A lightweight distributed solid-state transformer cluster cooperative scheduling system and method

Abstract

The application belongs to the technical field of power electronics and distributed energy, and discloses a kind of light weight distributed solid state transformer cluster collaborative scheduling system and method, comprising: the distributed cluster formed by multiple solid state transformer nodes;Dynamic role assignment and consensus module, for based on the election index of preset, through lightweight consensus algorithm in cluster temporary coordinator node is dynamically elected, and periodically carries out role rotation;Local fast self-balancing controller, for based on local sensing port state information, perform millisecond level autonomous power regulation;Sparse communication and neighborhood negotiation module, for make node only with the temporary coordinator node or the few nodes adjacent to topology, any one node or two nodes in necessary data exchange are carried out together with each other.The application can realize efficient, flexible, reliable global scheduling, especially suitable for complex scenes such as edge power grid, microgrid group and multi-station integration with different communication conditions and dynamic topology.

Description

Technical Field

[0001] This invention belongs to the field of power electronics and distributed energy technology, specifically relating to a lightweight distributed solid-state transformer cluster collaborative scheduling system and method. Background Technology

[0002] As application scale expands, cluster systems composed of multiple solid-state transformers face severe challenges in collaborative operation. Existing technologies mainly adopt hierarchical or centralized collaborative architectures. For example, in AC / DC hybrid distribution networks, modular solid-state transformers are interconnected to form a system, and centralized or local control is used to achieve various operating modes such as power flow optimization and power mutual assistance. In the scenario of multi-station integration of "source, grid, load, and storage", a centralized device consisting of multi-station data acquisition modules, a global collaborative scheduling unit, and a fusion management platform is usually deployed, and a global scheduling strategy is formulated through a built-in multi-energy complementary optimization model. Although such centralized or hierarchical architectures are functionally complete, they expose obvious defects when facing scenarios with dynamically changing distributed solid-state transformer cluster sizes, non-fixed network topologies, or limited communication conditions: First, the architecture is rigid, relying on fixed central nodes or communication levels, resulting in high deployment costs and the risk of single-point failures; second, global optimization calculations are complex, decision-making delays are large, and it is difficult to match the millisecond-level rapid response capability of solid-state transformers themselves; finally, there is a lack of effective adaptability to dynamic node additions, removals, and heterogeneity.

[0003] Chinese patent publication number CN114243727A, entitled "A Source-Grid-Load Self-Optimizing Intelligent Regulation System and Real-Time Control Optimization Technique Based on Wide-Area Sensing Prediction," discloses a method comprising an observer-based state diagnosis method, a situation-aware distributed source-grid-load control method, and a source-grid-load time-scale intelligent regulation energy management method. The observer-based state diagnosis method and the situation-aware distributed self-optimizing control method support the source-grid-load self-optimizing intelligent regulation system and real-time control optimization technique. However, this patent application fails to address the problem of autonomous collaborative scheduling of a decentralized, dynamically rotating, lightweight consensus-based solid-state transformer cluster. Summary of the Invention

[0004] In order to overcome the problems existing in the prior art, the present invention aims to provide a lightweight distributed solid-state transformer cluster collaborative scheduling system and method, which enables cluster nodes to organize autonomously and make collaborative decisions to achieve efficient, flexible and reliable global scheduling. It is particularly suitable for complex scenarios such as edge power grids, microgrid groups and multi-station integration with different communication conditions and dynamic topology changes.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a lightweight distributed solid-state transformer cluster collaborative scheduling system, comprising: A distributed cluster consisting of multiple solid-state transformer nodes; The dynamic role allocation and consensus module is deployed in each of the solid transformer nodes. It is used to dynamically elect temporary coordinator nodes in the cluster based on preset election indicators and through a lightweight consensus algorithm, and to periodically rotate roles. A local fast self-balancing controller is deployed in each of the solid-state transformer nodes to perform millisecond-level autonomous power regulation based on locally sensed port status information; The sparse communication and neighborhood negotiation module is deployed in each of the solid-state transformer nodes. It is used to enable the node to perform necessary data exchange with only one or two of the temporary coordinator node or a few nodes that are topologically adjacent, and to perform consistency correction on the local adjustment command through a distributed negotiation algorithm.

