Fault processing method and device of power distribution network and electronic equipment

Abstract

The invention discloses a fault processing method and device for a power distribution network and electronic equipment. The method relates to the technical field of power systems, and comprises the following steps: in response to a fault of a power distribution network, establishing a distributed energy resource model of the power distribution network based on an energy storage device and a new energy power supply in the power distribution network; based on an electric vehicle cluster in the dynamic traffic system, an electric vehicle time-varying polymeric battery model is established, and the electric vehicle time-varying polymeric battery model is used for representing electric energy consumption and discharge power of the electric vehicle cluster to a power distribution network; based on the distributed energy resource model and the electric vehicle time-varying polymeric battery model, constructing an island division model and island division constraint conditions of the power distribution network; solving the island division model based on island division constraint conditions to obtain an island elastic recovery strategy of the power distribution network; and performing fault recovery on the power distribution network by using an island elastic recovery strategy. According to the invention, the technical problem of low recovery capability of the power distribution network during fault recovery in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular, to a fault processing method, device, and electronic device for a distribution network. Background Art

[0002] In recent years, extreme weather has occurred frequently, and the safe and stable operation of the power system faces relatively serious challenges. Therefore, the distribution network should have the ability to maintain elastic operation and recovery throughout the disaster period. Multiple faults may occur in the distribution network system under extreme conditions, and the distribution network may be in an island operation state without the support of a large power source. Island operation can ensure that the power-off load in the non-disaster area can still be reliably powered for a short time in an emergency. Island division is the premise and basis for the island operation of a distribution network with new energy. By formulating a reasonable and effective island division plan, the continuous power supply of the grid load can be guaranteed to the greatest extent, and the ability of the system to operate elastically can be improved.

[0003] For the island division of the distribution network, due to the access of new energy, its intermittency and randomness will affect the reliable and continuous power supply of the load during the disaster process. This leads to a low recovery ability of the distribution network when performing fault recovery in related technologies.

[0004] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a fault processing method, device, and electronic device for a distribution network, so as to at least solve the technical problem of low recovery ability of the distribution network when performing fault recovery in related technologies.

[0006] According to one aspect of the embodiments of the present invention, a fault processing method for a distribution network is provided, including: in response to a fault occurring in the distribution network, based on energy storage devices and new energy power sources in the distribution network, establishing a distributed energy resource model for the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources; based on an electric vehicle cluster in a dynamic traffic system, establishing an electric vehicle time-varying aggregated battery model, where the electric vehicle time-varying aggregated battery model is used to characterize the power consumption and discharge power of the electric vehicle cluster on the distribution network; based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, constructing an island division model and island division constraint conditions for the distribution network; solving the island division model based on the island division constraint conditions to obtain an island elastic recovery strategy for the distribution network; and using the island elastic recovery strategy to perform fault recovery on the distribution network.

[0007] Optionally, based on the energy storage devices and new energy power sources in the distribution network, a distributed energy resource model of the distribution network is established, including: based on the energy storage characteristics of the energy storage devices, an energy storage device model of the distribution network is established, where the energy storage characteristics include at least one of the following: charging power, discharging power, charging coefficient state, discharging coefficient state, state of charge within a preset time period; based on the fluctuation characteristics of the new energy power sources, a new energy power source model of the distribution network is established, where the fluctuation characteristics include at least one of the following: predicted output value of the new energy power source, predicted error value of the output of the new energy power source, set of new energy power source units; based on the energy storage device model and the new energy power source model, a distributed energy resource model is obtained.

[0008] Optionally, based on the association relationship between the electric vehicle cluster in the dynamic traffic system and the distribution network, an electric vehicle time-varying aggregated battery model is established, including: based on the dynamic traffic flow model, the electric vehicle cluster in the dynamic traffic system is determined, where the dynamic traffic flow model is used to characterize the time-varying travel demand and traffic dynamic flow of the electric vehicle cluster; based on the charging and discharging characteristics of a single electric vehicle in the electric vehicle cluster, a charging and discharging model of a single electric vehicle is established, where the charging and discharging characteristics include at least one of the following: charging active power output, discharging active power output, state of charge when off-grid, state of charge when on-grid, electrical energy capacity, charging efficiency, discharging efficiency, target time period for connecting to the distribution network; based on the charging and discharging model of a single electric vehicle, the electric vehicle time-varying aggregated battery model is determined.

[0009] Optionally, based on the charging and discharging model of a single electric vehicle, the electric vehicle time-varying aggregated battery model is determined, including: based on the charging and discharging model of a single electric vehicle, the total power consumption of the electric vehicle cluster is determined; based on the charging and discharging model of a single electric vehicle, the dischargeable power of the electric vehicle cluster is determined; based on the total power consumption and the dischargeable power, the electric vehicle time-varying aggregated battery model is obtained.

[0010] Optionally, based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, an island division model and island division constraint conditions of the distribution network are constructed, including: based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, a first objective function and island division constraint conditions are constructed; based on the number of branch breakings in the distribution network, a second objective function is constructed; based on the first objective function and the second objective function, an island division model is constructed.

[0011] Optionally, based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, a first objective function is constructed, including: based on the island division scope of the distribution network, the output of the distributed energy resources in the distributed energy resource model, and the output of the electric vehicles in the electric vehicle time-varying aggregated battery model, decision variables are constructed; based on the state of charge storage of the electric vehicle time-varying aggregated battery model, a penalty term is constructed; based on the decision variables and the penalty term, a first objective function is constructed.

[0012] Optionally, based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, island division constraint conditions are constructed, including: constructing an island power balance constraint condition based on the power generation power of the distributed energy resources in the distributed energy resource model and the power consumption power of the electric vehicles in the electric vehicle time-varying aggregated battery model; constructing an island division topology constraint condition based on the island division range of the distribution network; constructing an electric vehicle load charge and discharge constraint condition based on the charge and discharge power, load energy, and electrical energy of the electric vehicles in the electric vehicle time-varying aggregated battery model; constructing a new energy power source output constraint condition based on the active power output, reactive power output, active power, capacity, and minimum power factor of the new energy power sources in the distributed energy resource model; establishing a power flow constraint condition and a system safe operation voltage constraint condition; and establishing an island division constraint condition based on the island power balance constraint condition, the island division topology constraint condition, the electric vehicle load charge and discharge constraint condition, the new energy power source output constraint condition, the power flow constraint condition, and the system safe operation voltage constraint condition.

[0013] Optionally, the island division model is solved based on the island division constraint conditions to obtain an island elastic recovery strategy for the distribution network, including: relaxing the output of the island division constraint conditions to obtain the processed constraint conditions; converting the island division model into a second-order cone programming model; and using an optimization solver and the processed constraint conditions to solve the second-order cone programming model to obtain the island elastic recovery strategy.

[0014] According to another aspect of the embodiments of the present invention, a fault processing device for a distribution network is further provided, including: a first establishment module, configured to, in response to a fault occurring in the distribution network, establish a distributed energy resource model of the distribution network based on energy storage devices and new energy power sources in the distribution network, where the new energy power sources include at least one of the following: a wind power source and a photovoltaic power source; a second establishment module, configured to establish an electric vehicle time-varying aggregated battery model based on an electric vehicle cluster in a dynamic traffic system, where the electric vehicle time-varying aggregated battery model is used to characterize the power consumption and discharge power of the electric vehicle cluster for the distribution network; a third establishment module, configured to construct an island division model and island division constraint conditions of the distribution network based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model; a solving module, configured to solve the island division model based on the island division constraint conditions to obtain an island elastic recovery strategy for the distribution network; and a recovery module, configured to use the island elastic recovery strategy to perform fault recovery on the distribution network.

