A power distribution network climate adaptability reconstruction method

Abstract

This invention provides a method for climate-adaptive reconfiguration of power distribution networks, comprising: obtaining typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; constructing power distribution network optimization models based on the typical, extreme, and expected climate datasets respectively; performing a risk quantification assessment of the power distribution network based on the optimization models to form candidate reconfiguration schemes; and selecting the candidate reconfiguration scheme with the smallest normalized integration assessment value as the climate-adaptive reconfiguration scheme through normalized integration evaluation of the expected net present value and expected integration level of the power distribution network under each candidate reconfiguration scheme. This invention enables power distribution network reconfiguration to adapt to various climate scenarios.

Description

Technical Field

[0001] This invention relates to the field of distribution network reconfiguration technology, and more specifically, to a method for climate-adaptive reconfiguration of distribution networks. Background Technology

[0002] Urban energy systems face multiple challenges under extreme weather conditions. First, extreme weather events can lead to power outages, disrupting normal urban operations. Second, surges in electricity demand may exceed the system's capacity, causing further power outages. Against this backdrop, methods for predicting and reconfiguring distribution network faults under extreme weather conditions have become an important research topic. While existing technologies offer some solutions, they still have many shortcomings.

[0003] Current technologies lack the ability to quantify the impact of extreme weather on urban power distribution networks. This makes it difficult for traditional power distribution network reconfiguration methods to perform targeted optimization of faults caused by climate anomalies in the power distribution network. Faults in lines and nodes tend to recur under the same conditions, resulting in insufficient robustness of urban power distribution networks. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a climate-adaptive reconfiguration method for power distribution networks, which enables the network to adapt to various climate scenarios through reconfiguration.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a method for climate-adaptive reconfiguration of a power distribution network, comprising the following steps:

[0006] Step S1: Obtain typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models;

[0007] Step S2: Construct power distribution network optimization models based on typical climate datasets, extreme climate datasets, and expected climate datasets, respectively;

[0008] Step S3: Conduct a quantitative risk assessment of the distribution network based on the distribution network optimization model to generate candidate schemes for distribution network reconfiguration;

[0009] Step S4: By normalizing the integration assessment of the expected net present value and expected integration level of the distribution network under each distribution network reconfiguration candidate scheme, the distribution network reconfiguration candidate scheme with the smallest normalized integration assessment value is selected as the distribution network climate-adaptive reconfiguration scheme.

[0010] Further, step S1 includes the following sub-steps:

[0011] Step S1.1: The global climate model is used to provide the basic trends of global climate change. Several regional climate models are deployed under the global climate model, and each regional climate model is used to provide climate data for the corresponding region.

[0012] Step S1.2: Based on the cumulative distribution of regional climate data, select the most representative period under the long-term trend to form a typical climate dataset;

[0013] Step S1.3: Using the Finkelstein–Schafer statistical method, select the typical time period that best represents the climate characteristics of the region from the regional climate data, and based on the time step, identify the extreme values ​​of the climate data at each time step to construct an extreme climate dataset.

[0014] Step S1.4: Calculate the cumulative distribution of each climate variable in the regional climate data at different time steps, obtain the percentiles at each time point, divide the climate data intervals, generate multiple expected climate scenarios, and obtain the expected climate dataset.

[0015] Further, step S2 constructs a deterministic optimization model of the distribution network based on typical climate datasets to obtain the decision variables of the distribution network under the current normal weather scenario; constructs a robust optimization model of the distribution network based on extreme climate datasets to obtain the decision variables of the distribution network under extreme climate events; constructs a stochastic optimization model of the distribution network based on expected climate datasets to obtain the decision variables of the distribution network under expected climate; and forms a comprehensive decision variable of the distribution network for the future time period by weighted fusion of the decision variables of the distribution network under extreme climate events and the decision variables of the distribution network under expected climate.

