sequence, multi-step forward extrapolation is performed, using the current operating conditions as initial conditions and the identified energy flow trends as physical constraints, to predict the state at one or more future time steps, thus forming an estimate of the energy supply and demand situation for the future period. Finally, to enhance the practicality of the forecasts, a confidence assessment of the energy supply and demand situation was conducted, the uncertainties of the forecasts were analyzed, and key periods with large forecast fluctuations or tight supply-demand relationships were selected to generate an energy fluctuation range containing expected values ​​and possible fluctuation ranges. This energy fluctuation range, containing information on uncertainties, constitutes the final short-term energy forecast data, providing a more comprehensive and reliable input for subsequent energy coordination decisions. By integrating real-time operating conditions, physical power flow, historical patterns, and environmental factors, a three-dimensional energy forecasting framework was constructed, enhancing the stability, economy, and adaptability to renewable energy fluctuations in microgrid operation.

[0058] Based on the adaptive weights and the short-term energy prediction data, a global energy allocation consensus is reached through distributed iterative negotiation.

[0059] Optionally, the step of fusing the adaptive weights and the short-term energy prediction data to reach a global energy allocation consensus through distributed iterative negotiation includes:

[0060] The adaptive weights are used as voting coefficients for each control node in energy allocation negotiation, and combined with the corresponding short-term energy prediction data, a local energy allocation proposal is formed.

[0061] Multiple rounds of information exchange are performed between adjacent control nodes, and the local energy allocation proposal is dynamically modified according to the weight ratio of each control node to gradually reduce the power allocation difference between nodes.

[0062] When the local energy allocation proposals of all the control nodes satisfy the power balance constraints and node operation limits, and the power deviation between adjacent nodes is less than the preset power threshold, it is determined that a global energy allocation consensus has been reached.

[0063] Specifically, the adaptive weights and short-term energy forecast data mentioned above are first effectively integrated to initiate the initial negotiation phase. Each control node uses its acquired adaptive weights as its voting coefficient in energy allocation negotiation, which determines the influence of its viewpoint on group decision-making. Simultaneously, combined with its own short-term energy forecast data, an initial power setpoint that meets its own supply-demand balance or operational goals is preliminarily determined, forming a local energy allocation proposal. The core of this process lies in the multi-round information exchange and dynamic correction between adjacent control nodes. Within an iteration cycle, each control node broadcasts its local energy allocation proposal to all its directly connected neighboring nodes. This local energy allocation proposal is usually not a direct power value, but a variable reflecting its marginal energy cost or power surplus / deficit status, which can be called a negotiation variable. After receiving the negotiation variable from neighboring nodes, each control node corrects its own negotiation variable according to a preset update rule. The core idea of ​​this correction is to converge towards the weighted average of its neighboring nodes, gradually reducing the differences in allocation viewpoints between nodes. For computing nodes... In the The updated negotiated variable in the next iteration ,have:

[0064] ;

[0065] in, Representative control node In the Negotiated variables in subsequent iterations, such as local marginal energy cost; It is a step size factor used to control the convergence speed, and is used in most microgrid applications. The valid value range is from 0.1 to 0.3. For initial debugging, it is recommended to start from... Initially, by observing the convergence curve, if the convergence is too slow, increase the value appropriately; if oscillations occur, decrease the value. It is with nodes The set of neighboring nodes that communicate directly; Neighboring nodes In the Negotiated variables during the next iteration; Representing neighbor nodes The original adaptive weights; Representative node The sum of the original adaptive weights of all neighboring nodes; Represents neighboring nodes The normalized adaptive weights reflect the nodes To the neighbors The degree to which opinions are adopted. A neighboring node with a higher adaptive weight will have a greater influence on the node's opinion. The updates have a greater impact. This process is repeated continuously, with each round of interaction and correction bringing the negotiated variables of the entire network closer to consensus. The termination condition for the iterative process is reaching consensus. Two key conditions are continuously checked. The first is convergence, i.e., when all neighboring nodes agree on the consensus... When the absolute values ​​of the differences in the negotiated variables are all less than a preset power threshold, it indicates that the views of the nodes have basically reached a consensus. The second is feasibility; that is, under the current convergent negotiated variables, the power allocation value calculated by each control node must simultaneously satisfy the global power balance constraints, i.e., the total power generation is approximately equal to the sum of the total load and network losses, and the power value of each node is within its allowed node operating limits, such as the maximum and minimum output of generators and the charging and discharging power limits of energy storage. When the local energy allocation proposals of all control nodes in the network can simultaneously satisfy these two conditions, the distributed iterative negotiation is considered complete, and a global energy allocation consensus has been reached. Figure 5 As shown in the figure, the convergence process of the local energy allocation proposals of multiple control nodes (nodes 1 to 4) after k iterations is illustrated. With the increase in the number of iterations, the proposals of each node gradually converge, ultimately reaching a global energy allocation consensus when power balance constraints, node operating limits, and power deviations between adjacent nodes are less than a preset threshold are met. By introducing a distributed iterative negotiation mechanism based on adaptive weights, a globally suitable energy allocation scheme can be spontaneously and collaboratively found without relying on a central control unit. This improves the rational allocation of resources and enhances the economy and security of microgrid operation.

