Power distribution network connection mode optimization method and device, computer equipment and program product

Abstract

The application relates to a power distribution network wiring mode optimization method and device, computer equipment and computer program product. The method comprises the following steps: acquiring basic data required by power distribution network planning, and extracting target features of the basic data; screening candidate wiring mode schemes based on the target features; constructing a target function and corresponding constraint conditions with the minimum life cycle economic cost, the minimum system power shortage, the minimum green loss, the minimum operation stability deviation and the maximum flexible device utilization efficiency as targets; continuously iterating the candidate wiring mode schemes, and outputting a wiring mode scheme set; calculating the matching degrees of all wiring mode schemes and the target function, taking the wiring mode scheme corresponding to the highest matching degree as a target wiring mode scheme; performing regular scene verification and extreme scene verification, and outputting a final wiring mode scheme when all verifications are passed. The method can improve the comprehensive operation effect of the power distribution network.

Description

Technical Field

[0001] This application relates to the field of power system and optimized operation technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for optimizing the wiring mode of a distribution network. Background Technology

[0002] Against the backdrop of the "dual carbon" goals and the construction of a new power system, high-voltage distribution networks are transforming into a "multi-dimensional interaction model of source, grid, load and storage," facing challenges such as prominent uncertainties on the source side, the impact of high-density flexible loads on the load side, and significant pressure to upgrade existing power grids.

[0003] Existing technologies mainly focus on single wiring topology optimization, which has problems such as single optimization variable dimension, incomplete economic cost accounting, lack of multi-dimensional collaboration of the target system, limited verification scenarios, coarse constraint system, and insufficient accuracy and efficiency of traditional intelligent algorithms. It is difficult to achieve full-dimensional optimization of wiring topology, flexible equipment configuration and collaboration of existing and incremental equipment. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product for optimizing the wiring mode of a distribution network, which can comprehensively improve the economy, reliability, greenness, and operational stability of the distribution network, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for optimizing the wiring patterns of a power distribution network, including:

[0006] Obtain the basic data required for power distribution network planning, and extract the target features of the basic data;

[0007] Based on the target characteristics, candidate wiring pattern schemes are selected from a preset wiring pattern library;

[0008] The topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment.

[0009] Based on the objective function and the constraints, the candidate wiring pattern schemes are iterated continuously until the preset convergence condition is met, and then the iteration stops and the set of wiring pattern schemes is output.

[0010] Calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme;

[0011] The target wiring scheme is subjected to regular scenario verification and extreme scenario verification in sequence. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme.

[0012] In one embodiment, the target characteristics include the uncertainty characteristics of the source load, the influence intensity characteristics of distributed energy, the degree of access, the utilization capacity characteristics of existing equipment, and the adjustment capacity characteristics of flexible equipment.

[0013] In one embodiment, the topology parameters include typical wiring type, number of line segments, location of tie points, and conductor cross-section selection; the flexible equipment parameters include the location and rated capacity of solid-state switches, the location and rated capacity of back-to-back converters, the location, rated power, and rated capacity of energy storage devices; and the existing and incremental parameters include the existing equipment reuse rate and the location, selection, and capacity parameters of incremental equipment.

[0014] In one embodiment, the step of iteratively evaluating candidate wiring pattern schemes based on the objective function and the constraints until a preset convergence condition is met, and then stopping the iteration and outputting a set of wiring pattern schemes, includes:

[0015] The topology parameters, the flexible device parameters, and the stock-increment parameters are encoded into a hybrid coding chromosome;

[0016] An initial population is generated, and unreasonable individuals in the initial population are removed to obtain an initialized population;

[0017] Preset weights are assigned to the full life cycle economic cost, system power shortage, green loss, operational stability deviation and flexible equipment utilization efficiency, respectively, and weighted summation is performed to transform the objective function into a single objective fitness function. A penalty function is applied to individuals in the initialized population that do not meet the constraints.

[0018] Based on the single-objective fitness function and the penalty function, the population is continuously iterated, and a set of wiring mode schemes is output when the preset convergence condition is met.

[0019] In one embodiment, the target wiring mode scheme is validated under standard scenarios, including:

[0020] Obtain the first voltage of all nodes, the first load rate of all lines, and the short-circuit current of each node during the operation of the target wiring mode scheme;

[0021] In the event that any line goes out of service, obtain the second load rate of all lines and the second voltage of all nodes;

[0022] If the first voltage and the second voltage are within a preset voltage range, the first load rate and the second load rate are within a preset load rate range, and the cost corresponding to the target wiring scheme is within a preset cost range, then the target wiring scheme is determined to pass the conventional scenario verification.

[0023] In one embodiment, extreme scenario verification is performed on the target wiring mode scheme, including:

[0024] Based on the target wiring mode scheme, simulations were conducted in distributed energy complete shutdown scenario, overloaded scenario, and extreme weather multi-line fault scenario, and the operation status of the target area under each scenario was determined.

[0025] If the operating conditions meet the preset operating conditions, the target wiring mode scheme is determined to have passed the extreme scenario verification.

[0026] Secondly, this application also provides a distribution network wiring mode optimization device, comprising:

[0027] The acquisition module is used to acquire the basic data required for power distribution network planning and extract the target features of the basic data;

[0028] The filtering module is used to filter candidate wiring pattern schemes from a preset wiring pattern library based on the target characteristics.

[0029] The module is used to construct an objective function and corresponding constraints by taking the topology parameters, flexible equipment parameters, and existing and incremental parameters of the wiring within the target area of ​​the distribution network as optimization variables, with the objectives of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment.

[0030] The output module is used to iterate the candidate wiring pattern schemes based on the objective function and the constraints until the preset convergence condition is met, and then stop the iteration and output the set of wiring pattern schemes.

[0031] The calculation module is used to calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the target function, and to take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme.

[0032] The verification module is used to perform normal scenario verification and extreme scenario verification on the target wiring mode scheme in sequence. If all verifications pass, the target wiring mode scheme is adopted as the final wiring mode scheme.

[0033] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0034] Obtain the basic data required for power distribution network planning, and extract the target features of the basic data;

[0035] Based on the target characteristics, candidate wiring pattern schemes are selected from a preset wiring pattern library;

[0036] The topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment.

[0037] Based on the objective function and the constraints, the candidate wiring pattern schemes are iterated continuously until the preset convergence condition is met, and then the iteration stops and the set of wiring pattern schemes is output.

[0038] Calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme;

[0039] The target wiring scheme is subjected to regular scenario verification and extreme scenario verification in sequence. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme.

[0040] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0041] Obtain the basic data required for power distribution network planning, and extract the target features of the basic data;

[0042] Based on the target characteristics, candidate wiring pattern schemes are selected from a preset wiring pattern library;

[0043] The topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment.

[0044] Based on the objective function and the constraints, the candidate wiring pattern schemes are iterated continuously until the preset convergence condition is met, and then the iteration stops and the set of wiring pattern schemes is output.

[0045] Calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme;

[0046] The target wiring scheme is subjected to regular scenario verification and extreme scenario verification in sequence. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme.

[0047] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0048] Obtain the basic data required for power distribution network planning, and extract the target features of the basic data;

[0049] Based on the target characteristics, candidate wiring pattern schemes are selected from a preset wiring pattern library;

[0050] The topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment.

[0051] Based on the objective function and the constraints, the candidate wiring pattern schemes are iterated continuously until the preset convergence condition is met, and then the iteration stops and the set of wiring pattern schemes is output.

