Regional power supply parameter determination method and device and electronic equipment

Abstract

The invention discloses a regional power supply parameter determination method and device and electronic equipment. The method comprises the following steps: acquiring a region parameter corresponding to a fault region; determining a plurality of traffic routes corresponding to the fault area according to the area position parameters; determining group power supply parameters respectively corresponding to the plurality of mobile energy storage equipment groups according to the regional environment parameters and the plurality of traffic routes; dividing the fault area according to the group power supply parameters respectively corresponding to the plurality of mobile energy storage equipment groups to obtain sub-fault areas respectively corresponding to the plurality of mobile energy storage equipment groups; and obtaining target power supply parameters respectively corresponding to the plurality of sub-fault areas according to the area power utilization parameters and the group power supply parameters respectively corresponding to the plurality of mobile energy storage equipment groups. According to the method and the device, the technical problem of inaccurate regional power supply parameter determination caused by power supply uncertainty and the like of the electric vehicle when the electric vehicle is matched with the power distribution network for power generation in the related technology is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular, to a method, apparatus, and electronic device for determining regional power supply parameters. Background Art

[0002] When extreme events such as large-scale power outages or system failures occur, the normal power supply of the distribution network in the area is affected. In the related art, when relying on electric vehicles to cooperate with the distribution network for power generation, due to the uncertainty of the power supply of electric vehicles, etc., there is a technical problem that the determination of regional power supply parameters is inaccurate.

[0003] For the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide a method, apparatus, and electronic device for determining regional power supply parameters, so as to at least solve the technical problem in the related art that when relying on electric vehicles to cooperate with the distribution network for power generation, due to the uncertainty of the power supply of electric vehicles, etc., the determination of regional power supply parameters is inaccurate.

[0005] According to an aspect of an embodiment of the present invention, there is provided a method for determining regional power supply parameters, including: obtaining regional parameters corresponding to a fault area, where the regional parameters include regional position parameters, regional power consumption parameters, and regional environmental parameters; determining a plurality of traffic routes corresponding to the fault area according to the regional position parameters; determining group power supply parameters corresponding to a plurality of mobile energy storage device groups respectively according to the regional environmental parameters and the plurality of traffic routes, where the plurality of mobile energy storage device groups correspond to the plurality of traffic routes one by one; dividing the fault area according to the group power supply parameters corresponding to the plurality of mobile energy storage device groups respectively to obtain sub-fault areas corresponding to the plurality of mobile energy storage device groups respectively, where the plurality of mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic route; obtaining target power supply parameters corresponding to a plurality of sub-fault areas respectively according to the regional power consumption parameters and the group power supply parameters corresponding to the plurality of mobile energy storage device groups respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas respectively according to the corresponding target power supply parameters.

[0006] Optionally, determining group power supply parameters corresponding to multiple mobile energy storage device groups according to the regional environmental parameters and the multiple traffic routes includes: determining traffic flow parameters corresponding to the multiple traffic routes according to the regional environmental parameters and the multiple traffic routes, where the corresponding traffic flow parameters are parameters used to represent the number of sub-mobile energy storage devices in the corresponding traffic route; determining the group power supply parameters corresponding to the multiple mobile energy storage device groups according to the multiple traffic flow parameters.

[0007] Optionally, determining traffic flow parameters corresponding to the multiple traffic routes according to the regional environmental parameters and the multiple traffic routes includes: determining travel demand parameters corresponding to the multiple traffic routes according to the regional environmental parameters, where the corresponding travel demand parameters represent the degree of travel demand of the sub-mobile energy storage devices on the corresponding traffic route; determining traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes according to the multiple traffic routes and the travel demand parameters corresponding to the multiple traffic routes; determining traffic flow parameters corresponding to the multiple traffic routes according to the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes.