[0006] Optionally, the dynamic role allocation and consensus module includes: The node status evaluation unit is used to monitor and evaluate the election indicators of this node in real time. The lightweight consensus algorithm unit is used to execute the consensus process based on reputation voting or practical Byzantine fault tolerance algorithm. Based on the election indicators broadcast by each node, the cluster reaches a consensus on the identity of the temporary coordinator node without the intervention of the central node. The role management and rotation unit is used to manage the coordination responsibilities of the temporary coordinator node after it is elected, and to automatically initiate a new round of elections to transfer roles when the rotation conditions are triggered.

[0007] Optionally, the sparse communication and neighborhood negotiation module is configured as follows: After the temporary coordinator node is elected, the cluster enters a coordination cycle; the temporary coordinator node broadcasts coordination trigger instructions and global status reference information to all nodes within its communication range or to a subset of nodes selected according to the topology. Upon receiving the instruction, each node first undergoes a rapid initial adjustment based on local information by the local fast self-balancing controller; each node then sends its own boundary information to the temporary coordinator node via a sparse communication network. Based on the collected boundary information, the temporary coordinator node runs a fast consensus algorithm to calculate the coordination parameters used to correct the local instructions of each node, and broadcasts them to the relevant nodes.

[0008] Optionally, the temporary coordinator node is configured to send a coordination trigger instruction only to a subset of nodes that meet the conditions when selecting negotiation nodes, based on preset topological adjacency rules or communication hop count thresholds.

[0009] Optionally, the lightweight distributed solid-state transformer cluster collaborative scheduling system also includes a contribution proof and incentive unit integrated into the dynamic role allocation and consensus module or deployed independently, used to record the behavior data and adjustment effect of each solid-state transformer node as a coordinator or participant in each collaborative scheduling, and generate an immutable local contribution certificate.

[0010] Optionally, the local fast self-balancing controller adopts an autonomous power control rule based on port voltage partitioning. The autonomous power control rule includes: real-time monitoring of the voltage status of each AC / DC port of the solid-state transformer; when a port voltage deviates from the rated range, automatically adjusting the power command of the converter connected to that port, prioritizing the use of locally connected energy storage or controllable loads for power balancing; and simultaneously, coordinating the power flow between different ports through internal high-frequency isolation and power routing capabilities, guiding local unbalanced power to ports with adjustment capabilities.

[0011] Optionally, the dynamic role allocation and consensus module is configured with a redundant architecture logical view, which is used to quickly elect a backup node to take over its load through consensus when a node failure is detected.

[0012] Optionally, the sparse communication and neighborhood negotiation module integrates new energy output and computing load prediction algorithms.

[0013] Secondly, the present invention provides a lightweight distributed solid-state transformer cluster collaborative scheduling method, characterized in that it employs the aforementioned lightweight distributed solid-state transformer cluster collaborative scheduling system, comprising the following steps: Power on each solid-state transformer node and establish a sparse communication network connection; Each node runs a dynamic role allocation and consensus process, and elects a temporary coordinator node for the current period based on real-time status indicators; Each node continuously runs local fast self-balancing control to maintain local stability of its own port voltage and power; When a global power disturbance is detected or an upper-level scheduling instruction is received, the temporary coordinator node triggers a cooperative scheduling cycle. During the coordination period, each node, while maintaining local adjustment, reports boundary information to the coordinator through sparse communication and receives coordinated correction parameters generated by the coordinator through negotiation. Each node applies the corrected parameters, updates its local control commands, and completes a round of distributed collaborative adjustment to bring the overall operating state of the cluster closer to the optimal or safe target. Record the contribution data of each node in this round of collaboration, and update the contribution ledger and incentive information; Determine if the role rotation conditions are met. If they are met, return to the dynamic role allocation and consensus process steps and start a new election and coordination cycle. If they are not met, return to each node to continue running local fast self-balancing control and continue running.

[0014] Optionally, the dynamic role allocation and consensus process may also include: When a new node is detected to be powered on and broadcasting a join request, the current temporary coordinator node verifies its identity, updates the cluster member list and broadcasts it, and includes the new node in the subsequent election and coordinated scheduling. When the heartbeat of the current temporary coordinator node times out or its status indicator falls below the threshold, the healthy node automatically triggers an emergency election process to select a new temporary coordinator node within a preset time window, which will take over the collaborative scheduling responsibilities and achieve uninterrupted operation.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention, through sparse communication and neighborhood negotiation mechanisms, reduces the communication complexity of a single coordination cycle from that of traditional fully interconnected systems. Reduce to The amount of communication data is only 10%-30% of that of the full-state synchronization solution, making it especially suitable for edge applications with limited communication conditions.

