Power distribution system emergency power supply and energy storage planning method and system oriented to toughness improvement

Abstract

The invention discloses a power distribution system emergency power supply and energy storage planning method and system oriented to toughness improvement, and belongs to the technical field of power system analysis, the system comprises a simulation module, an optimization module and a solving module, the optimization module comprises a constraint unit and an optimization unit, and the solving module comprises a solving unit and an output unit; the simulation module simulates photovoltaic output and a fault scene of a power distribution system through input historical parameters; the optimization module gathers system parameters and data generated by the simulation module, and multiple constraints are considered for optimization; and the solving module is used for solving the data in the optimization module to obtain an optimal plan and outputting the optimal plan. According to the power distribution system emergency power supply and energy storage planning method and system oriented to toughness improvement, single-layer planning is carried out on the power distribution system under the condition of extreme disasters, the requirements of important loads can be met, and redundant electric energy can be stored.

Description

Technical Field

[0001] The present invention relates to the technical field of power system analysis, and in particular to a method and system for planning emergency power supply and energy storage for a power distribution system aimed at improving resilience. Background Art

[0002] In recent years, due to the vigorous exploitation of natural resources and the increasing development and use of fossil fuels, the resulting resource reduction and carbon dioxide emissions have increased. Such unreasonable human activities will cause drastic fluctuations in the weather, leading to frequent extreme natural disasters, causing more and more regions to suffer from extreme compound events. Extreme weather events can easily cause damage to power supply facilities, bring great damage to the reliability of power supply in the distribution system, and cause large-scale power outages in the power grid, which in turn causes huge economic losses and causes adverse social impacts.

[0003] When the distribution system is damaged, how to reduce the losses it brings has gradually become a research topic for researchers. In order to make the distribution network more resistant to damage and disturbances, the concept of "elastic grid" or "resilient grid" came into being. "Resilient grid" refers to a grid that can accurately, quickly and completely judge the operating status of the grid, coordinate the resources of the system, actively respond to various interferences, quickly restore important power loads and continue to learn and improve.

[0004] At present, the methods for improving resilience are mainly implemented through three stages: pre-disaster optimization planning, emergency control during disasters, and rapid recovery after disasters. In the pre-disaster stage, it is mainly prediction and optimization configuration. Reasonable pre-disaster planning can improve the resilience of the power grid to a certain extent. For example, the location and capacity planning of various distributed energy sources and energy storage can reduce the loss of key loads while ensuring low costs. Distributed power sources such as diesel units, wind power, and photovoltaics can be used as emergency power generation resources. They are relatively independent and controllable. When a disaster occurs and the distribution infrastructure is destroyed, and the upper power grid cannot supply energy to the distribution network, these emergency power sources can continue to supply energy to the distribution network, enhance the distribution network's ability to respond to extreme disasters, and reduce losses. In recent years, the technology of energy storage equipment has gradually matured. Because of its flexible and variable configuration, stable output, and fast response speed, it plays a very important role in enhancing the resilience of the distribution system, and its proportion in the distribution system is gradually increasing. However, the current planning schemes to ensure the supply capacity of key loads in the power grid are relatively simple, and most of them adopt a multi-layer interactive mode. Therefore, a single-layer planning method for distribution system resilience based on random planning is needed. Summary of the invention

[0005] The purpose of the present invention is to provide an emergency power supply and energy storage planning method and system for distribution system with improved resilience. In extreme disaster situations, single-layer planning of the distribution system can not only meet the needs of important loads and reduce the losses of important loads, but also store excess electric energy.

[0006] To achieve the above-mentioned object, the present invention provides an emergency power supply and energy storage planning system for a power distribution system for improving resilience, comprising a simulation module, an optimization module and a solution module, wherein the optimization module comprises a constraint unit and an optimization unit, and the solution module comprises a solution unit and an output unit; The simulation module simulates the photovoltaic output and distribution system failure scenarios by inputting historical parameters; The optimization module combines system parameters and data generated by the simulation module, taking into account multiple constraints for optimization; The constraint unit constrains the system parameters and the data generated by the simulation module; The optimization unit optimizes the data constrained by the constraint unit; The solution module solves the data in the optimization module, obtains the optimal plan and outputs it; The solving unit solves the data in the optimization module; The output unit outputs the data obtained by the solving unit.

