A power distribution cyber physical system collaborative planning method and device, terminal equipment and computer readable storage medium

Abstract

The application discloses a power distribution cyber-physical system collaborative planning method and device, terminal equipment and a computer readable storage medium, and belongs to the technical field of power distribution network planning. The method comprises the following steps: obtaining network topology data, equipment configuration state data and dynamic coordination parameters to obtain planning basic data; evaluating a static configuration dependency relationship according to the equipment configuration state data, and constructing a coupling risk complexity; quantifying bottom-layer communication calling and running load overhead according to the dynamic coordination parameters, and constructing a fault handling coupling complexity; taking the minimum of the two complexities as an optimization target, and taking the upper limit of the system annual expected power supply shortage as a constraint condition, constructing and solving a double-target collaborative planning model, and generating a configuration state combination of each device to perform collaborative planning. The application solves the problem that the existing power distribution network planning ignores the bottom-layer communication and physical running overhead, leading to excessive configuration of automatic equipment and causing system overload and communication congestion.

Description

Technical Field

[0001] This invention relates to the field of power distribution network planning technology, and in particular to a collaborative planning method, apparatus, terminal equipment, and computer-readable storage medium for power distribution cyber-physical systems. Background Technology

[0002] In the field of distribution network construction, most existing distribution network collaborative planning focuses solely on reducing the system's expected annual power shortage as a single objective or constraint, neglecting the actual carrying capacity of the system's underlying resources. Specifically, existing technologies do not deeply integrate the configuration status of primary equipment, automation functions, and communication links for static risk assessment, and they are detached from the actual operating conditions of dynamic coordination parameters such as the number of terminals involved in observation, the number of switches involved in control, the number of communication links invoked, and the number of manually operated switches during fault handling.

[0003] Because existing technologies lack a comprehensive evaluation based on the above-mentioned equipment configuration status and dynamic coordination parameters, and fail to optimize by minimizing the coupling complexity of risk and fault handling, the system is prone to problems such as underlying communication congestion and operational overload due to over-configuration of automated equipment, and cannot output the optimal configuration combination. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal equipment, and computer-readable storage medium for collaborative planning of power distribution cyber-physical systems, which can solve the problem of system overload and communication congestion caused by the over-configuration of automation equipment due to the neglect of underlying communication and physical operation overhead in existing power distribution network planning.

[0005] An embodiment of the present invention provides a collaborative planning method for a power distribution cyber-physical system, comprising: The network topology data of the distribution network to be planned, the equipment configuration status data of each device, and the dynamic coordination parameters during the fault handling process are obtained to obtain the basic planning data of the distribution cyber-physical system. Based on the equipment configuration status data, assess the static configuration dependencies between various devices in the power distribution network and construct the coupling risk complexity. The underlying communication calls and operational overhead caused by the fault handling process are quantified based on the dynamic coordination parameters, and the fault handling coupling complexity is constructed based on the underlying communication calls and operational overhead. With the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, a dual-objective collaborative planning model for the power distribution cyber-physical system is constructed, and reliability constraints are set, wherein the system's annual expected power shortage meets a set upper limit. Under the constraints of the reliability constraints, the dual-objective collaborative planning model is solved to generate the configuration status of candidate segmented switches, the configuration status of candidate tie switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. Based on the configuration status of the candidate sectionalizing switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links, collaborative planning of the power distribution cyber-physical system is carried out.

[0006] Furthermore, the expected annual power shortage of the system is calculated from the fault handling time of each faulty object in the distribution network and the power restoration capability of each power-loss load point; the fault handling time of each faulty object is calculated through the following steps: Based on the equipment configuration status data, the operation mode adopted by each faulty object during fault handling is determined; wherein, the operation mode includes manual mode, remote two-way mode, remote three-way mode or distributed collaborative mode; For each faulty object, the communication transmission delay time and mechanical action time required to process the faulty object are determined according to the operation mode corresponding to the faulty object. Calculate the fault location time and fault isolation time of the faulty object based on the communication transmission delay time and the mechanical action time; The fault location time of the faulty object is added together with the corresponding fault isolation time to obtain the fault handling time of the faulty object.

[0007] Furthermore, the power restoration capability of each power-loss load point in each of the aforementioned faulty objects is calculated in the following manner: For each faulty object, after the fault handling time of the faulty object ends, based on the equipment configuration status data, determine the interconnection-based power transfer recovery power and distributed power islanding recovery power of each power-loss load point corresponding to the faulty object; construct mutual exclusion selection constraints for each power-loss load point; wherein, the mutual exclusion selection constraints are used to restrict each power-loss load point to only be restored by either interconnection-based power transfer or distributed power islanding at the same time; based on the mutual exclusion selection constraints and the preset power restoration priority order, select one of the interconnection-based power transfer recovery power and distributed power islanding recovery power corresponding to each power-loss load point, and determine the selected recovery power as the power restoration capacity of each power-loss load point.

[0008] Furthermore, the expected annual power shortage of the system is calculated in the following manner: For each faulty object, obtain the annual fault rate, manual recovery time, load power corresponding to each power loss load point in the faulty object, and recovery operation time of each power loss load point when power is restored. Based on the fault handling time of the fault object and the load power corresponding to each power loss load point in the fault object, calculate the power shortage of each power loss load point during the fault handling stage. Based on the manual recovery time of the fault object, the load power corresponding to each power loss load point in the fault object, the power supply recovery capability corresponding to the power loss load point, and the recovery operation time, calculate the power supply shortage of each power loss load point corresponding to the fault object during the recovery phase. The power shortage at each of the power-loss load points during the fault handling phase is added to the power shortage during the recovery phase to obtain the node power shortage at each power-loss load point. The total power supply deficit of the fault object is obtained by summing the power supply deficit of each of the aforementioned power loss load points. Using the annual failure rate corresponding to each of the aforementioned fault objects, the total power supply shortage of each fault object is weighted and summed to obtain the expected annual power supply shortage of the system.

[0009] Furthermore, the coupling risk complexity is calculated using the following formula: ; ; In the formula, Let S be the coupling risk complexity; S, T, and G are the candidate segmented switch set, candidate tie switch set, and candidate distributed power source access location set in the primary and secondary collaborative planning set, respectively; s and r are elements in set S, and t and g are elements in sets T and G, respectively. Let (i,j) be the set of candidate cooperative communication links between terminals, and (i,j) be the set of... Elements in; The number of downstream load points of the sectionalizing switch S; The number of downstream load points corresponding to element r; This represents the maximum number of downstream load points among all candidate sectionalizing switches. For segmented switches The topological importance coefficient; , , These are the configuration state variables for the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively. These are the remote function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; These are the remote control function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; Configure state variables for distributed collaborative terminals; Configure state variables for distributed power supply islanding control functionality; , , These are sectional switches. , contact switch Configure status variables for the communication link between the automated terminal and the master station on the distributed power source; Configure state variables for the cooperative communication link between sectional switch i and sectional switch j.

[0010] Furthermore, the fault handling coupling complexity is calculated using the following formula: ; ; in, The fault handling coupling complexity is given by K; K is the set of fault objects, and k is an element in set K. is the annual failure rate of faulty object k; m is the index of the operating mode number. The selection variable for the mode m corresponding to the faulty object k; The dynamic coordination parameters respectively characterize the number of terminals participating in observation, the number of switches participating in control, the number of communication links being invoked, and the number of manually operated switches when the faulty object k adopts mode m. , , , These are the unit technical cost indicators for the corresponding observation terminal, remote control action, communication link, and manual operation, respectively. This refers to the increase in communication bandwidth usage caused by the access of a single observation terminal. The increase in CPU computing load caused by the main station processing data from a single observation terminal; The incremental bandwidth usage of control messages caused by a single remote control action; The CPU computational load increment caused by the main station executing a single remote control command; This represents the increase in wear on the mechanical action of the switch caused by a single remote control operation. This represents the bandwidth usage increment when a single communication link is invoked. This represents the communication latency increment when a single communication link is invoked. The time cost incurred by a single manual inspection or manual operation; , , , , , , , These are the rated upper limit or normalized benchmark value for the corresponding indicator.