[0016] Furthermore, step S3 includes the following sub-steps:

[0017] Step S3.1: Calculate the power supply shortage risk index, power load loss risk index, and power load loss risk index considering the impact of extreme weather events for the power distribution network under the current normal weather scenario, based on the decision variables or comprehensive decision variables of the power distribution network at the corresponding time.

[0018] Step S3.2: Identify the critical routes and critical node areas with the highest probability of disaster in the distribution network. Based on the equipment status and redundancy in the critical routes and critical node areas, determine whether the corresponding equipment needs to be repaired, upgraded or replaced, and obtain multiple candidate schemes for distribution network reconfiguration.

[0019] Furthermore, the calculation process for the power supply shortage risk index of the power distribution network is as follows:

[0020]

[0021] in, T Indicates time,t express T index, This represents the total number of climate scenarios. s express index, Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios Indicates the distribution network in the first t Hour, First s Renewable energy generation under various climate scenarios This indicates the rated maximum output power of the generator in the distribution network. Indicates the distribution network in the first t Hour, First s The maximum discharge power that the battery pack can provide under various climate scenarios. This indicates the maximum power limit that allows the purchase of electricity from the grid.

[0022] Furthermore, the calculation process for the power load loss risk index of the distribution network is as follows:

[0023]

[0024] in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, LOLP This represents the most severe cumulative power shortage in the distribution network across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0025] Furthermore, the calculation process for the power load loss risk index of the distribution network considering the impact of extreme weather events is as follows:

[0026]

[0027] in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, d Indicates the expected duration of extreme weather events. Ex This represents the most severe cumulative power shortage in a distribution network considering extreme weather events across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0028] Furthermore, the calculation process for the expected net present value of the distribution network is as follows:

[0029]

[0030] Among them, E( NPV () represents the expected net present value of the distribution network. ICC Indicates the initial cost of capital. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, C Represents a set of components in a power distribution network. c express C index, H Indicates the lifespan of the power distribution network. h express H index, Indicates the first s Probability weights for each climate scenario Indicates the first in the distribution network c The component in the first s Fixed operation and maintenance costs under various climate scenarios Indicates the first c The capital recovery factor of the component Indicates the real interest rate. Indicates the first in the distribution network c The component in the first h Year, No. s Variable operation and maintenance costs under different climate scenarios.

[0031] Furthermore, the calculation process for the integration level of the distribution network is as follows:

[0032]

[0033] Among them, E( GI This indicates the expected level of integration of the distribution network. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, Indicates the first s Probability weights for each climate scenario Indicates the distribution network in the first t Hour, First s Electricity purchased from the grid under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0034] Furthermore, the normalized integrated evaluation value F The calculation process is as follows:

[0035]

[0036] in, E( NPV The normalized coefficient of ) E( GI The normalized coefficient of ).

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) The distribution network climate adaptive reconstruction method of the present invention constructs a distribution network optimization model based on typical climate datasets, extreme climate datasets and expected climate datasets respectively, providing decision variables under different climate scenarios. The three models are used in a complementary manner, which can not only provide a benchmark operation strategy under normal climate conditions, but also capture future climate change trends and the most unfavorable conditions under extreme events, and generate multi-dimensional optimal decision variables with climate adaptability. This method overcomes the problem of insufficient adaptability of a single model when facing climate fluctuations and improves the robustness of the optimization results under different climate conditions.

[0039] (2) This invention constructs three risk quantification indicators to build candidate schemes for power distribution network reconfiguration, thereby realizing a quantitative assessment of the stability of power distribution network operation under extreme weather conditions. Based on this, high-risk lines and nodes are identified, and targeted reconfiguration candidate schemes are generated in combination with equipment status, thereby reducing the fault recurrence rate of the reconfigured power distribution network.

[0040] (3) The present invention determines the final reconfiguration scheme based on economic efficiency and grid integration, and realizes the dual consideration of investment benefits and energy autonomy on a macro level. Through unified dimension processing, the optimal reconfiguration scheme that takes into account both economic efficiency and grid integration is finally selected, thereby improving the overall benefits of the distribution network system reconfiguration operation under climate challenges. Attached Figure Description

[0041] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the distribution network climate-adaptive reconfiguration method of the present invention;

[0043] Figure 2 This is a schematic diagram of the distribution network climate-adaptive reconfiguration system of the present invention. Detailed Implementation

[0044] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings.