[0015] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a memory storing an executable program; and a processor configured to run the program, where when the program runs, it executes the methods in the various embodiments of the present invention.

[0016] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the methods in various embodiments of the present invention.

[0017] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0018] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in each embodiment of the present invention is implemented.

[0019] According to another aspect of the embodiments of the present invention, a computer program is further provided. When the computer program is executed by a processor, the methods in the embodiments of the present invention are implemented.

[0020] In an embodiment of the present invention, in response to a failure in the distribution network, a distributed energy resource model of the distribution network is established based on energy storage devices and new energy power supplies in the distribution network, wherein the new energy power supplies include at least one of the following: wind power supplies and photovoltaic power supplies; based on an electric vehicle cluster in a dynamic traffic system, an electric vehicle time-varying aggregate battery model is established, wherein the electric vehicle time-varying aggregate battery model is used to characterize the electric energy consumption and discharge power of the electric vehicle cluster on the distribution network; based on the distributed energy resource model and the electric vehicle time-varying aggregate battery model, an island partition model and island partition constraints of the distribution network are constructed; based on the island partition constraints, the island partition model is solved to obtain an island resilience recovery strategy for the distribution network; and a method for recovering the distribution network from faults using the island resilience recovery strategy. It is easy to notice that by establishing a distributed energy resource model for the distribution network and a time-varying aggregate battery model for electric vehicles, it is possible to fully consider the operating time and fluctuations of the power supply and load during island operation, the randomness of user willingness and road conditions, thereby achieving the purpose of improving the emergency resilience recovery capability of the distribution network during disasters, thereby achieving the technical effect that the distribution network can recover from faults with a higher recovery capability, and thus solving the technical problem of low recovery capability of the distribution network during fault recovery in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0022] Figure 1It is a flowchart of a fault handling method for a distribution network according to an embodiment of the present invention;

[0023] Figure 2 It is a flowchart of an optional islanding division strategy for a distribution network according to an embodiment of the present invention;

[0024] Figure 3a It is an optional islanding division topology result under Strategy 1 according to an embodiment of the present invention;

[0025] Figure 3b It is an optional islanding division topology result under Strategy 2 according to an embodiment of the present invention;

[0026] Figure 4 It is an optional electric vehicle cluster scheduling operation result according to an embodiment of the present invention;

[0027] Figure 5 It is an optional island 1 resilience recovery index and electric vehicle cluster state of charge according to an embodiment of the present invention;

[0028] Figure 6 It is an optional island 1 resilience recovery index and electric vehicle cluster state of charge according to an embodiment of the present invention;

[0029] Figure 7 It is an optional load recovery amount and energy storage power station output according to an embodiment of the present invention;

[0030] Figure 8 It is an optional island 2 resilience recovery index according to an embodiment of the present invention;

[0031] Figure 9 It is a schematic diagram of a fault handling device for a distribution network according to an embodiment of the present invention. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] According to an embodiment of the present invention, an embodiment of a fault handling method for a distribution network is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0035] Figure 1 is a flowchart of a fault handling method for a distribution network according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0036] Step S102, in response to a fault occurring in the distribution network, based on the energy storage device and new energy power sources in the distribution network, establish a distributed energy resource model of the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources.

[0037] The above-mentioned fault may be a fault that occurs in the distribution network under extreme conditions. Among them, the extreme conditions may include but are not limited to: extreme weather conditions, natural disasters, human factors, technical failures, environmental factors, etc. Multiple faults may occur in the distribution network system under extreme conditions, and the distribution network may be in an island operation state without the support of a large power source.

[0038] The above energy storage device refers to a device used to store electrical energy in a distribution network system (hereinafter simply referred to as the distribution network). It can store energy when the power supply is excessive and release energy during peak power demand to balance the supply and demand relationship of the power grid. The functions of energy storage devices in the distribution network mainly include the following points: Peak shaving and valley filling: Store energy during low power demand (valley time) and release energy during high demand (peak time), reducing dependence on traditional power plants and lowering energy costs. Improving grid stability: By the fast response ability of energy storage devices, the fluctuations of the power grid can be reduced, improving the stability and reliability of the power grid. Promoting the utilization of renewable energy: Energy storage devices can store the electrical energy generated by renewable energy such as wind energy and solar energy, reducing energy waste caused by weather changes. Emergency backup power supply: In the event of a power grid failure or emergency, energy storage devices can serve as backup power supplies to ensure the power supply of critical facilities. Demand-side management: Through energy storage devices, more refined management of power demand can be achieved, optimizing the allocation of power resources. Supporting smart grid: Energy storage devices are an important part of the smart grid. They can respond to the real-time needs of the power grid and achieve more efficient energy management. Energy storage devices can include but are not limited to: Battery energy storage systems: such as lithium-ion batteries, lead-acid batteries, etc., which are one of the most commonly used energy storage methods. Pumped-storage power plants: Pump water to a high-level reservoir during low power demand and release water to generate electricity during peak demand. Compressed air energy storage: Store compressed air during low power demand and release compressed air to generate electricity during peak demand. Flywheel energy storage: Store kinetic energy using a rotating flywheel. Supercapacitors: Store electrical energy in the form of an electric field and have the characteristics of fast charge and discharge.

[0039] In an alternative embodiment, in the case where the distribution network fails and operates in an island state under extreme conditions, in order to improve the relatively low recovery ability of the distribution network during fault recovery, in the inventive embodiment, first, a distributed energy resource model of the distribution network can be established based on the energy storage devices and new energy power sources within the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources.

[0040] Optionally, demand analysis can be carried out first to determine the purpose of the model, such as optimizing energy distribution, improving energy efficiency, reducing costs, etc., and analyze the existing structure and characteristics of the distribution network, including load characteristics, grid stability, etc. Secondly, data collection can be carried out. For example, relevant data of energy storage devices and new energy power sources in the distribution network can be collected, such as the capacity, charge and discharge efficiency, life, etc. of battery energy storage systems, and the output characteristics of new energy power sources (such as solar energy, wind energy), including power fluctuations, predictability, etc. Secondly, an energy storage device model, a new energy power source model and a distribution network model can be constructed. Establish a mathematical model of the energy storage device to describe the charge and discharge process, efficiency and state of the distribution network. Establish an output model of the new energy power source, considering the influence of factors such as weather and season. Construct a topological structure model of the distribution network, including lines, transformers, protection devices, etc. Finally, the energy storage device model and the new energy power source model can be integrated into the distribution network model to form a complete distributed energy resource model.

[0041] For another example, after obtaining relevant data through demand analysis and data collection, a distribution network energy storage device model, a distribution network wind power source model and a distribution network photovoltaic power source model can be established. Finally, the distribution network energy storage device model, the distribution network wind power source model and the distribution network photovoltaic power source model can be integrated to obtain a complete distributed energy resource model.

[0042] Step S104: Based on the electric vehicle cluster in the dynamic traffic system, establish a time-varying aggregated battery model of electric vehicles, where the time-varying aggregated battery model of electric vehicles is used to characterize the power consumption and discharge power of the electric vehicle cluster on the distribution network.