[0011] Furthermore, under the constraints of the reliability constraints, the dual-objective collaborative programming model is solved to generate the configuration states of candidate segmented switches, candidate tie switches, candidate distributed power supply access states, automated terminal functional configuration states, and communication link networking configuration states, including: The bi-objective collaborative programming model is iteratively solved using a multi-objective optimization algorithm, and the Pareto front solution set containing multiple candidate programming schemes is output. Extract the coupling risk complexity and fault handling coupling complexity of all candidate planning schemes in the Pareto front solution set respectively, and select the minimum coupling risk complexity and the minimum fault handling coupling complexity. The minimum value of the coupling risk complexity and the minimum value of the fault handling coupling complexity are normalized to construct a dual-objective normalized ideal point; The coupling risk complexity and the fault handling coupling complexity of each candidate planning scheme are normalized to obtain the normalized two-dimensional target coordinates of each candidate planning scheme. Calculate the Euclidean distance from the normalized two-dimensional target coordinates of each candidate planning scheme to the normalized ideal point of the dual objective, and determine the candidate planning scheme with the smallest Euclidean distance as the target candidate scheme; When multiple target candidate schemes with the same Euclidean distance exist, they are sequentially screened according to the priority order of minimizing the expected annual power shortage of the system, minimizing the coupling risk complexity, and minimizing the fault handling coupling complexity. The set of state variables corresponding to the screened target candidate schemes is determined as the configuration status of the candidate segment switch, the configuration status of the candidate tie switch, the access status of the candidate distributed power source, the functional configuration status of the automation terminal, and the networking configuration status of the communication link.

[0012] Another embodiment of the present invention provides a collaborative planning device for a power distribution cyber-physical system, comprising: a data acquisition module, a static evaluation module, a dynamic evaluation module, a model building module, a model solving module, and a collaborative planning module; The data acquisition module is used to acquire network topology data of the distribution network to be planned, equipment configuration status data of each device, and dynamic coordination parameters during the fault handling process, so as to obtain the basic planning data of the distribution cyber-physical system. The static evaluation module is used to evaluate the static configuration dependencies between various devices in the distribution network based on the equipment configuration status data in the planning basic data, and to construct the coupling risk complexity. The dynamic evaluation module is used to quantify the underlying communication calls and operational load overhead brought about by the fault handling process based on the dynamic coordination parameters, and to construct the fault handling coupling complexity based on the underlying communication calls and operational load overhead. The model building module is used to construct a dual-objective collaborative planning model of the power distribution cyber-physical system with the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, and to set reliability constraints, wherein the reliability constraints are that the system's annual expected power shortage meets a set upper limit. The model solving module is used to solve the dual-objective collaborative planning model under the constraints of the reliability constraints, and generate the configuration status of candidate segmented switches, the configuration status of candidate interconnection switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. The collaborative planning module is used to perform collaborative planning of the power distribution cyber-physical system based on the configuration status of the candidate segment switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links.

[0013] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the collaborative planning method for a power distribution cyber-physical system of the present invention.

[0014] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the collaborative planning method for a power distribution cyber-physical system of the present invention.

[0015] The implementation of embodiments of the present invention has the following beneficial effects: The present invention provides a collaborative planning method for a distribution cyber-physical system. By acquiring network topology data of the distribution network to be planned, equipment configuration status data of each device, and dynamic coordination parameters during fault handling, the basic planning data of the distribution cyber-physical system is obtained. Based on the equipment configuration status data in the basic planning data, the static configuration dependencies between devices in the distribution network are comprehensively evaluated, and coupling risk complexity is constructed. Based on the dynamic coordination parameters in the basic planning data, the underlying communication calls and operational load overhead brought about by the fault handling process are quantified, and fault handling coupling complexity is constructed. With the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, a collaborative planning method for the distribution cyber-physical system is constructed. The system employs a dual-objective collaborative planning model with reliability constraints, whereby the expected annual power shortage must meet a set upper limit. Under these constraints, the dual-objective collaborative planning model is solved to generate the configuration states of candidate sectionalizing switches, candidate tie switches, candidate distributed power sources, automated terminals, and communication links. Based on these configuration states, collaborative planning of the power distribution cyber-physical system is performed.

[0016] By adopting the above technical solution, this invention addresses the shortcomings of existing distribution network collaborative planning, which mostly focuses on reducing the system's expected annual power shortage as a single objective or constraint, neglecting the actual carrying capacity of the system's underlying resources and lacking a comprehensive evaluation based on equipment configuration status and dynamic coordination parameters. It innovatively lowers the planning evaluation dimension to the actual overhead of physical and communication equipment, comprehensively evaluates static configuration dependencies to construct coupling risk complexity, and accurately quantifies the underlying communication calls and operational load overhead in the fault handling process to construct fault handling coupling complexity. Simultaneously, this invention uses the minimization of these dual complexities as the optimization objective, supplemented by the system's expected annual power shortage as a hard reliability constraint, in calculating... The model enforces restrictions on excessive redundancy in distribution network resource allocation, transforming the originally mutually challenging high power supply reliability requirements and underlying hardware and software operational pressures into an optimization process that can be precisely solved. Ultimately, by generating a comprehensive optimal configuration combination of various primary and secondary equipment to guide on-site location installation and parameter deployment, it fundamentally eliminates systemic operational risks such as underlying communication channel congestion, master station computing power overload, and rapid decline in equipment mechanical lifespan that are easily caused by blindly piling up automated equipment. It achieves the optimal balance between distribution network power supply reliability and underlying physical / communication resource coordination overhead, significantly improving the engineering applicability and robustness of the distribution cyber-physical system planning scheme under complex operating conditions. Attached Figure Description

[0017] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a collaborative planning method for a power distribution cyber-physical system according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an apparatus for a collaborative planning method for a power distribution cyber-physical system provided in an embodiment of the present invention. Detailed Implementation

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

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0022] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0025] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0026] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0027] To address the problem of system overload and communication congestion caused by the over-configuration of automation equipment due to neglecting the underlying communication and physical operation overhead in existing distribution network planning, an embodiment of the present invention provides a collaborative planning method for distribution cyber-physical systems, comprising: Step S1: Obtain the network topology data of the distribution network to be planned, the equipment configuration status data of each device, and the dynamic coordination parameters during the fault handling process to obtain the basic planning data of the distribution cyber-physical system; In a preferred embodiment, the step of acquiring network topology data of the distribution network to be planned, equipment configuration status data of each device, and dynamic coordination parameters during fault handling to obtain the basic planning data of the distribution cyber-physical system specifically includes: First, importing the original network structure model through a distribution network geographic information system (GIS) or production management system (PMS) to identify and extract the network topology data of the distribution network; Second, based on the network topology data, establishing a primary and secondary collaborative planning set covering primary physical entity devices and secondary automation communication devices, and initializing the equipment configuration status data of each device; Finally, performing time-series simulations for each potential fault scenario in the network topology data, collecting and quantifying the dynamic coordination parameters during fault handling under different automation response modes, and summarizing and integrating them into the basic planning data of the distribution cyber-physical system.

[0028] Preferably, the network topology data of the distribution network to be planned is obtained through the following steps: A distribution cyber-physical system (CPS) refers to a complex network system that deeply integrates the primary physical equipment (such as conductors, switches, transformers, etc.) at the bottom layer of the distribution network with the secondary information equipment (such as smart terminals, communication optical fibers, master station servers, etc.) at the top layer. When obtaining network topology data, the distribution network to be planned is first abstracted into a graph theory model consisting of nodes (load points, power supply points) and edges (feeder segments). Subsequently, the graph theory model is traversed and all candidate locations available for resource planning are marked, specifically constructing the following set of data: Candidate sectionalizing switch set S: Represents the set of node locations on feeder lines where sectionalizing switches can be installed. Sectionalizing switches are mainly used to cut off short-circuit current and isolate fault areas when a single feeder fails; Candidate tie switch set T: Represents the set of node locations where tie switches can be installed between different feeders. Tie switches are normally open during operation and closed during faults to restore power supply to areas experiencing power outages; Candidate distributed generation access location set G: Represents the set of node locations where distributed generation (DG) such as photovoltaic and wind power can be connected; Simultaneously, based on the historical operation records of the distribution network and the geographical environment of the lines, a fault object set K containing all lines or nodes in the power grid that may experience faults, and the annual fault rate corresponding to each fault object k∈K, are generated. (Unit: times / year) and the corresponding set of affected load points L and load power .

