Power grid hybrid network performance optimization model considering construction cost

Abstract

The invention discloses a power grid hybrid network performance optimization model considering construction cost, and belongs to the technical field of intelligent power grid communication. According to the model, a parameter system including communication equipment, terrain and a global system is defined, a function with the purpose of minimizing the total cost of a life cycle and multivariate constraint conditions of transmission distance, retransmission times, delay, reliability and flow velocity are constructed, and optimal equipment and transfer device configuration is solved by adopting a node traversal and iterative optimization algorithm. Cost and performance collaborative optimization of a honeycomb, Wi-Fi and ZigBee heterogeneous hybrid communication network under a complex terrain is realized, planning accuracy and reliability are remarkably improved, and construction, operation and maintenance cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of smart grid communication technology, specifically to a performance optimization model for a hybrid power grid network that considers construction costs. In particular, it relates to a method and system for performance optimization and cost minimization planning of a hybrid communication network composed of heterogeneous wireless communication devices such as cellular communication, Wi-Fi, and ZigBee in complex terrain environments. Background Technology

[0002] In recent years, with the large-scale integration of renewable energy, the rapid development of distributed power sources, and the increasing diversification of load-side demands, the power system is undergoing profound changes, and its operational complexity has increased significantly. The power grid has gradually evolved from a traditional unidirectional power transmission network into a complex system integrating power flow, information flow, and business flow. In this evolution, the communication system, as the "nerve center" of the power grid, has become increasingly important. It is not only a channel for data interaction and information transmission, but also a key support for ensuring real-time grid dispatch, rapid fault diagnosis, and efficient consumption of new energy sources. Without an efficient and reliable communication network, the safe, stable, and coordinated operation of the smart grid is impossible.

[0003] However, traditional power communication modes based on power line communication, despite having a cost advantage in utilizing existing power infrastructure in the early stages of deployment, have revealed significant limitations when faced with the new requirements of smart grids for high speed, low latency, and high reliability, such as limited transmission rate, poor anti-interference ability, and weak environmental adaptability.

[0004] To address these challenges, smart distribution network communication offers a new solution by integrating various technologies such as power line communication and wireless communication. This hybrid architecture can improve system stability, flexibility, and redundancy while providing a solid guarantee for various advanced applications of the future power grid. However, a core problem remains unresolved: how to effectively control the construction and long-term operation and maintenance costs of the communication network while meeting diverse communication performance requirements. Without a scientifically sound architectural design and optimization model, even if communication performance is met in the short term, in the long run, it may be difficult to promote and apply it on a large scale due to excessive costs or insufficient scalability.

[0005] Currently, research in this field faces the following main technical challenges: 1. Lack of hybrid relay mechanism: Existing technologies lack sufficient research on hybrid power line and wireless relay communication, especially lacking effective solutions for signal relay and coordination between different types of communication devices such as cellular, Wi-Fi, and ZigBee. This results in limited practicality of the constructed hybrid communication architecture and low interoperability and coordination efficiency between devices.

[0006] 2. Models are out of touch with reality: Most hybrid communication network models are still in the stage of theoretical assumptions and laboratory simulations. Their premises are too idealistic and difficult to adapt to the complex and ever-changing terrain and business scenarios in actual engineering environments. This results in poor generalization ability of research results and poor universality of cases, making it impossible to directly transform them into large-scale applications.

[0007] 3. Limited Optimization Dimensions: Existing solutions often focus only on a few performance indicators such as latency and hop count when considering communication technology conditions. They fail to comprehensively and systematically consider service quality constraints from multiple dimensions such as transmission distance, retransmission count, link reliability, and flow rate. They also rarely take construction and operation and maintenance costs as core optimization objectives, making it difficult to achieve the best balance between performance and economic benefits.

[0008] Therefore, in response to the urgent needs of future smart grid development, it is imperative to propose an innovative optimization model that can not only construct a practical hybrid communication architecture, but also achieve rational equipment configuration and minimize life-cycle costs through systematic optimization algorithms in complex and ever-changing environments, thereby achieving a key balance between performance, cost, and engineering feasibility. Summary of the Invention

[0009] This invention proposes a performance optimization model for hybrid power grid networks that considers construction costs, aiming to address the shortcomings of current smart distribution network communication network systems in cost planning and integrated network planning. This model constructs an optimization framework that comprehensively considers power grid construction costs, communication converter configuration and layout strategies, and establishes a performance optimization model with cost optimization as the core objective. In terms of technical implementation, this invention comprehensively evaluates communication performance by considering multiple dimensions of service quality indicators, including transmission distance, retransmission count, delay, reliability factors limiting hop count, link unreliability, and rate. Innovatively, this invention organically combines terrain environmental factors with service quality indicators, making communication constraints more targeted and practical. Simultaneously, it introduces the design of signal repeaters between heterogeneous devices and their related cost factors into the optimization framework, effectively improving the overall system efficiency and engineering feasibility. Furthermore, this invention collects real communication data from different communication devices in on-site monitoring or simulated environments and compares it with ideal communication data. Based on the similarity of data change trends, it divides the region and fits it to corresponding terrain parameters, making the model closer to actual application scenarios and significantly improving the model's accuracy and robustness. Ultimately, this model can significantly reduce construction and operation costs while ensuring communication quality and system reliability, and at the same time enhance the scalability and application value of the power grid system.

[0010] The technical solution adopted in this invention is as follows: A performance optimization model for a hybrid power grid network considering construction costs, comprising: Parameter system section: Define a comprehensive parameter system that includes communication equipment parameters, terrain environment parameters, and global system parameters to provide quantitative input for the optimization model; Mathematical Modeling: Construct a mathematical optimization model with the objective function of minimizing total lifecycle cost, and subject to multivariate constraints such as transmission distance, retransmission count, data latency, reliability, and flow rate. The optimization algorithm section employs an optimization algorithm based on node traversal and iterative calculation to find the optimal configuration scheme of communication equipment and repeaters for each node that satisfies all constraints and has the lowest cost.

