Hybrid communication network configuration method for intelligent power distribution network

Abstract

The invention discloses a hybrid communication network configuration method for a smart power distribution network, and the method comprises the steps: constructing a smart power distribution network hybrid communication architecture based on a micro-grid, and dividing the smart power distribution network into a plurality of micro-grid units; the method comprises the following steps of: establishing a QoS (Quality of Service) optimal model by taking a weighted sum of minimum data packet end-to-end delay and unreliable probability of a communication link as a target function based on communication requirements of nodes in a hybrid communication architecture of the intelligent power distribution network; based on the QoS optimal model, solving an optimal communication technology combination of each branch in the radial power distribution network topology by adopting a recursive algorithm; and determining a hybrid communication network configuration scheme of the intelligent power distribution network according to the output of the recursive algorithm. The real-time performance, the reliability and the economical efficiency of the intelligent power distribution network communication system are effectively improved, and the problem of insufficient performance or high cost caused by a single technology or local planning is avoided.

Description

Technical Field

[0001] This invention relates to the field of smart grid communication technology, and in particular to a hybrid communication network configuration method for smart distribution networks. Background Technology

[0002] With the rapid construction and promotion of new power systems, the distribution network is playing an increasingly prominent role in the overall power system architecture. The increasing integration of distributed power sources, energy storage devices, and electric vehicles into the distribution network has transformed the traditional unidirectional power transmission mode into a complex bidirectional, multi-source power supply mode. Simultaneously, the digitalization and intelligence of power system operation are continuously improving. As a key infrastructure supporting its safe, reliable, and economical operation, the distribution network communication network undertakes crucial tasks such as equipment monitoring, status awareness, energy dispatch, and information exchange.

[0003] Existing power distribution network communication mainly employs single technologies such as fiber optics, cellular networks, and short-range wireless communication. Fiber optic communication offers advantages such as high bandwidth, low latency, and high reliability, but its construction and maintenance costs are extremely high, and its deployment flexibility is limited in complex power distribution environments. Cellular communication boasts wide coverage and strong access capabilities, suitable for accessing a large number of distributed devices, but its link stability depends on the operator's network, is prone to congestion in high-density scenarios, and experiences significant transmission delay fluctuations. Short-range wireless communication such as ZigBee is low-cost and easy to network, but its transmission distance is limited, its penetration capability is poor, and it is greatly affected by environmental interference. Independent applications cannot meet the stringent requirements of smart power distribution networks for end-to-end data packet latency and link reliability. Therefore, in practical applications, it is difficult for a single communication technology to simultaneously achieve multiple performance indicators such as low latency, high reliability, and low cost.

[0004] To overcome the aforementioned problems, in recent years, academia and engineering practice have proposed distribution network communication schemes based on hybrid communication architectures. The core idea of ​​these schemes is to comprehensively utilize the advantages of different communication technologies, forming complementarity and synergy to improve network flexibility and performance. However, most existing hybrid communication schemes remain at the architectural level or in local deployments, lacking quantitative optimization design from a system-wide perspective. Especially considering the complex radial topology of distribution networks, diverse service requirements, and the inherent unreliability of communication links, how to optimize key Quality of Service (QoS) indicators while controlling costs remains a pressing issue. Without a systematic optimization method, relying solely on experience or local planning often results in insufficient QoS, low resource utilization, or cost overruns. This not only affects the operational efficiency of the distribution network communication system but may also pose potential risks to the real-time dispatching and secure and stable control of the power system. Summary of the Invention

[0005] The purpose of this invention is to overcome the limitations of existing technologies where a single communication technology cannot simultaneously meet the multi-dimensional requirements of smart distribution networks for low latency, high reliability, and low cost. Furthermore, existing hybrid communication solutions often remain at the architectural level or involve localized deployments, lacking quantitative optimization design for the entire system. These solutions are unable to adapt to radial topologies, diverse services, and inherent link unreliability, easily leading to insufficient service quality, low resource utilization, or cost overruns. This invention provides a hybrid communication network configuration method for smart distribution networks. By constructing a microgrid-based hybrid communication architecture for smart distribution networks, a QoS optimal model with multiple constraints is established, aiming to minimize the weighted sum of end-to-end data packet latency and communication link unreliability probabilities. A recursive algorithm is used to solve for the optimal combination of communication technologies for each branch in the radial distribution network topology and determine the configuration scheme. This achieves the goal of improving the real-time performance, reliability, and economy of the smart distribution network communication system under strict cost constraints.