[0066] Power commands are generated based on the global energy allocation consensus, and energy allocation decisions are generated accordingly.

[0067] Optionally, the energy allocation decision includes:

[0068] The device output characteristics are obtained, and based on the power allocation values ​​of each control node in the global energy allocation consensus, the current operating conditions and the device output characteristics are coordinated and optimized to generate a preliminary power command.

[0069] Based on the preliminary power command, a local feasibility check is performed to determine whether the node voltage stability range and line transmission capacity limit are met, and the feasibility judgment result is obtained.

[0070] When the feasibility judgment result does not meet the local feasibility, a power recoupling mechanism is initiated between adjacent control nodes to form a corrected power command through local weight adjustment and power transfer.

[0071] Energy allocation decisions are generated based on the revised power commands and then sent to each of the control nodes for execution.

[0072] Specifically, the process begins with coordinated optimization to obtain the output characteristics of distributed power sources or energy storage units under each control node. These output characteristics detail the technical performance of each device. Then, based on the power allocation value assigned to each control node in the previous global energy allocation consensus, and considering the current operating conditions of the microgrid and the output characteristics of each device, optimization calculations are performed. The goal of this optimization is to generate a preliminary power command, taking into account factors such as power generation efficiency, equipment lifespan, and response speed, while meeting the consensus allocation value. This preliminary power command is detailed down to the specific power value that each power generation device or energy storage unit should output or absorb. Next, to ensure physical feasibility, a local feasibility check is performed on the generated preliminary power command. This check simulates the physical effects that may occur after the preliminary power command is applied to the grid, and uses power flow calculations and other analytical methods to determine whether key grid safety constraints are met. This mainly includes two aspects: first, node voltage stability range verification, which checks whether the voltage of all nodes in the network can be maintained within a preset safe range after the command is executed, typically ±5% or 7% of the rated voltage. Second, there is the line transmission capacity limit verification, which checks whether the power flow caused by the command will exceed the thermal stability limit of any transmission line. This verification yields a feasibility assessment of the initial command. If the feasibility assessment indicates that the initial power command does not meet local feasibility requirements, such as potentially causing voltage overruns at some nodes or overloads on a certain line, a power re-coordination mechanism will be initiated. This mechanism is a local, small-scale adjustment process, primarily conducted between the problematic node and its adjacent control nodes. Between these nodes, the command is corrected through local weight adjustments and power transfer. For example, if a line is about to overload, the weight of upstream nodes on that line may be temporarily reduced, while the weight of downstream nodes may be increased, prompting them to transfer some power to other paths through a new round of micro-negotiation, thereby alleviating line pressure. This process is a rapid, targeted fine-tuning aimed at resolving local physical bottlenecks without disrupting the core global consensus, ultimately resulting in a corrected power command. Finally, the corrected power command after re-coordination generates the final energy allocation decision. This energy allocation decision is a structured set of instructions that specifies the concrete operations to be performed by each control node and its subordinate devices in the next control cycle, including the setpoints for active and reactive power. Subsequently, this energy allocation decision is precisely distributed to each control node via the communication network, where it is parsed and executed by their local controllers, thus completing a closed-loop control process from global negotiation to physical execution. By implementing a refined instruction generation process that includes coordination optimization, feasibility verification, and local re-coordination, potential safety incidents such as voltage collapse or equipment damage caused by neglecting physical limitations are reduced, enhancing the practicality, safety, and overall reliability of the energy coordination control strategy.