[0011] Furthermore, the communication equipment parameters include: equipment parameters, communication technology parameters, and repeater parameters; Equipment parameters include: total equipment cost Monthly maintenance costs and signal coverage ; Communication technical parameters include: data delay parameters and link transmission delay. Delay in sending data packets by smart devices Data packet reception delay The cost of a single communication technology is a parameter related to communication costs. Technology capacity requirements and costs The data packet flow rate parameter. Maximum technical speed The link unreliability probability, a reliability parameter. Reliability factor Maximum delay allowed for data packets to reach their destination And the data packet transmission frequency of the data packet transmission parameters. Number of retransmissions ; The parameters for the repeater include: the total cost of repeaters between similar devices. Monthly maintenance costs Signal coverage radius And the cost of repeaters required to adapt to heterogeneous communication devices. .

[0012] Furthermore, the terrain environment parameters include: equipment cost parameters. Communication cost parameters Data delay parameter Device communication range parameters Flow rate parameters Retransmission count parameter Unreliability probability parameters ; The global system parameters include: equipment service life. The maximum distance between the source and the target Number of smart buildings in microgrids Number of devices Total cost .

[0013] Furthermore, the objective function of the mathematical model is: ; in, , which is the total cost of the repeater; In the formula, Total cost; Total cost of the repeater; For the number of devices; Total equipment cost; Monthly maintenance costs; For the cost of communication technology; This refers to the data packet transmission frequency; This represents the number of retransmissions. Cost of information capacity requirements; The number of smart buildings in the microgrid; The service life of the equipment; For equipment cost terrain parameters; For communication cost terrain parameters; Number of retransmissions of terrain parameters; Cost of inter-device transfer devices; The total cost of a transfer device between similar equipment; This refers to monthly maintenance costs.

[0014] Furthermore, the communication constraints of the mathematical model include at least one of the following: Transmission distance constraints ensure that the combined effective transmission range of the communication equipment and repeater can cover the maximum distance between the source and the target. Retransmission count constraints ensure that the system has sufficient retransmission capacity to cope with link unreliability; Data latency constraints ensure that the total end-to-end transmission latency does not exceed the maximum latency allowed by the service. Reliability constraints limit the maximum number of hops in data transmission using reliability factors. Flow rate constraints ensure that the data packet flow rate does not exceed the maximum physical rate of the communication technology used.

[0015] Furthermore, the transmission distance constraint is expressed as: ; The retransmission count constraint is expressed as: ; Data latency constraints are expressed as follows: ; The reliability constraint is expressed as: ; The flow rate constraint is expressed as: ; The communication constraints also include boundary constraints, limiting the number of devices to non-negative and the overall unreliability probability of the communication link to greater than or equal to zero and less than 1; the boundary constraints are expressed as: , ; In the formula, For the number of devices; For equipment coverage area; Terrain parameters for the device's communication range; This represents the probability of link unreliability. These are unreliable probabilistic terrain parameters; The coverage area of ​​the repeater; The maximum distance; It is a rounding function; This represents the number of retransmissions. Terrain parameters for the number of retransmissions; For data delay terrain parameters; This refers to data latency in the link; Delay in sending data packets; Delay in receiving data packets; Maximum allowable delay; Reliability factor; The data packet flow rate; For flow rate topographic parameters; This represents the maximum speed of the technology.

[0016] Furthermore, the implementation process of the optimization algorithm includes the following steps: S1 is initialized, communication device parameters are read, and different repeater combinations are pre-sorted by cost; The S2 node loop divides the power transmission line into multiple nodes and performs optimization on each node in turn. S3 node-wide iterative optimization: For the current node, execute: Read the terrain parameters of the current node; generate multiple combinations of the number of communication devices; verify each combination of the number of devices using the communication constraints; for combinations that pass the verification, calculate their total cost and compare it with the current optimal cost; if the cost is lower, update the optimal solution. S4 Results Summary and Output: After all nodes have been optimized, the globally optimal hybrid communication network construction scheme is summarized and output.

[0017] Furthermore, the performance optimization model for the hybrid power grid also includes a model validation section; Model validation section: Simulations are used to verify the effectiveness and practicality of the mathematical optimization model in terms of iterative convergence, rationality of equipment configuration, and cost advantages of hybrid solutions; Convergence: During iterative optimization, the total cost converges and tends to stabilize as the number of iterations increases; Equipment configuration rationality: Under different transmission distance requirements, the distribution of the number of configured communication devices conforms to their technical characteristics, that is, the cost of devices with short transmission distances is low, and the cost of devices with long transmission distances is high. Cost advantage: The total cost of the hybrid communication scheme is lower than that of using any single communication technology scheme at different transmission distances.

[0018] An electronic device includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor, when executing the program, implements any one of the above-described power grid hybrid network performance optimization models.

[0019] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements any of the above-described power grid hybrid network performance optimization models.

[0020] The beneficial effects of this invention are: 1. Significant Optimization of Lifecycle Costs: With minimizing total construction and operation costs as its core objective, the model comprehensively considers costs across all dimensions, including equipment procurement, installation, maintenance, communication technology usage, information capacity requirements, and repeaters, through refined mathematical formulas. Simulation results demonstrate that, at different transmission distances, the total cost of the proposed hybrid communication scheme is consistently lower than that of any single communication technology used alone, proving its significant economic advantages and providing economic assurance for the long-term sustainable development of power grid communication networks.

[0021] 2. Enhancing the Accuracy and Applicability of the Model in Practical Engineering: This invention innovatively introduces a terrain parameter system to quantify and abstract the impact of complex geographical environments on communication performance. By comparing and fitting real-world monitoring data with ideal data, the model can accurately adapt to diverse real-world terrains such as plains, mountains, and hills. This design greatly overcomes the shortcomings of existing technology models that are disconnected from reality, significantly improving the model's accuracy, robustness, and applicability in practical engineering scenarios.

[0022] 3. Ensuring Communication Performance and System Reliability through Multidimensional Constraints: The model does not simply pursue low cost, but rather establishes a complete multidimensional service quality constraint system, including transmission distance, retransmission count, data latency, reliability, and flow rate, to ensure that the optimized solution meets stringent communication quality and system reliability requirements while minimizing cost. This effectively avoids performance degradation caused by excessive cost reduction, achieving the optimal balance between cost and performance.