[0006] The objective of this invention is achieved through the following technical solution: A method for configuring a hybrid communication network for smart distribution networks, comprising: Construct a hybrid communication architecture for smart distribution networks based on microgrids, dividing the smart distribution network into multiple microgrid units; Based on the communication requirements of each node in the hybrid communication architecture of smart distribution network, an optimal QoS model is established with the objective function of minimizing the weighted sum of data packet end-to-end delay and communication link unreliability probability. Based on the QoS optimal model, a recursive algorithm is used to solve for the optimal combination of communication technologies for each branch in a radial distribution network topology. Based on the output of the recursive algorithm, determine the hybrid communication network configuration scheme for the smart distribution network.

[0007] Preferably, the microgrid unit integrates intelligent user nodes, intelligent distribution nodes, intelligent renewable energy devices, intelligent transformer nodes (STN), microgrid relays, and a microgrid control station.

[0008] As a preferred embodiment, the QoS optimal model also includes constraints, such as total lifecycle cost, installation cost, maintenance cost, communication cost, communication range, number of retransmissions, hop count limit, number of devices, unreliability probability range, and bandwidth requirements.

[0009] Preferably, the recursive algorithm is a QoS optimal combination recursive algorithm, whose execution steps include: Identify the farthest branch in the radial distribution network topology; Constraint verification and optimal solution for the farthest branch: Call the objective function to find the optimal combination of communication techniques for the farthest branch under all constraints and the corresponding optimal objective function value; Recursively perform constraint checks and optimal solution calculations on all sub-branches of the identified branches until the entire radial distribution network topology has been traversed. The optimal communication technology configuration scheme for smart distribution networks under cost constraints is summarized and output.

[0010] As a preferred approach, if a sub-branch fails to find a feasible solution during the execution of the recursive algorithm, the backtracking adjustment of the parent branch's technology combination is triggered. This reduces the number of devices using low-priority communication technologies in the parent branch and re-solves for the optimal technology combination of the sub-branch until the sub-branch satisfies all constraints or it is confirmed that the fully intelligent distribution network has no feasible solution.

[0011] Preferably, the intelligent power distribution node is equipped with a heterogeneous communication function module. The configuration rules for the heterogeneous communication function module are as follows: only when the adjacent nodes connected to the intelligent power distribution node use different communication technologies, a heterogeneous adaptation module corresponding to the two communication technologies is configured; if the adjacent nodes use the same communication technology, the intelligent power distribution node is configured with a single communication technology module consistent with the adjacent nodes, and the heterogeneous adaptation module supports real-time conversion of communication protocols.

[0012] Preferably, the weight coefficients in the objective function are determined by the analytic hierarchy process (AHP). The business types of the smart distribution network are divided into real-time control, data monitoring, and information interaction, and each business is assigned a priority weight. Then, based on the sensitivity of each business to delay and the probability of unreliable communication links, the values ​​of the weight coefficients are derived in reverse.

[0013] Preferably, when there is a scenario of dense access of distributed power sources in the smart distribution network, the optimal communication technology combination prioritizes communication technologies that support wide bandwidth and low interference, and the redundancy of the data packet stream bandwidth constraint is greater than the set redundancy threshold, while the unreliability probability of the communication link is less than the set unreliability probability threshold, so as to adapt to the high-frequency transmission requirements of distributed power source output data.

[0014] A computer device includes a processor, a memory, an input / output interface, a communication interface, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a hybrid communication network configuration method for smart distribution networks.

[0015] A computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements a hybrid communication network configuration method for smart distribution networks.

[0016] The beneficial effects of this invention are as follows: This invention, through the complete technical link of microgrid hybrid communication architecture construction, QoS optimal model establishment, recursive algorithm solution, and configuration scheme determination, achieves the synergistic minimization of end-to-end delay of data packets and the probability of unreliability of communication links under multiple constraints such as total life cycle cost and installation cost. This effectively improves the real-time performance, reliability, and economy of smart distribution network communication systems and avoids performance deficiencies or cost overruns caused by single technologies or local planning.