[0043] The above-mentioned electric vehicle cluster refers to that in the dynamic traffic system, electric vehicles are organized and coordinated according to specific rules, algorithms or strategies to achieve more efficient, environmentally friendly and intelligent traffic operation. Among them, the electric vehicle cluster can include multiple electric vehicles, which optimize traffic flow, reduce energy consumption and improve safety through communication, data exchange and cooperative control.

[0044] In an alternative embodiment, when it is determined that a fault occurs in the distribution network, a time-varying aggregated battery model of electric vehicles can also be established based on the electric vehicle cluster in the dynamic traffic system.

[0045] For example, first, data collection and analysis can be carried out: collect the operation data of electric vehicles, including driving speed, acceleration, driving time, charging status, etc. Secondly, the battery usage patterns of electric vehicles can be analyzed, including the frequency, depth, and duration of charging and discharging. Then, model establishment can be carried out: establish a dynamic model of the electric vehicle battery, considering factors such as the charging status of the battery, the battery health status, and the temperature impact. Then, the driving patterns of electric vehicles can be considered, and a time-varying aggregation model can be established, which can dynamically adjust according to the real-time traffic conditions and the driving status of electric vehicles. Then, algorithm development can also be carried out: develop algorithms to predict the battery status of electric vehicles, including predicting the remaining power, charging demand, and discharging capacity. Use optimization algorithms to manage the battery resources of electric vehicle clusters to maximize energy efficiency and minimize costs. Finally, system integration can be carried out: integrate the electric vehicle battery model with a traffic management system (such as an intelligent transportation system), establish a time-varying aggregation battery model for electric vehicles, and achieve real-time data exchange and control. Ensure that the system integration can handle large-scale data and has good scalability and flexibility.

[0046] For another example, after obtaining data based on data collection and analysis, first, the time-varying travel demand and traffic dynamic flow of electric vehicles can be considered to obtain an electric vehicle cluster. Secondly, the time-varying traffic flow propagation volume under road conditions can be considered for the electric vehicle cluster to obtain a charging and discharging model for a single electric vehicle. Finally, based on the charging and discharging model of a single electric vehicle, a time-varying aggregation battery model for electric vehicles can be obtained.

[0047] Step S106, based on the distributed energy resource model and the time-varying aggregation battery model of electric vehicles, construct an islanding division model and islanding division constraint conditions for the distribution network.

[0048] The above-mentioned islanding division model is mainly used for the effective management and control of the distribution network under extreme conditions (such as natural disasters, equipment failures, etc.), and can cooperate with new energy power sources, electric vehicles, and energy storage power stations to achieve goals such as improving power supply reliability, optimizing resource allocation, enhancing the stability of the distribution network, supporting distributed generation, emergency response, reducing economic losses, and environmental friendliness. Among them, the goals of the islanding division model can include but are not limited to: minimizing the power-off nodes, reducing the number of branch breakings, and reducing the breaking cost. The above-mentioned islanding division constraint conditions can ensure the operation efficiency and safety of the distribution network in the face of extreme conditions.

[0049] In an optional embodiment, in the case of obtaining the distributed energy resource model and the time-varying aggregation battery model of electric vehicles, based on the distributed energy resource model and the time-varying aggregation battery model of electric vehicles, construct an islanding division model and islanding division constraint conditions for the distribution network.

[0050] For example, with the goal of minimizing the power - lost nodes, as many power - lost nodes as possible can be included in the island. The island division range, the output of the wind - solar - storage power sources, and the output of the electric vehicles are used as decision variables, and the power storage state of the time - varying aggregated battery model of the electric vehicles is used as a penalty term. A first objective function is set. At the same time, with the goal of reducing the number of branch breakings and lowering the breaking cost, a second objective function can be set. Finally, the first objective function and the second objective function can be aggregated to obtain the island division model.

[0051] For another example, based on the balanced power of the distribution network in the island state, the island division topology, the charging and discharging power of the electric vehicle load, the output of the wind power source, the output of the photovoltaic power source, as well as the power flow and the safety of the distribution network, the island division constraint conditions can be constructed.

[0052] Step S108: Solve the island division model based on the island division constraint conditions to obtain the island elastic recovery strategy of the distribution network.

[0053] The above - mentioned elastic recovery strategy can, when the distribution network in the island state is in a power shortage state of the power source, through the flexible adjustment of the charge - discharge of the energy storage power station and the coordinated cooperation of the new - energy power sources, preferentially ensure the load recovery amount of the important loads inside the island, extend the load recovery duration, and improve the overall elastic index of the island.

[0054] In an alternative embodiment, when the island division model and the island division constraint conditions are obtained, the island division model can be solved based on the island division constraint conditions to obtain the island elastic recovery strategy of the distribution network. For example, through the island division constraint conditions, the island division model can be solved based on an optimization algorithm to obtain the island elastic recovery strategy of the distribution network. For another example, the convex relaxation method can be used to relax the constraint conditions, the disaster prevention model can be converted into a second - order cone programming model using the second - order cone transformation, and further through MATLAB R2018a programming, an optimization solver is used to solve the strategy to obtain the island elastic recovery strategy of the distribution network.

[0055] Step S1010: Use the island elastic recovery strategy to perform fault recovery on the distribution network.

[0056] In an alternative embodiment, when the island elastic recovery strategy of the distribution network is obtained, the distribution network can be fault - recovered through the island elastic recovery strategy to improve the recovery ability of the distribution network during fault recovery.

[0057] Through the above steps, it is possible to establish a distributed energy resource model of the distribution network in response to a failure of the distribution network based on the energy storage device and the new energy power supply in the distribution network, wherein the new energy power supply includes at least one of the following: a wind power supply and a photovoltaic power supply; establish an electric vehicle time-varying aggregate battery model based on the electric vehicle cluster in the dynamic traffic system, wherein the electric vehicle time-varying aggregate battery model is used to characterize the electric energy consumption and discharge power of the electric vehicle cluster to the distribution network; construct an island partition model and island partition constraints of the distribution network based on the distributed energy resource model and the electric vehicle time-varying aggregate battery model; solve the island partition model based on the island partition constraints to obtain the island resilience recovery strategy of the distribution network; and use the island resilience recovery strategy to perform fault recovery on the distribution network. It is easy to notice that by establishing a distributed energy resource model for the distribution network and a time-varying aggregate battery model for electric vehicles, it is possible to fully consider the operating time and fluctuations of the power supply and load during island operation, the randomness of user willingness and road conditions, thereby achieving the purpose of improving the emergency resilience recovery capability of the distribution network during disasters, thereby achieving the technical effect that the distribution network can recover from faults with a higher recovery capability, and thus solving the technical problem of low recovery capability of the distribution network during fault recovery in related technologies.

[0058] Optionally, based on the energy storage device and the new energy power supply in the distribution network, a distributed energy resource model of the distribution network is established, including: based on the energy storage characteristics of the energy storage device, an energy storage device model of the distribution network is established, wherein the energy storage characteristics include at least one of the following: charging power, discharging power, charging coefficient state, discharging coefficient state, and charge state within a preset time period; based on the fluctuation characteristics of the new energy power supply, a new energy power supply model of the distribution network is established, wherein the fluctuation characteristics include at least one of the following: predicted output value of the new energy power supply, predicted error value of the new energy power supply output, and a set of new energy power supply units; based on the energy storage device model and the new energy power supply model, a distributed energy resource model is obtained.