[0023] 4. Solving the Challenge of Heterogeneous Device Integration and Enhancing System Flexibility: This invention clearly proposes a solution and cost model for signal repeaters between heterogeneous devices, successfully achieving efficient hybrid relaying between different types of devices such as cellular communication, Wi-Fi, and ZigBee. This not only solves the pain point of poor interoperability between devices and constructs a highly practical hybrid communication system architecture, but also significantly improves the communication coverage, system flexibility, and resource utilization of smart distribution networks.

[0024] 5. Verification of the algorithm's high efficiency and stability, and good convergence: The optimization algorithm of the model has been verified to have good convergence and stability. As the number of iterations increases, the total cost result converges quickly and tends to stabilize, proving the efficiency and reliability of the algorithm's solution process, and ensuring the reproducibility of the optimization scheme and the feasibility of engineering implementation.

[0025] In summary, this invention effectively solves the core challenges of current smart distribution network communication networks in terms of cost planning, heterogeneous integration, and engineering applicability. It successfully constructs a high-performance optimization model that balances economy, reliability, accuracy, and practicality, providing key communication support technologies for the reliable, efficient, and long-term sustainable development of smart grids. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0027] Figure 1 This is a flowchart illustrating the operation of the power grid hybrid network performance optimization model of the present invention. Figure 2 This is an iterative graph of node results during the simulation analysis process of this invention; Figure 3 This is a distance-number of devices graph during the simulation analysis process of this invention; Figure 4 This is a distance-cost diagram used in the simulation analysis process of this invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention proposes a power grid hybrid network performance optimization model that takes into account construction costs, aiming to address the shortcomings of current smart distribution network communication network systems in terms of cost planning and integrated network planning.

[0030] The power grid hybrid network performance optimization model considering construction costs includes three main components: parameter system, mathematical model, and optimization algorithm, each of which is detailed below: I. Parameter System Section: The parameter system forms the data foundation for constructing the entire optimization model, providing comprehensive, quantitative, and realistic inputs for subsequent mathematical modeling and optimization algorithms. The parameter system aims to accurately characterize the technical characteristics and economic factors of smart grid hybrid communication networks in complex real-world environments. For example... Figure 1 As shown, the model operation begins with data reading, and the parameters defined in this section are the core data being read.

[0031] The parameter system mainly consists of the following three interrelated parts: 1. Communication Equipment Parameters: Communication equipment parameters are used to accurately describe the inherent attributes, performance indicators, and economic costs of various physical devices and communication technologies in a network. They can be further subdivided into three categories: 1.1 Equipment Parameters: Equipment parameters are directly related to the physical deployment and basic maintenance of the communication equipment itself, and are the core basis for fixed asset investment in computing equipment. The equipment parameters are shown in the table below: Total equipment cost : refers to the type of technology adopted The total purchase cost of a single smart device, =1, 2, and 3 correspond to cellular, Wi-Fi, and ZigBee respectively, thus covering the equipment purchase price and on-site installation and commissioning costs. Monthly maintenance costs. : refers to the technology used in a single unit The monthly operating expenses for routine maintenance, inspections, and consumables required for the equipment. Signal coverage area. : refers to the technology used in a single unit The maximum effective communication distance that a device can achieve in an ideal open environment is the basis for evaluating network coverage capabilities.

[0032] 1.2 Communication Technical Parameters: Communication technical parameters characterize the inherent protocol characteristics and performance limits of different communication technology standards, such as the inherent protocol characteristics and performance limits of Cellular, Wi-Fi, and ZigBee. They are crucial for evaluating the Quality of Service (QoS) of communication. The communication technical parameters are shown in the table below: Data latency parameters include: link transmission latency This represents the inherent time it takes for a signal to propagate through a physical medium. (This refers to the delay in data packet transmission by smart devices.) This represents the processing time required for the device to receive data from the application layer and complete the physical layer transmission. (Receive data packet delay) This indicates the processing time required for the device to receive a signal from the physical layer and deliver it to the application layer.

[0033] Communication cost parameters include: cost of individual communication technologies. , indicating the use of technology The cost incurred for successfully sending a single data transmission. Technical capacity requirement cost. , indicating technology The cost of infrastructure resources required to meet its information carrying and aggregation capabilities.

[0034] Traffic rate parameters include: packet flow rate This represents the actual data transmission rate on the communication link. Maximum technical rate. , indicating technology The maximum speed limit that the physical layer can support.

[0035] Reliability parameters include: link unreliability probability This represents the base probability of a single data transmission failure. Reliability factor This is a key constant used to constrain the maximum number of hops in a network and ensure system stability. It represents the maximum delay allowed for data packets to reach their destination. This represents the upper limit of the total end-to-end latency that the business can tolerate.

[0036] Data packet transmission parameters include: data packet transmission frequency This represents the number of data packets sent per unit of time, directly reflecting the service load. (Retransmission count) This indicates the maximum number of repeated transmissions set by the communication protocol under ideal conditions to ensure reliability.

[0037] 1.3 Repeater Parameters: Repeater parameters are specifically set for signal relay equipment and are the core of achieving heterogeneous network convergence and extending communication distance. The repeater parameters are shown in the table below: Total cost of inter-equipment transfer devices A repeater used to connect devices using the same communication technology; its unit purchase and installation cost. Monthly maintenance cost. This represents the monthly operation and maintenance cost of a single repeater. Signal coverage radius. This represents the monthly operation and maintenance cost of a single repeater. The repeater cost required to adapt to heterogeneous communication equipment. The additional costs incurred for special gateways or conversion equipment required to achieve signal conversion and relay between different types of communication devices, such as cellular and ZigBee.

[0038] 2. Terrain and Environmental Parameters: It is well known that large-scale power transmission equipment is typically built in sparsely populated areas and often involves ultra-long-distance power transmission. Therefore, power transmission lines often need to traverse various complex terrains. Against this backdrop, a modeling approach is proposed that abstracts the actual geographical transmission conditions of communication equipment into data. Specifically, by conducting on-site monitoring or simulation of different types of communication equipment, their actual communication performance data is obtained. This data is then compared with the communication performance of the equipment under ideal conditions, thereby identifying regions with similar trends in communication performance. Subsequently, a fitting method is used to generate corresponding terrain parameters for these regions, thus reflecting the impact of different terrains on the performance of communication equipment in the model. This ensures that the model possesses both abstract universality and a high degree of consistency with actual application scenarios.