[0073] Optionally, the power output characteristics of the acquisition device include:

[0074] Based on the historical operating data, the power response speed, adjustable output range and efficiency curves of the power generation or energy storage equipment under typical operating conditions at each control node are extracted to form basic output characteristics.

[0075] Real-time monitoring of equipment runtime, environmental conditions and maintenance status; assessment of its current suitable output capacity and adjustment sensitivity; generation of dynamic capability status.

[0076] By combining the basic output characteristics with the dynamic capability status, a device output characteristic reflecting the real-time controllability of the device is constructed.

[0077] Specifically, the basic output characteristics are first constructed through data mining. Historical operational data is accessed and processed, and performance records under various typical operating conditions are extracted for each power generation or energy storage device under each control node. By analyzing this data, key static parameters of the equipment can be quantified. For example, by analyzing the time delay and slope between power command issuance and actual power response, the power response speed can be determined; by statistically analyzing the upper and lower limits of power output during long-term operation, the adjustable output range can be clarified; and by correlating input energy such as light intensity and fuel consumption with output electrical energy, efficiency curves can be plotted. These parameters together constitute the basic output characteristics of the equipment, forming a static benchmark model characterizing its inherent performance. However, the actual capacity of the equipment changes in real time with time and the environment, therefore, dynamic capacity status assessment is further introduced. Through a real-time sensor network, dynamic factors affecting equipment performance are continuously monitored. This includes recording the cumulative or continuous operating time of the equipment, monitoring its environmental conditions such as temperature and humidity, and receiving maintenance status information from the equipment management system, such as fault alarms or periodic maintenance reminders. Based on this real-time information, the current health status and performance degradation of the equipment are assessed, thereby quantifying its current suitable output capacity and adjustment sensitivity. For example, excessively high surface temperatures of photovoltaic panels will reduce their power generation efficiency, and the usable capacity of energy storage batteries will decrease after multiple deep charge-discharge cycles. These assessment results collectively constitute the dynamic capability state of the equipment. Finally, the static baseline model is fused with the real-time state assessment to construct the final equipment output characteristics that reflect the real-time controllability of the equipment. This step uses the basic output characteristics as a base and performs real-time corrections using the dynamic capability state. This is used to calculate the equipment's current real-time maximum output power. ,have:

[0078] ;

[0079] in, It is the theoretical maximum output power obtained from the pre-established basic power output characteristics; It is a dynamic correction factor with a value between 0 and 1, determined by the dynamic capability state, such as when the ambient temperature is too high or the equipment is in a maintenance-ready state. The value will decrease accordingly. By combining historical experience with real-time conditions in this way, a precise and dynamically updated equipment output characteristic is generated, providing a reliable basis for subsequent power command generation. By constructing a dynamic, hierarchical equipment output characteristic model, the precision and realism of energy coordination control are improved.

[0080] Real-time energy data is obtained from the real-time sensor information. While executing the energy allocation decision, it is determined whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold, and an event trigger signal is generated.

[0081] Optionally, the event trigger signal includes:

[0082] Receive the topology change event and generate a topology anomaly flag when the topology change event occurs;

[0083] The deviation value is obtained by calculating the difference between the real-time energy data and the short-term energy prediction data.

[0084] Determine whether the deviation value exceeds a preset deviation threshold, and generate a prediction anomaly flag if it does.

[0085] Based on the activation status of the topology anomaly flag or the predicted anomaly flag, an event trigger signal is output.

[0086] Specifically, firstly, in the communication topology monitoring channel, once any form of topology change event is received, such as node joining, leaving, or link interruption, a topology anomaly flag is immediately generated. This topology anomaly flag is a Boolean variable or a specific signal state; its activation directly indicates that the communication foundation supporting distributed coordination has undergone a substantial change. Simultaneously, in the energy prediction monitoring channel, a deviation calculation and threshold judgment process is executed. Real-time energy data of the microgrid is acquired, including extracting the current real-time active and reactive power through real-time sensor information. Then, this real-time energy data is compared point-by-point or time-by-time with previously generated short-term energy prediction data for the corresponding time points, calculating the deviation value between the two. For calculating the deviation value... ,have:

[0087] ;

[0088] in, Represents the relative deviation of power prediction; It is the actual power value from the real-time energy data obtained from the sensor; It is the predicted power value corresponding to the short-term energy forecast data; This is the rated power of the equipment or the reference power of the system, used for normalization to eliminate scale effects. The calculated deviation value... The prediction is compared with a preset deviation threshold, which is set based on operational experience and tolerance for prediction accuracy. Once the deviation exceeds the threshold, a prediction anomaly flag is generated. Activation of this flag indicates that the operating state has significantly deviated from the expected trajectory, and the original energy allocation decisions may no longer be applicable. Finally, the results from the two monitoring channels are summarized and output as a final event trigger signal based on a logical OR gate relationship. Specifically, as long as either flag is activated—whether it's a change in communication topology or a large deviation in energy prediction—a valid event trigger signal will be immediately output. This event trigger signal will serve as an interrupt request or status update notification to update the control strategy. By establishing a dual-track parallel event trigger mechanism, an efficient, on-demand updating operating mode is introduced for the energy coordination control of the microgrid, realizing a shift from "time-driven" to "event-driven" operation.

[0089] The control strategy is updated based on the event trigger signal.

[0090] Specifically, the process begins by reading the currently effective energy allocation decisions and real-time energy data. Then, based on the specific event type indicated by the event trigger signal, the corresponding strategy update logic is selected. If the event trigger signal is caused by a topology change event, the update process focuses on reassessing the communication relationships between control nodes and their influence on energy coordination, and recalculating adaptive weights accordingly. If the event trigger signal is caused by a deviation between short-term energy prediction data and real-time energy data exceeding a preset threshold, the update process focuses on correcting or regenerating short-term energy prediction data using the latest real-time energy data, and may restart the distributed negotiation process. The update of the control strategy ultimately manifests as the generation of a new set of power commands, which replaces the original energy allocation decisions and is issued to each control node for execution. The entire update process ensures that the microgrid's energy management strategy can respond promptly to changes in operating status.

[0091] Optionally, sending a check signal to the neighboring nodes of the control node through a preset heartbeat mechanism includes:

[0092] Obtain the current network communication load and generate network load parameters;

[0093] Based on the network load parameters, the preset basic heartbeat interval is dynamically adjusted to generate a dynamic heartbeat interval.

[0094] The check signal is sent during each of the dynamic heartbeat intervals, and a timer is started to wait for the heartbeat response signal.

[0095] Specifically, the method first monitors indicators such as local network interface traffic, packet transmission queue length, and communication channel occupancy to obtain real-time information on the current network load. These raw indicators are quantified and normalized, then integrated into a comprehensive network load parameter that directly reflects the current network activity level. Next, the core of this method lies in dynamically adjusting a preset base heartbeat interval based on the acquired network load parameter, thereby generating an adaptive dynamic heartbeat interval. The base heartbeat interval is a fixed time value preset during design and used under ideal network conditions. The logic of dynamic adjustment is as follows: when the network load parameter is low, i.e., the communication network is relatively idle, the heartbeat interval is shortened to achieve faster topology change detection; conversely, when the network load parameter is high, i.e., the network is congested, the heartbeat interval is appropriately lengthened to reduce the additional burden of the heartbeat signal itself on the network, freeing up channel resources for more important energy coordination data. The calculation of the dynamic heartbeat interval... ,have:

[0096] ;

[0097] in, It is the preset baseline heart rate interval, which is a fixed design value; It is a network load parameter that is acquired in real time and normalized, with a value between 0 and 1; It is a positive load regulation coefficient used to control the sensitivity of the heartbeat interval to network load. It can be determined by collecting communication performance data under different load conditions in actual or simulated networks, such as packet delay, packet loss rate, and channel occupancy rate. Then, based on this data, the impact of the heartbeat signal transmission frequency on the network load can be analyzed. The coefficient can be determined through regression analysis or sensitivity analysis methods in control theory. For a typical low-speed wireless network... The empirical value range is typically between 1.0 and 5.0. Finally, heartbeat detection is performed under the adjusted time rhythm. At the end of each dynamic heartbeat interval, the control node sends a check signal to its neighboring nodes on time. At the same moment the signal is sent, a timer inside the node is started, begins timing, and waits to receive the corresponding heartbeat response signal from the target neighbor. The timeout limit of this timer is usually also associated with the dynamic heartbeat interval. By introducing a dynamic heartbeat interval adjustment mechanism based on network load, microgrid communication topology monitoring is endowed with a self-regulating intelligence, achieving a dynamic balance between the timeliness of topology change detection and the effective utilization of communication resources.