[0029] Preferably, the equipment configuration status data of each device is obtained through the following steps: After determining the above-mentioned candidate physical location set, a 0-1 Boolean decision variable matrix describing the configuration of primary and secondary equipment is further established, namely, the equipment configuration status data. This data specifically includes the primary equipment configuration status, the automation function configuration status, and the communication link configuration status: (1) Primary equipment configuration status: including candidate segmented switch configuration status variables Candidate contact switch configuration state variables and candidate distributed power source access state variables The above variable takes a value of 1 to indicate that the physical equipment has been actually installed at the corresponding location, and takes a value of 0 to indicate that it has not been installed. (2) Automation function configuration status: used to define the "intelligent" level of the installed primary switch, specifically including: Remote function configuration status variables ( , , ): Two remote functions refer to telemetry (real-time acquisition of current and voltage) and remote signaling (acquiring switch opening and closing status), which only have the ability to sense status and upload information, but do not have the ability to receive remote command actions; Three remote functions configure status variables ( , , Three-remote control adds remote control functionality to two-remote control, enabling the distribution master station to directly send control messages to drive the switch mechanical mechanism to perform opening and closing actions; Distributed collaborative terminal configures state variables ( : Refers to a smart distribution terminal (STU) configured with edge computing and peer-to-peer communication capabilities, capable of directly interacting with adjacent terminals and independently determining fault isolation strategies without relying on a master station; Distributed power islanding control function configuration state variables ( ): Characterizes whether the distributed power source has the ability to automatically switch to voltage source mode to support the operation of the local islanded microgrid when the main grid loses power. (3) Communication link configuration status: includes communication link configuration status variables from the terminal to the main station ( , , (e.g., 5G wireless or fiber optic private networks) and terminal-to-terminal cooperative communication link configuration state variables ( (e.g., GOOSE / SV direct sampling communication link).

[0030] Preferably, the dynamic coordination parameters during the fault handling process are obtained through the following steps: The dynamic coordination parameters refer to the quantitative indicators of the number of underlying communication resources and physical equipment actions that the system must call to complete fault location, fault isolation and power restoration when a real short circuit or ground fault occurs in the distribution network.

[0031] For each faulty object k∈K, search according to the given topology path Given the current assumed equipment configuration, the system will uniquely fall into one target operation mode m (m=0 represents pure manual mode, m=1 represents remote two-way mode, m=2 represents remote three-way mode, and m=3 represents distributed collaborative mode). Based on the determined mode m, the following four types of dynamic coordination parameters are extracted through graph theory connectivity analysis and signaling flow simulation: (1) Number of terminals participating in the observation : Refers to the total number of secondary terminals that passively or actively send fault current or voltage surge alarm signals to the main station along the fault search path. This parameter directly determines the concurrent message processing pressure of the main station's communication front-end at the moment of the fault; (2) Number of switches involved in the control : Refers to the total number of physical switches that the master station actually sends remote control commands through the communication network to cut off the fault area and successfully drives the tripping / closing. This parameter is directly related to the bandwidth occupancy of the power grid's control channel and the mechanical life loss of the switch body's operating mechanism; (3) Number of communication links invoked : refers to the total number of communication channels actually activated during information interaction (especially the inter-terminal collaborative communication link). This parameter reflects the data congestion risk of the underlying communication bearer network; (4) Number of manually operated switches This refers to the number of switches that must be switched at one time in areas with no automation coverage or communication failures, requiring maintenance personnel to drive to the site for operation. This parameter directly determines the timeliness of fault handling and the extreme labor costs of maintenance.

[0032] In this embodiment, through the aforementioned multi-dimensional graph theory topology analysis, 0-1 Boolean variable matrix construction, and event-driven fault timing deduction, this invention not only digitally maps physical assets such as power distribution network wires and switches, but also accurately captures and quantifies the underlying operational costs (i.e., dynamic coordination parameters) hidden in the digital space, such as communication concurrency, CPU message parsing pressure, and switch mechanical action losses. This multi-dimensional, in-depth basic data extraction mechanism breaks the technical bias of traditional planning that only focuses on blind financial cost accounts while ignoring underlying operational costs. It provides a highly granular and confident data foundation for the subsequent accurate construction of models representing the coupled risk complexity and fault handling coupling complexity of system overhead. This ensures that the final collaborative planning scheme, when implemented in actual engineering projects, will not cause system computing power downtime or channel congestion due to the blind stacking of automated equipment.

[0033] Step S2: Based on the equipment configuration status data in the planning basic data, evaluate the static configuration dependencies between various devices in the distribution network and construct the coupling risk complexity; Preferably, the coupling risk complexity is calculated using the following formula: ; ; In the formula, Let S be the coupling risk complexity; S, T, and G are the candidate segmented switch set, candidate tie switch set, and candidate distributed power source access location set in the primary and secondary collaborative planning set, respectively; s and r are elements in set S, and t and g are elements in sets T and G, respectively. Let (i,j) be the set of candidate cooperative communication links between terminals, and (i,j) be the set of... The elements in the array represent the peer-to-peer communication channel between nodes i and j; The number of downstream load points of the sectionalizing switch S; The number of downstream load points corresponding to element r; This represents the maximum number of downstream load points among all candidate sectionalizing switches. For segmented switches The topological importance coefficient; , , These are the configuration state variables for the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively. These are the remote function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; These are the remote control function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; Configure state variables for distributed collaborative terminals; Configure state variables for distributed power supply islanding control functionality; , , These are sectional switches. , contact switch Configure status variables for the communication link between the automated terminal and the master station on the distributed power source; Configure state variables for the cooperative communication link between sectional switch i and sectional switch j.

[0034] In a preferred embodiment, the step of evaluating the static configuration dependencies among various devices in the distribution network and constructing the coupling risk complexity based on the device configuration status data in the planning basic data specifically includes: First, extracting and parsing the device configuration status data in the planning basic data to clarify the spatial distribution and functional nesting of various heterogeneous devices in the distribution network; Second, based on the parsed device functional nesting, sorting out and quantifying the static configuration dependencies between the primary physical network structure and the secondary automation communication system of the distribution network; Finally, combining the topological importance of the nodes where each device is located, performing an algebraic evaluation of the static configuration dependencies, and constructing and outputting the coupling risk complexity characterizing the vulnerability of the system structure through multi-dimensional weighted and aggregated operations.

[0035] It is worth noting that, in order to ensure the accuracy and computability of the above assessment process, the core concepts and assessment mechanisms involved in this step are defined and explained in detail as follows: (1) Equipment configuration status data: refers to the set of 0-1 Boolean decision variables used in the planning stage to characterize whether various primary and secondary hardware and software resources on a specific node of the distribution network are actually deployed. It not only includes the installation status of the underlying primary physical equipment (such as sectionalizing switches, tie switches, and distributed power sources), but also includes the secondary automation function levels attached to the primary equipment (such as the status of remote control, remote control, distributed collaborative control, and island control functions) and the communication link connectivity status as an information bridge (such as the link status from terminal to master station and from terminal to terminal). This data essentially constitutes the static digital gene map of the distribution cyber-physical system (CPS) to be planned. (2) Static configuration dependency relationship: refers to the structural functional binding and nesting risk formed by primary equipment, automation functions, and information support system in the distribution cyber-physical system. Specifically, any advanced secondary automation function cannot exist independently; it must be attached to the underlying physical equipment and depend on the communication link. For example, for a switch to achieve distributed collaborative control, its static prerequisite is that the node must be configured with a physical switch entity, must have a basic three-remote execution module, and must have a terminal-to-terminal collaborative communication link. This nested inclusion relationship means that once there is a local equipment anomaly (such as damage to the underlying communication module or disconnection of the link), it will cause all the high-level control functions of the upper layer to fail or be downgraded. This static vulnerability formed by complex structure, functional mismatch or communication support sensitivity is the static configuration dependency relationship. (3) Evaluating the static configuration dependency relationship between various devices in the distribution network: refers to the process of transforming the above-mentioned abstract functional nesting and sensitivity into quantifiable mathematical penalty indicators. The specific evaluation logic is as follows: add up all the state variables (i.e., a series of 0-1 values) of all the automation functions and communication links piled up on a single physical node. The more complex the configured functions, the larger the base of the sum, which means that the dependency link inside the node is longer and more prone to failure; at the same time, the topological importance of the physical node in the global power grid is introduced as a penalty weight to amplify the weighted base of the sum. By using this aggregation method that combines the complex cardinality of a node with its global importance, an objective quantitative assessment of the overall system dependency risk can be achieved.

[0036] Preferably, the topology importance coefficients of each candidate sectionalizing switch required in the above evaluation process are calculated through the following steps: In distribution networks operating in a radial or weak loop configuration, the topology level of a physical switch directly determines its safety importance. The closer a sectionalizing switch is to the substation outgoing line (upstream), the greater the impact range of a power outage caused by its functional failure. To accurately quantify this spatial topology attribute, this embodiment constructs a topology importance coefficient. The computational model.

[0037] Extract the number of downstream load points corresponding to each candidate segment switch s Simultaneously, iterate through all elements r in the candidate segmented switch set S to find the maximum number of downstream load points. .