[0059] The above-mentioned preset time period may be a period of time after a failure occurs in the distribution network, and the specific duration is not limited in this embodiment.

[0060] In an optional embodiment, the energy storage device model can be obtained by the following formula:

[0061]

[0062] α f +α c ≤1,α f ,α c ∈{1,0};

[0063]

[0064] Among them, are the charging and discharging powers of the energy storage device respectively; α c and α f represent the charging and discharging coefficient states of the energy storage device respectively; represents the maximum value of the power of the energy storage device, represents the state of charge of the energy storage device at time period t, represents the value at the initial moment of the power of the energy storage device, and Δt is the unit time.

[0065] Optionally, the output of the new energy power source can be calculated by the following formula:

[0066]

[0067] Among them, P i DG· is the predicted output value of the i-th power source in the distributed wind and solar power source unit; g i is the uncertainty coefficient; is the predicted error value of the output of the distributed wind and solar power source; P i DG is the actual output value of the distributed wind and solar power source, and Ω DG is the set of distributed power source units; represents that any i-th power source is one of the distributed power source unit sets, β is the uncertainty cost, and by adjusting the value of β, the volatility of the distributed power source uncertainty can be controlled, and ∑ is for summation.

[0068] Optionally, after establishing the energy storage device model and the new energy power source model, the energy storage device model and the new energy power source model can be summarized, that is, the distributed energy resource model can be obtained.

[0069] Optionally, based on the correlation between the electric vehicle cluster in the dynamic traffic system and the distribution network, an electric vehicle time-varying aggregated battery model is established, including: based on the dynamic traffic flow model, determining the electric vehicle cluster in the dynamic traffic system, where the dynamic traffic flow model is used to characterize the time-varying travel demand and traffic dynamic flow of the electric vehicle cluster; based on the charging and discharging characteristics of a single electric vehicle in the electric vehicle cluster, establishing a charging and discharging model of a single electric vehicle, where the charging and discharging characteristics include at least one of the following: charging active power output, discharging active power output, off-grid state of charge, on-grid state of charge, electric energy capacity, charging efficiency, discharging efficiency, target time period for accessing the distribution network; based on the charging and discharging model of a single electric vehicle, determining the electric vehicle time-varying aggregated battery model.

[0070] In an alternative embodiment, the electric vehicle cluster can be obtained by the following formula:

[0071]

[0072] Among them, x a (t) is the traffic flow of traffic arc a within a preset time period; d is a constant; t is time; u a (t) is the flow rate flowing into traffic arc a within a preset time period; v a (t) is the flow rate flowing out of traffic arc a at time period t; C(j) is the set of arcs flowing out of node j; D(j) is the set of arcs flowing out of node j; e w (t) represents the traffic flow reaching destination d within a preset time period; It means that any outflow node j is not zero; It means that any route r is not zero; E r,w (t) represents the cumulative flow state variable reaching destination d through route r within a preset time period.

[0073] Optionally, the charging and discharging model of a single electric vehicle can be obtained through the following formula:

[0074]

[0075] T a =[T i,ar ,T i,eq ;

[0076] Among them, η ln , η gn respectively represent the charging and discharging efficiencies of the electric vehicle; T a represents the time period when the electric vehicle is connected to the distribution network; t represents time; P i,ln,t , P i,gn,t respectively represent the active charging and discharging powers of the i-th electric vehicle; respectively represent the state of charge of the i-th electric vehicle when it disconnects and connects to the grid; represents the electric energy capacity of the electric vehicle; respectively represent the minimum and maximum values of the active power output of the electric vehicle; S i,t represents the state of charge of the i-th electric vehicle within a preset time period; S i，t-1 represents the state of charge of the i-th electric vehicle at time t - 1; represents the active power output of the electric vehicle; T i,ar , T i,eq respectively represent the grid connection and disconnection time periods of the i-th electric vehicle; Δt represents the unit time step.

[0077] Optionally, after obtaining the charging and discharging model of a single electric vehicle, the time-varying aggregated battery model of the electric vehicle can be determined based on the charging and discharging model of the single electric vehicle.

[0078] Optionally, based on the charging and discharging model of a single electric vehicle, a time-varying aggregated battery model of electric vehicles is determined, including: determining the total power consumption of an electric vehicle cluster based on the charging and discharging model of a single electric vehicle; determining the dischargeable power of an electric vehicle cluster based on the charging and discharging model of a single electric vehicle; and obtaining a time-varying aggregated battery model of electric vehicles based on the total power consumption and the dischargeable power.

[0079] In an alternative embodiment, the time-varying aggregated battery model of electric vehicles can be obtained through the following formula:

[0080]

[0081] where P ln,t represents the total power consumption, x a，w (t) represents the number of electric vehicles connected to the grid at time t; η ln , η gn respectively represent the charging and discharging efficiencies of electric vehicles; H ev,j,t represents the connection of the j-th electric vehicle to the distribution network within a preset time period, H ev,j,t =0 indicates that the j-th electric vehicle is not connected to the distribution network, H ev,j,t =1 indicates that the j-th electric vehicle is connected to the distribution network; P j，ln，t represents the power consumption of the j-th electric vehicle within a preset time period; P gn,t represents the dischargeable power; P j，gn，t represents the dischargeable power of the j-th electric vehicle at time t.

[0082] Optionally, based on a distributed energy resource model and a time-varying aggregated battery model of electric vehicles, an islanding division model and islanding division constraint conditions of a distribution network are constructed, including: constructing a first objective function and islanding division constraint conditions based on the distributed energy resource model and the time-varying aggregated battery model of electric vehicles; constructing a second objective function based on the number of branch breakings in the distribution network; and constructing an islanding division model based on the first objective function and the second objective function.

[0083] In an alternative embodiment, first, a first objective function and islanding division constraint conditions can be constructed based on the distributed energy resource model and the time-varying aggregated battery model of electric vehicles.

[0084] Optionally, the second objective function can be obtained through the following formula:

[0085]

[0086] where the second objective function f2 is used to reduce the number of branch breakings and lower the breaking cost, Ω l represents the set of lines, z ij represents the decision state variable of the breaking of line ij, zij When z ij = 1, it indicates that line ij is closed, and when z

[0087] Optionally, after obtaining the first objective function and the second objective function, an island division model can be constructed based on the first objective function and the second objective function.

[0088] Optionally, based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, a first objective function is constructed, including: constructing decision variables based on the island division range of the distribution network, the output of the distributed energy resources in the distributed energy resource model, and the output of the electric vehicles in the electric vehicle time-varying aggregated battery model; constructing a penalty term based on the state of charge of the electric vehicle time-varying aggregated battery model; and constructing the first objective function based on the decision variables and the penalty term.

[0089] In an optional embodiment, the goal can be to minimize the power-off nodes, include as many power-off nodes as possible in the island, use the island division range, the output of the wind-solar-storage power source, and the output of the electric vehicles as decision variables, and secondly, use the state of charge of the electric vehicle time-varying aggregated battery model as a penalty term.