[0007] The present invention provides a method for planning emergency power supply and energy storage for a power distribution system with improved resilience, comprising the following steps: S1. The Latin hypercube sampling method is used to construct the uncertain scenario of photovoltaic units, and the Kantorovich distance method is used to obtain the output of different photovoltaic units; S2, consider the line active power and reactive power transmission to establish the line power flow model and constraints; S3. Establish a distribution network planning optimization model for improving resilience, build a site selection and capacity model for planning equipment, and take the planning equipment investment cost and load loss cost as the objective function; S4. Use historical data to select lines that often fail and form a failure scenario; S5. Establish an extreme scenario operation model with the goal of minimizing load loss. The resilience of the distribution network is expressed by load loss.

[0008] Preferably, there are N sets of photovoltaic random variables in S1 , where the Nth variable The cumulative probability distribution function of Described as: ; The scene constructed based on the Latin hypercube sampling method is called the LHS scene. The LHS scene generation method includes the following steps: S1.1.1. The sampling scale is R, and the cumulative probability distribution function The distribution curve of is divided into several probability intervals, each of which has a range of 1 / R; S1.1.2. Randomly select any number in each probability interval; then the sampling point of the xth interval is The cumulative probability for: ; in, is a random number in the interval [0, 1]; S1.1.3. Substitute into the inverse cumulative probability distribution function In the corresponding interval, the sampling value is obtained : ; S1.1.4, repeat the sampling R times through S1.1.1~S1.1.3, that is, R sampling results about the photovoltaic unit will be generated; S1.1.5. Generate an N×R dimensional matrix, randomly sort the rows, and generate R photovoltaic output scenarios.

[0009] Preferably, in S1, the generated photovoltaic output scene is reduced by using the Kantorovich distance method and multiple typical representative photovoltaic output situations are retained to restore the actual photovoltaic output situation. The number of photovoltaic output scenes generated is N, and the number of scenes after reduction is n. The LHS scene reduction method includes the following steps: S1.2.1, Initialization processing, the probability value of each photovoltaic power prediction value scenario is , the initial reduction number of scenes is ; S1.2.2. Calculate each scene Kantorovich distance : ; in, is the photovoltaic power size in a certain scenario, is the photovoltaic power value in other scenarios; S1.2.3. Select the scene with the smallest distance to the scene, and calculate the product of the Kantorovich distance and the scene probability: ; in, For the scene r The probability value of S1.2.4. Repeat S1.2.3 for each scenario, then select the smallest scenario as scenario d and delete it, and update the number of reduced scenarios. , then the probability value of scene r can be updated as: ; in, is the probability value of scene d; S1.2.5, repeat S1.2.2~S1.2.4 until the final number of scenes is .

[0010] Preferably, the line flow constraints in S2 are as follows: For each node ,have: (1) represent t Time Node i The voltage phasor, represent t Time Node i The square of the voltage amplitude; (2) represent t Time Node i The current phasor, represent t Time Node i The square of the current amplitude; (3) , Respectively represent t Inject nodes at all times i Active power and reactive power; (4) , Respectively represent t Time branch Active power and reactive power flowing through; (5) , Represents branches Resistance and reactance values ​​on the ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; in, is the set of all nodes; is a collection of nodes containing emergency energy storage; is the set of nodes containing original energy storage; A time collection of 24 hours; is a collection of nodes containing emergency power supplies; is a collection of nodes containing the original distributed power sources; is a collection of nodes including wind and solar generators; Represents a node i The node set that is the parent node; Indicates i Node is a node collection whose child nodes are; , are the lower and upper limits of the square of the voltage amplitude respectively; , Node i exist t Active and reactive power requirements of loads during the time period; , Node i In the period t Lost load active and reactive power; , Node i In the period t The total active and reactive output of all resources connected to it; , Node g In the period t The active and reactive output of the power supply connected to it; , For Node d In the period t The active and reactive output of the photovoltaic unit; , For Node d In the period t The abandoned active and reactive power under , The time periodst The charging and discharging power of the energy storage device; , For Node d In the period t Active and reactive output of downstream wind turbines; , For Node d In the period t The abandoned wind active and reactive power; is the power delivered by IES to the distribution system, The power of IES energy discarded to the system.