[0045] like Figure 1 The flowchart below shows the distribution network climate-adaptive reconfiguration method of the present invention, which includes the following steps:

[0046] Step S1: Obtain typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; this includes the following sub-steps:

[0047] Step S1.1: The global climate model is used to provide the basic trends of global climate change. Several regional climate models are deployed under the global climate model, and each regional climate model is used to provide more refined climate data for the corresponding region.

[0048] Step S1.2: Based on the cumulative distribution of regional climate data, select the most representative period under the long-term trend to form a typical climate dataset, which is used to construct decision variables under normal weather scenarios and will be mainly used to construct deterministic optimization models in the future.

[0049] Step S1.3: Using the Finkelstein–Schafer statistical method, select the typical time period that best represents the climate characteristics of the region from the regional climate data. Based on the time step, identify the extreme values ​​of the climate data at each time step, construct an extreme climate dataset, and use it as boundary condition constraint variables to support the robust optimization model modeling.

[0050] Step S1.4: Calculate the cumulative distribution of each climate variable in the regional climate data at different time steps, obtain the percentiles at each time point, divide the climate data intervals, generate multiple expected climate scenarios, and obtain the expected climate dataset, which is used as the probability distribution input for constructing the stochastic optimization model.

[0051] Step S2: Construct power distribution network optimization models based on typical climate datasets, extreme climate datasets, and expected climate datasets, providing decision variables for different climate scenarios. These three models complement each other, providing not only baseline operating strategies under normal climate conditions but also capturing future climate change trends and the most unfavorable conditions under extreme events. This generates climate-adaptive multi-dimensional optimal decision variables. This method overcomes the insufficient adaptability of single models in the face of climate fluctuations and improves the robustness of optimization results under different climate conditions. Specifically:

[0052] Construct a deterministic optimization model for the power distribution network based on typical climate datasets. This model is a deterministic scenario model that assumes stable climate conditions, ignores extreme weather factors, and obtains the decision variables of the distribution network under the current normal weather scenario. When the expected cost overrun is considered as an irreparable failure of the line or equipment, then... This represents the decision variables of the power distribution network under current normal weather scenarios, such as equipment selection and power distribution. This represents a typical climate scenario. C ( ) represents the expected cost function.

[0053] A robust optimization model for the power distribution network was constructed based on extreme climate datasets. To obtain the decision variables of the distribution network under extreme weather events, and to ensure that the system cost is minimized in the worst case, the expected cost overrun is considered as an irreparable failure of the line or equipment. Indicates the first j An extreme climate scenario These represent the decision variables for the power distribution network under extreme weather scenarios. This represents the set of all possible extreme weather scenarios. The model uses a robust optimization algorithm to handle the potential impact of extreme weather on the system, with particular consideration for the impact of extreme weather events on the energy system. By employing robust techniques to address the effects of extreme events, it ensures that the distribution network design remains stable even under extreme conditions.

[0054] Construct a stochastic optimization model for the power distribution network based on the expected climate dataset. This model obtains the decision variables for the power distribution network under anticipated climate conditions. It uses a stochastic formula algorithm to model different climate change scenarios, taking into account the uncertainty of climate change but not the impact of extreme weather events. The model weights the probabilities of each climate scenario to minimize the expected cost; any excess of expected cost is considered as irreparable damage to the lines or equipment. N This represents the total number of expected climate scenarios. n express index, Indicates the first n Various expected climate scenarios Indicates the first n Decision variables for power distribution networks under anticipated climate conditions Indicates climate scenario The probability of occurrence This indicates the decision variables for the power distribution network under the expected climate conditions.