[0090] Optionally, the first objective function can be obtained through the following formula:

[0091]

[0092] where f1 is the first objective function; i represents the load node number including the electric vehicle load; Ω t is the set of time periods; Ω V is the set of load nodes; ω i represents the weight of load node i, which is determined by the importance of the load connected to this node; θ it represents whether the load at node i is cut off during the preset time period, θ it = 1 indicates that load node i is cut off during the preset time period, θ it = 0 indicates that node i is included in the operation range of the distribution network; L it represents the magnitude of the node load i during the preset time period; λ is the penalty coefficient; E i,max represents the maximum electric energy capacity of the i-th electric vehicle; Ω ta is the set of fault duration periods; E i,t represents the electric energy of the i-th electric vehicle that goes off-grid during the preset time period.

[0093] Optionally, based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, island division constraint conditions are constructed, including: constructing an island power balance constraint condition based on the power generation power of distributed energy resources in the distributed energy resource model and the power consumption power of electric vehicles in the electric vehicle time-varying aggregated battery model; constructing an island division topology constraint condition based on the island division range of the distribution network; constructing an electric vehicle load charge and discharge constraint condition based on the charge and discharge power, load energy, and electrical energy of electric vehicles in the electric vehicle time-varying aggregated battery model; constructing a new energy power source output constraint condition based on the active power output, reactive power output, active power, capacity, and minimum power factor of the new energy power source in the distributed energy resource model; establishing a power flow constraint condition and a system safe operation voltage constraint condition; and establishing an island division constraint condition based on the island power balance constraint condition, the island division topology constraint condition, the electric vehicle load charge and discharge constraint condition, the new energy power source output constraint condition, the power flow constraint condition, and the system safe operation voltage constraint condition.

[0094] In an alternative embodiment, the island power balance constraint condition can be obtained through the following formula:

[0095]

[0096] Among them, P DG,it 、P B,jt 、P gn,t respectively represent the power generation powers of distributed power sources, energy storage, and electric vehicles within a preset time period; P Load,t is the load magnitude at time t; P ln,t represents the power consumption power of electric vehicles at time t; i represents the load node number including the electric vehicle load; n represents the total number of load nodes; j represents the energy storage system number, and m represents the total number of energy storage systems.

[0097] Optionally, the island division topology constraint condition can be obtained through the following formula:

[0098]

[0099] Among them, each load node in the distribution network can only be included in one of the islands, and e is is the node island division status quantity; when e is = 1, node i is included in island s; when e is = 0, the load node is not included in island s; S is the island set; Ω b is the distribution network load node set.

[0100] Among them, after dividing the islands, in addition to satisfying connectivity, the topology should also remain radial. After linearizing this constraint, the following constraint is obtained:

[0101]

[0102] In the formula, represents the line island division status variable, when the line (i, j) is included in the island s, when the line is not included in the island s; e is is the line island division status quantity; e js is the node island division status quantity; Ω l represents the set of distribution network lines; S is the set of islands.

[0103] Similarly, the islands formed after the division of the distribution network should still satisfy connectivity and maintain a radial operation state:

[0104]

[0105] In the formula, ij represents the line, Ω l represents the set of distribution network lines; z ij represents the decision status quantity of the opening of the line ij; |Ω b | is the number of all load nodes in the distribution network; |S| is the number of islands formed after division.

[0106] Optionally, the charge and discharge constraint conditions of the electric vehicle load can be obtained through the following formula:

[0107]

[0108] Among them, are respectively the minimum and maximum values of the charge and discharge power of the i-th electric vehicle load within the preset time period; is the charge and discharge power of the i-th electric vehicle load within the preset time, with discharge being positive and charge being negative; is the minimum value of the electric vehicle load energy within the preset time period, E i,t are respectively the sizes of the electric vehicle load energy at the i-th node at the initial moment and within the preset time period; and are respectively the minimum and maximum values of the electric energy of the i-th electric vehicle within the preset time period.

[0109] Optionally, the output constraint conditions of the new energy power source can be obtained through the following formula:

[0110]

[0111]

[0112] Among them, are the maximum and minimum values of the active power of the wind and light power sources at the i-th node during the time period t; PDG,it and Q DG,it are respectively the active and reactive power output magnitudes of the wind and photovoltaic power sources at node i during time period t; is the minimum power factor of the wind and photovoltaic power sources at node i; S DG,it is the installed capacity of wind power and photovoltaic power connected to node i.

[0113] Optionally, the voltage constraint conditions for the safe operation of the system can be obtained through the following formula:

[0114]

[0115] where and are respectively the upper and lower limits of the voltage at node i, U t , i is the voltage at node i during time period t.

[0116] Optionally, the line operation capacity constraint can be obtained through the following formula:

[0117]

[0118] where I t,ij is the current of line ij during time period t, is the maximum value of the current of line ij.

[0119] Optionally, the power flow constraint conditions can be obtained through the following formula:

[0120]

[0121]

[0122] where P jkt is the active power flowing into node j from line k during time period t; k is the line; f(i) and s(i) respectively represent the parent and child node sets of node i in the distribution network; P ijt , Q jit are respectively the active and reactive power magnitudes of the power flow of line ij during time period t; R ij is the resistance value of line ij; I ijt is the current magnitude flowing from node i to node j during time period t; P jt , Q jt are respectively the active and reactive power magnitudes injected into node j during time period t; Ω b is the set of distribution network branches; Q jkt is the reactive power flowing into node j from line k during time period t; f(j) and s(j) are respectively the parent and child node sets of node j in the distribution network; X ij is the reactance value of line ij; P DG,jt , QDG,jt are the active and reactive power magnitudes injected by the distributed power source into node j at time period t; P S，jt , Q s,jt are the active and reactive power magnitudes released by the energy storage power station at node j at time period t; P Load,jt , Q Load,jt are the active and reactive power magnitudes of the load at node j in time period t; P gn，t , Q gn,t are the active and reactive power magnitudes of the electric vehicle in time period t; U jt represents the voltage magnitude of node j at time period t; U it is the voltage magnitude of node i at time period t; Q ijt is the reactive power magnitude of the power flow of line ij in time period t; I jit is the current magnitude flowing from node j to node i at time period t; a ij is a constant.

[0123] Optionally, the island power balance constraint conditions, the island division topology constraint conditions, the electric vehicle load charge and discharge constraint conditions, the new energy power source output constraint conditions, the power flow constraint conditions, and the system safe operation voltage constraint conditions can be summarized, that is, the island division constraint conditions can be established.

[0124] Optionally, based on the island division constraint conditions, the island division model is solved to obtain the island elastic recovery strategy of the distribution network, including: relaxing the output of the island division constraint conditions to obtain the processed constraint conditions; converting the island division model into a second-order cone programming model; using an optimization solver and the processed constraint conditions to solve the second-order cone programming model to obtain the island elastic recovery strategy.

[0125] In an alternative embodiment, the island division model is still a non-convex non-linear model. At this time, first, the convex relaxation method can be used to relax the constraint conditions to obtain the processed constraint conditions. Second, the island division model can be converted into a second-order cone programming model by using the second-order cone transformation. Further, the second-order cone programming model can be solved for the strategy by using the Cplex optimization solver and the processed constraint conditions to obtain the island elastic recovery strategy. Among them, Cplex is a powerful mathematical programming solver, mainly used to solve large-scale linear programming, mixed integer linear programming, quadratic programming, and mixed integer quadratic programming problems. It is famous for its efficient algorithm and wide application range and is widely used in fields such as supply chain management, production scheduling, and resource allocation.