[0016] The local fast self-balancing controller of this invention achieves initial power regulation at the millisecond level (≤5ms), and global collaborative correction is completed within tens of milliseconds through negotiation, perfectly matching the high dynamic response requirements of solid-state transformers.

[0017] This invention is based on dynamic role allocation and rotation, which completely eliminates the single point of failure bottleneck of traditional architecture, and supports plug-and-play nodes and fault self-healing. When a single node fails, the cluster can resume normal collaboration within a preset time window (200ms).

[0018] Furthermore, the contribution proof and lightweight blockchain incentive unit of this invention introduce distributed ledger technology into SST cluster scheduling, constructing a closed-loop trusted economic system and fundamentally solving the initiative problem of heterogeneous entities participating in collaboration. Attached Figure Description

[0019] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and do not specifically limit the shapes and proportions of the components. In the drawings: Figure 1 This is a schematic diagram of the architecture of the lightweight distributed solid-state transformer cluster collaborative scheduling system according to an embodiment of the present invention; Figure 2This is a flowchart illustrating the dynamic role allocation and consensus process in an embodiment of the present invention. Figure 3 This is a timing diagram of a complete cooperative scheduling cycle according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the data flow of the contribution proof and incentive unit in an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed during use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0023] When an element is referred to as being "set on" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only embodiments. The use of the term "horizontal" does not imply that the component is required to be absolutely horizontal, but rather that it may be slightly tilted. "Horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it may be slightly tilted.

[0024] It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification and appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] The present invention will now be described in detail with reference to the accompanying drawings.

[0027] This invention provides a lightweight distributed solid-state transformer cluster collaborative scheduling system, comprising: A distributed cluster consisting of multiple solid-state transformer nodes; The dynamic role allocation and consensus module is deployed in each of the solid transformer nodes. It is used to dynamically elect temporary coordinator nodes in the cluster based on preset election indicators and through a lightweight consensus algorithm, and to periodically rotate roles. A local fast self-balancing controller is deployed in each of the solid-state transformer nodes to perform millisecond-level autonomous power regulation based on locally sensed port status information; The sparse communication and neighborhood negotiation module is deployed in each of the solid-state transformer nodes. It is used to enable the node to perform necessary data exchange with only one or two of the temporary coordinator node or a few nodes that are topologically adjacent, and to perform consistency correction on the local adjustment command through a distributed negotiation algorithm.

[0028] This invention reduces the communication complexity of a single coordination cycle through sparse communication and neighborhood negotiation mechanisms, making it particularly suitable for edge applications with limited communication conditions. The local fast self-balancing controller of this invention achieves initial power regulation at the millisecond level, and global coordinated correction is completed within tens of milliseconds through negotiation, perfectly matching the high dynamic response requirements of solid-state transformers. Based on dynamic role allocation and rotation, this invention completely eliminates the single-point bottleneck of traditional architectures and supports plug-and-play nodes and fault self-healing. In the event of a single node failure, the cluster can resume normal coordination within a preset time window.

[0029] Example 1 This invention provides a lightweight distributed solid-state transformer cluster collaborative scheduling system, comprising a distributed cluster consisting of multiple solid-state transformer nodes, each node possessing independent local sensing, computing, and communication capabilities. The core innovation of the system lies in its distributed collaborative architecture, specifically implemented through the collaborative efforts of the following modules: Dynamic role allocation and consensus module: Deployed on each node, it is the cornerstone of this invention's architecture. This module abandons the fixed master-slave relationship, enabling the cluster to self-organize and operate; the dynamic role allocation and consensus module includes: The node status evaluation unit is used to monitor and evaluate the election indicators of this node in real time.

[0030] The election metric is a weighted composite score that integrates current available adjustment capacity, historical average response speed, communication link quality with neighboring nodes (such as Received Signal Strength Indicator (RSSI) or Bit Error Rate), and remaining device energy or health status. An exemplary calculation formula is as follows:

[0031] The weights can be flexibly configured according to the application scenario.

[0032] Lightweight consensus algorithm unit: Employs reputation-based voting or optimized practical Byzantine fault tolerance algorithms. Each node broadcasts its own state periodically or as needed, and through multiple rounds of lightweight information exchange without the intervention of a central node, reaches a cluster-wide consensus on the identity of the current coordinator node.