[0098] Based on the same inventive concept, such as Figure 6 As shown, the present invention also provides a microgrid energy coordination control system that takes into account dynamic changes in communication topology, the system comprising:

[0099] The communication topology monitoring module is used to monitor the communication connection status between all control nodes in the microgrid in real time and generate topology change events.

[0100] An adaptive weight generation module is used to perform real-time energy supply monitoring and communication reliability assessment based on the control node, obtain real-time energy supply capacity and communication reliability parameters, and dynamically adjust the influence of the control node in energy coordination in combination with the topology change event to generate adaptive weights.

[0101] The short-term energy prediction module is used to acquire real-time sensor information and historical operating data, perform prediction processing, and generate short-term energy prediction data.

[0102] The distributed energy negotiation module is used to perform fusion processing based on the adaptive weights and the short-term energy prediction data, and to reach a global energy allocation consensus through distributed iterative negotiation.

[0103] The energy allocation decision module is used to generate power commands based on the global energy allocation consensus and generate energy allocation decisions.

[0104] The event triggering judgment module is used to obtain real-time energy data from the real-time sensor information, and while executing the energy allocation decision, determine whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold, and generate an event triggering signal.

[0105] The control strategy update module is used to update the control strategy based on the event trigger signal.

[0106] To verify the feasibility of this invention in practice, it was applied to the energy coordination and control of a microgrid in a remote area. This microgrid integrates a photovoltaic power station, wind turbine generators, energy storage power stations, and residential and small industrial loads, with each unit acting as a control node connected via a wireless communication network. Due to geographical influences, the communication links are often unstable due to weather conditions, and there are intermittent distributed power sources and load fluctuations, making it difficult for traditional control methods to guarantee the stable and economical operation of the grid. This microgrid project aims to use the method of this invention to achieve rapid adaptation to changes in communication topology and efficient energy coordination.

[0107] In this embodiment, the microgrid in the region is equipped with the control system proposed in this invention. The system uses a communication topology monitoring module to perceive the connection status between each control node in real time; an adaptive weight generation module to dynamically evaluate the influence of each node in coordination; a short-term energy prediction module to forecast future energy supply and demand; a distributed energy negotiation module to achieve global power allocation consensus; an energy allocation decision module to generate executable power commands; and an event trigger judgment module and a control strategy update module to achieve rapid response to system disturbances.

[0108] To verify the effectiveness of the present invention, the system's operating data for a week in a certain month of 2025 was recorded. During that week, the system experienced a variety of typical scenarios, including the grid connection of a new photovoltaic array, communication link interruption and recovery caused by a storm, and a sudden increase in industrial load.

[0109] Regarding topology change monitoring, at 9:00 AM on a certain day, a newly built 50kW photovoltaic array was connected to the grid as a new control node, namely node 1. The system quickly identified the addition of this node through a heartbeat mechanism and generated a topology change event. During this period, to cope with network congestion, the system dynamically adjusted the heartbeat interval from the basic 2 seconds to 3.5 seconds. On the night of the third day, due to the impact of a storm, the communication link to wind turbine generator No. 3 was interrupted. The system did not receive a heartbeat response within the preset timeout period, immediately determined that the link was interrupted, and generated another topology change event.

[0110] The adaptive weight generation module then responds to these events. Node 1, the photovoltaic system, is assigned a high real-time energy supply capability after grid connection due to its high real-time output power; simultaneously, the new node's historical communication data is empty, so the system assigns it a neutral initial communication reliability parameter. However, after the link to wind turbine 3 is interrupted, its communication reliability parameter is immediately penalized and degraded. Node 1's adaptive weight increases from 0 to 0.25, while Node 3's adaptive weight drops sharply from 0.22 to 0.05, significantly weakening its bargaining power in subsequent energy negotiations.