[0055] By weighted fusion of decision variables for the distribution network under extreme weather events and those under anticipated weather conditions, a comprehensive set of decision variables for the distribution network in the future time period is formed, taking into account both long-term climate trends and extreme events. This serves as the overall operational strategy for the distribution network in the future. This invention provides the operational status of the distribution network under different times and climate scenarios through current decision variables and comprehensive decision variables for the future time period, providing a decision-making basis for subsequently developing various post-disaster reconstruction schemes.

[0056] Step S3: Based on the distribution network optimization model, a quantitative risk assessment of the distribution network is conducted to quantitatively evaluate the disaster situation of the distribution network under different climate scenarios, thereby forming candidate schemes for distribution network reconfiguration. This achieves a quantitative assessment of the operational stability of the distribution network under extreme climate conditions. Based on this, high-risk lines and nodes are identified, and combined with equipment status, targeted reconfiguration candidate schemes are generated, reducing the fault recurrence rate of the reconfigured distribution network. This includes the following sub-steps:

[0057] Step S3.1: Calculate the power supply shortage risk index, power load loss risk index, and power load loss risk index considering the impact of extreme weather events for the power distribution network under the current normal weather scenario, based on the decision variables or comprehensive decision variables of the power distribution network at the corresponding time.

[0058] The power supply insufficiency risk index of the distribution network measures its ability to meet load demand by calculating the proportion of power loss under different climate scenarios, especially under extreme weather conditions caused by climate change. This assesses the stability of the distribution network's power supply; if the distribution network cannot provide the required power in a timely manner, its power supply insufficiency risk is assessed. LPS The value will increase, reflecting a power shortage. The calculation process for the power shortage risk index of this distribution network is as follows:

[0059]

[0060] in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, Indicates the distribution network in the first t Hour, First s The power shortage under various climate scenarios, i.e., the difference between demand and supply. Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios Indicates the distribution network in the first t Hour, First s Renewable energy generation under various climate scenarios This indicates the rated maximum output power of the generator in the distribution network. Indicates the distribution network in the first t Hour, First s The maximum discharge power that the battery pack can provide under various climate scenarios. This indicates the maximum power limit that allows the purchase of electricity from the grid.

[0061] The power load loss risk index of a distribution network measures the risk that the network may be unable to meet demand during a specific period under climate uncertainty or extreme weather scenarios. The calculation process for this power load loss risk index is as follows:

[0062]

[0063] in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, LOLP This represents the most severe cumulative power shortage in the distribution network across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0064] The power load loss risk index for distribution networks that considers the impact of extreme weather events can more accurately assess the probability of load loss in extreme weather scenarios. The calculation process is as follows:

[0065]

[0066] in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, d This indicates the expected duration of extreme weather events, used to adjust for statistical periods of power shortages. Ex This represents the most severe cumulative power shortage in a distribution network considering extreme weather events across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0067] Step S3.2: By identifying long-term high temperatures in multiple climate scenarios LPS, LOLP or Ex By identifying the locations where values ​​frequently appear, we can determine the critical routes and critical node areas with the highest probability of disaster in the distribution network. Combining the equipment status and redundancy in the critical routes and critical node areas, we can determine whether the corresponding equipment needs to be repaired, upgraded, or replaced, thus obtaining multiple candidate schemes for distribution network reconfiguration.

[0068] Step S4: By normalizing the expected net present value and expected integration level of the distribution network under each distribution network reconfiguration candidate scheme, the distribution network reconfiguration candidate scheme with the smallest normalized integration evaluation value is selected as the distribution network climate-adaptive reconfiguration scheme. This achieves a dual consideration of investment benefits and energy autonomy on a macro level. Through unified dimensional processing, the optimal reconfiguration scheme that balances economic efficiency and grid integration is finally selected, thereby improving the overall benefits of distribution network system reconfiguration operation under climate challenges.