[0033] Role Management and Rotation Unit: Manages the responsibilities of the elected temporary coordinator node and automatically and safely initiates a new round of elections when preset conditions (such as the end of the time period, node performance degradation, or detection of a fault) are triggered, realizing the dynamic and periodic rotation of the coordinator role and fundamentally avoiding single-point bottlenecks and failures.

[0034] Local Fast Self-Balancing Controller: Deployed on each node, this controller ensures rapid autonomous response capabilities. Based on the real-time status of the solid-state transformer's multiple ports (such as port voltage and power), it performs millisecond-level autonomous power regulation. For example, employing a port voltage-based partitioning strategy, when a port voltage deviation is detected, it prioritizes balancing using locally connected energy storage or controllable loads and coordinates internal power routing. This provides a fast and stable local foundation for subsequent cluster collaboration.

[0035] Sparse Communication and Neighborhood Negotiation Module: Deployed on each node, this module is key to achieving lightweight collaboration. It does not require fully interconnected communication but instead establishes an efficient sparse interaction pattern. The Sparse Communication and Neighborhood Negotiation Module is configured as follows: After the temporary coordinator node is elected, the cluster enters a coordination cycle. The temporary coordinator node broadcasts coordination trigger instructions and global state reference information to all nodes within its communication range or to a subset of nodes selected based on the topology. After the temporary coordinator node for the current cycle is generated, it broadcasts coordination trigger instructions to a subset (not all) of the nodes within its communication range.

[0036] Upon receiving the instruction, each node first performs a rapid initial adjustment based on local information by the local fast self-balancing controller; each node then sends its own boundary information to the temporary coordinator node via a sparse communication network. While running local fast self-balancing, each node only needs to send its own adjustment margin, power demand, and other key boundary information to the coordinator or a few topologically adjacent nodes.

[0037] After collecting this sparse information, the temporary coordinator node runs a fast consensus algorithm (such as the average consensus algorithm) to calculate the coordination parameters used to fine-tune the local instructions of each node, and feeds them back to the relevant nodes.

[0038] Each node receives and applies the coordination parameters to complete this round of coordination adjustment, enabling the cluster to achieve self-balancing without the need for global information synchronization and complex optimization calculations.

[0039] The temporary coordinator node is configured to: when selecting a negotiation node, based on a preset topology adjacency rule or communication hop count threshold, send a coordination trigger instruction only to a subset of nodes that meet certain conditions, including: the number of communication hops with the coordinator node is within N hops, or the available adjustment capacity of the node is greater than a preset threshold.

[0040] Through this localized, rapid adjustment and coordinator-guided sparse negotiation mode, the system can quickly achieve global power balance and voltage stability of the cluster with extremely low communication overhead, perfectly matching the dynamic response characteristics of solid-state transformers.

[0041] The contribution proof and incentive unit can be deployed independently in the lightweight distributed solid-state transformer cluster collaborative scheduling system or integrated into the dynamic role allocation and consensus module.

[0042] The contribution proof and incentive unit is used for: Record the behavior data and adjustment effects of each solid-state transformer node as a coordinator or participant in each collaborative scheduling, and generate an immutable local contribution certificate.

[0043] Within the cluster, lightweight blockchain or distributed ledger technology is used to store and reach consensus on contribution credentials, forming a globally recognized contribution ledger.

[0044] Based on the contribution ledger, economic incentives or system points that nodes should receive for providing collaborative services (such as acting as a coordinator, providing support power, and responding quickly) are automatically calculated and allocated through predefined incentive functions or smart contracts, so as to achieve a precise match between contribution and reward.

[0045] A lightweight distributed solid-state transformer cluster collaborative scheduling method based on the above system includes the following steps: S1: Cluster initialization, each node is powered on and a sparse communication network connection is established.

[0046] S2: Runs a dynamic role allocation and consensus process, electing a temporary coordinator node for the current period based on real-time status indicators.

[0047] S3: Each node continuously runs local fast self-balancing control to maintain the stability of its own port.

[0048] S4: When a global disturbance is detected or a scheduling instruction is received, the temporary coordinator node triggers a coordination cycle.

[0049] S5: During the coordination period, each node reports boundary information through sparse communication and receives coordination correction parameters generated by the coordinator.

[0050] S6: Each node applies the corrected parameters, updates the local control commands, and completes a round of distributed collaborative adjustment.

[0051] S7: Record the contribution data of each node in this round of collaboration, and update the contribution ledger and incentive information.

[0052] S8: Determine if the role rotation condition is met. If it is, return to S2; otherwise, return to S3 and continue running.