[0038] By dividing the number of load points of a single switch by the maximum number of load points in the entire network, i.e. This maps topological importance to a dimensionless weight coefficient within the interval (0, 1). This coefficient clearly defines the importance of segmented switches located in the core backbone network. Approaching 1, while the switch located at the end branch... The smaller value provides a solid mathematical basis for the differentiated risk weighting of subsequent static configuration dependencies.

[0039] Preferably, the coupling risk complexity is calculated using the following formula: Based on the above evaluation logic and topology coefficients, this embodiment substitutes the configuration state variables of each device into the following algebraic summation model for the final evaluation calculation: ; ; In the formula, Let S be the coupling risk complexity; S, T, and G are the candidate segmented switch set, candidate tie switch set, and candidate distributed power source access location set in the primary and secondary collaborative planning set, respectively; s and r are elements in set S, and t and g are elements in sets T and G, respectively. Let (i,j) be the set of candidate cooperative communication links between terminals, and (i,j) be the set of... The elements in the array represent the peer-to-peer communication channel between nodes i and j; The number of downstream load points of the sectionalizing switch S; The number of downstream load points corresponding to element r; This represents the maximum number of downstream load points among all candidate sectionalizing switches. For segmented switches The topological importance coefficient; , , These are the configuration state variables for the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively. These are the remote function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; These are the remote control function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; Configure state variables for distributed collaborative terminals; Configure state variables for distributed power supply islanding control functionality; , , These are sectional switches. , contact switch Configure status variables for the communication link between the automated terminal and the master station on the distributed power source; Configure state variables for the cooperative communication link between sectional switch i and sectional switch j.

[0040] In the above In its computational architecture, the static dependency evaluation logic for four types of key resources in the distribution network is clearly decoupled and quantified: First item Specifically designed for sectionalizing switches; since sectionalizing switches are the first line of defense against faults, the more automation functions they have, the more severe the cross-dependencies between internal modules and communications become. This model sums up these functional state variables and modifies them with topological importance, which reflects their global status. Multiplicative weighting was applied. This means that if complex automated functions are excessively piled up at highly critical core nodes, the amplification effect of these functions on inducing systemic failures (such as communication board crashes causing backbone malfunctions) will be realistically assessed and presented. Second item Regarding the handshake switch, the third item For distributed power sources (with the introduction of islanded control variables) These two items, which play a more backup role in power restoration, have a relatively high fault tolerance rate, so their configuration functions are directly evaluated without weighting. Fourth item Regarding the horizontal interaction of the underlying communication architecture, the denser the connections between terminals, the greater the risk of information storms and mutual interference. This is quantified by directly accumulating link variables. By combining the above four parts, it is possible to accurately capture the spatial dependency risks of various heterogeneous devices in the power grid.

[0041] In this embodiment, by performing in-depth analysis of the equipment configuration status data and introducing topology importance to evaluate static configuration dependencies, this invention can change the blind belief in traditional power distribution network planning that the more complete the configuration of automation equipment, the better. By explicitly translating and transforming the physical interweaving of primary network topology (downstream load level), secondary control functions (remote control, coordination, islanding), and communication link status into a mathematical algebraic sum composed of 0-1 Boolean variables and topology weights, and by identifying the configuration redundancy of the power distribution system, and thereby constructing coupling risk complexity, it can perceive and punish unreasonable planning caused by excessive nesting of complex hardware and software devices at key electrical nodes with extremely high granularity. This reveals the vulnerability risk of global paralysis caused by local failures due to excessive coupling of equipment functions and protocol mismatch from the root, thus providing a quantitative constraint scale that is dimensionally complete, physically transparent, and computationally efficient for using this complexity as the core optimization direction of the dual-objective optimization model.

[0042] Step S3: Quantify the underlying communication calls and operational overhead brought about by the fault handling process according to the dynamic coordination parameters, and construct the fault handling coupling complexity based on the underlying communication calls and operational overhead; In a preferred embodiment, the annual expected power shortage of the system is calculated from the fault handling time of each faulty object in the distribution network and the power restoration capability of each power-loss load point; the fault handling time of each faulty object is calculated through the following steps: Based on the equipment configuration status data, the operation mode adopted by each faulty object during fault handling is determined; wherein, the operation mode includes manual mode, remote two-way mode, remote three-way mode or distributed collaborative mode; For each faulty object, the communication transmission delay time and mechanical action time required to process the faulty object are determined according to the operation mode corresponding to the faulty object. Calculate the fault location time and fault isolation time of the faulty object based on the communication transmission delay time and the mechanical action time; The fault location time of the faulty object is added together with the corresponding fault isolation time to obtain the fault handling time of the faulty object.

[0043] Preferably, the power restoration capability of each power-loss load point in each of the faulty objects is calculated in the following manner: For each faulty object, after the fault handling time of the faulty object ends, based on the equipment configuration status data, determine the interconnection-based power transfer recovery power and distributed power islanding recovery power of each power-loss load point corresponding to the faulty object; construct mutual exclusion selection constraints for each power-loss load point; wherein, the mutual exclusion selection constraints are used to restrict each power-loss load point to only be restored by either interconnection-based power transfer or distributed power islanding at the same time; based on the mutual exclusion selection constraints and the preset power restoration priority order, select one of the interconnection-based power transfer recovery power and distributed power islanding recovery power corresponding to each power-loss load point, and determine the selected recovery power as the power restoration capacity of each power-loss load point.

[0044] Preferably, the annual expected power shortage of the system is calculated in the following manner: For each faulty object, obtain the annual fault rate, manual recovery time, load power corresponding to each power loss load point in the faulty object, and recovery operation time of each power loss load point when power is restored. Based on the fault handling time of the fault object and the load power corresponding to each power loss load point in the fault object, calculate the power shortage of each power loss load point during the fault handling stage. Based on the manual recovery time of the fault object, the load power corresponding to each power loss load point in the fault object, the power supply recovery capability corresponding to the power loss load point, and the recovery operation time, calculate the power supply shortage of each power loss load point corresponding to the fault object during the recovery phase. The power shortage at each of the power-loss load points during the fault handling phase is added to the power shortage during the recovery phase to obtain the node power shortage at each power-loss load point. The total power supply deficit of the fault object is obtained by summing the power supply deficit of each of the aforementioned power loss load points. Using the annual failure rate corresponding to each of the aforementioned fault objects, the total power supply shortage of each fault object is weighted and summed to obtain the expected annual power supply shortage of the system.

[0045] Preferably, the fault handling coupling complexity is calculated using the following formula: ; ; in, The fault handling coupling complexity is given by K; K is the set of fault objects, and k is an element in set K. is the annual failure rate of faulty object k; m is the index of the operating mode number; The selection variable for the mode m corresponding to the faulty object k; The dynamic coordination parameters respectively characterize the number of terminals participating in observation, the number of switches participating in control, the number of communication links being invoked, and the number of manually operated switches when the faulty object k adopts mode m. , , , These are the unit technical cost indicators for the corresponding observation terminal, remote control action, communication link, and manual operation, respectively. This refers to the increase in communication bandwidth usage caused by the access of a single observation terminal. The increase in CPU computing load caused by the main station processing data from a single observation terminal; The incremental bandwidth usage of control messages caused by a single remote control action; The CPU computational load increment caused by the main station executing a single remote control command; This represents the increase in wear on the mechanical action of the switch caused by a single remote control operation. This represents the bandwidth usage increment when a single communication link is invoked. This represents the communication latency increment when a single communication link is invoked. The time cost incurred by a single manual inspection or manual operation; , , , , , , , These are the rated upper limit or normalized benchmark value for the corresponding indicator.