[0017] The optimal combination recursive algorithm for QoS is precisely adapted to the radial distribution network topology. Through the logic of prioritizing the farthest branch and recursively traversing sub-branches, it customizes the optimal combination of communication technologies for different branches. The model and algorithm are highly robust to changes in link unreliability, transmission distance, cost parameters, etc., and can flexibly cope with complex geographical environments, differences in equipment density, and cost fluctuations, adapting to the diverse deployment needs of the distribution network.

[0018] The QoS optimal model, through the design of a weighted sum objective function and multiple technical constraints, can dynamically adjust the combination of communication technologies according to the priority and sensitivity of different services such as real-time control, data monitoring, and information interaction in smart distribution networks. It not only meets the specific requirements of high-frequency transmission and low interference, but also ensures the comprehensive feasibility of communication services across the entire network, and adapts to the complex business scenarios after the access of multiple source devices in new power systems. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention; Figure 2 This is the overall flowchart of the recursive algorithm execution of this invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0022] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0024] Example: A hybrid communication network configuration method for smart distribution networks, such as Figure 1 As shown, it includes: S11, Construct a hybrid communication architecture for a smart distribution network based on microgrids, dividing the smart distribution network into multiple microgrid units; To meet the real-time dynamic data sharing needs of spatially distributed devices in smart distribution networks, the entire smart distribution network is divided into several independent yet collaboratively operating microgrid units. A hybrid communication architecture with unitized management and on-demand communication is constructed. Each microgrid unit serves as a basic functional module of the smart distribution network communication system, integrating various intelligent device nodes such as Smart User Nodes (SCNs), Smart Distribution Nodes (SDNs), Renewable Energy Intelligent Devices (REIDs), Smart Transformer Nodes (STNs), Microgrid Relays (MGRelays), and Microgrid Control Stations (MCSs) to achieve efficient data transmission, real-time interaction, and precise control within the network. All intelligent device nodes are equipped with sensing and execution modules, computing modules, and communication modules. The sensing and execution module is responsible for collecting key physical quantities of the power grid such as current, voltage, and temperature and executing control commands issued from higher levels. The computing module performs preliminary preprocessing operations such as filtering, noise reduction, and format standardization on the collected data to reduce redundancy during data transmission. The communication module serves as the core carrier for data interaction, and its technology selection and functional configuration are differentiated according to the specific functional positioning of the nodes—the SCN, as the source node for data acquisition, is responsible for generating Consumer Data Packets (CDPs). The data packet contains key information such as real-time user power load and equipment operating status. SDN, as an intermediate node for data transmission, undertakes data forwarding functions and is configured with heterogeneous communication functions only at node locations in the network where there is a need for communication technology switching (such as the junction of short-range ZigBee technology and medium-range Wi-Fi technology). Other conventional locations are configured with a single communication technology module to minimize the redundancy in hardware procurement and deployment costs caused by full node heterogeneity. MCS, as the destination node for data processing and control command generation, receives and parses the CDP data uploaded by each SCN and generates corresponding scheduling and control commands based on the real-time operating status of the microgrid. REID, STN, and MGReaes, as auxiliary functional nodes, flexibly access the communication network according to the actual operating needs of the microgrid and respectively realize specialized functions such as renewable energy power generation data acquisition, transformer operating status monitoring, and microgrid inter-mode switching control.

[0025] S12, Based on the communication requirements of each node in the hybrid communication architecture of the smart distribution network, an optimal QoS model is established with the objective function of minimizing the weighted sum of the end-to-end delay of data packets and the unreliability probability of communication links. To optimize key service quality indicators (QoS) of a smart distribution network's hybrid communication network under cost constraints, a QoS optimization model is constructed. This model takes minimizing the weighted sum of end-to-end data packet delay and communication link unreliability probability as its core optimization objective. Simultaneously, it transforms the total lifecycle cost, individual cost components (installation, maintenance, and communication), and key technical limitations (such as communication range and bandwidth) into strict mathematical constraints, forming a deeply coupled optimization system of performance objectives, economic constraints, and technical constraints.

[0026] The model's objective function focuses on minimizing the weighted sum of end-to-end data packet latency and the probability of communication link unreliability, aiming to synergistically optimize the real-time performance and reliability of data transmission. Meanwhile, to ensure the economic feasibility of the solution, total lifecycle cost, installation cost, maintenance cost, and communication cost are considered as key constraints.