[0053] Furthermore, to incentivize heterogeneous nodes to actively participate and ensure the system's sustainable operation, the system can also integrate a contribution proof and incentive unit. This unit records the collaborative behavior and effects of each node, generates contribution credentials, and uses lightweight blockchain or distributed ledger technology for notarization and consensus. Based on a trusted contribution ledger, smart contracts automatically achieve precise matching of contributions and rewards, constructing a built-in economic driving mechanism.

[0054] Optionally, step S2 also includes a dynamic node addition and fault self-healing mechanism: When a new node is detected to be powered on and broadcasting a join request, the current temporary coordinator node verifies its identity, updates the cluster member list and broadcasts it, and includes the new node in the subsequent election and coordinated scheduling. When the heartbeat of the current temporary coordinator node times out or its status indicator falls below the threshold, the healthy node automatically triggers an emergency election process to select a new temporary coordinator node within a preset time window, which will take over the collaborative scheduling responsibilities and achieve uninterrupted operation.

[0055] The dynamic role allocation and consensus module is also used to support plug-and-play nodes and cluster self-healing: When a new node is detected joining the network, it is automatically added to the consensus group, the member list is updated, and it participates in subsequent role election and collaborative scheduling. When the exit or failure of the existing temporary coordinator node is detected, a new round of election is immediately triggered, and other healthy nodes take over the coordination responsibilities to ensure that the collaborative scheduling process is not interrupted.

[0056] This invention addresses the problems of rigidity, high communication overhead, and slow response speed in existing solid-state transformer cluster collaborative scheduling architectures. It enables each solid-state transformer node to dynamically elect a temporary coordinator node based on real-time performance indicators using a lightweight consensus algorithm, and rotates the coordinator periodically according to preset conditions, eliminating single points of failure and reducing communication complexity and overhead. The system consists of multiple nodes with autonomous decision-making capabilities. Each node achieves millisecond-level autonomous adjustment through a local fast self-balancing controller, while engaging in sparse communication only with the coordinator and a few adjacent nodes to negotiate and correct local commands to achieve global power balance and voltage stability for the cluster. Furthermore, it integrates proof-of-contribution and a lightweight blockchain, recording node collaborative behavior as tamper-proof contribution credentials. Smart contracts automatically match contributions and rewards, constructing an endogenous economic incentive. This invention integrates dynamic role allocation, sparse communication consensus, fast local self-balancing, and trusted contribution incentives, forming a new generation of solid-state transformer cluster collaborative scheduling system with high adaptability, low overhead, fast response, and self-driven economic benefits, suitable for edge power grids, microgrids, and new distribution systems.

[0057] Example 2 like Figure 1 As shown, this embodiment provides a lightweight distributed solid-state transformer cluster collaborative scheduling system, including three solid-state transformer nodes: a first solid-state transformer node (currently acting as a temporary coordinator), a second solid-state transformer node, and a third solid-state transformer node. The three nodes exchange data through a sparse communication network, without relying on any fixed central control node.

[0058] Each solid-state transformer node contains three core modules: a dynamic role allocation and consensus module, a local fast self-balancing controller, and a sparse communication and neighborhood negotiation module. These three modules are arranged vertically within the node and work together.

[0059] In this embodiment, the first solid-state transformer node is connected to photovoltaic and energy storage via a DC port, forming a new energy power generation and energy storage unit; the second solid-state transformer node is connected to a charging station via a DC port, representing a typical controllable load; and the third solid-state transformer node is connected to the power grid via an AC port, realizing power exchange with the upstream distribution network. Each node can connect to various types of source-load-storage devices according to the actual scenario; the figure is only an exemplary configuration.

[0060] Example 3 Figure 2 The internal workflow of the dynamic role assignment and consensus module is shown in detail.

[0061] Once a node is powered on and ready, the node status assessment unit calculates the node's election metrics in real time. These metrics are a weighted composite score that integrates current available adjustment capacity, historical average response speed, communication link quality with neighboring nodes (such as Received Signal Strength Indicator (RSSI) or Bit Error Rate), and remaining energy or health status of the device.

[0062] Each node packages its own state assessment score and node identifier into a state declaration message and broadcasts it to the cluster. Subsequently, the lightweight consensus algorithm unit executes a consensus process based on reputation voting: after receiving each other's state declarations, all nodes vote independently according to preset score comparison rules. Through multiple rounds of lightweight message interaction, the cluster reaches a consensus on the identity of the temporary coordinator node without the intervention of a central node.