[0052] Calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme;

[0053] The target wiring scheme is subjected to regular scenario verification and extreme scenario verification in sequence. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme.

[0054] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for optimizing the wiring patterns of the distribution network first acquire the basic data required for distribution network planning and extract the target features of the basic data; based on the target features, candidate wiring pattern schemes are screened from a pre-set wiring pattern library; the topology parameters, flexible equipment parameters, and existing and incremental parameters of the wiring within the target area of ​​the distribution network are used as optimization variables to construct an objective function and corresponding constraints with the objectives of minimizing the full life-cycle economic cost, minimizing system power shortage, minimizing green energy loss, minimizing operational stability deviation, and maximizing the utilization efficiency of flexible equipment; based on the objective function and constraints, the candidate wiring pattern schemes are iterated continuously until the pre-set convergence condition is met, at which point the iteration stops and a set of wiring pattern schemes is output; the matching degree between all wiring pattern schemes in the set and the objective function is calculated, and the wiring pattern scheme with the highest matching degree is taken as the target wiring pattern scheme; the target wiring pattern scheme is sequentially verified under normal scenarios and extreme scenarios, and if all verifications pass, the target wiring pattern scheme is taken as the final wiring pattern scheme. Thus, by incorporating wiring topology, flexible equipment deployment / capacity, and existing equipment reuse rate into a unified optimization variable system, the millisecond-level fault isolation of solid-state switches, peak load smoothing of energy storage, and power flow regulation capabilities of back-to-back converters are deeply coupled with the grid structure, systematically solving the voltage over-limit and power flow backflow problems caused by uncertainties on both the source and load sides. Secondly, by comprehensively calculating the costs of existing equipment upgrades, flexible equipment investment and maintenance, and carbon emissions through a full life-cycle cost model, combined with optimized design of existing equipment reuse rate, "large-scale demolition and construction" and equipment configuration redundancy are avoided, improving the economic rationality of the plan. Thirdly, the synergistic effect of the five-dimensional objective function and the eight categories of constraints takes into account the multi-dimensional balance of reliability, greenness, operational stability, and flexible equipment utilization. In addition, the dual-layer verification mechanism forcibly verifies the robustness of the solution under extreme conditions such as complete DG shutdown, overload, and multi-line faults, making up for the safety blind spots of conventional scenario verification. In summary, this invention provides a systematic solution for high-quality planning of high-voltage distribution networks under new power systems, which combines economy, safety, and engineering feasibility. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is an application environment diagram of the power distribution network wiring mode optimization method in one embodiment;

[0057] Figure 2 This is a flowchart illustrating a method for optimizing the wiring patterns of a power distribution network in one embodiment.

[0058] Figure 3 This is a structural block diagram of a power distribution network wiring mode optimization device in one embodiment;

[0059] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0061] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0062] The power distribution network wiring mode optimization method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0063] In one exemplary embodiment, such as Figure 2 As shown, a method for optimizing the wiring patterns of a power distribution network is provided, which can be applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps 202 to 212. Wherein:

[0064] Step 202: Obtain the basic data required for distribution network planning and extract the target features of the basic data.

[0065] The basic data includes grid basic parameters, load parameters, distributed energy parameters, flexible equipment parameters, existing and incremental equipment parameters, economic parameters, reliability parameters, and environmental and policy parameters; the target characteristics are source-load fluctuations, distributed generation penetration, overcharging impact, existing value, and flexible potential characteristics in the distribution network.

[0066] Among them, the basic parameters of the power grid include substation parameters, line parameters, and switchgear parameters; load parameters include conventional loads and special loads; distributed energy parameters include distributed photovoltaic, distributed wind power, and energy storage equipment; flexible equipment parameters include solid-state switches and back-to-back converters; existing and incremental equipment parameters include existing equipment and incremental equipment; economic parameters include cost parameters and revenue parameters; reliability parameters include equipment reliability and system reliability; and environmental and policy parameters include environmental parameters and policy parameters.

[0067] Substation parameters include main transformer capacity (e.g., 50MVA, 100MVA), number of units, number of outgoing line bays, and main transformer short-circuit impedance; line parameters include existing line type, length, resistance, reactance, susceptance, and allowable current carrying capacity; switchgear parameters include circuit breaker rated current (e.g., 1250A), breaking capacity (e.g., 40kA), load switch, and disconnector type and rated parameters.

[0068] Conventional loads include the total load of the target area (e.g., 100MW), spatiotemporal distribution characteristics (e.g., load density divided by administrative region / functional area), peak-valley difference, and annual load growth rate; special loads include the location, number, single-station capacity (e.g., 10MW / station) of high-power supercharging stations, charging curves (e.g., 0~10min constant current charging, 10~60min constant voltage charging), peak load time distribution (e.g., 18:00~22:00 on weekdays); power supply reliability level (e.g., first-level load, second-level load) and allowable outage time (e.g., ≤5min) of important loads such as data centers and industrial parks.

[0069] Distributed photovoltaic (PV) includes installed capacity (e.g., 50MW), location distribution, output probability distribution (described using Beta distribution), power factor (e.g., 0.9~1.0), and penetration rate limit (e.g., 25%); distributed wind power (DW) includes installed capacity (e.g., 30MW), hub height, wind speed-output curve, and output fluctuation standard deviation (e.g., ±15%); energy storage equipment (ESS) includes candidate deployment locations, rated power / capacity ratio (e.g., 1C / 2h), charge / discharge efficiency (e.g., ≥90%), response time (e.g., ≤100ms), SOC operating range (e.g., 20%~90%), lifespan (e.g., 10 years), and investment / operation and maintenance costs (e.g., 1.2 yuan / Wh, 0.05 yuan / Wh·year).

[0070] Solid-state switches (SS) include rated voltage (e.g., 20kV, 35kV), rated current (e.g., 1250A), breaking time (e.g., ≤5ms), conduction loss (e.g., ≤0.5W / A), and investment / maintenance cost (e.g., 500,000 RMB / unit, 20,000 RMB / unit / year); back-to-back converters (B2B) include rated capacity (e.g., 20MVA), voltage level (e.g., 20kV / 20kV), converter efficiency (e.g., ≥98%), regulation range (e.g., -100%~100%), and investment / maintenance cost (e.g., 800 RMB / kVA, 30 RMB / kVA / year).

[0071] Existing equipment includes the service life of lines / switches / main transformers (e.g., 10 years, 15 years), remaining capacity (e.g., 80% of rated capacity), performance degradation coefficient (e.g., 0.9), renovation cost (e.g., replacement conductor cost of 2 million yuan / km, equipment maintenance cost of 100,000 yuan / unit), and maximum utilization rate (e.g., 100%, i.e., full utilization; 80%, i.e., partial utilization); incremental equipment includes: candidate conductor models, switch equipment models, new main transformer capacity, investment cost (e.g., LGJ-300 conductor of 4.5 million yuan / km, 20kV circuit breaker of 300,000 yuan / unit), and construction period (e.g., 6 months, 12 months).

[0072] Cost parameters include the equipment lifecycle discount rate (e.g., 4.9%), unit investment cost of lines / switches / main transformers, annual operation and maintenance cost coefficient (e.g., 2% of investment cost per year), network loss cost coefficient (e.g., 0.6 yuan / kWh), and power outage loss cost coefficient (e.g., 10 yuan / kWh); harvest parameters include distributed energy consumption subsidies (e.g., 0.05 yuan / kWh), carbon emission trading value (e.g., 80 yuan / ton CO2), and harvest from flexible equipment ancillary services (e.g., peak-valley arbitrage harvest, frequency regulation harvest).