[0011] Preferably, the site selection and capacity model of the planned equipment in S3 is as follows: ; ; ; ; ; ; ; ; ; ; ; in, A collection of emergency energy storage equipment; is the set of nodes containing original energy storage; A time collection of 24 hours; for t The electrical energy stored in the energy storage device at all times; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the time interval; are respectively the charging and discharging power of the energy storage device at the current moment; are 0-1 state variables, representing the current charging and discharging status of the energy storage device; is the planned value of the emergency energy storage equipment; The minimum capacity of the emergency energy storage equipment; are the maximum and minimum capacities of the original energy storage equipment respectively; It is the initial energy storage of the original energy storage equipment; is the initial capacity of emergency energy storage; are the minimum and maximum power for charging the energy storage device respectively; , are the minimum and maximum power of energy storage equipment. The number of emergency energy storage devices installed; Constraints of existing distributed power and emergency power in the system: ; ; in, Output of distributed power supply and emergency power supply; The output limit of each power source; A collection of nodes containing emergency power supplies; is a set of nodes containing distributed power sources.

[0012] Preferably, the objective function for planning equipment investment cost and load loss cost in S3 is: ; ; ; ; in, is the upper objective function; Cost of emergency power supply for planning; Cost of planned emergency energy storage; is the total number of disasters that occur within the life of emergency resources, assumed to be 10; It represents the expectation of the load loss cost caused by a disaster; The unit price of purchasing the fuel energy of a unit of emergency power supply; The total amount of fuel required for emergency power supply; The unit price for purchasing the emergency power supply unit; Provide emergency power supply; is the capacity unit price of the energy storage equipment; The capacity of the emergency energy storage equipment; is the set of all nodes; where A time collection of 24 hours; They are the sets consisting of the first, second and third level load nodes respectively; They are the unit prices of the first, second and third level loads for the unit power loss respectively; , Node i The primary, secondary and tertiary loads are t The load loss power caused by the period.

[0013] Therefore, the present invention adopts the above-mentioned distribution system emergency power supply and energy storage planning method and system for improving resilience, and performs single-layer planning of the distribution system under extreme disaster conditions, which can not only meet the needs of important loads and reduce the losses of important loads, but also store excess electric energy.

[0014] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the structure of the present invention; Figure 2 It is a schematic diagram of the process of the present invention; Figure 3 is a photovoltaic scene generation diagram of the present invention; Figure 4 It is a processing scene diagram of three photovoltaic units of the present invention; Figure 5 It is a 62-node power distribution system line topology diagram of the present invention; Figure 6 is a node load demand diagram of the present invention; Figure 7 It is a load loss diagram after a disaster occurs when the present invention is not planned; Figure 8 It is a diagram of the electric energy that the system can provide after a disaster occurs in the present invention; Fig. 9 It is the emergency power output diagram after planning of the present invention; Fig.10 It is the change of each type of energy storage state of the present invention; Fig.11 The system provides an electric energy diagram after planning by the present invention; Fig.12 It is a load loss diagram after planning by the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0017] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0018] Embodiment 1 like Figure 1 As shown, the present invention provides an emergency power supply and energy storage planning system for a distribution system aimed at improving resilience, including a simulation module, an optimization module and a solution module, the optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit.

[0019] The simulation module simulates the photovoltaic output and distribution system failure scenarios by inputting historical parameters.

[0020] The optimization module combines system parameters and data generated by the simulation module, and optimizes them by considering multiple constraints. The constraint unit constrains system parameters and data generated by the simulation module. The optimization unit optimizes the data constrained by the constraint unit.

[0021] The solving module solves the data in the optimization module, obtains the optimal plan and outputs it. The solving unit solves the data in the optimization module. The output unit outputs the data obtained by the solving unit.