[0063] Determine if a consensus has been reached within the cluster: if not, return to the previous step and proceed to the next consensus iteration; if yes, elect a temporary coordinator node. The elected node then operates as a temporary coordinator, continuously monitoring its own status and heartbeat status, and its coordination responsibilities are managed by the role management and rotation unit.

[0064] The role management and rotation unit continuously determines whether the rotation conditions are met. The rotation conditions include: reaching a preset time period (e.g., a coordination cycle ends in 5 seconds), the status indicator of the elected node drops beyond a preset threshold (e.g., available capacity drops by more than 50%), or not receiving a heartbeat message from the current coordinator for multiple consecutive heartbeat cycles (determined as a fault). If the rotation conditions are not met, the unit returns to the role management and rotation unit (308) to manage its coordination responsibilities and continue to perform its coordination duties; if the conditions are met, role rotation is triggered, and a new round of election is started.

[0065] This embodiment further supports plug-and-play and fault self-healing of nodes. When a new node is detected to be powered on and broadcasting a join request, the current temporary coordinator node verifies its identity and legality, updates the cluster member list, and broadcasts the member update message to the entire cluster. The new node is then included in subsequent elections and collaborative scheduling. When the current coordinator node's heartbeat times out or its status indicators fall below a threshold, healthy nodes in the cluster automatically trigger an emergency election process, electing a new temporary coordinator node within a preset time window (e.g., 200 milliseconds) to take over the collaborative scheduling responsibilities, ensuring uninterrupted operation.

[0066] Example 4 Figure 3 The sequence diagram illustrates in detail the message exchange order between the temporary coordinator node and solid transformer nodes A and B during a single collaborative scheduling cycle.

[0067] At the start of the coordination cycle, the temporary coordinator node first broadcasts the coordination trigger command and global state reference information to the relevant nodes within the communication range. After receiving the command, each node immediately executes local fast self-balancing control, and performs rapid initial adjustment based on local port voltage and power information (this is an internal action performed synchronously by each node, which is represented by the activation bar on the lifeline of each node in the timing diagram).

[0068] After the initial local adjustment is completed, each node reports its own adjustment margin (upward and downward adjustment capacity), power demand, or voltage deviation and other boundary information to the temporary coordinator node through a sparse communication network. At this time, what is reported are the boundary constraints rather than the complete node operating status, and the amount of data is much smaller than that of the full-state synchronization scheme.

[0069] After collecting boundary information from each node, the temporary coordinator node runs a fast consensus algorithm (such as the average consensus algorithm) to calculate the coordination parameters (e.g., a uniform proportional allocation coefficient) used to correct the local instructions of each node. ), and broadcast the parameter to all relevant nodes.

[0070] After receiving the coordination parameters, each node calculates the final power command increment based on its own boundary conditions, updates its local control commands, and executes them, completing this round of coordinated adjustment. Through this local rapid adjustment + coordinator-guided sparse negotiation mode, the entire coordination cycle can be completed in just tens of milliseconds, enabling the cluster to achieve self-balancing without the need for global information synchronization and complex optimization calculations.

[0071] In this embodiment, the local fast self-balancing controller adopts an autonomous power control rule based on port voltage partitioning.

[0072] The autonomous power control rules based on port voltage zoning include: real-time monitoring of the voltage status of each AC / DC port of the solid-state transformer; when a port voltage deviates from the rated range, automatically adjusting the power command of the converter connected to that port, prioritizing the use of locally connected energy storage or controllable loads for power balancing; and simultaneously, coordinating the power flow between different ports through the high-frequency isolation and power routing capabilities within the solid-state transformer, guiding local unbalanced power to ports with regulation capabilities.

[0073] Example 5 Figure 4 The entire data flow process of contribution proof and incentive unit is shown.

[0074] Each time a round of coordinated scheduling is completed, each participating node (including nodes acting as temporary coordinators and ordinary participants) automatically generates a digitally signed local contribution certificate. This certificate contains the following key data: the node's role in this round of coordination (coordinator or participant), the amount of regulated electricity provided (in kWh), the average response time (the time from receiving the coordination trigger instruction to completion), and an identifier indicating whether the execution was successful.