[0126] The beneficial effects of the present invention are as follows:

[0127] 1. The present invention effectively reduces the algorithm model variables by establishing an aggregated battery model for an electric vehicle cluster based on a dynamic traffic system. It collaborates with distributed energy in an island with full power supply to sufficiently and continuously restore the load inside the island, and fully improves the load restoration amount on the premise of meeting the satisfaction of electric vehicle users.

[0128] 2. The present invention improves the coordination between distributed energy and load by proposing a distribution network island division strategy, establishing a distributed energy model and a controllable load model using a clustering modeling method, and fully tapping the controllable power supply potential on the load side. At the same time, the number of switch closures is minimized during island division, significantly reducing the island division cost.

[0129] 3. The present invention improves the overall resilience index of the island by proposing an operation control strategy considering the coordination of power sources, loads, and energy storage. When the island is in a state of power shortage, it flexibly adjusts the charging and discharging of the energy storage power station in coordination with the new energy power source to preferentially ensure the load restoration amount of important loads inside the island, extend the duration of load restoration, and

[0130] Figure 2 is a flowchart of an optional distribution network island division strategy according to an embodiment of the present invention. As Figure 2 shown, the method includes the following steps:

[0131] Step S201, a system fault occurs;

[0132] Step S202, initialize the distribution network parameters;

[0133] Step S203, search for energy storage stations and electric vehicle dispatching points in the distribution network that can be used as main power sources;

[0134] Step S204, minimize the power-off load nodes and minimize the branch breakings to form an island division result, and enter steps S205, S206, and S207;

[0135] Step S205, establish a box-type wind and light fluctuating output model, and enter steps S208 and S209;

[0136] Step S206, aggregately establish an electric vehicle charging and discharging output model, and enter steps S208 and S209;

[0137] Step S207, establish an energy storage device output model, and enter steps S208 and S209;

[0138] Step S208, explore the power supply potential of the main power source in an island with sufficient main power;

[0139] Step S209, preferentially restore important loads in an island with a shortage of main power;

[0140] Step S210: Determine whether the fault is repaired. If so, proceed to step S211; if not, proceed to step S212;

[0141] Step S211: The distribution network resumes normal operation;

[0142] Step S212: Update the fault repair information and return to step S204.

[0143] Among them, a distributed energy model can be obtained through step S205, step S206, and step S207.

[0144] In this embodiment, an improved 48-node distribution network system of a certain city is taken as the basic topology. Assuming that under the influence of extreme conditions, all 110 / 10 kV substations in the distribution system have a total power failure accident during the period from 12:00 to 18:00, and the overall distribution network system needs to operate in island mode for 6 hours. The time interval is taken as 15 minutes, that is, Δt = 0.25 h, and the actual operation time of the island is divided into 24 time periods.

[0145] The principles for dividing the distribution network into islands are formulated as follows:

[0146] When starting the island planning, first search for all energy storage devices and electric vehicle connection points that can be used as the main power source in the distribution network. With the goal of minimizing the loss of power value and minimizing the number of branch breakings, and taking the power balance of island operation as the constraint condition, try to include all power loss nodes in different islands as much as possible. Secondly, use the method of clustering modeling to establish an available distributed energy model within the island. By considering the dynamic traffic flow and user behavior habits, cluster and group electric vehicles to establish controllable load models for dischargeable groups and non-dischargeable groups, use the box-type uncertain set to establish a prediction output model for wind and solar power sources, and use the battery principle to establish an energy storage device model. Finally, with the goal of minimizing the power loss time of load nodes within the divided island and minimizing the number of switch breakings, combined with the satisfaction of electric vehicle users and other constraint conditions, coordinate and plan the output states of different power supply resources within the island.

[0147] To verify the effectiveness and superiority of the proposed strategy, two different strategies are used for island division and operation control of the power loss area when dividing the distribution network into islands.

[0148] Strategy 1: Use the island division and flexible operation strategy described in this article. When dividing the island, search for available distributed energy, establish an island division and flexible operation model considering the coordination of source-load-storage, and solve the operation control strategy of different distributed energy in the island.

[0149] Strategy 2: When dividing the island, only consider the power balance between distributed energy and load nodes in the island during the division period, without considering the output fluctuations of wind and solar power sources and the uncertainties of electric vehicles, and solve the island division and operation control strategy.

[0150] The islanding division topology diagrams under the two strategies can be obtained respectively: Figure 3a It is an optional islanding division topology result under Strategy 1 according to an embodiment of the present invention; Figure 3b It is an optional islanding division topology result under Strategy 2 according to an embodiment of the present invention.

[0151] (1) Analysis of the sufficient island 1 elastic recovery results of the main power supply under the two strategies:

[0152] The charging and discharging power conditions of the electric vehicle cluster in island 1 under the two strategies are as Figure 4 shown, Figure 4 It is an optional electric vehicle cluster scheduling operation result according to an embodiment of the present invention. When dividing the island under Strategy 1, based on considering the source-load power balance and the number of switch openings and closings, according to the prediction results of the fluctuating output of new energy, the charging and discharging power of the electric vehicle cluster is coordinated and adjusted to realize the division and operation of island 1. While when dividing the island under Strategy 2, only the power balance between the source and the load is considered, and the electric vehicle cluster is not controlled to charge and store energy during the period when the distributed resources output is sufficient. The system reserve capacity of island 1 is relatively low. Therefore, when the output of the wind and light power sources decreases, the electric vehicle cluster does not have enough electrical energy reserves to provide power restoration and it is difficult to cope with the island power deficit period.

[0153] Under the two strategies, the load recovery amount of island 1, the charging amount of the electric vehicle cluster, and the elastic indexes of each period of island 1 are respectively as Figure 5 and Figure 6 shown, Figure 5 It is an optional elastic recovery index of island 1 and the state of charge of the electric vehicle cluster according to an embodiment of the present invention; Figure 6 It is an optional elastic recovery index of island 1 and the state of charge of the electric vehicle cluster according to an embodiment of the present invention.

[0154] According to Figure 5 shown, both of the two islanding division strategies have achieved the recovery of all primary load amounts and most secondary load amounts in island 1. Strategy 1 fully coordinates and schedules the electric vehicle cluster and matches it with the fluctuating output of the distributed power source, and increases the charging power of the electric vehicle cluster before the output of the distributed power source decreases, thereby increasing the electrical energy reserve of the electric vehicle. It can be seen from Figure 6 that the overall charging amount of the electric vehicle cluster always remains above 50%, effectively meeting the vehicle use needs of users in the distribution network. During the period when the power supply is low, by increasing the reserve power of the electric vehicle cluster in advance, all secondary loads can be continuously powered, and most of the tertiary loads are also restored. Generally speaking, the all-period elastic index of the island remains at 0.769 on average.