[0073] Equipment reliability includes line failure rate (e.g., 0.05 times / year·km), switchgear failure rate (e.g., 0.02 times / year·unit), main transformer failure rate (e.g., 0.01 times / year·unit), and mean time to repair (MTTR) (e.g., 8 hours for lines and 4 hours for switchgear); system reliability includes the maximum allowable system power shortage (ENS) (e.g., 10MWh / year), the target value of SAIDI (system average outage duration) (e.g., ≤1 hour / household·year), and the target value of SAIFI (system average outage frequency) (e.g., ≤0.5 times / household·year).

[0074] Environmental parameters include carbon emission coefficients (e.g., 0.8 kg CO2 / kWh for coal-fired power generation and 0.02 kg CO2 / kWh for photovoltaic power generation) and DG curtailment penalty coefficients (e.g., 0.1 yuan / kWh); policy parameters include the lower limit of DG penetration rate (e.g., 15%) and grid planning and specification requirements (e.g., N-1 static safety and voltage deviation ≤ ±5%).

[0075] For example, the basic data required for distribution network planning is obtained, and the target features of the basic data are extracted.

[0076] Step 204: Based on the target characteristics, select candidate wiring pattern schemes from the preset wiring pattern library.

[0077] Optionally, candidate wiring pattern schemes can be selected from a preset wiring pattern library based on the determined target characteristics.

[0078] In some embodiments, the preset wiring pattern library includes standard libraries of typical wiring patterns such as radial, single ring network, double ring network, segmented multi-connection, and flexible interconnected ring network. Standardized parameters such as topology characteristics, flexible equipment adaptation type, existing equipment reuse adaptability, and applicable scenarios are defined for each type of wiring pattern, as shown in Table 1.

[0079] Table 1. Preset Wiring Mode Library

[0080] Typical wiring type Topological features Flexible equipment adaptation types Adaptability of existing equipment Applicable Scenarios Radial Single power supply, no tie switch, the line is divided into several sections. Distributed energy storage (deployment at segmented locations) High (can utilize existing wiring and switches) Rural power distribution networks and areas with low load density Single ring network Dual power supply, closed-loop circuit design, open-loop operation, equipped with 1-2 sectionalizing switches. Solid-state switches (fault isolation point placement) (A new interconnection switch is required; existing lines can be reused.) Urban power distribution network, general load area Double ring network Dual or multiple power supply, two independent ring networks in parallel, with multiple section and tie switches. Solid-state switches + distributed energy storage (deployment at segmentation points and interconnection points) (Some new wiring and switches are required; existing power supply can be reused) New urban areas and areas with concentrated heavy workloads Segmented multi-communication Multiple radial lines are interconnected via tie switches, and each line is divided into 3 to 4 sections. Distributed energy storage (deployed at segmented locations) + solid-state switches (deployed at interconnection points) High (existing radial circuit can be reused, and a new interconnecting switch can be added). Urban core area, high load density area Flexible interconnected ring network The dual-ring network structure interconnects the rings via back-to-back converters, with energy storage configured at each segment. Back-to-back converter + centralized energy storage + solid-state switch (Requires new converters and energy storage; existing ring network lines can be reused) High proportion of DG, dense supercharging access area

[0081] Step 206: Using the topology parameters, flexible equipment parameters, and existing and incremental parameters of the wiring within the target area of ​​the distribution network as optimization variables, construct an objective function and corresponding constraints with the goals of minimizing the economic cost throughout the entire life cycle, minimizing the power shortage in the system, minimizing the green loss, minimizing the deviation in operational stability, and maximizing the utilization efficiency of flexible equipment.

[0082] For example, the topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the economic cost throughout the entire life cycle, minimizing the power shortage in the system, minimizing the green loss, minimizing the deviation in operational stability, and maximizing the utilization efficiency of flexible equipment.

[0083] In some embodiments, the formula for minimizing the total life-cycle economic cost F1 is shown below.

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] in, , , , , , , , This is a cost synergy weighting coefficient, and the weights of each economic cost can be adjusted according to the actual situation. As a load correction factor, the impact of medium- and long-term load changes on network losses is incorporated into the objective function; Cost of upgrading existing equipment (ten thousand yuan / year). For the collection of existing equipment, Let k be the utilization rate of the existing equipment. The total cost of upgrading the kth existing piece of equipment is... For the entire life cycle of the equipment (e.g., 20 years); Annualized cost of investment in flexible equipment For flexible equipment collection, Let r be the initial investment cost of the m-th flexible device, and r be the discount rate. For the lifespan of flexible equipment; Annual maintenance costs for flexible equipment The annual operation and maintenance cost of the m-th flexible device; The annualized cost of incremental equipment investment is calculated using the same logic as that for flexible equipment. Annual maintenance cost for incremental equipment; Annual network loss cost Let t be the total network loss (kW) of the entire network. This is the network loss cost coefficient; Cost of annual power outage losses, This is the cost coefficient for power outage losses; Annual carbon emission cost (ten thousand yuan / year). This represents the total annual carbon emissions (tons of CO2 per year). Value for carbon emissions trading.

[0092] The system power shortage comprehensively reflects the overall power supply reliability of the distribution network, and also reflects the fault recovery contribution of flexible equipment. The calculation formula for the system power shortage F2 is as follows.

[0093]

[0094] in, The load importance weighting coefficient is used, with larger values ​​assigned to critical loads such as data centers and supercharging stations. This is a fault type influencing factor, distinguishing the severity of different faults in main line and branch line switches; n is the number of load nodes. A set of faults (such as line faults, switch faults, main transformer faults); Let be the load capacity (kW) of the i-th load node; Let f be the outage time (h) of the i-th load node under the f-th type of fault, taking into account the fault isolation and power transfer capabilities of flexible equipment (such as solid-state switches, which can shorten the outage time from 8 hours to 0.5 hours). Let f be the probability of occurrence of type f fault.

[0095] The green loss F3 comprehensively considers line network losses, DG curtailment, and carbon emissions, while also incorporating the peak-shaving and loss reduction contributions of flexible equipment. The calculation formula is shown below.

[0096]

[0097]

[0098] in, This is the source load fluctuation intensity coefficient; the larger the fluctuations in DG and overcharge load, the higher the value. As an incentive factor for low-carbon policies, it reflects the strengthening of carbon emission constraints under the dual-carbon objectives; The total annual network loss is (MWh / year). Annual network loss reduction (MWh / year) for flexible equipment; The total annual DG (Dual Generated Gas) wasted (MWh / year) The annual DG curtailment reduced by flexible equipment (MWh / year); This represents the total annual carbon emissions (tons of CO2 per year). The electricity purchased by the grid at time t (kW) Carbon emission coefficient for power grid supply, Let t be the output power of DG (kW). DG carbon emission factor; , , Normalized weighting coefficients for each indicator ( + + =1), determined by the entropy weight method.

[0099] The operational stability deviation F4 comprehensively quantifies the voltage deviation, load factor balance, and short-circuit current constraints of the distribution network, reflecting the voltage regulation and power flow optimization capabilities of flexible equipment. The calculation formula is shown below.