[0046] Specifically, the following detailed explanation is given regarding the deduction and calculation process of the above-mentioned fault handling time: After a fault occurs in the distribution network, the system must go through two stages: fault location and fault isolation. The total time consumed by these two stages is the fault handling time, and its length directly determines the size of the power outage loss. Since the equipment configuration status obtained in step S1 (such as whether remote control, remote control, and collaborative terminals are configured) determines the level of intelligence in the area, the system will uniquely establish one operating mode for specific fault objects: (1) Manual mode: When there are no automated devices in the fault area, it relies entirely on the manual inspection and manual circuit breaking by maintenance personnel. At this time, the communication transmission delay time is considered invalid, the fault location time is equal to the long manual inspection time, and the fault isolation time is equal to the manual operation switch time, resulting in an extremely long overall time consumption. (2) Remote control mode: A remote control terminal is configured, which can sense the fault current and upload alarm signals. At this time, the fault location time consists of the communication delay time of the terminal information being uploaded to the main station and the calculation time of the main station processing the information; however, since remote control cannot receive remote tripping commands, the isolation time is still equal to the time consumed by the machinery and personnel to drive to the site for operation. (3) Three-remote mode: Based on two-remote mode, it has remote control capability. Fault location relies on communication uploading, while the fault isolation time is completely free from manual intervention and depends on the communication delay time of the master station sending down the remote control command plus the mechanical action time of the switch body to perform the opening action, which greatly improves the processing speed. (4) Distributed collaborative mode: It is equipped with the highest level of collaborative terminal. The terminals interact directly through peer-to-peer communication (5G or fiber optic GOOSE messages) without the need for master station intervention. At this time, the location time is extremely short (only the establishment time of collaborative control synchronization within a very small range), and the isolation time is limited by the communication delay and mechanical action time of the underlying multi-switch parallel control. This embodiment accurately transforms the static hardware diagram into a dynamic time measurement through the above-mentioned rigorous parameter flow of equipment status → operation mode → action delay extraction → fault time accumulation.

[0047] Specifically, the quantitative logic for restoring power supply capacity is defined as follows: After a fault is successfully isolated, innocent load points downstream of the fault point or in healthy areas need to be restored to power as soon as possible. The power grid provides two types of restoration resources: one is to close the tie switches of adjacent lines, allowing other healthy substations to provide cross-line support (tie-transfer power restoration); the other is to activate local distributed power sources (such as photovoltaics and energy storage) to support an independent microgrid (distributed power islanding power restoration). To comply with the safe operation procedures of a real physical power grid, mutual exclusion selection constraints must be constructed; in the distribution network, it is absolutely forbidden for two asynchronous power sources (power transferred from the main grid and local islanded power) to simultaneously supply power to the same load point, otherwise it will lead to serious phase-to-phase short circuits or inverter burnout. Therefore, strict 0-1 mutual exclusion variables are introduced. At the same time, based on the physical characteristics of the large capacity and high stability of the main grid, this scheme sets a preset power restoration priority order of tie-transfer first, followed by distributed power islanding. Through this execution flow, the algorithm can automatically verify the channel capacity and make a unique power supply decision, just like a real power grid dispatcher, and finally output a scientifically feasible power restoration capacity.

[0048] Specifically, regarding the calculation process of the above-mentioned annual expected power supply deficit (EENS), the following supplementary explanation is provided: The power supply deficit (the total electrical energy lost due to power outages) is the ultimate hard indicator for evaluating the reliability of the distribution network. This step strictly divides the power outage process at the load point into two stages: the fault handling stage and the recovery stage, and performs refined integration. In the fault handling stage, since the fault has not yet been isolated, the entire line is in a power outage state. At this time, the power deficit is purely determined by the load power multiplied by the fault handling time. After entering the recovery stage, some load points receive support from power transfer or islanded power supply (i.e., the recovery power supply capability obtained in the previous step is incorporated). At this time, only the power outage during the extremely short period of power switching (recovery operation time) needs to be calculated. Finally, the power loss from both stages is merged into the power deficit of a single node, aggregated upwards to the total power deficit of a single fault scenario, and then multiplied by the probability of the fault occurring in a year (annual fault rate). This finally outputs the macroscopic annual expected power supply deficit index, perfectly closing the reliability assessment chain of time → power → expected power.

[0049] In particular, the specific explanation of the formula for the coupling complexity of fault handling is as follows: Traditional power distribution network planning algorithms often fall into the trap of solely relying on reliability, believing that simply replacing all switches with automated terminals will reduce power outage time to near zero. However, in reality, any advanced automation comes at the hidden cost of consuming massive amounts of computing power and wearing down physical equipment. This solution addresses this by establishing a fault handling coupled complexity... It can accurately monitor the system's operating costs; (1) Observation cost When a short-circuit fault occurs, the terminals along the line ( This will cause a massive number of sudden change alarm messages to be sent to the main station in a single millisecond. At this time, the uplink bandwidth of the main station's communication front-end will surge instantly. The server CPU needs to parse the fault characteristics of these concurrent messages. This model multiplies the number of terminals by the incremental weights of bandwidth and CPU computing power, accurately depicting the strain that information storms place on the main station's processing capacity.

[0050] (2) Controlling expenses After the master station identifies the fault location, it needs to issue a series of remote trip / close commands. This not only consumes downlink control channel bandwidth ( ) and main station verification computing power ( More seriously, each time a heavy physical vacuum circuit breaker performs an operation, the mechanical life of its internal springs and contacts is irreversibly shortened. By multiplying the number of switching operations by mechanical / computing power losses, the implicit quantification of equipment aging and degradation is achieved.

[0051] (3) Communication coordination overhead For advanced distributed collaborative modes, high-frequency communication links with mesh crossover need to be established between terminals. To establish a peer-to-peer handshake. Each new link connection consumes valuable fiber optic or 5G slice bandwidth resources. ), and inevitably increase control latency in complex network switching. ).

[0052] To overcome the differences in physical dimensions between bandwidth capacity (megabytes), computing load (CPU utilization), and mechanical lifespan (number of operations), this embodiment uses a method where each indicator is divided by its corresponding upper limit of the system's physical limits (e.g., ...). Normalization methods (such as...) are used. Thus, all heterogeneous technical overheads are compressed into dimensionless penalty weights between 0 and 1. Finally, through outer nesting... Multiply the penalty weight of these individual failures by the annual failure probability of the line in the real world to flatten the random events into the expected value of normalized operating costs for the whole year.

[0053] In this embodiment, the annual expected power shortage (EENS) of the system is calculated through rigorous time-series extrapolation, while simultaneously constructing a fault handling coupling complexity based on the underlying real physical overhead. This invention transforms abstract, hidden risks such as network communication congestion (bandwidth), server resource strain (CPU load), and underlying hardware degradation (mechanical wear) into concrete mathematical indicators that can be constrained and solved through algorithms. This two-dimensional evaluation mechanism allows the subsequent algorithm optimization process to restrainedly measure the cost of each terminal concurrent call and mechanical switching action while pursuing extreme reliability to reduce power outage losses. This fundamentally prevents situations where the main station's computing power crashes or large-scale failures to operate due to the excessive pursuit of full automation and full coverage in design drawings under real-world concurrent weather conditions such as thunderstorms and typhoons. This greatly enhances the engineering resilience and safety baseline of the collaborative planning scheme under extreme operating conditions.

[0054] Step S4: With the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, construct a dual-objective collaborative planning model for the power distribution cyber-physical system, and set reliability constraints, wherein the reliability constraints are that the system's annual expected power shortage meets a set upper limit. In a preferred embodiment, the step of constructing a dual-objective collaborative programming model for the distribution cyber-physical system with the objectives of minimizing the coupling risk complexity and the fault handling coupling complexity, and setting reliability constraints, specifically includes: using the expected annual power shortage of the system as a reliability constraint, and configuring other boundary constraints such as distribution network operation and equipment configuration logic; extracting the calculated coupling risk complexity and the fault handling coupling complexity as dual optimization objectives; and constructing a dual-objective collaborative programming model for optimizing the decision variables of primary and secondary equipment configuration in the distribution cyber-physical system based on the above-set multidimensional constraints and dual optimization objectives.

[0055] Preferably, reliability constraints and other boundary constraints are set through the following steps: First, set hard reliability constraints, requiring that the calculated annual expected power shortage (EENS) of the system must be less than or equal to the upper limit of the annual expected power shortage corresponding to the preset target reliability. That is, satisfying EENS≤ This ensures the basic power supply guarantee capability of the planning scheme and avoids the algorithm from completely omitting automation equipment in order to reduce complexity; Secondly, set equipment configuration logic constraints to limit the physical compatibility and functional nesting of primary and secondary equipment, specifically including: (1) Segment switch automation function configuration constraints: advanced functions must be backward compatible with low-level functions, that is, when segment switches are configured with three remote functions, they must also have two remote functions, and when distributed collaborative terminals are configured, they must also have two remote and three remote functions; (2) Tie switch automation function configuration constraints: it is stipulated that once a tie switch is configured, it should be forced to have two remote and three remote functions; (3) Distributed power supply automation function configuration constraints: if the distributed power supply has island control function, then it should be forced to configure three remote functions; (4) Communication link constraints: when configuring automation equipment, the communication link from the terminal to the master station must be configured synchronously, and when configuring distributed collaborative terminals, the collaborative communication link from the terminal to the terminal must be configured synchronously, and the collaborative terminal must not exist in isolation in the physical topology. Finally, resource coordination constraints for distribution network operation and restoration are set to ensure that the system always meets the constraints of Kirchhoff's laws on AC power flow equations, the constraints on node voltage and branch current not exceeding limits, the topology constraints for maintaining open-loop radial operation, and the capacity limits and 0-1 mutual exclusion constraints for interconnection transfer and islanding restoration during the power restoration phase.