[0155] However, Strategy 2 does not fully consider the prediction of distributed power sources and loads, and fails to optimize and control the operation of the electric vehicle cluster in advance. When the output of distributed power sources decreases while the total load increases, the discharge of the electric vehicle cluster surges, resulting in a sharp drop in the state of charge of electric vehicles. Even during periods when the supply of distributed power sources is sufficient, no active charging measures are taken to increase the power reserve, resulting in an average state of charge of the electric vehicle cluster of only 0.427, which fails to fully meet the needs of electric vehicle users when off-grid. In addition, the disorderly discharge of the electric vehicle cluster leads to insufficient power supply when the output of distributed power sources decreases, and some tertiary loads are cut off, significantly reducing the load recovery value. Generally speaking, the average all-time island elasticity index under Strategy 2 is 0.441. In contrast, Strategy 1 can more effectively recover the load value, improve the satisfaction of electric vehicle users, and make the island system more operationally resilient under island division operation.

[0156] (2) Analysis of the island 2 elasticity recovery results of the main power shortage under the two strategies:

[0157] The overall load recovery situation of island 2 and the output of the energy storage power station obtained under the two strategies are as Figure 7 shown, Figure 7 which is an optional load recovery amount and energy storage power station output according to an embodiment of the present invention. The overall island elasticity indexes obtained under the two strategies are as Figure 8 shown, Figure 8 which is an optional island 2 elasticity recovery index according to an embodiment of the present invention.

[0158] As Figure 7 can be seen, due to the large amount of load in island 2, the power supply resources are always in a relatively short supply state. It can be seen from the figure that the island load recovery state in Strategy 1 is always relatively stable. During periods when the photovoltaic output is large, in response to the fluctuations in photovoltaic output, the energy storage device reduces its output, while maintaining the power supply to all primary loads and most secondary loads, and fully utilizes the excess photovoltaic output for charging to cope with periods when the photovoltaic output decreases and maintain the overall stability of the load power supply. In Strategy 2, during the early period when the photovoltaic output is large, the load recovery amount is relatively large, and even a small part of the tertiary load is recovered. In order to maintain power balance, the energy storage outputs more electric energy. When the photovoltaic output decreases in the later stage of island operation, due to the small remaining electric energy storage of the energy storage power station, in order to stabilize the load recovery amount in the later stage, the output can only be reduced, and some secondary loads are cut off. As Figure 8 can be seen, the average all-time island elasticity index of Strategy 1 is 0.72, and the average island elasticity index of Strategy 2 is 0.65. Strategy 2 sacrifices the overall stability of island operation and increases the recovery amount of some tertiary loads in the early stage. Strategy 1 maintains the continuous and stable power supply of more important loads, enhances the load recovery amount during the all-time island operation, and makes the island more operationally resilient.

[0159] According to an embodiment of the present invention, an embodiment of a fault processing device for a distribution network is provided. It should be noted that this device can be used to execute the above-mentioned fault processing method for the distribution network.

[0160] Figure 9 It is a schematic diagram of a fault processing device for a distribution network according to an embodiment of the present invention. As Figure 9 shown, the device includes: a first establishment module 92, configured to, in response to a fault occurring in the distribution network, establish a distributed energy resource model of the distribution network based on energy storage devices and new energy power sources in the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources; a second establishment module 94, configured to establish an electric vehicle time-varying aggregated battery model based on an electric vehicle cluster in a dynamic traffic system, where the electric vehicle time-varying aggregated battery model is used to characterize the power consumption and discharge power of the electric vehicle cluster for the distribution network; a third establishment module 96, configured to construct an island division model and island division constraint conditions of the distribution network based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model; a solving module 98, configured to solve the island division model based on the island division constraint conditions to obtain an island elastic recovery strategy for the distribution network; and a recovery module 910, configured to use the island elastic recovery strategy to perform fault recovery on the distribution network.

[0161] Optionally, the first establishment module includes: a first establishment unit, configured to establish an energy storage device model of the distribution network based on the energy storage characteristics of the energy storage device, where the energy storage characteristics include at least one of the following: charging power, discharging power, charging coefficient state, discharging coefficient state, state of charge within a preset time period; a second establishment unit, configured to establish a new energy power source model of the distribution network based on the fluctuation characteristics of the new energy power source, where the fluctuation characteristics include at least one of the following: predicted output value of the new energy power source, predicted error value of the output of the new energy power source, set of new energy power source units; and a first processing unit, configured to obtain a distributed energy resource model based on the energy storage device model and the new energy power source model.

[0162] Optionally, the second establishment module includes: a first determination unit, configured to determine an electric vehicle cluster in the dynamic traffic system based on a dynamic traffic flow model, where the dynamic traffic flow model is used to characterize the time-varying travel demand and traffic dynamic flow of the electric vehicle cluster; a third establishment unit, configured to establish a charging and discharging model of a single electric vehicle based on the charging and discharging characteristics of a single electric vehicle in the electric vehicle cluster, where the charging and discharging characteristics include at least one of the following: charging active power output, discharging active power output, state of charge when off-grid, state of charge when on-grid, electric energy capacity, charging efficiency, discharging efficiency, target time period for connecting to the distribution network; and a second determination unit, configured to determine an electric vehicle time-varying aggregated battery model based on the charging and discharging model of a single electric vehicle.

[0163] Optionally, the second determination unit includes: a first determination subunit, configured to determine the total power consumption of the electric vehicle cluster based on the charge and discharge model of a single electric vehicle; a second determination subunit, configured to determine the dischargeable power of the electric vehicle cluster based on the charge and discharge model of a single electric vehicle; and a processing subunit, configured to obtain a time-varying aggregated battery model of the electric vehicle based on the total power consumption and the dischargeable power.

[0164] Optionally, the third establishment module includes: a first construction unit, configured to construct a first objective function and island division constraint conditions based on the distributed energy resource model and the time-varying aggregated battery model of the electric vehicle; a second construction unit, configured to construct a second objective function based on the number of branch breakings of the distribution network; and a third construction unit, configured to construct an island division model based on the first objective function and the second objective function.

[0165] Optionally, the first construction unit includes: a first construction subunit, configured to construct decision variables based on the island division range of the distribution network, the output of the distributed energy resources in the distributed energy resource model, and the output of the electric vehicles in the time-varying aggregated battery model of the electric vehicle; a second construction subunit, configured to construct a penalty term based on the power storage state of the time-varying aggregated battery model of the electric vehicle; and construct a first objective function based on the decision variables and the penalty term.

[0166] Optionally, the third establishment module further includes: a fourth construction unit, configured to construct island power balance constraint conditions based on the power generation power of the distributed energy resources in the distributed energy resource model and the power consumption power of the electric vehicles in the time-varying aggregated battery model of the electric vehicle; a fifth construction unit, configured to construct island division topology constraint conditions based on the island division range of the distribution network; a sixth construction unit, configured to construct electric vehicle load charge and discharge constraint conditions based on the charge and discharge power, load energy, and electrical energy of the electric vehicles in the time-varying aggregated battery model of the electric vehicle; a seventh construction unit, configured to construct new energy power source output constraint conditions based on the active power output, reactive power output, active power, capacity, and minimum power factor of the new energy power sources in the distributed energy resource model; an eighth construction unit, configured to establish power flow constraint conditions and system safe operation voltage constraint conditions; and a ninth construction unit, configured to establish island division constraint conditions based on the island power balance constraint conditions, the island division topology constraint conditions, the electric vehicle load charge and discharge constraint conditions, the new energy power source output constraint conditions, the power flow constraint conditions, and the system safe operation voltage constraint conditions.