[0100]

[0101]

[0102] in, Voltage safety margin coefficient, with increased weighting for voltage-weak areas; The average voltage deviation (%) of all nodes in the network. The average voltage deviation (%) reduced for flexible equipment regulation; The standard deviation of the line / main transformer load rate (reflecting load rate balance) is given by m, where m is the total number of lines / main transformers. Let j be the load factor of the j-th line / main transformer. Average load factor; The node short-circuit current is (kA). The breaking capacity of the circuit breaker is (kA). , Weighting coefficients (e.g.) =0.5、 =1.0), when ≤ When the time is right, the third term is 0.

[0103] The flexible equipment utilization efficiency F5 is a comprehensive quantitative measure of the actual utilization of flexible interconnected devices and energy storage devices. The calculation formula is shown below.

[0104]

[0105] in, / This is the ratio of the average annual power consumption of the energy storage device to its rated power. / This is the ratio of the actual annual operating time of a solid-state switch to its rated permissible operating time (e.g., rated permissible operating time of 1000 times / year). / This is the ratio of the average annual transmission power to the rated capacity of a back-to-back converter. , , Weighting coefficients ( + + =1), determined based on the importance of the flexible equipment in the system (e.g. =0.5、 =0.2、 =0.3), The standard deviation of the utilization rate of the three types of flexible equipment. A utilization balance penalty coefficient is used to force a balance in the utilization of various types of equipment.

[0106] In some embodiments, the constraints include flexible equipment constraints, existing and incremental equipment constraints, source-load and new energy constraints, power flow constraints, security and topology constraints, equipment operation constraints, economic and policy constraints, and flexible equipment configuration constraints.

[0107] In some embodiments, the flexible equipment is constrained as follows: Solid-state switches: breaking time ≤ 5ms; on-state current ≤ rated current; annual number of operations ≤ rated allowable number of operations (e.g., 1000 times / year); Back-to-back converters: transmission power ≤ rated capacity; commutation efficiency ≥ 98%; regulation range ∈ [-100% × rated capacity, 100% × rated capacity]; Energy storage devices: charging and discharging power ≤ rated power; SOC ∈ [20%, 90%]; charging and discharging efficiency ≥ 90%; response time ≤ 100ms; total regulation capacity of flexible equipment ≥ 1.2 times the source-load fluctuation (redundancy requirement).

[0108] In some embodiments, constraints on existing and incremental equipment are as follows: Existing equipment: Reuse rate ≤ maximum reuse rate (e.g., determined based on remaining lifespan, maximum reuse rate is 100% when remaining lifespan ≥ 5 years); Actual load rate ≤ remaining capacity × performance degradation coefficient; Incremental equipment: Layout location matches wiring topology and must not conflict with existing equipment; Current carrying capacity of new lines ≥ maximum load current of the segment; Total capacity of existing + incremental equipment ≥ 1.5 times the total load of the target area (N-1 safety requirement); Number of new main transformers ≤ number of outgoing line bays of substation.

[0109] In some embodiments, source load and new energy constraints are as follows: DG penetration rate ≤ planning upper limit (e.g., 25%); DG output power factor ∈ [0.9, 1.0]; Supercharging station: peak load power ≤ access node carrying capacity (considering the remaining capacity after N-1); charging curve must meet access node voltage constraints; source load uncertainty constraints: 100 typical scenarios are analyzed using the scenario method (covering extreme cases such as full DG generation, complete DG shutdown, supercharging peak load, and load trough), and the optimization scheme must meet the constraint requirements in all scenarios.

[0110] In some embodiments, the node power balance equation is: , Let t be the input power of the i-th node (including DG output and power supply from the upstream grid). Let t be the output power of the i-th node (including load consumption and power supply to the downstream grid). Let be the network loss power of the i-th node at time t; branch power constraint: , Let be the transmission power of the j-th branch at time t. Let be the rated transmission power of the j-th branch.

[0111] In some embodiments, safety and topology constraints: N-1 static safety constraint: After any branch / main transformer fails and exits operation, the load rate of all lines, switches, and main transformers in the non-faulty area is ≤80%, and the node voltage is ∈ [0.95U]. N, 1.05U N ](U N (Rated voltage); Voltage constraint: Voltage of all load nodes ∈ [0.95U] N 1.05U N The voltage fluctuation of the supercharging station access node is ≤3%; topology constraints: closed-loop design and open-loop operation of the distribution network (avoiding circulating current); no isolated nodes in the entire network (connectivity constraints); number of tie switches ≤ number of line segments × 2 (avoiding overly complex topology).

[0112] In some embodiments, equipment operating constraints are as follows: Line / Main Transformer: Long-term load rate ≤ 80%, short-time (≤ 1 hour) peak load rate ≤ 100%; Switching Equipment: Short-circuit current ≤ breaking capacity; Rated current ≥ maximum load current of the branch.

[0113] In some embodiments, economic and policy constraints include: annualized cost over the entire life cycle ≤ planned budget ceiling; DG curtailment rate ≤ 5% (policy requirement); carbon emission intensity ≤ planned target (e.g., 500 tons CO2 / MWh).

[0114] In some embodiments, the flexible equipment configuration is constrained as follows: the location of solid-state switches must coincide with areas with high failure rates and interconnection nodes; the location of energy storage devices should prioritize locations near supercharging stations, DG centralized access nodes, and nodes with weak voltage; and the location of back-to-back converters should be located at interconnection nodes of different power sources or different ring networks.

[0115] Step 208: Based on the objective function and constraints, iterate the candidate wiring pattern schemes until the preset convergence condition is met, then stop the iteration and output the wiring pattern scheme set.

[0116] For example, based on the objective function and constraints, the candidate wiring patterns are iterated continuously using a preset algorithm until the preset convergence condition is met, at which point the iteration stops and a set of wiring pattern schemes is output.

[0117] Step 210: Calculate the matching degree between all wiring pattern schemes in the wiring pattern scheme set and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme.

[0118] Optionally, the set of wiring mode schemes is determined to include m schemes, and the optimization objectives in the objective function are F1, F2, F3, F4 and F5. The original decision matrix is ​​constructed, and the specific formula is shown below.

[0119]

[0120] in, For the value of the i-th scheme on the j-th objective (e.g.) (This is the optimized target value for the total life-cycle economic cost of Scheme 1).

[0121] The objective function contains two categories: minimizing objective (F1-F4) and maximizing objective F5. Both categories are transformed into the same polarity (both are better the larger they are).

[0122] Inverse normalization is used for minimizing the objective (F1-F4), and the specific formula is shown below.

[0123]

[0124] in, The maximum value of the j-th target in the set of wiring pattern schemes. To find the minimum value, after transformation using the formula A larger value indicates a better objective.

[0125] For maximizing the objective (F5), a positive normalization is applied, and the specific formula is shown below.

[0126]

[0127] in, The maximum value of the j-th target in the set of wiring pattern schemes. To find the minimum value, after transformation using the formula A larger value indicates a better objective.

[0128] After homogenization, a standardized matrix is ​​obtained, ensuring that all target values ​​are on the same order of magnitude and eliminating the influence of dimensions. The specific formula is shown below (the data in the formula are assumed values).

[0129]

[0130] The objective weights of the objective function are determined using the entropy weight method. Information entropy reflects the "dispersion" of the objective value: the smaller the entropy value, the greater the difference in the objective value, the higher the distinguishability of the scheme selection, and the greater the weight should be; conversely, the larger the entropy value, the more concentrated the objective value, and the smaller the weight. The specific calculation formula is shown below.

[0131]

[0132]

[0133] in, Let i be the "proportion" of the i-th option under the j-th objective. =10−8 (avoid) =0 Meaningless), entropy range , =1 indicates that all possible solutions for this objective have the same value and have no distinguishing value.