[0056] Preferably, the dual-objective collaborative programming model of the power distribution cyber-physical system is constructed in the following manner: The reliability constraints, equipment configuration logic constraints, and power distribution network operation constraints are simultaneously solved to form a safe search space for the feasible solution of the model; the extracted coupling risk complexity (… The coupling complexity between ) and fault handling This serves as the joint minimization objective function, i.e., constructing the objective function matrix. The configuration state variables of primary equipment (candidate sectionalizing switches, tie switches, and distributed power supply access locations) and the configuration state variables of secondary equipment (remote control, remote control, collaborative terminals, islanded control, and communication links) are used as the set of decision variables X to be solved in the model. Thus, the abstract game of resource coordination in the distribution network is transformed into a standard bi-objective collaborative planning mathematical model with clear boundaries and target objectives.

[0057] In this embodiment, by setting the system's annual expected power shortage as a hard reliability constraint and minimizing the static coupling risk complexity reflecting underlying overhead and the dynamic fault handling coupling complexity, this invention successfully achieves a planning architecture that balances virtual algorithms and the real physical power grid. This architecture cleverly simulates the trade-off game thinking in engineering design: on the one hand, it uses the rigid upper limit of power shortage to force the system to configure necessary automated defenses to avoid prolonged power outages; on the other hand, it uses dual complexity indicators to limit excessive investment and blindly piling up hardware and software equipment. Simultaneously, by supplementing the underlying layer with rigorous equipment function compatibility logic and electrical operation constraints, it eliminates malformed, mismatched, or physically unsolvable solutions that may arise during algorithm crossover mutation, fundamentally resolving the contradiction between high power supply reliability requirements and low system operating overhead. This lays a clear and highly targeted execution path for subsequent scientific and directional Pareto optimization convergence using heuristic algorithms.

[0058] Step S5: Under the constraints of the reliability constraints, solve the dual-objective collaborative planning model to generate the configuration status of candidate segmented switches, the configuration status of candidate tie switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. Preferably, under the constraints of the reliability constraints, the dual-objective collaborative programming model is solved to generate the configuration states of candidate segmented switches, candidate tie switches, candidate distributed power supply access states, automated terminal functional configuration states, and communication link networking configuration states, including: The bi-objective collaborative programming model is iteratively solved using a multi-objective optimization algorithm, and the Pareto front solution set containing multiple candidate programming schemes is output. Extract the coupling risk complexity and fault handling coupling complexity of all candidate planning schemes in the Pareto front solution set respectively, and select the minimum coupling risk complexity and the minimum fault handling coupling complexity. The minimum value of the coupling risk complexity and the minimum value of the fault handling coupling complexity are normalized to construct a dual-objective normalized ideal point; The coupling risk complexity and the fault handling coupling complexity of each candidate planning scheme are normalized to obtain the normalized two-dimensional target coordinates of each candidate planning scheme. Calculate the Euclidean distance from the normalized two-dimensional target coordinates of each candidate planning scheme to the normalized ideal point of the dual objective, and determine the candidate planning scheme with the smallest Euclidean distance as the target candidate scheme; When multiple target candidate schemes with the same Euclidean distance exist, they are sequentially screened according to the priority order of minimizing the expected annual power shortage of the system, minimizing the coupling risk complexity, and minimizing the fault handling coupling complexity. The set of state variables corresponding to the screened target candidate schemes is determined as the configuration status of the candidate segment switch, the configuration status of the candidate tie switch, the access status of the candidate distributed power source, the functional configuration status of the automation terminal, and the networking configuration status of the communication link.

[0059] Preferably, the Pareto front solution set containing multiple candidate planning schemes is output through the following steps: A non-dominated sorting genetic algorithm (such as the NSGA-II algorithm) with an elite retention strategy is used as the multi-objective optimization algorithm. First, the decision variables, such as the physical location variables of the primary equipment and the configuration variables of the secondary automation functions, are encoded into binary gene segments to initialize and generate an original population containing a massive number of random configuration combinations. Second, during the crossover, mutation, and iterative optimization process of the algorithm, the constraint verification module in the previous steps is called in real time to directly eliminate or impose a large penalty function on invalid individuals that violate the system's annual expected power shortage upper limit constraint, distribution network topology operation constraint, or primary and secondary equipment configuration logic constraint. Finally, for surviving individuals that satisfy all rigid constraints, multi-dimensional non-dominated sorting and congestion calculation are performed based on the two objective function values ​​of their corresponding coupling risk complexity and fault handling coupling complexity. After sufficient breeding and iteration, the Pareto front solution set, consisting of a series of non-dominated optimal solutions, is finally converged and output. Any candidate program within this solution set is at the equilibrium boundary of the game, meaning it cannot improve one complexity objective without worsening the other.

[0060] Preferably, the dual-objective normalized ideal point is constructed and the Euclidean distance is calculated to determine the target candidate scheme through the following steps: First, all candidate planning schemes in the Pareto front solution set are traversed, and the globally absolutely minimum coupling risk complexity (denoted as minimum value X) and the globally absolutely minimum fault handling coupling complexity (denoted as minimum value Y) are extracted respectively. Considering that the static coupling risk and dynamic fault handling overhead have significant differences in physical dimensions and numerical absolute span, in order to avoid large numerical indicators dominating spatial distance calculation, the target space must be linearly normalized using their respective extreme value boundaries. That is, the two extracted absolute minimum values ​​are mapped to the origin of the two-dimensional coordinate system, constructing a theoretically perfect but practically unattainable virtual coordinate point, namely the dual-objective normalized ideal point (0, 0); Secondly, using the same normalization mapping rule, the true complexity index of each candidate planning scheme in the Pareto front solution set is scaled proportionally and converted into normalized two-dimensional target coordinates that all fall within the closed interval [0, 1]. ; Finally, using the spatial geometric straight-line distance formula, the Euclidean distance from the normalized two-dimensional target coordinates of each candidate planning scheme to the normalized ideal point (0, 0) of the two objectives is calculated (i.e., the distance is extracted). The smaller the Euclidean distance (the value of the distance), the closer the candidate planning scheme is to the theoretically perfect and lossless state when comprehensively balancing static configuration redundancy and dynamic operating overhead. Based on this, the scheme with the smallest Euclidean distance is initially selected as the target candidate scheme.

[0061] Preferably, the final equipment status configuration is output by prioritizing and filtering through the following steps: Due to the special nature of the discrete variable optimization space, there may be multiple candidate planning schemes with completely equal Euclidean distances to the bi-objective normalized ideal point in the two-dimensional target coordinate system (i.e., multiple parallel optimal solutions appear); When this equidistant parallel condition is triggered, a secondary breakthrough screening rule based on the underlying rigid logic of power operation is initiated: The first priority is to compare the annual expected power supply shortage corresponding to each parallel scheme, and forcibly select the scheme with the smallest power supply shortage value (i.e., the highest macroscopic power supply reliability); If the first priority is still the same, then proceed to the second priority and select the scheme with the shorter static dependency link and the lowest coupling risk complexity; If the second priority is still the same, then proceed to the third priority and directly select the scheme with the lowest fault handling coupling complexity, until a unique winning individual with engineering advantages is successfully selected; Finally, the chromosome gene segment of the sole winning individual is reverse-decoded and mapped to accurately restore and divide the 0-1 binary array carried within it into the configuration state of the candidate segment switch, the configuration state of the candidate contact switch, the access state of the candidate distributed power supply, the functional configuration state of the automated terminal, and the networking configuration state of the communication link required for physical implementation.

[0062] In this embodiment, by introducing the Pareto front solution set and the Euclidean distance measurement mechanism for normalized ideal points, the present invention can solve the multidimensional decision-making problem in the dual-objective collaborative planning model, where the static network configuration dependency and dynamic communication computing power overhead are mutually constrained and difficult to assign weights manually. By strictly mapping heterogeneous complexity with drastically different dimensions to a unified dimensionless two-dimensional normalized space, and using Euclidean distance to approximate the theoretically optimal utopian point, the algorithm can automatically select the solution with the most balanced risk resistance and operating load from tens of thousands of complex hardware and software configuration combinations, relying on the geometric tension of mathematics. At the same time, in response to the inevitable equidistant parallel phenomenon in genetic algorithms, this embodiment incorporates the prior expert control logic of power supply reliability as a safety net > static network simplification > dynamic overhead simplification as a secondary defense. Through this complete and logically self-consistent optimization decision system, not only is the engineering optimization deviation caused by subjective weight assignment in the traditional weighted summation method eliminated, but it also ensures that the output primary and secondary equipment collaborative planning scheme meets the safety bottom line of real power distribution network system scheduling.