[0069] In this invention, the expected net present value (NPV) of the distribution network is used to measure the economics of the distribution network and help assess its financial benefits in long-term operation. The NPV is derived by calculating the present value of annual revenues and subtracting the initial investment. NPV A positive value indicates a good rate of return on investment for the distribution network; a negative value suggests that the distribution network may not be economically viable. The calculation process for the expected net present value of a distribution network is as follows:

[0070]

[0071] Among them, E( NPV () represents the expected net present value of the distribution network. ICC This represents the initial capital cost, including equipment purchase and installation costs. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, C Represents a set of components in a power distribution network. c express C index, H Indicates the lifespan of the power distribution network. h express H index, Indicates the first s Probability weights for each climate scenario Indicates the first in the distribution network c The component in the first s Fixed operation and maintenance costs under various climate scenarios Indicates the first c The capital recovery factor of the component Indicates the real interest rate. Indicates the first in the distribution network c The component in the first h Year, No. s Variable operation and maintenance costs under different climate scenarios.

[0072] In this invention, the integration level of the distribution network is measured by the degree of integration between the distributed energy system and the power grid, ensuring stable system operation during load fluctuations. A higher integration level is achieved through grid integration. GI A higher value indicates that the distribution network relies more on the external power grid to meet energy demand, while a lower value means the system is more autonomous and can operate without relying on the external power grid. The integration level of the distribution network is calculated using the power grid integration level as follows:

[0073]

[0074] Among them, E( GI This indicates the expected level of integration of the distribution network, i.e., the proportion of electricity purchased from the grid relative to total demand. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, Indicates the first sProbability weights for each climate scenario Indicates the distribution network in the first t Hour, First s Electricity purchased from the grid under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

[0075] Normalized integrated evaluation value in this invention F The calculation process is as follows:

[0076]

[0077] in, E( NPV The normalized coefficient of ) E( GI The normalized coefficient of ).

[0078] In one technical solution of the present invention, a distribution network climate adaptive reconfiguration system is also provided, including: a climate data preprocessing module, a hybrid optimization model module, a risk quantification module, and a scheme evaluation module;

[0079] The climate data preprocessing module obtains typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models.

[0080] The hybrid optimization model module constructs power distribution network optimization models based on typical climate datasets, extreme climate datasets, and expected climate datasets, respectively.

[0081] The risk quantification module performs risk quantification assessment of the distribution network based on the distribution network optimization model, and generates candidate schemes for distribution network reconfiguration.

[0082] The scheme evaluation module evaluates the expected net present value and expected integration level of the distribution network under each distribution network reconfiguration candidate scheme using a normalized integration assessment. The distribution network reconfiguration candidate scheme with the smallest normalized integration assessment value is selected as the distribution network climate-adaptive reconfiguration scheme.

[0083] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A method for climate-adaptive reconfiguration of a power distribution network, characterized in that, Includes the following steps: Step S1: Obtain typical climate datasets, extreme climate datasets, and expected climate datasets based on global and regional climate models; Step S2: Construct distribution network optimization models based on typical climate datasets, extreme climate datasets, and expected climate datasets respectively: Construct a deterministic optimization model for the distribution network based on the typical climate dataset to obtain the decision variables of the distribution network under the current normal weather scenario; Construct a robust optimization model for the distribution network based on the extreme climate dataset to obtain the decision variables of the distribution network under extreme climate events; Construct a stochastic optimization model for the distribution network based on the expected climate dataset to obtain the decision variables of the distribution network under the expected climate; By weighted fusion of the decision variables of the distribution network under extreme climate events and the decision variables of the distribution network under the expected climate, a comprehensive decision variable for the distribution network in the future time period is formed. Step S3: Conduct a quantitative risk assessment of the distribution network based on the distribution network optimization model to generate candidate schemes for distribution network reconfiguration; this includes the following sub-steps: Step S3.1: Calculate the power supply shortage risk index, power load loss risk index, and power load loss risk index considering the impact of extreme weather events for the power distribution network under the current normal weather scenario, based on the decision variables or comprehensive decision variables of the power distribution network at the corresponding time. Step S3.2: Identify the critical routes and critical node areas with the highest probability of disaster in the distribution network. Based on the equipment status and redundancy in the critical routes and critical node areas, determine whether the corresponding equipment needs to be repaired, upgraded or replaced, and obtain multiple candidate schemes for distribution network reconfiguration. Step S4: By normalizing the integration assessment of the expected net present value and expected integration level of the distribution network under each distribution network reconfiguration candidate scheme, the distribution network reconfiguration candidate scheme with the smallest normalized integration assessment value is selected as the distribution network climate-adaptive reconfiguration scheme.