[0134] Objective weights are calculated based on entropy values ​​and converted into weights through "difference degree". Difference degree = 1 - entropy value. The greater the difference degree, the greater the weight. The specific formula is shown below.

[0135]

[0136] The constraints are as follows: .

[0137] The weighted normalized decision matrix is ​​constructed, and the specific calculation formula is shown below.

[0138]

[0139] Determine the positive ideal solution (A+) and the negative ideal solution (A-), and extract the optimal and worst values ​​from the weighted normalized matrix as the positive ideal solution (A+) and the negative ideal solution (A-). The specific calculation formula is shown below.

[0140]

[0141]

[0142] The Euclidean distance is used to measure the spatial distance between each scheme and the positive and negative ideal solutions. The closer the scheme is to the positive ideal solution and the farther the scheme is from the negative ideal solution, the better the scheme is. The formulas for calculating the distance of scheme i to the positive ideal solution and the distance of scheme i to the negative ideal solution are as follows.

[0143]

[0144]

[0145] in, ≥0, the smaller the value, the closer the solution is to the optimal state; ≥0, the larger the value, the further the solution is from the worst-case scenario; if =0 indicates that the solution perfectly matches the ideal solution (theoretically optimal).

[0146] Calculate the matching degree C i The calculation formula is shown below, and the value range is [0,1].

[0147]

[0148]

[0149] Where Ci represents the matching degree of the i-th solution; the higher the matching degree, the better the solution. Let be the Euclidean distance between the positive ideal solution (A+) and the negative ideal solution (A−).

[0150] The wiring pattern scheme with the highest matching degree is taken as the target wiring pattern scheme.

[0151] Step 212: Perform normal scenario verification and extreme scenario verification on the target wiring mode scheme in sequence. If all verifications pass, the target wiring mode scheme shall be adopted as the final wiring mode scheme.

[0152] For example, the target wiring scheme is verified in both normal scenarios and extreme scenarios. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme and applied to the distribution network.

[0153] In some embodiments, the final wiring scheme includes topology parameters: typical wiring type, number of line segments, tie point location, conductor cross-section selection; flexible equipment configuration list: location, rated power / capacity, quantity, and investment value of solid-state switches / back-to-back converters / energy storage; existing equipment reuse scheme: reuse rate, modification content, and modification cost of each existing equipment; incremental equipment investment list: location, model, capacity, and investment value of new lines / switches / main transformers; target indicator values ​​for each dimension: annualized cost per life cycle, ENS, DG curtailment rate, total carbon emissions, voltage deviation, and flexible equipment utilization rate; and normal / extreme scenario operation indicators: maximum line load rate, maximum node voltage deviation, short-circuit current, and fault recovery time.

[0154] In the above-mentioned method for optimizing the wiring patterns of distribution networks, the basic data required for distribution network planning are obtained, and the target features of the basic data are extracted. Based on the target features, candidate wiring pattern schemes are screened from a pre-set wiring pattern library. The topology parameters, flexible equipment parameters, and existing and incremental parameters of the wiring within the target area of ​​the distribution network are used as optimization variables to construct an objective function and corresponding constraints with the objectives of minimizing the full life cycle economic cost, minimizing system power shortage, minimizing green loss, minimizing operational stability deviation, and maximizing the utilization efficiency of flexible equipment. Based on the objective function and constraints, the candidate wiring pattern schemes are iterated continuously until the pre-set convergence condition is met, at which point the iteration stops and a set of wiring pattern schemes is output. The matching degree between all wiring pattern schemes in the set and the objective function is calculated, and the wiring pattern scheme with the highest matching degree is taken as the target wiring pattern scheme. The target wiring pattern scheme is then subjected to regular scenario verification and extreme scenario verification. If all verifications pass, the target wiring pattern scheme is taken as the final wiring pattern scheme. Thus, by incorporating wiring topology, flexible equipment deployment / capacity, and existing equipment reuse rate into a unified optimization variable system, the millisecond-level fault isolation of solid-state switches, peak load smoothing of energy storage, and power flow regulation capabilities of back-to-back converters are deeply coupled with the grid structure, systematically solving the voltage over-limit and power flow backflow problems caused by uncertainties on both the source and load sides. Secondly, by comprehensively calculating the costs of existing equipment upgrades, flexible equipment investment and maintenance, and carbon emissions through a full life-cycle cost model, combined with optimized design of existing equipment reuse rate, "large-scale demolition and construction" and equipment configuration redundancy are avoided, improving the economic rationality of the plan. Thirdly, the synergistic effect of the five-dimensional objective function and the eight categories of constraints takes into account the multi-dimensional balance of reliability, greenness, operational stability, and flexible equipment utilization. In addition, the dual-layer verification mechanism forcibly verifies the robustness of the solution under extreme conditions such as complete DG shutdown, overload, and multi-line faults, making up for the safety blind spots of conventional scenario verification. In summary, this invention provides a systematic solution for high-quality planning of high-voltage distribution networks under new power systems, which combines economy, safety, and engineering feasibility.

[0155] In one exemplary embodiment, the target characteristics include the uncertainty characteristics of the source load, the impact intensity characteristics of distributed energy, the degree of access, the utilization capacity characteristics of existing equipment, and the adjustment capacity characteristics of flexible equipment.

[0156] In practice, the uncertainty characteristics of the source load are quantified by the standard deviation of load fluctuation and the standard deviation of DG (Distributed Gain) processing fluctuation. Specifically, the standard deviation of load fluctuation... and DG's handling of fluctuation standard deviation The calculation formula is shown below.

[0157]

[0158]

[0159] Where T is the statistical duration (e.g., a typical day of 24 hours or a year of 8760 hours for a project). The average load power during the statistical period is calculated using the following formula: L(t) represents the total load power at time t, including conventional load and overcharge load. Let be the total output of the distributed energy source at time t, calculated using the following formula: Let t be the total photovoltaic output. Let t be the total wind power output (kW). The average output of DG during the statistical period is calculated using the following formula: .

[0160] In some embodiments, , The larger the load fluctuation, the more severe the load fluctuation, requiring enhanced flexibility in equipment adjustments. Based on this, some parameters can be increased or some constraint limits tightened (such as the source load fluctuation intensity coefficient). Voltage fluctuation limits).

[0161] The ratio of DG installed capacity to maximum load reflects the intensity of the impact of distributed energy resources on the distribution network. The specific calculation formula is shown below.

[0162]

[0163] in, The total installed capacity of DG in the target area (kW). The target area's maximum load power (kW) is determined by taking the typical daily peak load or annual maximum load (selected according to planning specifications).

[0164] In some embodiments, by The value can be adjusted accordingly to adjust the limits of the curtailment rate constraint and the N-1 safety constraint.

[0165] By overcharging capacity ratio and overcharge load impact strength Reflecting the degree of accessibility, among which, the proportion of supercharging capacity. and overcharge load impact strength The calculation formula is shown below.

[0166]

[0167]

[0168] in, The sum of the rated capacities of all supercharging stations. For the maximum load of the target area, This represents the maximum simultaneous charging power of the supercharging station. The target area load during peak load periods.

[0169] In some embodiments, if A higher value corresponds to a higher weighting for energy storage configuration. ,like A higher value will result in a tighter voltage fluctuation constraint on the supercharging access node.

[0170] The utilization capacity characteristics of existing equipment are quantified by comprehensively considering remaining capacity, performance degradation, and renovation costs. The calculation formula is shown below.