[0027] Specifically, the objective function is expressed as: The constraints include: Total lifecycle cost constraint: Installation cost constraints: Maintenance cost constraints: Communication cost constraints: Constraints on the Relationship between Communication Range and Unreliability Probability: Data retransmission count constraint: Jump count growth limit constraint: Constraints on the range of the number of smart devices: Unreliable probability range constraints: Data packet stream bandwidth constraints: in, Indicates the number of retransmissions. Indicates link data latency. This indicates the latency of data packet transmission by smart devices. This indicates the latency of a smart device receiving data packets. Indicates equipped with technology The number of smart devices These represent the weight coefficients of the objective function. This represents the average probability of an unreliable communication link. This indicates the installation cost of smart devices. Indicates the number of years of operation. This indicates the monthly maintenance cost of smart devices. Indicates the cost of data communication. This represents the cost of information capacity requirements. Indicates a pre-set budget. This indicates the upper limit of installation costs. This represents the maximum monthly maintenance cost. Indicates a separate upper limit for communication costs. R Indicates the communication range based on a specific technology. K Represents the path loss constant. Indicates the maximum number of data retransmissions. H Indicates the number of hops in data packet transmission. Represents the reliability factor. Indicates the maximum allowed number of hops. This indicates the minimum number of devices. This indicates the maximum number of devices. Indicates the bandwidth of the data packet stream. This indicates the maximum bandwidth of a particular technology.

[0028] S13, based on the QoS optimal model, uses a recursive algorithm to solve for the optimal communication technology combination for each branch in a radial distribution network topology; the specific process is as follows: Figure 2 As shown.

[0029] The main branch of a radial distribution network will give rise to multiple branches. For this type of radial distribution network topology, a recursive algorithm is needed to determine the optimal QoS combination of the intelligent devices in each branch under cost constraints.

[0030] For the RAQOSCCT (QoS Optimal Combination Recursive) algorithm, the input parameters include: distribution network topology (PGT), selected communication technology (CT), and expected smart grid lifespan. The transmission frequency of the selected data packets Allowable delay of selected data packets Bandwidth requirements of the selected data packets probability of link unreliability Reliability constant Objective function weighting coefficients λ, cost budget Installation cost ceiling Maintenance cost ceiling and communication cost ceiling The algorithm first identifies the furthest branch in the input distribution network topology (PGT); then, within the function FINDQOSC (find the optimal QoS combination), the function FINDQOSVAL (find the optimal QoS value) runs an optimization model to determine the optimal QoS communication technology combination for the intermediate devices in that branch. The function FINDQOSVAL calculates the objective function. (That is, the weighted sum of delay and unreliability probability), and iteratively finds the minimum objective function value and the corresponding technology combination. Simultaneously, the function checks whether all constraints are met (including total cost, installation cost, maintenance cost, communication cost, communication range, retransmission count, hop count limit, range of device numbers, range of unreliability probability, and bandwidth requirements): if satisfied, it returns the QoS optimal solution; otherwise, it returns no feasible solution. The function GETCOMMDET (Get Communication Details) retrieves information on delay, unreliability probability, cost, bandwidth, and communication range for each communication technology that the distribution network topology (PGT) may use.

[0031] Returning the optimal After determining the number of devices equipped with each technology and the optimal objective function value, the FINDQOSC function fills in the sub-branches of the identified furthest branch. Then, the FINDQOSC function is recursively called until the optimal QoS value for the technology combination of all sub-branches of the furthest branch is determined. When the radial distribution network topology traversal is complete or all branches are covered, the RAQOSCCT algorithm exits and terminates the function.

[0032] S14. Based on the output of the recursive algorithm, determine the hybrid communication network configuration scheme for the smart distribution network.

[0033] The calculation results of the RAQOSCCT recursive algorithm are transformed into a clear, specific deployment blueprint that can guide actual engineering implementation. This scheme clarifies the types of communication technologies (such as ZigBee, Wi-Fi, and Cellular) and the corresponding number of smart devices to be deployed on different branches and nodes of the entire radial distribution network, ensuring that the overall end-to-end latency and link unreliability probability of the entire network are comprehensively optimized without exceeding the cost budget.