2. The method for climate-adaptive reconfiguration of a power distribution network according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S1.1: The global climate model is used to provide the basic trends of global climate change. Several regional climate models are deployed under the global climate model, and each regional climate model is used to provide climate data for the corresponding region. Step S1.2: Based on the cumulative distribution of regional climate data, select the most representative period under the long-term trend to form a typical climate dataset; Step S1.3: Using the Finkelstein–Schafer statistical method, select the typical time period that best represents the regional climate characteristics from the regional climate data, and based on the time step, identify the extreme values ​​of the climate data at each time step to construct an extreme climate dataset. Step S1.4: Calculate the cumulative distribution of each climate variable in the regional climate data at different time steps, obtain the percentiles at each time point, divide the climate data intervals, generate multiple expected climate scenarios, and obtain the expected climate dataset.

3. The method for climate-adaptive reconfiguration of a power distribution network according to claim 1, characterized in that, The calculation process for the power supply shortage risk index of the power distribution network is as follows: in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios Indicates the distribution network in the first t Hour, First s Renewable energy generation under various climate scenarios This indicates the rated maximum output power of the generator in the distribution network. Indicates the distribution network in the first t Hour, First s The maximum discharge power that the battery pack can provide under various climate scenarios. This indicates the maximum power limit that allows the purchase of electricity from the grid.

4. The method for climate-adaptive reconfiguration of a power distribution network according to claim 1, characterized in that, The calculation process for the power load loss risk index of the distribution network is as follows: in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, LOLP This represents the most severe cumulative power shortage in the distribution network across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

5. The method for climate-adaptive reconfiguration of a power distribution network according to claim 1, characterized in that, The calculation process for the power load loss risk index of the distribution network considering the impact of extreme weather events is as follows: in, T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, d Indicates the expected duration of extreme weather events. Ex This represents the most severe cumulative power shortage in a distribution network considering extreme weather events across all climate scenarios. Indicates the distribution network in the first t Hour, First s Power shortages under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

6. The method for climate-adaptive reconfiguration of a power distribution network according to claim 1, characterized in that, The calculation process for the expected net present value of the distribution network is as follows: Among them, E( NPV () represents the expected net present value of the distribution network. ICC Indicates the initial cost of capital. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, C Represents a set of components in a power distribution network. c express C index, H Indicates the lifespan of the power distribution network. h express H index, Indicates the first s Probability weights for each climate scenario Indicates the first in the distribution network c The component in the first s Fixed operation and maintenance costs under various climate scenarios Indicates the first c The capital recovery factor of the component Indicates the real interest rate. Indicates the first in the distribution network c The component in the first h Year, No. s Variable operation and maintenance costs under different climate scenarios.

7. The method for climate-adaptive reconfiguration of a power distribution network according to claim 6, characterized in that, The calculation process for the integration level of the distribution network is as follows: Among them, E( GI This indicates the expected level of integration of the distribution network. T Indicates time, t express T index, This represents the total number of climate scenarios. s express index, Indicates the first s Probability weights for each climate scenario Indicates the distribution network in the first t Hour, First s Electricity purchased from the grid under various climate scenarios Indicates the distribution network in the first t Hour, First s Electricity load demand under various climate scenarios.

8. The method for climate-adaptive reconfiguration of a power distribution network according to claim 7, characterized in that, The normalized integrated evaluation value F The calculation process is as follows: in, E( NPV The normalized coefficient of ) E( GI The normalized coefficient of ).

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