[0063] Step S6: Based on the configuration status of the candidate segment switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links, perform collaborative planning of the power distribution cyber-physical system.

[0064] In a preferred embodiment, the collaborative planning of the distribution cyber-physical system based on the configuration status of the candidate sectionalizing switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links specifically includes: First, converting the configuration status of the candidate sectionalizing switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links output in step S5 into specific engineering implementation instructions; Second, according to the engineering implementation instructions, performing the addressing and installation of primary physical equipment and secondary automation terminals at the physical node locations corresponding to the distribution network to be planned; Third, writing communication parameters and control permissions to the secondary automation terminals after the addressing and installation are completed, and completing the deployment of network mapping relationships and fault handling strategies in the distribution automation master station database; Finally, performing system-level communication connectivity and control action tests, and after the tests are passed, officially putting the distribution cyber-physical system into closed-loop operation.

[0065] Preferably, the addressing and installation of primary physical equipment and secondary automation terminals are performed through the following steps: The addressing and installation of primary physical equipment and secondary automation terminals is essentially the process of transforming the virtual optimal solution output by a multi-objective optimization algorithm in digital space into hardware assets in the real physical world. Specifically, the system analyzes the configuration status of the candidate sectionalizing switches, the configuration status of the candidate tie switches, and the access status of the candidate distributed power sources (i.e., a one-dimensional 0-1 Boolean decision sequence), mapping them to concrete spatial coordinates in the distribution network geographic information system (GIS), guiding on-site construction personnel to accurately install the sectionalizing switch bodies, tie switch bodies, and connected distributed power source equipment at the corresponding physical nodes; simultaneously, it analyzes the functional configuration status of the automation terminals, strictly adhering to the hierarchical limitations of state variables (such as two-remote mode, three-remote mode, or distributed collaborative mode), and adds matching level intelligent distribution terminals (STU / DTU / FTU) to each installed primary equipment; and based on the network configuration status of the communication links, it completes the establishment of fiber optic / 5G wireless channels from the terminals to the master station, and the deployment of vertical / horizontal physical transmission media for collaborative control between terminals.

[0066] Preferably, the deployment of network mapping relationships and fault handling strategies is completed through the following steps: After the hardware entities are in place, deep information layer configuration is required to activate the collaborative control capabilities of the Cyber-Physical System (CPS). Specifically, field engineers or the automation system, through the engineering implementation instructions, write proprietary device IP addresses, feeder topology numbers, communication baud rate parameters, and remote control action control permissions into the underlying firmware of each installed smart terminal; Subsequently, a digital twin architecture is established within the distribution automation master station database, and parameters such as the mapping relationship between the terminals and the master station, the topological adjacency relationship between distributed collaborative terminals, and preset fault isolation and power restoration strategies (such as first transferring power and then islanding logic) are written into the system kernel; Further, the distribution automation master station uniformly distributes service parameter configuration packages such as remote signaling, telemetry, remote control, collaborative control, and distributed power islanding control to each field terminal, thoroughly completing the network addressing, communication link establishment, and underlying defense strategy solidification of the terminals at the software protocol and information architecture level.

[0067] Preferably, the system-level communication connectivity and control action tests are performed and put into operation in a closed loop through the following steps: To ensure that the deployed primary and secondary hardware and software resources can fully meet the static network architecture simplification requirements and dynamic operating load limitation standards expected by the planning model, a full-scale functional verification must be performed before grid connection. Specifically, the master station system sends periodic heartbeat messages and control probe commands to each terminal, performing the following four core verifications: First, a communication connectivity test is performed to verify whether the data transmission delay meets the parameter standards extracted from the model; second, a remote control action test is performed to verify the end-to-end execution correctness of the master station's commands to the mechanical opening and closing of the switch mechanism; third, a collaborative control test is performed to verify whether adjacent terminals equipped with peer communication links can complete fault isolation logic interaction without master station intervention; fourth, an islanding switching test is performed to verify the independent voltage source support capability of distributed power sources when the main grid loses power. After all the above test procedures have passed, the power distribution cyber-physical system is officially put into energized operation. When a real short circuit or ground fault occurs in the subsequent distribution network, the master station or distributed collaborative terminal will strictly follow the written configuration parameters and control strategies to trigger the corresponding fault location and isolation timing actions. In this embodiment, by rigorously and accurately transforming the abstract multidimensional state variable matrix output by the algorithm into field engineering implementation instructions, and progressively guiding the placement of primary switches, the assembly of secondary terminals, the establishment of communication networks, and the solidification of master station strategies, this invention completes a physical closed loop from top-level algorithm planning to bottom-level engineering implementation. This step breaks down the barrier of traditional power grid planning research that emphasizes theory over practice, avoiding problems such as improper equipment selection and communication protocol mismatch caused by the disconnect between planning and construction; at the same time, the extremely rigorous connectivity and action closed-loop testing procedures ensure that the physical system put into operation in the field maintains a high degree of consistency with the low fault handling coupling complexity and low static configuration dependency risk state derived in the dual-objective planning model. The implementation of this step not only ensures that the collaborative planning scheme of the distribution information physical system has high operability, but also prevents communication channel congestion and avoids master station computing power overload from the physical execution terminal link, giving the power grid extremely high operational reliability and intelligent fault tolerance resilience.

[0068] like Figure 2 As shown, another embodiment of the present invention also provides a collaborative planning device for a power distribution cyber-physical system, including: a data acquisition module, a static evaluation module, a dynamic evaluation module, a model building module, a model solving module, and a collaborative planning module; The data acquisition module is used to acquire network topology data of the distribution network to be planned, equipment configuration status data of each device, and dynamic coordination parameters during the fault handling process, so as to obtain the basic planning data of the distribution cyber-physical system. The static evaluation module is used to evaluate the static configuration dependencies between various devices in the distribution network based on the equipment configuration status data in the planning basic data, and to construct the coupling risk complexity. The dynamic evaluation module is used to quantify the underlying communication calls and operational load overhead brought about by the fault handling process based on the dynamic coordination parameters, and to construct the fault handling coupling complexity based on the underlying communication calls and operational load overhead. The model building module is used to construct a dual-objective collaborative planning model of the power distribution cyber-physical system with the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, and to set reliability constraints, wherein the reliability constraints are that the system's annual expected power shortage meets a set upper limit. The model solving module is used to solve the dual-objective collaborative planning model under the constraints of the reliability constraints, and generate the configuration status of candidate segmented switches, the configuration status of candidate interconnection switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. The collaborative planning module is used to perform collaborative planning of the power distribution cyber-physical system based on the configuration status of the candidate segment switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links.

[0069] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the collaborative planning method for a power distribution cyber-physical system provided by any of the above-described method embodiments of the present invention.

[0070] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0071] Based on the above embodiment of the collaborative planning method for a power distribution cyber-physical system, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, 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 any one of the collaborative planning methods for a power distribution cyber-physical system of the present invention.

[0072] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0073] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0074] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0075] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution cyber-physical system collaborative planning method described in any of the above-described method embodiments of the present invention.

[0076] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0077] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A collaborative planning method for a power distribution cyber-physical system, characterized in that, include: The network topology data of the distribution network to be planned, the equipment configuration status data of each device, and the dynamic coordination parameters during the fault handling process are obtained to obtain the basic planning data of the distribution cyber-physical system. Based on the equipment configuration status data, assess the static configuration dependencies between various devices in the power distribution network and construct the coupling risk complexity. The underlying communication calls and operational overhead caused by the fault handling process are quantified based on the dynamic coordination parameters, and the fault handling coupling complexity is constructed based on the underlying communication calls and operational overhead. With the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, a dual-objective collaborative planning model for the power distribution cyber-physical system is constructed, and reliability constraints are set, wherein the reliability constraints are the upper limit value that the system expects to meet the annual power shortage. Under the constraints of the reliability constraints, the dual-objective collaborative planning model is solved to generate the configuration status of candidate segmented switches, the configuration status of candidate tie switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. Based on the configuration status of the candidate sectionalizing switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links, collaborative planning of the power distribution cyber-physical system is carried out.