[0171]

[0172] in, The percentage of remaining capacity of existing equipment (%). The performance degradation coefficient is calculated using the following formula: , For the current year, For economic lifespan, The cost of upgrading existing equipment, This refers to the unit investment cost of incremental equipment.

[0173] In some embodiments, if A higher value increases the feasible range for the reuse rate, while a lower value decreases the cost synergy weight. .

[0174] The adjustment capability characteristics of flexible equipment are determined by rated capacity, adjustment range, and response time. The quantification is performed, and the calculation formula is shown below.

[0175]

[0176] in, The total rated capacity (kW) for flexible equipment. To adjust the range coefficient, Response time coefficient.

[0177] In some embodiments, the total adjustment capability of the flexible device must be greater than or equal to a preset multiple of the source load fluctuation.

[0178] In the above embodiments, by extracting five key characteristics—source-load uncertainty, the intensity of distributed energy impact, the density of supercharging network access, the availability of existing equipment, and the adjustment capability of flexible equipment—the high-voltage distribution network planning is transformed from experience-driven to quantitative data-driven. The synergistic effect of these five characteristics achieves an upgrade from a "passive adaptation" to an "active quantitative-driven" optimization model, significantly improving the economy, reliability, environmental friendliness, and engineering adaptability of high-voltage distribution network planning schemes.

[0179] In an exemplary embodiment, topology parameters include typical wiring type, number of line segments, location of tie points, and conductor cross-section selection; flexible equipment parameters include location and rated capacity of solid-state switches, location and rated capacity of back-to-back converters, location, rated power, and rated capacity of energy storage devices; and existing and incremental parameters include existing equipment utilization rate and location, selection, and capacity parameters of incremental equipment.

[0180] In practical implementation, the topology parameters of the wiring include typical wiring types, number of line segments, location of tie points, and selection of conductor cross-sections; the parameters of flexible equipment include the location and rated capacity of solid-state switches, the location and rated capacity of back-to-back converters, the location, rated power, and rated capacity of energy storage equipment; and the parameters of existing and incremental equipment include the utilization rate of existing equipment and the location, selection, and capacity parameters of incremental equipment.

[0181] Among them, the typical wiring type is obtained by selecting feasible initial wiring schemes, such as double ring network and segmented multi-connection; the number of line segments is the number of segments in each line, such as 3 segments or 4 segments; the connection point location is the installation node of the connection switch, such as the interconnection of the second segment of line 1 and the third segment of line 2; the conductor cross-section selection includes the conductor cross-section selection for adding or replacing lines, such as 240mm², 300mm², and 400mm².

[0182] Among them, the location of solid-state switches (e.g., high-fault nodes, interconnection nodes) and rated capacity (e.g., 1250A); the location of back-to-back converters (e.g., ring network interconnection points, power supply interconnection points) and rated capacity (e.g., 10MVA, 20MVA); the location of energy storage devices (e.g., near supercharging stations, DG centralized access nodes), rated power (e.g., 5MW, 10MW) and rated capacity (e.g., 10MWh, 20MWh).

[0183] Among them, the utilization rate of existing equipment includes the utilization rate of each existing line / switch / main transformer (0≤utilization rate≤1, utilization rate=1 means full utilization, 0 means replacement with incremental equipment); the location of incremental equipment (location of newly added lines / switch / main transformers), selection and capacity parameters (such as adding a 20kV circuit breaker with a rated current of 2500A and a breaking capacity of 40kA).

[0184] In the above embodiments, the optimization of wiring topology parameters (typical wiring type, number of segments, tie point location, conductor cross-section) provides a standardized physical skeleton for the power grid, ensuring the rationality and scalability of the grid structure; the optimization of flexible equipment parameters (solid-state switches, back-to-back converters, energy storage device placement and capacity) enables millisecond-level fault isolation, cross-regional power flow regulation, and peak load smoothing capabilities to be precisely embedded in key topology nodes, effectively mitigating the operational risks caused by uncertainties on both the source and load sides; the optimization of existing and incremental parameters (existing equipment reuse rate, incremental equipment placement and selection) maximizes the utilization of existing assets while ensuring power supply security, avoiding resource waste.

[0185] In an exemplary embodiment, candidate wiring pattern schemes are iterated continuously based on an objective function and constraints until a preset convergence condition is met, at which point the iteration stops and a set of wiring pattern schemes is output. This includes: encoding topology parameters, flexible equipment parameters, and existing / incremental parameters into a hybrid encoded chromosome; generating an initial population and removing unreasonable individuals from the initial population to obtain an initialized population; assigning preset weights to the full lifecycle economic cost, system power shortage, green energy loss, operational stability deviation, and flexible equipment utilization efficiency, and performing weighted summation to transform the objective function into a single-objective fitness function; applying a penalty function to individuals in the initialized population that do not meet the constraints; and iterating the population continuously based on the single-objective fitness function and the penalty function, outputting a set of wiring pattern schemes when the preset convergence condition is met.

[0186] In practice, the Chaotic Whale-Beetle Whiskers Hybrid Optimization Algorithm (CWOA-BAS) is used to solve the model for the three categories of optimization variables, the five-dimensional objective function, and the constraints.

[0187] In some embodiments, a hybrid encoding of real numbers, integers, and binary numbers is adopted. The chromosome length is determined according to the number of optimization variables and is divided into five segments: wiring mode encoding, flexible equipment layout / capacity encoding, existing equipment reuse rate encoding, incremental equipment parameter encoding, and conductor cross-section encoding. Each segment of the encoding corresponds one-to-one with the core optimization variables to ensure that the encoding has no invalid solutions and fits the actual engineering situation.

[0188] Example coding structures are as follows: Wiring mode coding (integer): 1-Single ring network, 2-Double ring network, 3-Segmented multi-connection, 4-Flexible interconnected ring network (e.g., "2" represents a double ring network); Flexible equipment deployment / capacity coding (real number + integer): Solid-state switch deployment node (integer, e.g., "3" represents node 3), rated capacity (real number, e.g., "1.25" represents 1250A); Energy storage equipment deployment node (integer), rated power (real number, e.g., "5.0" represents 5MW), rated capacity (real number, e.g., "10.0" represents 10MWh); Existing equipment reuse rate coding (real number): e.g., "0.9" represents a reuse rate of 90% for an existing line; Incremental equipment parameter coding (integer + real number): New switch deployment node (integer), rated current (real number); Conductor cross-section coding (integer): 1-240mm², 2-300mm², 3-400mm² (e.g., "2" represents 300mm²).

[0189] A Logistic chaotic mapping is introduced to generate the initial population, replacing traditional random initialization. This leverages the ergodicity and randomness of the chaotic sequence to improve population diversity and avoid the algorithm getting trapped in local optima. The specific steps are as follows:

[0190] (1) Generating chaotic sequences: using the Logistic mapping formula ,in ∈(0,1), =4 (completely chaotic state), initial value Take non-fixed values ​​within (0,1) (such as 0.3, 0.5, 0.7) to generate N chaotic sequences (N is the population size, such as 100).

[0191] (2) Variable mapping: The chaotic sequence is mapped to the actual feasible region of each optimization variable through linear transformation to obtain the individual positions of the initial population. For example, the feasible region of the utilization rate of existing equipment is [0,1], and the chaotic sequence value is directly adopted; the feasible region of the conductor cross section is {1,2,3}, and the chaotic sequence value is mapped to the corresponding integer by rounding.