[0167] Optionally, the solution module includes: a second processing unit for relaxing the output of the island division constraint conditions to obtain the processed constraint conditions; a conversion unit for converting the island division model into a second-order cone programming model; and a solution unit for using an optimization solver and the processed constraint conditions to solve the second-order cone programming model to obtain an island elastic recovery strategy.

[0168] An embodiment of the present application further provides an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein when the program runs, it executes the methods in various embodiments of the present invention.

[0169] An embodiment of the present application further provides a computer-readable storage medium, which includes a stored executable program, wherein when the executable program runs, it controls the device where the computer-readable storage medium is located to execute the methods in various embodiments of the present invention.

[0170] An embodiment of the present application further provides a computer program product, including a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0171] An embodiment of the present application further provides a computer program product, including a non-volatile computer-readable storage medium for storing a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0172] An embodiment of the present application further provides a computer program, which implements the methods in various embodiments of the present invention when executed by a processor.

[0173] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0174] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0175] The unit described as a separating component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed over multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, each functional unit in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0177] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks or optical discs and other various media that can store program codes.

[0178] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A fault handling method for a distribution network, characterized in that, Including: In response to a fault occurring in the distribution network, based on the energy storage devices and new energy power sources within the distribution network, establish a distributed energy resource model of the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources; Based on the electric vehicle cluster in the dynamic traffic system, establish an electric vehicle time-varying aggregated battery model, where the electric vehicle time-varying aggregated battery model is used to characterize the power consumption and discharge power of the electric vehicle cluster for the distribution network; Based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, construct an island division model and island division constraint conditions for the distribution network; Solve the island division model based on the island division constraint conditions to obtain the island elastic restoration strategy for the distribution network; Use the island elastic restoration strategy to perform fault restoration on the distribution network.

2. The method according to claim 1, wherein Based on the energy storage devices and new energy power sources within the distribution network, establishing the distributed energy resource model of the distribution network includes: Based on the energy storage characteristics of the energy storage devices, establish an energy storage device model of the distribution network, where the energy storage characteristics include at least one of the following: charging power, discharging power, charging coefficient state, discharging coefficient state, state of charge within a preset time period; Based on the fluctuation characteristics of the new energy power sources, establish a new energy power source model of the distribution network, where the fluctuation characteristics include at least one of the following: predicted output value of the new energy power source, predicted error value of the output of the new energy power source, set of new energy power source units; Based on the energy storage device model and the new energy power source model, obtain the distributed energy resource model.

3. The method according to claim 1, characterized in that, Based on the association relationship between the electric vehicle cluster in the dynamic traffic system and the distribution network, establishing the electric vehicle time-varying aggregated battery model includes: Based on the dynamic traffic flow model, determine the electric vehicle cluster in the dynamic traffic system, where the dynamic traffic flow model is used to characterize the time-varying travel demand and traffic dynamic flow of the electric vehicle cluster; Based on the charging and discharging characteristics of a single electric vehicle in the electric vehicle cluster, establish a charging and discharging model of the single electric vehicle, where the charging and discharging characteristics include at least one of the following: charging active power output, discharging active power output, state of charge when off-grid, state of charge when on-grid, electrical energy capacity, charging efficiency, discharging efficiency, target time period for connecting to the distribution network; Based on the charging and discharging model of the single electric vehicle, determine the electric vehicle time-varying aggregated battery model.

4. The method according to claim 3, wherein Based on the charging and discharging model of the single electric vehicle, determining the electric vehicle time-varying aggregated battery model includes: Based on the charging and discharging model of the single electric vehicle, determine the total power consumption of the electric vehicle cluster; Based on the charging and discharging model of the single electric vehicle, determine the dischargeable power of the electric vehicle cluster; Based on the total power consumption and the dischargeable power, obtain the electric vehicle time-varying aggregated battery model.

5. The method according to any one of claims 1 to 4, characterized in that Based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model, constructing the island division model and island division constraint conditions for the distribution network includes: Based on the distributed energy resource model and the time-varying aggregated battery model of electric vehicles, construct the first objective function and the island division constraint conditions; Based on the number of branch breakings in the distribution network, construct the second objective function; Based on the first objective function and the second objective function, construct the island division model.

6. The method according to claim 5, wherein Based on the distributed energy resource model and the time-varying aggregated battery model of electric vehicles, constructing the first objective function includes: Based on the island division scope of the distribution network, the output of the distributed energy resources in the distributed energy resource model, and the output of the electric vehicles in the time-varying aggregated battery model of electric vehicles, construct decision variables; Based on the state of charge of the time-varying aggregated battery model of electric vehicles, construct a penalty term; Based on the decision variables and the penalty term, construct the first objective function.

7. The method according to claim 5, wherein Based on the distributed energy resource model and the time-varying aggregated battery model of electric vehicles, constructing the island division constraint conditions includes: Based on the power generation power of the distributed energy resources in the distributed energy resource model and the power consumption power of the electric vehicles in the time-varying aggregated battery model of electric vehicles, construct the island power balance constraint conditions; Based on the island division scope of the distribution network, construct the island division topology constraint conditions; Based on the charging and discharging power, load energy, and electrical energy of the electric vehicles in the time-varying aggregated battery model of electric vehicles, construct the electric vehicle load charging and discharging constraint conditions; Based on the active power output, reactive power output, active power, capacity, and minimum power factor of the new energy power sources in the distributed energy resource model, construct the new energy power source output constraint conditions; Establish power flow constraint conditions and system safe operating voltage constraint conditions; Based on the island power balance constraint conditions, the island division topology constraint conditions, the electric vehicle load charging and discharging constraint conditions, the new energy power source output constraint conditions, the power flow constraint conditions, and the system safe operating voltage constraint conditions, establish the island division constraint conditions.

8. The method according to any one of claims 1 to 4, characterized in that, Based on the island division constraint conditions, solve the island division model to obtain the island elastic restoration strategy of the distribution network, including: Relax the output of the island division constraint conditions to obtain the processed constraint conditions; Convert the island division model into a second-order cone programming model; Use an optimization solver and the processed constraint conditions to solve the second-order cone programming model to obtain the island elastic restoration strategy.

9. A fault handling device for a distribution network, characterized in that, Including: The first establishment module is used to, in response to a fault occurring in the distribution network, establish a distributed energy resource model of the distribution network based on the energy storage devices and new energy power sources in the distribution network, where the new energy power sources include at least one of the following: wind power sources, photovoltaic power sources; The second establishment module is used to establish a time-varying aggregated battery model of electric vehicles based on an electric vehicle cluster in a dynamic traffic system, where the time-varying aggregated battery model of electric vehicles is used to characterize the power consumption and discharge power of the electric vehicle cluster for the distribution network; A third establishment module, configured to construct an island division model and island division constraint conditions of the distribution network based on the distributed energy resource model and the electric vehicle time-varying aggregated battery model; A solution module, configured to solve the island division model based on the island division constraint conditions to obtain an island elastic restoration strategy of the distribution network; A restoration module, configured to perform fault restoration on the distribution network by using the island elastic restoration strategy.

10. An electronic device, characterized in that, Comprising: A memory storing an executable program; A processor, configured to run the program, wherein when the program runs, it executes the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, it controls the device where the storage medium is located to execute the method according to any one of claims 1 to 8.

12. A computer program product, characterized in that, Comprising a computer program, which when executed by a processor implements the method according to any one of claims 1 to 8.

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