[0022] like Figure 2 As shown, the present invention provides a distribution system emergency power supply and energy storage planning method for improving resilience, comprising the following steps: S1. The Latin hypercube sampling method is used to construct the uncertain scenario of photovoltaic units, and the Kantorovich distance method is used to obtain the output of different photovoltaic units; There are N sets of photovoltaic random variables , where the Nth variable The cumulative probability distribution function of Described as: ; The scene constructed based on the Latin hypercube sampling method is called the LHS scene. The LHS scene generation method includes the following steps: S1.1.1. The sampling scale is R, and the cumulative probability distribution function The distribution curve of is divided into several probability intervals, each of which has a range of 1 / R; S1.1.2. Randomly select any number in each probability interval; then the sampling point of the xth interval is The cumulative probability for: ; in, is a random number in the interval [0, 1]; S1.1.3. Substitute into the inverse cumulative probability distribution function In the corresponding interval, the sampling value is obtained : ; S1.1.4, repeat the sampling R times through S1.1.1~S1.1.3, that is, R sampling results about the photovoltaic unit will be generated; S1.1.5. Generate an N×R dimensional matrix, randomly sort the rows, and generate R photovoltaic output scenarios.

[0023] In the model constructed by the present invention, 1000 different scenarios are obtained based on the historical data of photovoltaics, such as Figure 3 shown.

[0024] When a large number of photovoltaic output scenarios are generated, a high proportion of scenarios have a high degree of similarity. In order to eliminate high-proportion similar scenarios or extremely low-probability scenarios, the generated photovoltaic output scenarios are reduced using the Kantorovich distance method and multiple typical representative photovoltaic output situations are retained to restore the actual photovoltaic output situation. The number of photovoltaic output scenarios generated is N, and the number of scenarios after reduction is n. The LHS scenario reduction method includes the following steps: S1.2.1, Initialization processing, the probability value of each photovoltaic power prediction value scenario is , the initial reduction number of scenes is ; S1.2.2. Calculate each scene Kantorovich distance : ; in, is the photovoltaic power size in a certain scenario, is the photovoltaic power value in other scenarios; S1.2.3. Select the scene with the smallest distance to the scene, and calculate the product of the Kantorovich distance and the scene probability: ; in, For the scene r The probability value of S1.2.4. Repeat S1.2.3 for each scenario, then select the smallest scenario as scenario d and delete it, and update the number of reduced scenarios. , then the probability value of scene r can be updated as: ; in, is the probability value of scene d.

[0025] S1.2.5, repeat S1.2.2~S1.2.4 until the final number of scenes is .

[0026] In the constructed model, 1000 scenarios are generated according to the uncertainty of photovoltaic output, and the three scenarios with the most reasonable probability distribution are retained as the output of the three photovoltaic units, such as Figure 4 shown.

[0027] S2, consider the line active power and reactive power transmission to establish the line power flow model and constraints; The line flow constraints are as follows: For each node ,have: (1) represent t Time Node i The voltage phasor, represent t Time Node i The square of the voltage amplitude; (2) represent t Time Node i The current phasor, represent t Time Node i The square of the current amplitude; (3) , Respectively represent t Inject nodes at all times i Active power and reactive power; (4) , Respectively represent t Time Branch Active power and reactive power flowing through; (5) , Represents branches Resistance and reactance values ​​on the ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; in, is the set of all nodes; is a collection of nodes containing emergency energy storage; is the set of nodes containing original energy storage; A time collection of 24 hours; is a collection of nodes containing emergency power supplies; is a collection of nodes containing the original distributed power sources; is a collection of nodes including wind and solar generators; Represents a node i The node set that is the parent node; Indicates i Node is a node collection whose child nodes are; , are the lower and upper limits of the square of the voltage amplitude respectively; , Node i exist t Active and reactive power requirements of loads during the time period; , Node i In the period t Lost load active and reactive power; , Node i In the period t The total active and reactive output of all resources connected to it; , Node g In the period t The active and reactive output of the power supply connected to it; , For Node d In the period t The active and reactive output of the photovoltaic unit; , For Node d In the period t The abandoned active and reactive power under , The time periods t The charging and discharging power of the energy storage device; , For Node d In the period t Active and reactive output of downstream wind turbines; , For Node d In the period t The abandoned wind active and reactive power; is the power delivered by IES to the distribution system, The power of IES energy discarded to the system.