[0192] (3) Population screening: Based on engineering experience, individuals with redundant flexible equipment configuration (such as energy storage equipment capacity exceeding 50% of the load of the node) and unreasonable reuse rate of existing equipment (such as reuse rate of equipment with remaining life ≤ 3 years ≥ 80%) are removed to further improve the initial quality of the population.

[0193] Based on the three core operations of the Whale Optimization Algorithm (WOA)—surrounding predation, bubble web attack, and random search—a global search is performed on the initial population, traversing the solution space to find the optimal solution region. The specific operations are as follows:

[0194] Fitness function calculation: The five-dimensional optimization objective function is transformed into a single-objective fitness function through weighted summation (weights are determined using the entropy weight method). Simultaneously, a penalty function is applied to individuals that violate constraints (the higher the degree of constraint violation, the larger the penalty function value). The fitness function expression is:

[0195]

[0196] in, , , , , These are the normalized values ​​of each objective function. To preset weights, The value is the penalty function value.

[0197] The search range is narrowed by updating the position to move closer to the current best individual. The formula is:

[0198]

[0199]

[0200]

[0201]

[0202] Where t is the current iteration number, This indicates the current location of the individual whale. is the current global optimal individual position; 'a' decreases linearly from 2 to 0 with the number of iterations, and 'r' is a random vector within (0,1).

[0203] A combination of shrinking encirclement and spiral update is used to perform a local search in the vicinity of the optimal individual, as shown in the formula:

[0204]

[0205]

[0206] Where b=1 (spiral shape control parameter). (Random number); the algorithm selects "shrink wrap" or "spiral update" with a 50% probability.

[0207] when When the value is greater than 1, abandon the current optimal solution and randomly select an individual within the population as a reference point for the search, as shown in the formula:

[0208]

[0209] in, To achieve traversal of the global solution space, the positions of randomly selected individuals within the population are used.

[0210] The optimal solution obtained from the global search using the whale algorithm is used as the initial position of the beetle whisker search algorithm (BAS). Utilizing the characteristic that the left and right whiskers of the beetle can sense the concentration difference, local fine-tuning is performed on the optimal solution region. The specific operation is as follows:

[0211] Longhorn beetle individual initialization: The global optimal solution obtained by the whale algorithm is used as the initial position of the longhorn beetle. ;

[0212] Direction and step size settings: Generate unit direction vector ( (a random vector within (0,1)) with an initial step size Take 10% of the solution space range (e.g., the step size for optimizing the conductor cross-section is 0.2), and the step size decay coefficient η=0.95 (the step size decreases exponentially with the number of iterations during the iteration process).

[0213] Left and right positions must be calculated:

[0214]

[0215] in, The length of the longhorn beetle's whiskers is 0.5 cm.

[0216] Position Update: Calculate the fitness values ​​for the left and right must positions. , ,like < (A lower fitness value is better), then the longhorn beetle moves to the left, and vice versa. The update formula is:

[0217]

[0218] Boundary constraint handling: When the position of the beetle after moving exceeds the feasible region of the variable, the reflection method is used to pull it back into the region. The formula is as follows:

[0219]

[0220]

[0221] in, , These are the lower and upper limits of the optimization variables, respectively.

[0222] The global search of the whale algorithm is alternated with the local optimization of the beetle whisker algorithm. After each global search of the whale algorithm is completed (e.g., 20 iterations), the beetle whisker algorithm is immediately launched to perform local fine-tuning of the current optimal solution (e.g., 10 iterations), forming an iterative loop. At the same time, an elite retention strategy is introduced to directly retain the best individuals of each generation to the next generation, avoiding the loss of excellent genes. Preset convergence conditions are set, such as the number of iterations ≥ 150 generations or the fitness value change ≤ 10⁻⁴ for 20 consecutive generations. Iteration stops when either condition is met, and the combined wiring mode scheme (containing 10~20 sets of non-dominated solutions) is output.

[0223] In the above embodiments, the solution efficiency and accuracy of the multi-objective optimization model for high-voltage distribution networks are significantly improved through the collaborative design of hybrid encoding, chaotic initialization, weighted fitness function, and penalty function: the use of hybrid encoding of real numbers, integers, and binary numbers unifies the three types of heterogeneous parameters—topology, flexible equipment, and existing and incremental parameters—into a chromosome structure, ensuring the complete representation and engineering interpretability of optimization variables; by eliminating unreasonable individuals in the initial population, the invalid solution space is effectively compressed, improving the initial quality of the population and the convergence speed; the five-dimensional objective function is transformed into a single-objective fitness function through entropy weighting and summation, and a penalty function is used to punish individuals that violate constraints, transforming the complex multi-constraint optimization problem into an efficient unconstrained problem, avoiding the blindness of traditional algorithms in constraint handling.

[0224] In an exemplary embodiment, a routine scenario verification of the target wiring scheme includes: obtaining the first voltage of all nodes, the first load rate of all lines, and the short-circuit current of each node during the operation of the target wiring scheme; if any line exits operation, obtaining the second load rate of all lines and the second voltage of all nodes; and if the first voltage and the second voltage are within a preset voltage range, the first load rate and the second load rate are within a preset load rate range, and the cost corresponding to the target wiring scheme is within a preset cost range, then the target wiring scheme is determined to have passed the routine scenario verification.

[0225] In actual implementation, the first voltage of all nodes, the first load rate of all lines, and the short-circuit current of each node are obtained during the operation of the target wiring scheme. If any line is out of operation, the second load rate of all lines and the second voltage of all nodes are obtained. If the first voltage and the second voltage are within a preset voltage range, the first load rate and the second load rate are within a preset load rate range, and the cost corresponding to the target wiring scheme is within a preset cost range, the target wiring scheme is determined to have passed the conventional scenario verification.

[0226] In the above embodiments, by performing routine scenario verification on the target wiring mode scheme, the safety and economy of the power grid under normal operating conditions are systematically verified before the scheme is implemented: the line load rate and node voltage constraints at each moment are verified to ensure that the power grid does not experience overload or voltage exceedance during normal operation; the three-phase and two-phase short-circuit currents of each node are calculated and compared with the breaking capacity of the circuit breaker to ensure that the protection device can operate reliably when a fault occurs; the N-1 static safety condition after the fault exit of any branch or main transformer is simulated to verify that the load rate and voltage of the non-faulty area still meet the safety margin requirements and prevent the fault cascading expansion.

[0227] In an exemplary embodiment, extreme scenario verification of the target wiring mode scheme includes: based on the target wiring mode scheme, simulating distributed energy complete shutdown scenario, overload scenario, and extreme weather multi-line fault scenario respectively, and determining the operation status of the target area under each scenario; if the operation status meets the preset operation status, the target wiring mode scheme is determined to have passed the extreme scenario verification.

[0228] In actual implementation, based on the target wiring mode scheme, simulations were conducted in distributed energy complete shutdown scenario, overloaded scenario, and extreme weather multi-line fault scenario, and the operating conditions of the target area under each scenario were determined. If the operating conditions meet the preset operating conditions, the target wiring mode scheme is determined to have passed the extreme scenario verification.

[0229] In some embodiments, the distributed energy outage scenario involves all distributed photovoltaic / wind power plants shutting down to verify that the entire grid voltage deviation is ≤±5%, there is no overload on lines / equipment, and the supercharging stations are supplying power normally; the supercharging full-load scenario involves all supercharging stations charging at 100% rated power simultaneously to verify the peak load smoothing effect of energy storage equipment (supercharging peak load reduction ≥30%), voltage fluctuation ≤3%, and line load rate ≤80%; the extreme weather multi-line fault scenario involves two or more main lines in the target area failing simultaneously (such as line tripping caused by heavy rain) to verify the millisecond-level isolation capability of solid-state switches (isolation time ≤5ms), the power supply recovery rate of non-faulty areas ≥99%, and no power outages for important loads.