[0039] In the modeling process, each region with similar communication performance variations is defined as a node. The terrain parameters of a node not only characterize the terrain features of that region but also reflect its impact on various performance indicators of the communication equipment. These terrain parameters, in turn, influence the selection of equipment within that node, and the final characteristics of the node are determined by the planned equipment configuration and its own terrain conditions. By progressively completing the selection and configuration of equipment between each node, the overall planning and optimization of the communication equipment for the entire power transmission line can ultimately be achieved. The terrain environment parameters are shown in the table below: Equipment cost parameters This represents the impact coefficient of specific terrain (such as mountains and hills) on the costs of equipment transportation, installation, and special protection. (Communication cost parameter) This represents the impact of terrain on signal propagation loss, and thus a correction factor for the cost of using communication technology and the cost of capacity requirements. Data delay parameter. This represents the aggravation factor of various transmission delays caused by terrain-induced signal multipath effects and diffraction loss. Equipment communication range parameters. This characterizes the reduction factor of terrain obstacles on the effective coverage area of ​​equipment. Flow rate parameter. This is a coefficient characterizing the impact of terrain on signal quality, which in turn leads to a decrease in effective transmission rate. Retransmission count parameter. This is a coefficient characterizing the increased probability of packet retransmission due to additional interference and signal fading introduced by terrain. It is a probability parameter for unreliability. This comprehensively characterizes the inherent unreliability probability of terrain on communication links. The amplification effect.

[0040] These parameters are obtained through the following process: conducting on-site monitoring or high-fidelity simulation of the target area, collecting real performance data of different communication devices, such as actual rate, bit error rate, and coverage, comparing them with benchmark data under ideal conditions, dividing the area based on the similarity of data change trends, and finally generating a set of corresponding terrain parameter values ​​for each area using methods such as curve fitting.

[0041] 3. Global System Parameters: Global system parameters define the macroscopic boundary conditions and overall objective of the entire optimization problem. The global system parameters are shown in the table below: Equipment service life This refers to the planned operating cycle of the entire communication network system or the depreciation period of the equipment, used to calculate the total lifecycle cost. The maximum distance between the source and the target. The distance between the two farthest nodes in the power transmission lines to be covered is a direct constraint on network coverage. The number of smart buildings in a microgrid... This represents the total number of base stations, such as smart meters and monitoring terminals, that need to transmit user data and access the transmission network, reflecting the network's service load. (Number of devices) , representing the number of a certain type of communication equipment deployed at a specific node or within a planned area, is the core decision variable of the model. Total cost , which is the total cost calculated by the objective function, is the ultimate goal of model optimization, and represents the sum of all relevant costs required to cover a specific node or line within the planning and operation cycle Y years.

[0042] The parameter system systematically integrates equipment attributes, technical characteristics, geographical influences, and economic factors to form a comprehensive quantitative framework describing the "performance-environment-cost" relationship of a hybrid power grid communication network. This not only ensures the completeness and accuracy of the model input but also serves as an absolute prerequisite for subsequent mathematical model construction and optimization algorithm execution, laying a solid data foundation for generating economically efficient and technically feasible network construction solutions.

[0043] II. Mathematical Model Section: The mathematical model is the theoretical core and decision engine of the entire optimization model. Its role is to transform the actual engineering optimization problem into a computable mathematical problem. Based on the aforementioned parameter system, this model constructs an optimization framework with economic efficiency as the ultimate goal, while being strictly constrained by multiple physical and technical conditions, ensuring that the solution has both cost optimization and engineering feasibility.

[0044] like Figure 1 As shown, after reading the terrain parameters during node iteration, the iterative loop phase begins. In this phase, each iteration generates a combination of device numbers that must be validated under the constraints of the mathematical model. The total cost is calculated using the model's objective function, and the optimal solution is ultimately selected based on the principle of cost minimization. Therefore, the following objective function and constraints are the core computational rules driving the iterative optimization process.

[0045] The mathematical model consists of an objective function and a set of constraints, as follows: The objective function aims to calculate and minimize the total cost of ownership of the power grid hybrid communication network over the entire planning period. Its core formula is as follows: ; in, Total cost refers to the sum of all relevant costs required to cover a specific node or line during the planning and operation cycle, and is the ultimate goal of model optimization.

[0046] This formula systematically integrates four major categories of costs: 1. Equipment purchase and maintenance costs: Calculate the fixed asset investment of all communication equipment over its life cycle.

[0047] The number of devices refers to the number of a certain type of communication devices deployed in a specific node or planned area. The total cost of the equipment refers to the cost of using communication technology. The total purchase cost of a single smart device typically includes the purchase price of the device itself and the initial installation cost; Monthly maintenance cost refers to the cost per unit using the technology. The monthly operating and maintenance costs of smart devices; The service life of the equipment refers to the planned operating cycle of the entire communication network system or the depreciation period of the equipment. This is the terrain parameter for equipment cost, reflecting the comprehensive impact coefficient of a specific terrain environment on the costs of equipment purchase, installation, and maintenance. This sum is obtained through the equipment cost terrain parameter. Make geographical adjustments, then multiply by the number of devices. The total cost of the equipment at that node is obtained.

[0048] 2. Cost of using communication technology: Calculate the operating costs incurred due to data transmission.

[0049] The cost of communication technology refers to the cost of using communication technology. The cost per successful data transmission; This is a terrain parameter for communication costs, which reflects the influence coefficient of a specific terrain environment on the cost of using communication technologies and the cost of information capacity requirements. Data packet transmission frequency refers to the number of data packets sent per unit of time, reflecting the communication volume of the service. The retransmission count refers to the inherent maximum number of retransmissions set by the communication protocol to ensure reliable delivery of data packets under ideal conditions. The retransmission count terrain parameter reflects the impact of a specific terrain environment on signal transmission quality, directly leading to an increase in the actual number of retransmissions required. To take into account the influence of terrain The actual maximum number of retransmissions was then used to ensure that the cost calculation covered the repeated transmissions performed to guarantee reliability; the overall communication cost was assessed using terrain parameters. Correct and multiply by the number of devices. .