[0028] S3. Establish a distribution network planning optimization model for improving resilience, build a site selection and capacity model for planning equipment, and take the planning equipment investment cost and load loss cost as the objective function; The site selection and capacity model for planning equipment is as follows: ; ; ; ; ; ; ; ; ; ; ; Among them, A collection of emergency energy storage equipment; is the set of nodes containing original energy storage; A time collection of 24 hours; for t The electrical energy stored in the energy storage device at all times; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the time interval; are respectively the charging and discharging power of the energy storage device at the current moment; are 0-1 state variables, representing the current charging and discharging status of the energy storage device; is the planned value of the emergency energy storage equipment; The minimum capacity of the emergency energy storage equipment; are the maximum and minimum capacities of the original energy storage equipment respectively; It is the initial energy storage of the original energy storage equipment; is the initial capacity of emergency energy storage; are the minimum and maximum power for charging the energy storage device respectively; , are the minimum and maximum power of energy storage equipment. The number of emergency energy storage devices installed.

[0029] Constraints of existing distributed power and emergency power in the system: ; ; in, Output of distributed power supply and emergency power supply; The output limit of each power source; A collection of nodes containing emergency power supplies; is a set of nodes containing distributed power sources.

[0030] The objective function for planning equipment investment cost and load loss cost is: ; ; ; ; in, is the upper objective function; Cost of emergency power supply for planning; Cost of planned emergency energy storage; is the total number of disasters that occur within the life of emergency resources, assumed to be 10; It represents the expectation of the load loss cost caused by a disaster; The unit price of purchasing the fuel energy of a unit of emergency power supply; The total amount of fuel required for emergency power supply; The unit price for purchasing the emergency power supply unit; Provide emergency power supply; is the capacity unit price of the energy storage equipment; The capacity of the emergency energy storage equipment; is the set of all nodes; where A time collection of 24 hours; They are the sets consisting of the first, second and third level load nodes respectively; They are the unit prices of the first, second and third level loads for the unit power loss respectively; , Node i The primary, secondary and tertiary loads are t The load loss power caused by the period.

[0031] S4. Use historical data to select lines that often fail and form a failure scenario; like Figure 5 As shown in Figure 1, based on the existing historical data, a 62-node distribution system line topology diagram is established. Figure 6 The node load demand diagram is constructed as shown.

[0032] Select the lines that often fail to function and create different scenarios: Table 1 Summary of disconnection situations ;

[0033] S5. Establish an extreme scenario operation model with the goal of minimizing load loss. The resilience of the distribution network is expressed by load loss. Establish an extreme scenario operation model. Take disconnection scenario 1 as an example. When no emergency equipment is planned, the load loss is as follows: Figure 7 As shown in Figure 1, without planning, once a disaster occurs, the load in the system will be almost completely lost. The power provided by the system after a disaster occurs is as follows: Figure 8 As shown, when no planning is performed, the system itself can only provide very little power, which is difficult to support the load demand of the node.

[0034] Depend on Figure 7 and Figure 8 It can be seen that when emergency resources are not planned, most of the load in the system will be lost when extreme natural disasters occur.

[0035] Table 2 Location and capacity of planned equipment ; The output of emergency power supply and emergency energy storage is as follows: Fig. 9 As shown in the figure, it can be seen that the emergency power supply approximately meets the distribution of high output during the day and less output in the early morning and at night, which is similar to the trend of the node load demand curve, indicating that the emergency power supply always changes with the changes in the node load demand. Fig.10 As shown in the figure, the node load demand is relatively small in the early morning, so the energy storage device mainly stores electric energy during this period. After noon, due to the increase in load demand, some energy storage devices begin to release energy to meet the power gap of the node. After dusk, in order to minimize load loss, the energy storage device releases all its energy.

[0036] The energy storage equipment is mainly in a state of charging or not releasing energy in the morning and at noon. The reason is that the load demand in the morning is not high. At this time, the distributed power supply can meet the needs of important loads and store excess electricity. Although the load demand is large at noon, the photovoltaic units also output and provide electricity. The photovoltaic units and distributed power sources work together to reduce the loss of important loads. If there is any surplus, it can be stored in the energy storage device.

[0037] At this time, the power that the system can provide and the load loss are as follows: Fig.11 and Fig.12 As shown by Fig.11 It can be seen that compared with the system power supply diagram before planning, it can be found that the power supply has increased significantly. Fig.12 and Figure 7In comparison, it can be seen that the system's load loss has been reduced, but a considerable part of the load is still discarded. This is mainly because the system is disconnected, which makes it difficult to transmit some electricity to the next node, and the system mainly gives priority to meeting the needs of important loads. The tertiary load will be appropriately discarded.