[0034] When executing the steps of the recursive algorithm, if a sub-branch cannot find a feasible solution, the parent branch's technology combination is backtracked and adjusted. This reduces the number of devices with low-priority communication technologies in the parent branch and re-solves for the optimal technology combination of the sub-branch until the sub-branch satisfies all constraints or it is confirmed that the fully intelligent distribution network has no feasible solution.

[0035] The intelligent power distribution node is equipped with a heterogeneous communication function module. The configuration rules for the heterogeneous communication function module are as follows: only when the adjacent nodes connected to the intelligent power distribution node use different communication technologies, a heterogeneous adaptation module corresponding to the two communication technologies is configured; if the adjacent nodes use the same communication technology, the intelligent power distribution node is configured with a single communication technology module consistent with the adjacent nodes, and the heterogeneous adaptation module supports real-time conversion of communication protocols.

[0036] The weight coefficients in the objective function are determined by the analytic hierarchy process (AHP). The business types of the smart distribution network are divided into real-time control, data monitoring, and information interaction, and each business is assigned a priority weight. Then, based on the sensitivity of each business to delay and the probability of unreliable communication links, the values ​​of the weight coefficients are derived in reverse.

[0037] When there are scenarios with dense distributed power source access in a smart distribution network, the optimal communication technology combination prioritizes communication technologies that support wide bandwidth and low interference, and the redundancy of the data packet stream bandwidth constraint is greater than the set redundancy threshold. At the same time, the unreliability probability of the communication link is less than the set unreliability probability threshold, so as to adapt to the high-frequency transmission requirements of distributed power source output data.

[0038] This embodiment aims to systematically verify the superior performance of the proposed QoS optimal model and RAQOSCCT recursive algorithm in terms of effectiveness, reliability, robustness, and applicability through a series of simulation experiments.

[0039] The basic simulation environment settings are as follows: All experiments were conducted in a smart distribution network topology modeled after the standard IEEE 33 bus radial distribution system.

[0040] To verify the effectiveness of the hybrid communication QoS optimization algorithm, this embodiment compares the "hybrid communication technology solution" with the "single communication technology solution" under varying network scale and service load conditions, and evaluates end-to-end latency and link unreliability probability.

[0041] Control group 1: The set of available technologies was limited to {ZigBee}; Control group 2; limiting the set of optional technologies to {Wi-Fi}; Control group 3: The set of optional technologies is limited to {Cellular}; Control group 4: Allows free combination of the technology set {ZigBee, Wi-Fi, Cellular}.

[0042] Experimental Results: The results show that the hybrid communication scheme is significantly better than the single scheme in terms of latency and unreliability probability. In all experimental scenarios, the total cost of the scheme is strictly controlled within the preset budget, which fully demonstrates the effectiveness of the model in achieving service quality optimization under given economic constraints.

[0043] This embodiment verifies the impact of link unreliability probability on QoS metrics. The experiment uses sensitivity analysis to vary the communication link unreliability probability parameter, observing the correlation between delay and source-destination distance, as well as the dynamic adjustment of the technology combination. Other parameters are kept constant, and the delay variation trend is recorded.

[0044] Control group 1: The link quality is good and the probability of unreliability is low; Control group 2: The link quality is poor and the probability of unreliability is high.

[0045] Experimental results: When the probability of unreliability is low, the latency increases gradually with distance, and the technology combination mainly uses low-latency technology; when the probability of unreliability is high, the latency increases sharply, and the algorithm automatically switches to high-reliability technology to ensure that the probability of unreliability is minimized while maintaining the latency optimization objective.

[0046] To verify the synergistic impact of transmission distance on QoS and cost constraints, this embodiment analyzes the correlation between latency and link unreliability probability, as well as the changing trend of the number of intermediate devices, by varying the maximum distance parameter between the source and destination.

[0047] Control group 1: The maximum distance between the source and destination ends is shorter; Control group 2: The maximum distance between the source and destination ends is longer.

[0048] Experimental results show that increasing transmission distance exacerbates end-to-end latency and increases its sensitivity to link unreliability. Nevertheless, the RAQOSCCT algorithm effectively suppresses drastic performance degradation by dynamically increasing the number of relay devices and optimizing the technology combination. In all distance scenarios, the total cost and the cost of each component are strictly limited within the upper bound of the constraints, demonstrating the economic feasibility of the solution when expanding coverage.