[0050] 3. Information capacity requirement cost: Calculate the basic resource costs required to meet the system's data aggregation and carrying capacity.

[0051] The cost of information capacity requirements refers to the cost of communication technology. The cost of infrastructure or resources required to meet its information carrying capacity requirements; The cost refers to the number of smart buildings in a microgrid, specifically the number of base stations or smart terminals on the lines that need to transmit user data and connect to the transmission network. This cost increases with the total number of devices in the system. The increase in capacity decreases exponentially, reflecting economies of scale—more devices can more efficiently distribute and aggregate data streams, thereby reducing the cost per unit capacity. Communication cost terrain parameters Adjust and multiply by the equipment's lifespan. .

[0052] 4. Integration cost of the repeater Total cost of the repeater To cover all costs associated with relay equipment incurred in signal relaying and heterogeneous device interconnection, denoted as: ; The cost of repeaters between heterogeneous devices refers to the additional cost incurred for special repeaters required to achieve signal conversion and relay between different types of communication devices, such as cellular and ZigBee. The total cost of a transfer device between similar equipment; This refers to monthly maintenance costs. It is the lifecycle cost of a single transfer device between similar equipment; This is the minimum number of repeaters required; this part is also affected by equipment cost and terrain parameters. Influence.

[0053] In summary, this objective function, through a comprehensive formula, achieves accurate modeling and overall optimization of the entire lifecycle cost of the constructed equipment, transceivers, and operation and maintenance, including maintenance, communication, and capacity.

[0054] The constraints ensure that the cost-optimal solution simultaneously meets the necessary technical performance indicators, preventing infeasible solutions from arising. The constraints and their physical meanings are as follows: 1. Transmission distance constraints: The transmission distance constraint ensures that the entire communication link from the source point to the destination point can be covered by the effective signals of the devices and repeaters.

[0055] The coverage area of ​​a device is the maximum transmission distance of a single communication device under ideal conditions. This is a terrain parameter representing the communication range of the equipment, and it is a coefficient that characterizes the influence of terrain on the signal coverage of the equipment. This represents the probability of link unreliability. This is a probabilistic terrain parameter representing the influence coefficient of terrain on link unreliability; The coverage area of ​​a repeater is the maximum transmission distance of a single repeater under ideal conditions. This represents the maximum distance between the source and target nodes. The left side of the formula is the sum of the "effective coverage distance of the device" and the "effective coverage distance of the repeater." As a reliability squared factor, based on the path loss and connectivity model, the ideal coverage distance is reduced based on reliability, making the model more consistent with the actual radio propagation characteristics.

[0056] 2. Retransmission count constraint: The retransmission count constraint ensures that the retransmission capability configured in the system is sufficient to overcome packet loss caused by link unreliability and terrain interference.

[0057] This is a rounding function to ensure that the number of retransmissions is an integer. The retransmission count is the maximum number of retransmissions allowed by the communication protocol under ideal conditions. The retransmission count is a terrain parameter that characterizes the coefficient by which the terrain environment increases the number of retransmissions required. It is the theoretical minimum retransmission capability required to guarantee successful transmission, calculated based on the comprehensive unreliability probability. The inherent retransmission capability of the equipment is affected by terrain. The actual capability after amplification and addition of 1 to provide a margin. Retransmission count constraints are crucial for ensuring communication reliability.

[0058] 3. Data latency constraints: Data latency constraints ensure that the total time from data packet transmission to reception, i.e. end-to-end latency, meets the real-time requirements of the business.

[0059] This is a terrain parameter representing the data delay, which is a coefficient characterizing the influence of terrain on various delays during data transmission. Data delay in the link refers to the fixed delay caused by the signal propagating along the physical link; The delay in sending data packets is the delay that occurs when a smart device processes and sends a data packet. To receive data packet delay, the delay that occurs when a smart device receives and processes a data packet; The maximum permissible latency is the maximum total end-to-end latency that the business can tolerate. Data latency constraints include single-hop latency, i.e., link latency. + Device processing delay + After correction by terrain parameter c, multiplied by the total hop delay considering retransmissions, the hop count is approximately proportional to... Constraints are based on the maximum allowable latency of the business. Within.

[0060] 4. Reliability constraints: Reliability constraints are achieved by introducing reliability factors. To limit the maximum size of the network.

[0061] The reliability factor is a constant used to limit the maximum number of hops in a system, approximately proportional to the number of devices. This is to ensure the network does not become unstable due to excessive complexity. With the increasing number of devices... This is similar to an increase in hop count, or link unreliability. × As the scale increases, the overall probability of system failure rises. Reliability constraints directly limit the product of these probabilities, preventing the network from losing stability due to excessive size or complexity. This is a simplified yet effective means of ensuring system-level robustness.

[0062] 5. Flow rate constraint: The flow rate constraint ensures that the data transmission rate requirements of the service will not exceed the physical limits of the selected communication technology.

[0063] This refers to the data packet flow rate, the actual data transmission rate on the communication link. This is a topographic parameter representing the influence of topography on the effective flow rate; The maximum data rate is the maximum physical layer rate that the communication technology employed can support. Actual flow rate. Flow rate topographic parameters After correction, it must be less than or equal to the maximum physical speed of the technology. To prevent channel overload.

[0064] 6. Boundary Constraints: Communication constraints also include boundary constraints, which limit the number of devices to non-negative and the overall unreliability probability of the communication link to greater than or equal to zero and less than 1; the boundary constraints are expressed as: , .

[0065] These two are fundamental mathematical constraints that ensure the physical meaning and mathematical validity of the solution. To prevent negative numbers of equipment; Ensuring that unreliability is a valid probability value, and making the denominator in the retransmission constraint formula ( The value is not zero to avoid calculation errors.

[0066] The mathematical model combines cost objectives and performance constraints within a unified mathematical framework and innovatively introduces terrain parameters to perform on-site environmental corrections for various calculations. This enables multi-objective, high-fidelity, and computable optimization decisions for smart grid hybrid communication network construction schemes under complex real-world conditions, providing a solid mathematical foundation for ultimately solving the optimal configuration scheme through optimization algorithms.