[0230] In the above embodiments, extreme scenario verification of the target wiring scheme systematically verifies the survivability and anti-interference resilience of the high-voltage distribution network under extreme conditions: in the scenario of complete power outage of distributed energy, voltage deviation and equipment overload are verified to ensure that the grid can still maintain basic power supply security when renewable energy output is zero; in the scenario of supercharging load, the effect of energy storage equipment on the smoothing of supercharging peak load is verified to ensure that the grid can withstand the extreme impact of high-power flexible load; in the scenario of multi-line faults in extreme weather, the self-healing capability of the grid under severe faults is verified. This extreme scenario verification layer, as a "safety redundancy enhancement mechanism" for conventional scenario verification, effectively makes up for the safety blind spot caused by traditional planning methods that only verify conventional operating conditions. It enables the optimized scheme to maintain safe and stable operation under extreme and harsh conditions such as typhoons, rainstorms, complete power outage of renewable energy, and supercharging stacking, significantly improving the robustness and engineering applicability of the high-voltage distribution network in the complex operating environment of new power systems.

[0231] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0232] Based on the same inventive concept, this application also provides a distribution network wiring pattern optimization device for implementing the above-mentioned distribution network wiring pattern optimization method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more distribution network wiring pattern optimization device embodiments provided below can be found in the limitations of the distribution network wiring pattern optimization method described above, and will not be repeated here.

[0233] In one exemplary embodiment, such as Figure 3 As shown, a wiring mode optimization device for a power distribution network is provided, comprising: an acquisition module 301, a filtering module 302, a construction module 303, an output module 304, a calculation module 305, and a verification module 306, wherein:

[0234] The acquisition module is used to acquire the basic data required for power distribution network planning and extract the target features of the basic data.

[0235] The filtering module is used to filter candidate wiring pattern schemes from a preset wiring pattern library based on the target characteristics.

[0236] The module is used to construct an objective function and corresponding constraints by taking the topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area as optimization variables. The objective functions are to minimize the economic cost throughout the entire life cycle, minimize the power shortage in the system, minimize the green loss, minimize the deviation in operational stability, and maximize the utilization efficiency of flexible equipment.

[0237] The output module is used to iterate the candidate wiring pattern schemes based on the objective function and the constraints until a preset convergence condition is met, and then stop iterating and output the set of wiring pattern schemes.

[0238] The calculation module is used to calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and to take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme.

[0239] The verification module is used to perform normal scenario verification and extreme scenario verification on the target wiring mode scheme in sequence. If all verifications pass, the target wiring mode scheme is adopted as the final wiring mode scheme.

[0240] Each module in the aforementioned power distribution network wiring mode optimization device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0241] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for optimizing the wiring patterns of a power distribution network.

[0242] The display unit of this computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of this computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0243] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0244] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0245] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0246] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0247] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0248] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0249] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0250] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for optimizing the wiring patterns of a power distribution network, characterized in that, The method includes: Obtain the basic data required for power distribution network planning, and extract the target features of the basic data; Based on the target characteristics, candidate wiring pattern schemes are selected from a preset wiring pattern library; The topology parameters, flexible equipment parameters, and existing and incremental parameters of the distribution network within the target area are used as optimization variables to construct an objective function and corresponding constraints with the goals of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment. Based on the objective function and the constraints, the candidate wiring pattern schemes are iterated continuously until the preset convergence condition is met, and then the iteration stops and the set of wiring pattern schemes is output. Calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the objective function, and take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme; The target wiring scheme is subjected to regular scenario verification and extreme scenario verification in sequence. If all verifications pass, the target wiring scheme is adopted as the final wiring scheme.

2. The method according to claim 1, characterized in that, The target characteristics include the uncertainty of source load, the intensity of the impact of distributed energy, the degree of access, the utilization capacity of existing equipment, and the adjustment capacity of flexible equipment.

3. The method according to claim 1, characterized in that, The topology parameters include typical wiring types, number of line segments, location of tie points, and conductor cross-section selection; the flexible equipment parameters include the location and rated capacity of solid-state switches, the location and rated capacity of back-to-back converters, the location, rated power, and rated capacity of energy storage devices; the existing and incremental parameters include the utilization rate of existing equipment and the location, selection, and capacity parameters of incremental equipment.

4. The method according to claim 1, characterized in that, The process of iterating over candidate wiring pattern schemes based on the objective function and the constraints until a preset convergence condition is met, then stopping the iteration and outputting a set of wiring pattern schemes, includes: The topology parameters, the flexible device parameters, and the stock-increment parameters are encoded into a hybrid coding chromosome; An initial population is generated, and unreasonable individuals in the initial population are removed to obtain an initialized population; Preset weights are assigned to the full life cycle economic cost, system power shortage, green loss, operational stability deviation and flexible equipment utilization efficiency, respectively, and weighted summation is performed to transform the objective function into a single objective fitness function. A penalty function is applied to individuals in the initialized population that do not meet the constraints. Based on the single-objective fitness function and the penalty function, the population is iterated continuously, and a set of wiring mode schemes is output when the preset convergence condition is met.

5. The method according to claim 1, characterized in that, Perform routine scenario verification on the target wiring mode scheme, including: Obtain the first voltage of all nodes, the first load rate of all lines, and the short-circuit current of each node during the operation of the target wiring mode scheme; In the event that any line goes out of service, obtain the second load rate of all lines and the second voltage of all nodes; If the first voltage and the second voltage are within a preset voltage range, the first load rate and the second load rate are within a preset load rate range, and the cost corresponding to the target wiring scheme is within a preset cost range, then the target wiring scheme is determined to pass the conventional scenario verification.

6. The method according to claim 1, characterized in that, Extreme scenario verification is performed on the target wiring mode scheme, including: Based on the target wiring mode scheme, simulations were conducted in distributed energy complete shutdown scenario, overloaded scenario, and extreme weather multi-line fault scenario, and the operation status of the target area under each scenario was determined. If the operating conditions meet the preset operating conditions, the target wiring mode scheme is determined to have passed the extreme scenario verification.

7. A device for optimizing the wiring mode of a power distribution network, characterized in that, The device includes: The acquisition module is used to acquire the basic data required for power distribution network planning and extract the target features of the basic data; The filtering module is used to filter candidate wiring pattern schemes from a preset wiring pattern library based on the target characteristics. The module is used to construct an objective function and corresponding constraints by taking the topology parameters, flexible equipment parameters, and existing and incremental parameters of the wiring within the target area of ​​the distribution network as optimization variables, with the objectives of minimizing the full life cycle economic cost, minimizing the system power shortage, minimizing the green loss, minimizing the operational stability deviation, and maximizing the utilization efficiency of flexible equipment. The output module is used to iterate the candidate wiring pattern schemes based on the objective function and the constraints until the preset convergence condition is met, and then stop the iteration and output the set of wiring pattern schemes. The calculation module is used to calculate the matching degree between all wiring pattern schemes in the set of wiring pattern schemes and the target function, and to take the wiring pattern scheme with the highest matching degree as the target wiring pattern scheme. The verification module is used to perform normal scenario verification and extreme scenario verification on the target wiring mode scheme in sequence. If all verifications pass, the target wiring mode scheme is adopted as the final wiring mode scheme.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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