2. The collaborative planning method for power distribution cyber-physical systems as described in claim 1, characterized in that, The expected annual power shortage of the system is calculated based on the fault handling time of each faulty object in the distribution network and the power restoration capability of each power-loss load point; the fault handling time of each faulty object is calculated through the following steps: Based on the equipment configuration status data, the operation mode adopted by each faulty object during fault handling is determined; wherein, the operation mode includes manual mode, remote two-way mode, remote three-way mode or distributed collaborative mode; For each faulty object, the communication transmission delay time and mechanical action time required to process the faulty object are determined according to the operation mode corresponding to the faulty object. Based on the communication transmission delay time and the mechanical action time, calculate the fault location time and fault isolation time of the faulty object; The fault location time of the faulty object is added together with the corresponding fault isolation time to obtain the fault handling time of the faulty object.

3. The collaborative planning method for power distribution cyber-physical systems as described in claim 2, characterized in that, The power restoration capability of each power-loss load point in each of the aforementioned fault objects is calculated using the following method: For each faulty object, after the fault handling time of the faulty object ends, based on the equipment configuration status data, determine the interconnection-based power transfer recovery power and distributed power islanding recovery power of each power-loss load point corresponding to the faulty object; construct mutual exclusion selection constraints for each power-loss load point; wherein, the mutual exclusion selection constraints are used to restrict each power-loss load point to only be restored by either interconnection-based power transfer or distributed power islanding at the same time; based on the mutual exclusion selection constraints and the preset power restoration priority order, select one of the interconnection-based power transfer recovery power and distributed power islanding recovery power corresponding to each power-loss load point, and determine the selected recovery power as the power restoration capacity of each power-loss load point.

4. The collaborative planning method for power distribution cyber-physical systems as described in claim 3, characterized in that, The expected annual power shortage of the system is calculated using the following method: For each faulty object, obtain the annual fault rate, manual recovery time, load power corresponding to each power loss load point in the faulty object, and recovery operation time of each power loss load point when power is restored. Based on the fault handling time of the fault object and the load power corresponding to each power loss load point in the fault object, calculate the power shortage of each power loss load point during the fault handling stage. Based on the manual recovery time of the faulty object, the load power corresponding to each power loss load point in the faulty object, the power supply recovery capability corresponding to the power loss load point, and the recovery operation time, calculate the power supply shortage of each power loss load point corresponding to the faulty object during the recovery phase. The power shortage at each of the power-loss load points during the fault handling phase is added to the power shortage during the recovery phase to obtain the node power shortage at each power-loss load point. The total power supply deficit of the fault object is obtained by summing the power supply deficit of each of the aforementioned power loss load points. Using the annual failure rate corresponding to each of the aforementioned fault objects, the total power supply shortage of each fault object is weighted and summed to obtain the expected annual power supply shortage of the system.

5. The collaborative planning method for power distribution cyber-physical systems as described in claim 4, characterized in that, The coupling risk complexity is calculated using the following formula: ； ； In the formula, The coupling risk complexity; S, T, and G are the candidate set of segmented switches, the candidate set of interconnection switches, and the candidate set of distributed power supply access locations in the primary and secondary collaborative planning set, respectively; s and r are elements in set S, and t and g are elements in sets T and G, respectively. Let (i,j) be the set of candidate cooperative communication links between terminals, and (i,j) be the set of... Elements in; The number of downstream load points of the sectionalizing switch S; The number of downstream load points corresponding to element r; This represents the maximum number of downstream load points among all candidate sectionalizing switches. For segmented switches The topological importance coefficient; , , The configuration status variables of the segmented switch s, the tie switch t, ​​and the candidate location distributed power source g in the device configuration status data respectively; These are the remote function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; These are the remote control function configuration state variables corresponding to the sectionalizing switch s, the tie switch t, ​​and the candidate location distributed power source g, respectively; Configure state variables for distributed collaborative terminals; Configure state variables for distributed power supply islanding control functionality; , , These are sectional switches. , contact switch Configure status variables for the communication link between the automated terminal and the master station on the distributed power source; Configure state variables for the cooperative communication link between sectional switch i and sectional switch j.

6. The collaborative planning method for power distribution cyber-physical systems as described in claim 5, characterized in that, The fault handling coupling complexity is calculated using the following formula: ； ； in, The fault handling coupling complexity is given by K; K is the set of fault objects, and k is an element in set K. is the annual failure rate of faulty object k; m is the index of the operating mode number. For the selection variable of the mode m corresponding to the faulty object k; The dynamic coordination parameters respectively characterize the number of terminals participating in observation, the number of switches participating in control, the number of communication links being invoked, and the number of manually operated switches when the faulty object k adopts mode m. , , , These are the unit technical cost indicators for the corresponding observation terminal, remote control action, communication link, and manual operation, respectively. This refers to the increase in communication bandwidth usage caused by the access of a single observation terminal. The increase in CPU computing load caused by the main station processing data from a single observation terminal; The incremental bandwidth usage of control messages caused by a single remote control action; The CPU computational load increment caused by the main station executing a single remote control command; This represents the increase in wear on the mechanical action of the switch caused by a single remote control operation. This represents the bandwidth usage increment when a single communication link is invoked. This represents the communication latency increment when a single communication link is invoked. The time cost incurred by a single manual inspection or manual operation; , , , , , , , These are the rated upper limit or normalized benchmark value of the corresponding indicator.

7. The collaborative planning method for power distribution cyber-physical systems as described in claim 6, characterized in that, Under the constraints of the reliability conditions, the bi-objective collaborative programming model is solved to generate the configuration states of candidate segmented switches, candidate tie switches, candidate distributed power supply access states, automated terminal functional configuration states, and communication link networking configuration states, including: The bi-objective collaborative programming model is iteratively solved using a multi-objective optimization algorithm, and the Pareto front solution set containing multiple candidate programming schemes is output. Extract the coupling risk complexity and fault handling coupling complexity of all candidate planning schemes in the Pareto front solution set respectively, and select the minimum coupling risk complexity and the minimum fault handling coupling complexity. The minimum value of the coupling risk complexity and the minimum value of the fault handling coupling complexity are normalized to construct a dual-objective normalized ideal point; The coupling risk complexity and the fault handling coupling complexity of each candidate planning scheme are normalized to obtain the normalized two-dimensional target coordinates of each candidate planning scheme. Calculate the Euclidean distance from the normalized two-dimensional target coordinates of each candidate planning scheme to the normalized ideal point of the dual objective, and determine the candidate planning scheme with the smallest Euclidean distance as the target candidate scheme; When multiple target candidate schemes with the same Euclidean distance exist, they are sequentially screened according to the priority order of minimizing the expected annual power shortage of the system, minimizing the coupling risk complexity, and minimizing the fault handling coupling complexity. The set of state variables corresponding to the screened target candidate schemes is determined as the configuration status of the candidate segment switch, the configuration status of the candidate tie switch, the access status of the candidate distributed power source, the functional configuration status of the automation terminal, and the networking configuration status of the communication link.

8. A collaborative planning device for a power distribution cyber-physical system, characterized in that, include: The system includes a data acquisition module, a static evaluation module, a dynamic evaluation module, a model building module, a model solving module, and a collaborative planning module. The data acquisition module is used to acquire network topology data of the distribution network to be planned, equipment configuration status data of each device, and dynamic coordination parameters during the fault handling process, so as to obtain the basic planning data of the distribution cyber-physical system. The static evaluation module is used to evaluate the static configuration dependencies between various devices in the distribution network based on the equipment configuration status data in the planning basic data, and to construct the coupling risk complexity. The dynamic evaluation module is used to quantify the underlying communication calls and operational load overhead brought about by the fault handling process based on the dynamic coordination parameters, and to construct the fault handling coupling complexity based on the underlying communication calls and operational load overhead. The model building module is used to construct a dual-objective collaborative planning model of the power distribution cyber-physical system with the goal of minimizing the coupling risk complexity and the fault handling coupling complexity, and to set reliability constraints, wherein the reliability constraints are that the system's annual expected power shortage meets a set upper limit. The model solving module is used to solve the dual-objective collaborative planning model under the constraints of the reliability constraints, and generate the configuration status of candidate segmented switches, the configuration status of candidate interconnection switches, the access status of candidate distributed power sources, the functional configuration status of automated terminals, and the networking configuration status of communication links. The collaborative planning module is used to perform collaborative planning of the power distribution cyber-physical system based on the configuration status of the candidate segment switches, the configuration status of the candidate tie switches, the access status of the candidate distributed power sources, the functional configuration status of the automation terminals, and the networking configuration status of the communication links.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the power distribution cyber-physical system collaborative planning method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the power distribution cyber-physical system collaborative planning method as described in any one of claims 1-7.

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