[0067] III. Optimization Algorithm Section: The optimization algorithm is the solution engine and execution controller of the entire model. Its function is to transform the parameter system and mathematical model into executable steps, thereby automatically and efficiently searching for the globally optimal cost solution that satisfies all engineering and technical constraints. The algorithm adopts a two-layer strategy combining "macro-node traversal" and "micro-iterative optimization" to ensure that it finds a near-optimal configuration of equipment and relays in the complex solution space.

[0068] The complete execution flow and logic of the algorithm are as follows: Figure 1 As shown: S1 Initialization: Prepares all the basic data and preprocessing information required for the algorithm to run.

[0069] Data Reading: The system reads all communication device parameters defined in the parameter system section from the storage medium, including device parameters, communication technology parameters, and repeater parameters. This data forms the basis for all subsequent calculations.

[0070] Pre-sorting: The algorithm pre-sorts all possible combinations of relays, including relay schemes between homogeneous devices and between heterogeneous devices, according to their total cost. This preprocessing step ensures that the lowest-cost relay scheme can be quickly selected in subsequent optimization, thereby improving the overall optimization efficiency.

[0071] S2 node loop: Implements a divide-and-conquer optimization strategy for large-scale power transmission lines.

[0072] Node partitioning: Long power transmission lines are divided into a series of continuous nodes based on geographical features, distance, or management needs. Each node represents a relatively independent communication planning area.

[0073] Execute in a loop: such as Figure 1 As shown in the node loop and the sequence of steps to the next node, the algorithm starts from the first node and executes step S3 independently for each node until all nodes have been optimized. This loop ensures that every part of the entire line is adequately planned.

[0074] Iterative optimization within the S3 node: This is the core optimization step of the algorithm, such as... Figure 1 The iteration loop from the beginning to the end node is the part in the code. This step focuses on a single node and searches for its local optimum through iteration.

[0075] S31 Read Terrain Parameters: Obtain the specific terrain environment parameters (a, b, c, d, e, f, g) of the currently being processed node. This step associates the abstract mathematical model with the specific geographical environment and is crucial for the model's accuracy.

[0076] S32 generates device combinations: Within the known device types (Cellular, Wi-Fi, ZigBee) and allowed quantity range, it randomly generates multiple combinations of communication device types. Each combination represents a possible network construction scheme.

[0077] S33 Constraint Verification: For each combination of device quantities generated in step S32, the algorithm calls upon the communication constraints defined in the mathematical model: transmission distance, retransmission count, data latency, reliability, flow rate, etc., and verifies them one by one. For example... Figure 1 As shown, the number of devices and communication limits for each device are calculated, and a pass / fail judgment is made.

[0078] If the combination fails, it is immediately discarded, and the algorithm continues to check the next combination.

[0079] If approved, proceed to the cost assessment and comparison stage.

[0080] S34 Cost Comparison and Solution Update: For feasible combinations that pass all constraint checks, the algorithm performs the following operations: Calculate total cost: Based on the objective function formula in the mathematical model section, calculate the total lifecycle cost of this equipment combination. .

[0081] Selecting a Transmitter: From the pre-sorted list of transmitter combinations in S1, select the lowest-cost feasible transmitter option for this equipment combination, and set its cost... Included in total cost.

[0082] Comparison and Update: Compare the calculated total cost with the minimum cost recorded in the current node.

[0083] If the cost is higher or equal: such as Figure 1 As shown in the path with the larger amount, this option was discarded.

[0084] If the cost is lower: such as Figure 1 If the path with a smaller amount is shown, then the amount and number of devices are overridden, that is, the current better solution, including the number of devices, combination and its relay configuration, is updated to the temporary best solution for this node.

[0085] S4 Results Summary and Output: This step completes the integration from local optimum to global optimum.

[0086] Summary: After all nodes have completed S3 optimization, the algorithm summarizes the temporary optimal solution for each node, namely the equipment and repeater configuration and its cost.

[0087] Output: Finally, the algorithm outputs a complete, globally cost-optimal hybrid communication network construction scheme. This scheme details the types and quantities of communication equipment to be used at each node along the entire power transmission line, the repeater configuration, and the expected total construction and maintenance costs.

[0088] The optimization algorithm transforms the complex network planning problem into an automated computation process through the aforementioned steps, avoiding the subjectivity and inaccuracy caused by relying on human experience. It ensures the optimization of the overall route through node looping and finds the lowest-cost feasible solution at each local node through iterative optimization, thereby approaching the global optimum. Through rigorous constraint verification, it ensures that each considered solution is technically feasible and meets performance standards. By combining relay pre-sorting, random generation, and iterative screening, it performs a directional search in the huge solution space, balancing computational efficiency and solution quality.

[0089] In summary, the power grid hybrid network performance optimization model considering construction costs provided in this embodiment innovatively solves the signal relay and coordination problem between different types of communication equipment. By proposing a clear relay solution, it constructs a highly practical hybrid communication system model. This model effectively overcomes the limitations of a single communication mode, significantly expands the communication coverage of the smart distribution network, and comprehensively improves the system's communication capacity and link reliability. Unlike most existing models that rely on ideal laboratory assumptions, this power grid hybrid network performance optimization model uses a data abstraction method to represent the impact of the actual geographical environment on communication transmission. Through on-site monitoring or simulation, it obtains real communication data and compares it with ideal values, divides regions, and fits and generates various terrain parameters. This core innovation enables the model to accurately adapt to complex and varied terrain environments such as plains, mountains, and hills, greatly improving the model's accuracy, robustness, and applicability in practical engineering applications. In the equipment compatibility assessment, the power grid hybrid network performance optimization model breaks through the limitations of the single optimization dimension of existing technologies. It constructs a multi-dimensional service quality constraint system, comprehensively considering key factors such as transmission distance, retransmission count, delay, link unreliability, reliability factor, and flow rate, and incorporates the impact of terrain. At the same time, the model takes minimizing the total life cycle cost as the ultimate optimization goal, ensuring that the output planning scheme is not only technically feasible and reliable, but also achieves optimal economic benefits, thereby realizing the best balance between communication performance and construction cost.