[0038] Table 3 Comparison of load loss and cost before and after planning ; From the results described above, we can know that: The emergency power supply is not always at full capacity, and the emergency energy storage is not always released, because the use of emergency resources also requires a certain cost. When a disaster occurs, the main purpose is to ensure the needs of important loads, and the third-level loads may be appropriately discarded; The primary load is almost not lost after planning emergency resources, but the secondary load is still partially unsatisfied. This is because the cost of emergency resources is limited, and it is impossible to ensure that emergency resources are set up for every important load node on the line. In addition, there is a transmission power limit on the line, and the secondary load far away from the planned equipment may not be able to guarantee energy supply; After setting up emergency resources in the distribution system and carrying out site selection and capacity planning for the emergency resources, the first-level important loads in the system can be guaranteed, the losses of the second-level important loads are reduced, the system losses are reduced, and the total cost is significantly reduced.

[0039] Therefore, the present invention adopts the above-mentioned distribution system emergency power supply and energy storage planning method and system for improving resilience, and performs single-layer planning of the distribution system under extreme disaster conditions, which can not only meet the needs of important loads and reduce the losses of important loads, but also store excess electric energy.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. Emergency power supply and energy storage planning system for distribution system with improved resilience, characterized by: It includes a simulation module, an optimization module and a solution module, the optimization module includes a constraint unit and an optimization unit, and the solution module includes a solution unit and an output unit; The simulation module simulates the photovoltaic output and distribution system failure scenarios by inputting historical parameters; The optimization module combines system parameters and data generated by the simulation module, taking into account multiple constraints for optimization; The constraint unit constrains the system parameters and the data generated by the simulation module; The optimization unit optimizes the data constrained by the constraint unit; The solution module solves the data in the optimization module, obtains the optimal plan and outputs it; The solving unit solves the data in the optimization module; The output unit outputs the data obtained by the solving unit.

2. A distribution system emergency power supply and energy storage planning method for improving resilience, characterized by: Using the above-mentioned distribution system emergency power supply and energy storage planning system for improving resilience includes the following steps: S1. The Latin hypercube sampling method is used to construct the uncertain scenario of photovoltaic units, and the Kantorovich distance method is used to obtain the output of different photovoltaic units; S2, consider the line active power and reactive power transmission to establish the line power flow model and constraints; S3. Establish a distribution network planning optimization model for improving resilience, build a site selection and capacity model for planning equipment, and take the planning equipment investment cost and load loss cost as the objective function; S4. Use historical data to select lines that often fail and form a failure scenario; S5. Establish an extreme scenario operation model with the goal of minimizing load loss. The resilience of the distribution network is expressed by load loss.

3. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 2, characterized in that: There are N groups of photovoltaic random variables in S1 , where the Nth variable The cumulative probability distribution function of Described as: ； The scene constructed based on the Latin hypercube sampling method is called the LHS scene. The LHS scene generation method includes the following steps: S1.1.

1. The sampling scale is R, and the cumulative probability distribution function The distribution curve of is divided into several probability intervals, each of which has a range of 1 / R; S1.1.

2. Randomly select any number in each probability interval; then the sampling point of the xth interval is The cumulative probability for: ； in, is a random number in the interval [0, 1]; S1.1.

3. Substitute into the inverse cumulative probability distribution function In the corresponding interval, the sampling value is obtained : ； S1.1.4, repeat the sampling R times through S1.1.1~S1.1.3, that is, R sampling results about the photovoltaic unit will be generated; S1.1.

5. Generate an N×R dimensional matrix, randomly sort the rows, and generate R photovoltaic output scenarios.

4. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 3, characterized in that: In S1, the generated photovoltaic output scenario is reduced by using the Kantorovich distance method and multiple typical representative photovoltaic output situations are retained to restore the actual photovoltaic output situation. The number of photovoltaic output scenarios generated is N, and the number of scenarios after reduction is n. The LHS scenario reduction method includes the following steps: S1.2.1, Initialization processing, the probability value of each photovoltaic power prediction value scenario is , the initial reduction number of scenes is ; S1.2.