[0111] Regarding energy forecasting and negotiation, on the evening of the fourth day, the short-term energy forecasting module, combining historical data and the immediate weather forecast, predicted a sharp drop in photovoltaic output while residential peak load would increase, creating a supply-demand gap of approximately 80kW. The distributed energy negotiation module was then activated. During the negotiation process, the local energy allocation proposals from Node 2 (the energy storage power station) and Node 1 (the photovoltaic power station), which have high adaptive weights, dominated the iterations. After five rounds of iterative negotiation, the power allocation differences between the nodes were less than the preset 1kW power threshold, ultimately reaching a consensus: the energy storage power station would discharge 65kW, and the photovoltaic power station with remaining output would provide 15kW to jointly fill the gap.

[0112] Based on this consensus and considering the current state of charge (85%) of the energy storage power station and other equipment output characteristics, the energy allocation decision module generated a preliminary power command. After local feasibility verification confirmed that the power command would not cause line overload or voltage exceedance, the system generated and issued the final energy allocation decision.

[0113] Regarding event triggering and control updates, on the morning of the fifth day, the communication link of wind turbine No. 3 was repaired, and the system immediately generated a topology change event, triggering the event trigger signal. The control strategy was updated, and the adaptive weights were recalculated. That same afternoon, a small factory suddenly started up, causing the deviation between real-time load data and predicted data to exceed a preset deviation threshold. The system again generated an event trigger signal and quickly initiated a new round of energy coordination processes, avoiding grid frequency fluctuations caused by prediction failure. Compared to traditional 5-minute periodic control, the event-driven mechanism of this invention completed response and adjustment in only 25 seconds after a load surge, thus improving response speed.

[0114] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0115] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A microgrid energy coordination control method considering dynamic changes in communication topology, characterized in that: Real-time monitoring of the communication connection status between all control nodes in the microgrid, generating topology change events; Based on the control node, real-time energy supply monitoring and communication reliability assessment are performed to obtain real-time energy supply capacity and communication reliability parameters. The influence of the control node in energy coordination is dynamically adjusted in conjunction with the topology change event to generate adaptive weights. Acquire real-time sensor information and historical operating data and perform predictive processing to generate short-term energy prediction data; Based on the adaptive weights and the short-term energy prediction data, a global energy allocation consensus is reached through distributed iterative negotiation. Power commands are generated based on the global energy allocation consensus, and energy allocation decisions are generated accordingly. Real-time energy data is obtained from the real-time sensor information. While executing the energy allocation decision, it is determined whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold, and an event trigger signal is generated. The control strategy is updated based on the event trigger signal.

2. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 1, characterized in that, The generated topology change events include: The control node sends a check signal to its neighboring nodes through a preset heartbeat mechanism and receives the heartbeat response signal returned by them. Based on the reception status of the heartbeat response signal, the link connection status between the control nodes is determined, and connection status data is generated. Based on the connection status data, time-series difference analysis is performed to identify nodes joining, leaving, or links being interrupted in the network, generating topology change events.

3. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 1, characterized in that, The generation of adaptive weights includes: Monitor the real-time output power or state of charge of the control node to generate real-time energy supply capacity; Record the historical link online duration and communication error rate of the control node and perform a comprehensive evaluation to generate communication reliability parameters; The communication reliability parameters are updated based on the topology change event to generate updated communication reliability parameters; By combining the real-time energy supply capacity and the updated communication reliability parameters, an adaptive weight is generated through weighted fusion calculation.

4. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 1, characterized in that, The generated short-term energy prediction data includes: Based on the real-time sensor information, feature extraction is performed to obtain real-time electrical parameters including voltage, current, power and frequency, thus forming the current operating condition; Based on the direction and magnitude of active power, network topology connections, and differences in node voltage phase angles in the real-time electrical parameters, the dominant paths and changing tendencies of internal energy are deduced, and energy flow trends are identified. Based on the historical operating data, trend features are extracted, and combined with environmental parameters and load fluctuation features from real-time sensor information, an energy dynamic evolution sequence is constructed. Based on the energy dynamic evolution sequence, a multi-step forward extrapolation is performed, and combined with the current operating conditions and the energy flow trend, an energy supply and demand forecast for the future period is formed. Based on the aforementioned energy supply and demand situation forecast, a confidence assessment is conducted to identify energy fluctuation ranges during key periods and generate short-term energy forecast data.

5. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 1, characterized in that, The process of fusing the adaptive weights and the short-term energy prediction data to reach a global energy allocation consensus through distributed iterative negotiation includes: The adaptive weights are used as voting coefficients for each control node in energy allocation negotiation, and combined with the corresponding short-term energy prediction data, a local energy allocation proposal is formed. Multiple rounds of information exchange are performed between adjacent control nodes, and the local energy allocation proposal is dynamically modified according to the weight ratio of each control node to gradually reduce the power allocation difference between nodes. When the local energy allocation proposals of all the control nodes satisfy the power balance constraints and node operation limits, and the power deviation between adjacent nodes is less than the preset power threshold, it is determined that a global energy allocation consensus has been reached.

6. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 4, characterized in that, The energy allocation decision includes: The device output characteristics are obtained, and based on the power allocation values ​​of each control node in the global energy allocation consensus, the current operating conditions and the device output characteristics are coordinated and optimized to generate a preliminary power command. Based on the preliminary power command, a local feasibility check is performed to determine whether the node voltage stability range and line transmission capacity limit are met, and the feasibility judgment result is obtained. When the feasibility judgment result does not meet the local feasibility, a power recoupling mechanism is initiated between adjacent control nodes to form a corrected power command through local weight adjustment and power transfer. Energy allocation decisions are generated based on the revised power commands and then sent to each of the control nodes for execution.

7. A microgrid energy coordination control method considering dynamic changes in communication topology according to claim 6, characterized in that, The power output characteristics of the acquisition device include: Based on the historical operating data, the power response speed, adjustable output range and efficiency curves of the power generation or energy storage equipment under typical operating conditions at each control node are extracted to form basic output characteristics. Real-time monitoring of equipment runtime, environmental conditions and maintenance status; assessment of its current suitable output capacity and adjustment sensitivity; generation of dynamic capability status. By combining the basic output characteristics with the dynamic capability status, a device output characteristic reflecting the real-time controllability of the device is constructed.

8. The microgrid energy coordination control method considering dynamic changes in communication topology according to claim 1, characterized in that, The event trigger signal includes: Receive the topology change event and generate a topology anomaly flag when the topology change event occurs; The deviation value is obtained by calculating the difference between the real-time energy data and the short-term energy prediction data. Determine whether the deviation value exceeds a preset deviation threshold, and generate a prediction anomaly flag if it does. Based on the activation status of the topology anomaly flag or the predicted anomaly flag, an event trigger signal is output.

9. A microgrid energy coordination control method considering dynamic changes in communication topology according to claim 2, characterized in that, The step of sending a check signal to the neighboring nodes of the control node through a preset heartbeat mechanism includes: Obtain the current network communication load and generate network load parameters; Based on the network load parameters, the preset basic heartbeat interval is dynamically adjusted to generate a dynamic heartbeat interval. The check signal is sent during each of the dynamic heartbeat intervals, and a timer is started to wait for the heartbeat response signal.

10. A microgrid energy coordination control system considering dynamic changes in communication topology, applied to a microgrid energy coordination control method considering dynamic changes in communication topology as described in any one of claims 1-9, characterized in that, The system includes: The communication topology monitoring module is used to monitor the communication connection status between all control nodes in the microgrid in real time and generate topology change events. An adaptive weight generation module is used to perform real-time energy supply monitoring and communication reliability assessment based on the control node, obtain real-time energy supply capacity and communication reliability parameters, and dynamically adjust the influence of the control node in energy coordination in combination with the topology change event to generate adaptive weights. The short-term energy prediction module is used to acquire real-time sensor information and historical operating data, perform prediction processing, and generate short-term energy prediction data. The distributed energy negotiation module is used to perform fusion processing based on the adaptive weights and the short-term energy prediction data, and to reach a global energy allocation consensus through distributed iterative negotiation. The energy allocation decision module is used to generate power commands based on the global energy allocation consensus and generate energy allocation decisions. The event triggering judgment module is used to obtain real-time energy data from the real-time sensor information, and while executing the energy allocation decision, determine whether the topology change event has occurred or whether the deviation between the short-term energy prediction data and the real-time energy data exceeds a preset deviation threshold, and generate an event triggering signal. The control strategy update module is used to update the control strategy based on the event trigger signal.

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