[0090] It should be noted that this power grid hybrid network performance optimization model, which takes into account construction costs, can be implemented in the form of a computer program within hardware-based electronic devices and computer-readable storage media, as detailed below: For example, an electronic device is provided, the core of which lies in the specific execution of the aforementioned optimization model through a combination of hardware and software. This electronic device includes at least one processor and at least one memory. The processor can be a hardware unit with computing capabilities, such as a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA). The memory can include non-transitory storage devices such as read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), used to store data and program instructions.

[0091] A computer program is stored in the memory. When the processor reads and executes this computer program, it controls the electronic device to implement the preceding hybrid power grid network performance optimization model. When executing the program, the electronic device will: read the parameter system from the memory or receive it from an external interface, including: communication equipment parameters, terrain parameters, and global parameters. The processor will then call memory to run the optimization algorithm. During execution, the processor will perform cost calculations based on the objective function of the mathematical model and conduct feasibility verification based on its multivariate constraints. Finally, through the processor's calculations, a globally optimal hybrid communication network construction scheme will be output.

[0092] To enable the power grid hybrid network performance optimization model to be easily stored, distributed, and loaded onto various computing devices, a computer-readable storage medium is provided. This medium is a non-transient physical carrier, and its specific form may include, but is not limited to: optical discs, USB flash drives, portable hard drives, memory cards, computer ROMs, RAMs, or server storage arrays, etc.

[0093] The storage medium contains a computer program. When this storage medium is connected to or installed in a computing device containing a processor and memory, i.e., the aforementioned electronic device, the stored program can be read and executed by the processor of that device. When the program is executed by the processor, the function it performs is completely consistent with that of the aforementioned electronic device, i.e., realizing the performance optimization model of the hybrid power grid network.

[0094] Furthermore, to verify the effectiveness, reliability, and practical value of the power grid hybrid network performance optimization model, this embodiment also conducted a comprehensive simulation analysis. The simulation settings and verification methods are described in detail below, along with... Figure 2 , Figure 3 , Figure 4 The verification results are analyzed and explained as follows: I. Simulation Environment and Parameter Settings: The core purpose of simulation is to test the performance of the model in a near-realistic scenario.

[0095] Data Sources: To ensure the authority and credibility of the simulation data, key technical parameters of wireless communication devices such as Cellular, Wi-Fi, and ZigBee involved in this simulation, including speed, coverage, and latency, are all derived from publicly released technical white papers and product specifications of mainstream equipment manufacturers. Cost and performance data for repeaters are obtained from technical documents of multiple suppliers, ensuring the objectivity of the input data.

[0096] Terrain Simulation: To comprehensively evaluate the model's terrain adaptability, the simulation environment was divided into five representative regions, covering common power transmission line terrains such as plains, mountains, and hills. The terrain parameters (a, b, c, d, e, f, g) corresponding to each terrain region were obtained using high-fidelity channel simulation software, thereby accurately reproducing the complex impact of the geographical environment on communication performance in the model.

[0097] Simulation Scenario: Based on the above parameters, a typical power communication line scenario with multiple nodes was constructed. The model uses three types of heterogeneous wireless communication devices—Cellular, Wi-Fi, and ZigBee—for hybrid network simulation. Key performance parameters are set as follows: ZigBee stream rate approximately 250kbps, Wi-Fi approximately 11Mbps, and Cellular approximately 75Mbps. These settings are consistent with the technical specifications of mainstream commercial equipment.

[0098] II. Model Validation: The model was validated from three dimensions, and the results correspond to... Figure 2 , Figure 3 , Figure 4 .

[0099] 1. Convergence verification: This verifies whether the optimization algorithm can stably find the optimal solution, that is, as the calculation proceeds, the quality of the solution no longer fluctuates drastically but tends to stabilize.

[0100] Verification methods and results: such as Figure 2 As shown, with other conditions fixed, such as maximum transmission distance, the initial cost was set at 6000 yuan, and iterative tests of different scales, ranging from 20 to 1000, were conducted. To facilitate horizontal comparison, all iteration sequences were normalized to a range of 20 iterations. Figure 2 The results clearly show that, at different iteration counts, the total cost decreases rapidly with the iteration process and eventually converges to a stable value. In particular, at 500 and 1000 iterations, the final cost remains essentially unchanged. This strongly demonstrates that the optimization algorithm exhibits good convergence and stability, and can efficiently and reliably search for the optimal equipment configuration.

[0101] 2. Verification of equipment configuration rationality: Verify whether the equipment combination scheme output by the model conforms to the inherent characteristics of different communication technologies and common engineering sense.

[0102] Verification methods and results: such as Figure 3 As shown, the maximum distance from the source to the target node was systematically varied, and the distribution of the number of three types of devices planned by the model to meet this distance requirement was observed. The results show: For short-distance transmission, low-cost ZigBee devices dominate.

[0103] As distance increases, the number of Wi-Fi devices with medium cost and coverage rises significantly.

[0104] In long-distance transmission scenarios, Cellular devices, which offer the strongest coverage but are also the most expensive, have become the mainstay.

[0105] The distribution of this number of devices evolved from ZigBee dominance to Wi-Fi transition and then to Cellular dominance, as follows: Figure 3 As shown, it fully conforms to the technical and economic characteristics of the three factors: "short-cheap, medium-medium, long-expensive," proving the logical correctness and engineering rationality of the model configuration results.

[0106] 3. Cost Advantage Verification: Verify whether the proposed hybrid communication solution has significant economic advantages compared to traditional single-technology solutions.

[0107] Verification methods and results: such as Figure 4 As shown, the total cost of using a hybrid communication scheme versus using Cellular, Wi-Fi, or ZigBee communication technologies alone is compared at different transmission distances. Simulation results clearly show: Across all transmission distances, the total cost curve of the hybrid communication solution consistently lies below the cost curve of any single communication technology solution. This means that by intelligently combining different technologies and optimizing their configurations, the hybrid communication solution can consistently achieve the lowest construction and maintenance costs while meeting performance requirements.

[0108] also, Figure 4 The occurrence of a single technology having a zero cost is because, at that specific distance, that single technology cannot meet the model's latency or other constraints, and is therefore directly excluded by the algorithm. This, in turn, confirms that the model strictly adheres to the quality of service requirements, ensuring the feasibility of the solution.