2. Calculate each scene Kantorovich distance : ； in, is the photovoltaic power size in a certain scenario, is the photovoltaic power value in other scenarios; S1.2.

3. Select the scene with the smallest distance to the scene, and calculate the product of the Kantorovich distance and the scene probability: ； in, For the scene r The probability value of S1.2.

4. Repeat S1.2.3 for each scenario, then select the smallest scenario as scenario d and delete it, and update the number of reduced scenarios. , then the probability value of scene r can be updated as: ； in, is the probability value of scene d; S1.2.5, repeat S1.2.2~S1.2.4 until the final number of scenes is .

5. The method for planning emergency power supply and energy storage for distribution system with improved resilience according to claim 2, characterized in that: The line flow constraints in S2 are as follows: For each node ,have: (1) represent t Time Node i The voltage phasor, represent t Time Node i The square of the voltage amplitude; (2) represent t Time Node i The current phasor, represent t Time Node i The square of the current amplitude; (3) , Respectively represent t Inject nodes at all times i Active power and reactive power; (4) , Respectively represent t Time Branch Active power and reactive power flowing through; (5) , Represents branches Resistance and reactance values ​​on the ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； in, is the set of all nodes; is a collection of nodes containing emergency energy storage; is the set of nodes containing original energy storage; A time collection of 24 hours; is a collection of nodes containing emergency power supplies; is a collection of nodes containing the original distributed power sources; is a collection of nodes including wind and solar generators; Represents a node i The node set that is the parent node; Indicates i Node is a node collection whose child nodes are; , are the lower and upper limits of the square of the voltage amplitude respectively; , Node i exist t Active and reactive power requirements of loads during the time period; , Node i In the period t Lost load active and reactive power; , Node i In the period t The total active and reactive output of all resources connected to it; , Node g In the period t The active and reactive output of the power supply connected to it; , For Node d In the period t The active and reactive output of the photovoltaic unit; , For Node d In the period t The abandoned active and reactive power under , The time periods t The charging and discharging power of the energy storage device; , For Node d In the period t Active and reactive output of downstream wind turbines; , For Node d In the period t The abandoned wind active and reactive power; is the power delivered by IES to the distribution system, The power of IES energy discarded to the system.

6. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 2, characterized in that: The location and capacity model of the planned equipment in S3 is as follows: ； ； ； ； ； ； ； ； ； ； ； in, A collection of emergency energy storage equipment; is the set of nodes containing original energy storage; A time collection of 24 hours; for t The electrical energy stored in the energy storage device at all times; The charging efficiency of the energy storage device; is the discharge efficiency of the energy storage device; is the time interval; are respectively the charging and discharging power of the energy storage device at the current moment; are 0-1 state variables, representing the current charging and discharging status of the energy storage device; is the planned value of the emergency energy storage equipment; The minimum capacity of the emergency energy storage equipment; are the maximum and minimum capacities of the original energy storage equipment respectively; It is the initial energy storage of the original energy storage equipment; is the initial capacity of emergency energy storage; are the minimum and maximum power for charging the energy storage device respectively; , are the minimum and maximum power of energy storage equipment. The number of emergency energy storage devices installed; Constraints of existing distributed power and emergency power in the system: ； ； in, Output of distributed power supply and emergency power supply; The output limit of each power source; A collection of nodes containing emergency power supplies; is a set of nodes containing distributed power sources.

7. The method for planning emergency power supply and energy storage for a power distribution system with improved resilience according to claim 2, characterized in that: The objective function for planning equipment investment cost and load loss cost in S3 is: ； ； ； ； in, is the upper objective function; Cost of emergency power supply for planning; Cost of planned emergency energy storage; is the total number of disasters that occur within the life of emergency resources, assumed to be 10; It represents the expectation of the load loss cost caused by a disaster; The unit price of purchasing the fuel energy of a unit of emergency power supply; The total amount of fuel required for emergency power supply; The unit price for purchasing the emergency power supply unit; Provide emergency power supply; is the capacity unit price of the energy storage equipment; The capacity of the emergency energy storage equipment; is the set of all nodes; where A time collection of 24 hours; They are the sets consisting of the first, second and third level load nodes respectively; They are the unit prices of the first, second and third level loads for the unit power loss respectively; , Node i The primary, secondary and tertiary loads are t The load loss power caused by the period.

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