[0049] To verify the robustness of QoS optimization under varying cost parameters, this embodiment studies the relationship between latency and unreliability probability and link unreliability probability by adjusting the cost parameters of key communication technologies. By simulating scenarios of rising costs, the performance of the algorithm under tight cost constraints is evaluated.

[0050] Control group 1: The installation costs of Wi-Fi and Cellular devices remained unchanged.

[0051] Control group 2: The installation cost of Wi-Fi and Cellular devices has increased.

[0052] Experimental results: The algorithm can still minimize latency and unreliability probability under varying cost parameters. The technology combination is dynamically switched according to cost constraints. Low-cost and low-latency technologies are preferred when the unreliability probability is low, while high-reliability technologies are introduced when the unreliability probability is high. The total cost is always under control, which verifies the robustness of the model.

[0053] To verify the applicability of recursive algorithms in complex topologies, this embodiment applies the RAQOSCCT algorithm to a radial distribution network topology to evaluate the algorithm's ability to output the optimal combination of QoS technologies.

[0054] Experimental setup: The RACOCCT algorithm was executed on a complete IEEE 33 bus topology. To simulate a real-world environment, the number of branches, node density, and environmental parameters were varied.

[0055] Experimental results: The algorithm can automatically generate the optimal QoS technology combination that satisfies cost constraints for each branch, prioritizes latency optimization in densely populated areas, enhances reliability in edge areas, and minimizes both global latency and unreliability probability; the recursive process terminates stably, verifying the applicability and scalability of the algorithm in complex topologies.

[0056] The above experiments verify the significant advantages of the hybrid communication network configuration method for smart distribution networks proposed in this invention: (1) Significant QoS performance optimization: By deeply integrating the hybrid communication scheme with the QoS optimal model, the end-to-end latency of data packets and the probability of unreliable communication links are minimized in a coordinated manner under strict cost constraints. Experimental verification shows that compared with a single communication technology scheme, the present invention significantly improves latency and reliability, and the total cost throughout the entire life cycle is always controlled within the preset budget, reflecting a balance between economy and performance.

[0057] (2) Adaptive reliability assurance: The model can intelligently sense changes in the probability of link unreliability and dynamically adjust the combination of communication technologies. Under harsh channel conditions, the algorithm automatically prioritizes high reliability technologies, sacrificing some economy in exchange for communication quality, thus ensuring the robustness of the system in complex environments.

[0058] (3) Handling transmission distance sensitivity: The algorithm can effectively cope with the performance challenges brought about by the increase in transmission distance. Experiments show that the sensitivity of delay to the probability of unreliability increases with the increase of distance, but by optimizing the combination of equipment and technology selection, the algorithm can effectively suppress performance degradation and maintain reliability within the cost constraint, demonstrating forward-looking optimization capabilities.

[0059] (4) Robustness of cost parameters: The model can still maintain QoS index optimization even under the scenario of fluctuating communication technology cost parameters. The technology combination is dynamically switched according to cost constraints. Low-cost technologies are selected first when the probability of unreliability is low, and high-reliability technologies are introduced when the probability of unreliability is high. The total cost is always under control, which proves its adaptability to market changes.

[0060] (5) Topology applicability and global optimization: The RAQOSCCT recursive algorithm can effectively analyze complex radial distribution network topologies and achieve "global optimization and local customization" through recursive solution. The algorithm can generate the optimal QoS technology combination that meets cost constraints for each branch, prioritize delay optimization in densely populated areas, enhance reliability in edge areas, and finally synthesize the optimal configuration for the entire network.

[0061] Compared to traditional configuration methods, this invention represents a fundamental shift from experience-driven to model-driven approaches. Traditional methods rely on single technologies or localized experience, making it difficult to simultaneously address multiple objectives such as latency, reliability, and cost optimization, and even more so to handle the planning complexities of radial topologies. In contrast, this invention constructs an objective function centered on QoS optimization, treating lifecycle costs, installation costs, and maintenance costs as hard constraints, systematically solving for the globally optimal solution and fundamentally avoiding resource waste. Simultaneously, the model incorporates link unreliability probability and dynamic retransmission mechanisms, enabling the scheme to adapt; the RAQOSCCT algorithm intelligently traverses the topology, customizing configurations for different branches, completely transforming the traditional localized and fragmented planning model. This configuration method, integrating QoS performance optimization, economic constraints, and topology adaptability, provides reliable technical support for the construction of smart distribution network communication infrastructure.