[0109] Through the above systematic simulation verification, the following conclusions can be drawn: The power grid hybrid network performance optimization model provided in this embodiment has a stable algorithm convergence, a reasonable output scheme configuration, and its planned hybrid communication network has a significant and continuous cost advantage compared with traditional schemes. These results fully demonstrate that the power grid hybrid network performance optimization model is not only theoretically correct but also has high engineering practical value, and can provide reliable and efficient decision support for the scientific planning and construction of smart grid hybrid communication networks.

[0110] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A performance optimization model for a hybrid power grid network considering construction costs, characterized in that: The performance optimization model for this hybrid power grid includes: Parameter system section: Define a comprehensive parameter system that includes communication equipment parameters, terrain environment parameters, and global system parameters to provide quantitative input for the optimization model; Mathematical Modeling: Construct a mathematical optimization model with the objective function of minimizing total lifecycle cost, and subject to multivariate constraints such as transmission distance, retransmission count, data latency, reliability, and flow rate. The optimization algorithm section employs an optimization algorithm based on node traversal and iterative calculation to find the optimal configuration scheme of communication equipment and repeaters for each node that satisfies all constraints and has the lowest cost.

2. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The communication equipment parameters include: equipment parameters, communication technology parameters, and repeater parameters; Equipment parameters include: total equipment cost Monthly maintenance costs and signal coverage ; Communication technical parameters include: data delay parameters and link transmission delay. Delay in sending data packets by smart devices Data packet reception delay The cost of a single communication technology is a parameter of communication cost. Technology capacity requirements and costs The data packet flow rate parameter. Maximum technical speed The link unreliability probability, a reliability parameter. Reliability factor Maximum delay allowed for data packets to reach their destination And the data packet transmission frequency of the data packet transmission parameters. Number of retransmissions ; The parameters for the repeater include: the total cost of repeaters between similar devices. Monthly maintenance costs Signal coverage radius And the cost of repeaters required to adapt to heterogeneous communication devices. .

3. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The terrain environment parameters include: equipment cost parameters. Communication cost parameters Data delay parameter Device communication range parameters Flow rate parameter Retransmission count parameter Unreliability probability parameters ; The global system parameters include: equipment service life. The maximum distance between the source and the target Number of smart buildings in microgrids Number of devices Total cost .

4. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The objective function of the mathematical model is: ； in, , which is the total cost of the repeater; In the formula, Total cost; Total cost of the repeater; For the number of devices; Total equipment cost; Monthly maintenance costs; For the cost of communication technology; This refers to the data packet transmission frequency; This represents the number of retransmissions. Cost of information capacity requirements; The number of smart buildings in the microgrid; The service life of the equipment; For equipment cost terrain parameters; For communication cost terrain parameters; Number of retransmissions of terrain parameters; Cost of inter-device transfer devices; The total cost of a transfer device between similar equipment; This refers to monthly maintenance costs.

5. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The communication constraints of the mathematical model include at least one of the following: Transmission distance constraints ensure that the combined effective transmission range of the communication equipment and repeater can cover the maximum distance between the source and the target. Retransmission count constraints ensure that the system has sufficient retransmission capacity to cope with link unreliability; Data latency constraints ensure that the total end-to-end transmission latency does not exceed the maximum latency allowed by the service. Reliability constraints limit the maximum number of hops in data transmission using reliability factors. Flow rate constraints ensure that the data packet flow rate does not exceed the maximum physical rate of the communication technology used.

6. The power grid hybrid network performance optimization model considering construction costs according to claim 5, characterized in that: The transmission distance constraint is expressed as: ; The retransmission count constraint is expressed as: ; Data latency constraints are expressed as follows: ; The reliability constraint is expressed as: ; The flow rate constraint is expressed as: ; The communication constraints also include boundary constraints, limiting the number of devices to non-negative and the overall unreliability probability of the communication link to greater than or equal to zero and less than 1; the boundary constraints are expressed as: , ; In the formula, For the number of devices; For equipment coverage area; Terrain parameters for the device's communication range; This represents the probability of link unreliability. These are unreliable probabilistic terrain parameters; The coverage area of ​​the repeater; The maximum distance; It is a rounding function; This represents the number of retransmissions. Terrain parameters for the number of retransmissions; For data delay terrain parameters; This refers to data latency in the link; Delay in sending data packets; Delay in receiving data packets; Maximum allowable delay; Reliability factor; The data packet flow rate; For flow rate topographic parameters; This represents the maximum speed of the technology.

7. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The implementation process of the optimization algorithm includes the following steps: S1 is initialized, communication device parameters are read, and different repeater combinations are pre-sorted by cost; The S2 node loop divides the power transmission line into multiple nodes and performs optimization on each node in turn. S3 node-wide iterative optimization: For the current node, execute: Read the terrain parameters of the current node; generate multiple combinations of the number of communication devices; verify each combination of the number of devices using the communication constraints; for combinations that pass the verification, calculate their total cost and compare it with the current optimal cost; if the cost is lower, update the optimal solution. S4 Results Summary and Output: After all nodes have been optimized, the globally optimal hybrid communication network construction scheme is summarized and output.

8. The power grid hybrid network performance optimization model considering construction costs according to claim 1, characterized in that: The performance optimization model for the hybrid power grid also includes a model validation section. Model validation section: Simulations are used to verify the effectiveness and practicality of the mathematical optimization model in terms of iterative convergence, rationality of equipment configuration, and cost advantages of hybrid solutions. Convergence: During iterative optimization, the total cost converges and tends to stabilize as the number of iterations increases; Equipment configuration rationality: Under different transmission distance requirements, the distribution of the number of configured communication devices conforms to their technical characteristics, that is, the cost of devices with short transmission distances is low, and the cost of devices with long transmission distances is high. Cost advantage: The total cost of using the hybrid communication scheme is lower than the cost of using any single communication technology scheme alone, regardless of the transmission distance.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the program, it implements the power grid hybrid network performance optimization model as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, it implements the power grid hybrid network performance optimization model as described in any one of claims 1 to 8.

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