[0075] Within a pre-defined settlement period (e.g., every 15 minutes), the current temporary coordinator node acts as the block packaging node, responsible for collecting signed contribution credentials broadcast by all nodes within that period, packaging these credentials into a candidate block, and broadcasting a verification request to the cluster. Multiple nodes within the cluster verify the validity of the credentials in the candidate block and the continuity of the block hash. After reaching a consensus, the block is added to the lightweight blockchain or distributed ledger, forming a globally recognized and tamper-proof contribution ledger.

[0076] The system has pre-defined incentive functions or smart contracts, the basic form of which is:

[0077] in The regulating power provided to the nodes The average response time of the node. This is the role coefficient of each node in this round of collaboration (temporary coordinators receive a higher role coefficient due to their greater responsibility for coordination, computation, and communication). Once a new block is confirmed and uploaded to the blockchain, the smart contract automatically reads the contribution data of each node in that block, substitutes it into the incentive function to calculate the system points or economic incentives (such as stablecoins, electricity bill credits, or system credit points) that each node should receive, and records the allocation results in the transactions of the next settlement cycle block.

[0078] Finally, the nodes that receive incentives will be more motivated to actively participate in the next coordinated scheduling, forming a positive economic loop where those who contribute more receive more benefits. Figure 4The return arrow pointing from each node to the solid-state transformer node, obtained from the system integral or economic incentives at each node, represents this closed loop.

[0079] Example 6 The lightweight distributed solid-state transformer cluster collaborative scheduling system in this embodiment is used in multi-station integration scenarios, wherein the solid-state transformer node serves as a key energy router connecting photovoltaic stations, energy storage stations, charging stations, and the main power grid.

[0080] The dynamic role allocation and consensus module dynamically elects the most suitable node for coordinating multi-energy complementary scheduling as a temporary coordinator based on multi-dimensional data (PV output forecast, energy storage state of charge (SOC), charging load demand, and real-time grid electricity price). For example, when the energy storage station has sufficient SOC and good communication conditions, the solid-state transformer node corresponding to the energy storage station is more likely to be elected as the coordinator.

[0081] Based on the global optimization objectives (such as maximizing energy utilization efficiency and minimizing electricity costs) set by the coordinator, the sparse communication and neighborhood negotiation module negotiates and generates real-time dispatch instructions among multiple sites. Taking peak electricity price periods as an example, the coordinator negotiates to ensure that all photovoltaic output is supplied to the charging station load, and the insufficient portion is supported by the energy storage station discharging at maximum power. The main grid power instruction is collaboratively corrected to zero, thereby achieving optimal control of photovoltaic curtailment rate and energy storage utilization rate.

[0082] Example 7 The lightweight distributed solid-state transformer cluster collaborative scheduling system in this embodiment is used in the scenario of microgrid and data center integration, in which the solid-state transformer cluster provides highly reliable power supply to the data center.

[0083] The dynamic role allocation and consensus module supports building a logical view of a redundant architecture. When the cluster detects a failure in a solid-state transformer node, it can quickly elect a backup node to take over its load within milliseconds through the consensus mechanism, achieving seamless switching and ensuring uninterrupted power supply to the data center.

[0084] The sparse communication and neighborhood negotiation module integrates new energy output prediction algorithms and computing load prediction algorithms, enabling the cluster to negotiate scheduling strategies in advance and smoothly respond to fluctuations in photovoltaic and wind power output and sudden changes in AI computing load. Through this prediction-negotiation-pre-adjustment mechanism, the voltage fluctuation rate of the microgrid's key bus is strictly controlled within ±1% of the rated value.

[0085] Unless otherwise specified, the equipment components involved in the above embodiments are all conventional equipment components, and the structural settings, working methods or control methods involved are all conventional settings, working methods or control methods in the art unless otherwise specified.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be covered within the scope of the claims of the present invention.

Claims

1. A lightweight distributed solid-state transformer cluster collaborative scheduling system, characterized in that, include: A distributed cluster consisting of multiple solid-state transformer nodes; The dynamic role allocation and consensus module is deployed in each of the solid transformer nodes. It is used to dynamically elect temporary coordinator nodes in the cluster based on preset election indicators and through a lightweight consensus algorithm, and to periodically rotate roles. A local fast self-balancing controller is deployed in each of the solid-state transformer nodes to perform millisecond-level autonomous power regulation based on locally sensed port status information; The sparse communication and neighborhood negotiation module is deployed in each of the solid-state transformer nodes. It is used to enable the node to perform necessary data exchange with only one or two of the temporary coordinator node or a few nodes that are topologically adjacent, and to perform consistency correction on the local adjustment command through a distributed negotiation algorithm.

2. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The dynamic role allocation and consensus module includes: The node status evaluation unit is used to monitor and evaluate the election indicators of this node in real time. The lightweight consensus algorithm unit is used to execute the consensus process based on reputation voting or practical Byzantine fault tolerance algorithm. Based on the election indicators broadcast by each node, the cluster reaches a consensus on the identity of the temporary coordinator node without the intervention of the central node. The role management and rotation unit is used to manage the coordination responsibilities of the temporary coordinator node after it is elected, and to automatically initiate a new round of elections to transfer roles when the rotation conditions are triggered.

3. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The sparse communication and neighborhood negotiation module is configured as follows: After the temporary coordinator node is elected, the cluster enters a coordination cycle; the temporary coordinator node broadcasts coordination trigger instructions and global status reference information to all nodes within its communication range or to a subset of nodes selected according to the topology. Upon receiving the instruction, each node first undergoes a rapid initial adjustment based on local information by the local fast self-balancing controller; each node then sends its own boundary information to the temporary coordinator node via a sparse communication network. Based on the collected boundary information, the temporary coordinator node runs a fast consensus algorithm to calculate the coordination parameters used to correct the local instructions of each node, and broadcasts them to the relevant nodes.

4. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The temporary coordinator node is configured to send a collaborative trigger command only to a subset of nodes that meet the conditions when selecting negotiation nodes, based on preset topology adjacency rules or communication hop count thresholds.

5. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The lightweight distributed solid-state transformer cluster collaborative scheduling system also includes a contribution proof and incentive unit integrated into the dynamic role allocation and consensus module or deployed independently, used to record the behavior data and adjustment effect of each solid-state transformer node as a coordinator or participant in each collaborative scheduling, and generate an immutable local contribution certificate.

6. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The local fast self-balancing controller adopts an autonomous power control rule based on port voltage partitioning. The autonomous power control rule includes: real-time monitoring of the voltage status of each AC / DC port of the solid-state transformer; when a port voltage deviates from the rated range, automatically adjusting the power command of the converter connected to that port, giving priority to using locally connected energy storage or controllable loads for power balancing; at the same time, through internal high-frequency isolation and power routing capabilities, coordinating the power flow between different ports, and guiding local unbalanced power to ports with adjustment capabilities.

7. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The dynamic role allocation and consensus module is configured with a redundant architecture logical view, which is used to quickly elect a backup node to take over its load when a node failure is detected.

8. The lightweight distributed solid-state transformer cluster collaborative scheduling according to claim 1, characterized in that, The sparse communication and neighborhood negotiation module integrates new energy output and computing load prediction algorithms.

9. A lightweight distributed solid-state transformer cluster collaborative scheduling method, characterized in that, The lightweight distributed solid-state transformer cluster collaborative scheduling system according to any one of claims 1 to 8 includes the following steps: Power on each solid-state transformer node and establish a sparse communication network connection; Each node runs a dynamic role allocation and consensus process, and elects a temporary coordinator node for the current period based on real-time status indicators; Each node continuously runs local fast self-balancing control to maintain local stability of its own port voltage and power; When a global power disturbance is detected or an upper-level scheduling instruction is received, the temporary coordinator node triggers a cooperative scheduling cycle. During the coordination period, each node, while maintaining local adjustment, reports boundary information to the coordinator through sparse communication and receives coordinated correction parameters generated by the coordinator through negotiation. Each node applies the corrected parameters, updates its local control commands, and completes a round of distributed collaborative adjustment to bring the overall operating state of the cluster closer to the optimal or safe target. Record the contribution data of each node in this round of collaboration, and update the contribution ledger and incentive information; Determine if the role rotation conditions are met. If they are met, return to the dynamic role allocation and consensus process steps and start a new election and coordination cycle. If they are not met, return to each node to continue running local fast self-balancing control and continue running.

10. A lightweight distributed solid-state transformer cluster collaborative scheduling method according to claim 9, characterized in that, The steps involved in running the dynamic role allocation and consensus process also include: When a new node is detected to be powered on and broadcasting a join request, the current temporary coordinator node verifies its identity, updates the cluster member list and broadcasts it, and includes the new node in the subsequent election and coordinated scheduling. When the heartbeat of the current temporary coordinator node times out or its status indicator falls below the threshold, the healthy node automatically triggers an emergency election process to select a new temporary coordinator node within a preset time window, which will take over the collaborative scheduling responsibilities and achieve uninterrupted operation.

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