[0062] This embodiment also provides a computer device, including a processor, a memory, an input / output interface, a communication interface, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a hybrid communication network configuration method for smart distribution networks.

[0063] This embodiment also provides a computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements a hybrid communication network configuration method for smart distribution networks.

[0064] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0065] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A hybrid communication network configuration method for smart distribution networks, characterized in that, include: Construct a hybrid communication architecture for smart distribution networks based on microgrids, dividing the smart distribution network into multiple microgrid units; Based on the communication requirements of each node in the hybrid communication architecture of smart distribution network, an optimal QoS model is established with the objective function of minimizing the weighted sum of data packet end-to-end delay and communication link unreliability probability. Based on the QoS optimal model, a recursive algorithm is used to solve for the optimal combination of communication technologies for each branch in a radial distribution network topology. Based on the output of the recursive algorithm, determine the hybrid communication network configuration scheme for the smart distribution network.

2. The hybrid communication network configuration method for smart distribution networks according to claim 1, characterized in that, The microgrid unit integrates intelligent user nodes, intelligent distribution nodes, intelligent renewable energy devices, intelligent transformer nodes (STN), microgrid relays, and microgrid control stations.

3. The hybrid communication network configuration method for smart distribution networks according to claim 1, characterized in that, The QoS optimal model also includes constraints, such as total lifecycle cost, installation cost, maintenance cost, communication cost, communication range, number of retransmissions, hop count limit, number of devices, unreliability probability range, and bandwidth requirements.

4. The hybrid communication network configuration method for smart distribution networks according to claim 3, characterized in that, The recursive algorithm described is a QoS optimal combination recursive algorithm, and its execution steps include: Identify the farthest branch in the radial distribution network topology; Constraint verification and optimal solution for the farthest branch: Call the objective function to find the optimal combination of communication techniques for the farthest branch under all constraints and the corresponding optimal objective function value; Recursively perform constraint checks and optimal solution calculations on all sub-branches of the identified branches until the entire radial distribution network topology has been traversed. The optimal communication technology configuration scheme for smart distribution networks under cost constraints is summarized and output.

5. The hybrid communication network configuration method for smart distribution networks according to claim 4, characterized in that, When executing the steps of the recursive algorithm, if a sub-branch cannot find a feasible solution, the parent branch's technology combination is backtracked and adjusted. This reduces the number of devices with low-priority communication technologies in the parent branch and re-solves for the optimal technology combination of the sub-branch until the sub-branch satisfies all constraints or it is confirmed that the fully intelligent distribution network has no feasible solution.

6. The hybrid communication network configuration method for smart distribution networks according to claim 2, characterized in that, The intelligent power distribution node is equipped with a heterogeneous communication function module. The configuration rules for the heterogeneous communication function module are as follows: only when the adjacent nodes connected to the intelligent power distribution node use different communication technologies, a heterogeneous adaptation module corresponding to the two communication technologies is configured; if the adjacent nodes use the same communication technology, the intelligent power distribution node is configured with a single communication technology module consistent with the adjacent nodes, and the heterogeneous adaptation module supports real-time conversion of communication protocols.

7. The hybrid communication network configuration method for smart distribution networks according to claim 1, characterized in that, The weight coefficients in the objective function are determined by the analytic hierarchy process (AHP). The business types of the smart distribution network are divided into real-time control, data monitoring, and information interaction, and each business is assigned a priority weight. Then, based on the sensitivity of each business to delay and the probability of unreliable communication links, the values ​​of the weight coefficients are derived in reverse.

8. The hybrid communication network configuration method for a smart distribution network according to claim 1, characterized in that, When there are scenarios with dense distributed power source access in a smart distribution network, the optimal communication technology combination prioritizes communication technologies that support wide bandwidth and low interference, and the redundancy of the data packet stream bandwidth constraint is greater than the set redundancy threshold. At the same time, the unreliability probability of the communication link is less than the set unreliability probability threshold, so as to adapt to the high-frequency transmission requirements of distributed power source output data.

9. A computer device, characterized in that, The system includes a processor, a memory, an input / output interface, a communication interface, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the hybrid communication network configuration method for smart distribution networks as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the hybrid communication network configuration method for smart distribution networks as described in any one of claims 1 to 8.

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