[0008] Optionally, dividing the fault area according to the group power supply parameters corresponding to the multiple mobile energy storage device groups to obtain sub-fault areas corresponding to the multiple mobile energy storage device groups includes: determining group power supply adjustment coefficients corresponding to the multiple mobile energy storage device groups according to the group power supply parameters corresponding to the multiple mobile energy storage device groups; determining multiple power loss nodes in the fault area, where the multiple power loss nodes are nodes that lose power supply in the fault area; determining power consumption weight parameters corresponding to the multiple power loss nodes, where the corresponding power consumption weight parameters are parameters used to represent the importance of the power consumption demands corresponding to the multiple power loss nodes; dividing the fault area according to the regional power consumption parameters, the power consumption weight parameters corresponding to the multiple power loss nodes, and the group power supply parameters and group power supply adjustment coefficients corresponding to the multiple mobile energy storage device groups to obtain sub-fault areas corresponding to the multiple mobile energy storage devices.

[0009] Optionally, dividing the fault area according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power-off nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, to obtain sub-fault areas respectively corresponding to the multiple mobile energy storage devices, includes: determining power supply access parameters respectively corresponding to the multiple power-off nodes according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power-off nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, where the corresponding power supply access parameters are parameters used to indicate whether to connect the corresponding power-off nodes to the power supply; dividing the fault area according to the power supply access parameters respectively corresponding to the multiple power-off nodes, to obtain sub-fault areas respectively corresponding to the multiple mobile energy storage devices.

[0010] Optionally, dividing the fault area according to the power supply access parameters respectively corresponding to the multiple power-off nodes, to obtain sub-fault areas respectively corresponding to the multiple mobile energy storage devices, includes: determining line connection parameters respectively corresponding to the multiple power-off nodes according to the power supply access parameters respectively corresponding to the multiple power-off nodes; determining initial sub-areas respectively corresponding to the multiple mobile energy storage devices according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power-off nodes, the line connection parameters respectively corresponding to the multiple power-off nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups; determining initial node sets respectively corresponding to the multiple initial sub-areas, where the corresponding initial node sets include multiple power-off nodes in the corresponding initial sub-areas; determining whether each initial node set has an intersection with other initial node sets, to obtain a judgment result; in the case that the judgment result is that each initial node set has no intersection with other initial node sets, dividing the fault area according to the initial sub-areas respectively corresponding to the multiple mobile energy storage devices, to obtain sub-fault areas respectively corresponding to the multiple mobile energy storage devices.

[0011] Optionally, obtaining target power supply parameters corresponding to multiple sub-fault areas based on the regional power consumption parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups includes: determining sub-power consumption parameters corresponding to the multiple sub-fault areas based on the regional power consumption parameters; determining power consumption constraint parameters corresponding to the multiple sub-fault areas; determining corrected power consumption parameters corresponding to the multiple sub-fault areas based on the power consumption constraint parameters and the sub-power consumption parameters corresponding to the multiple sub-fault areas; determining power supply constraint parameters corresponding to the multiple mobile energy storage device groups; determining corrected power supply parameters corresponding to the multiple mobile energy storage device groups based on the power supply constraint parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups; and obtaining target power supply parameters corresponding to the multiple sub-fault areas based on the corrected power consumption parameters corresponding to the multiple sub-fault areas and the corrected power supply parameters corresponding to the multiple mobile energy storage device groups.

[0012] According to one aspect of an embodiment of the present invention, there is provided a device for determining regional power supply parameters, including: an acquisition module configured to acquire regional parameters corresponding to a fault area, where the regional parameters include regional position parameters, regional power consumption parameters, and regional environmental parameters; a first determination module configured to determine multiple traffic routes corresponding to the fault area based on the regional position parameters; a second determination module configured to determine group power supply parameters respectively corresponding to multiple mobile energy storage device groups based on the regional environmental parameters and the multiple traffic routes, where the multiple mobile energy storage device groups correspond one-to-one to the multiple traffic routes; a third determination module configured to divide the fault area based on the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups to obtain sub-fault areas respectively corresponding to the multiple mobile energy storage device groups, where the multiple mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic routes; and a fourth determination module configured to obtain target power supply parameters respectively corresponding to the multiple sub-fault areas based on the regional power consumption parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas according to the corresponding target power supply parameters.

[0013] According to one aspect of an embodiment of the present invention, there is provided an electronic device, including: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the regional power supply parameter determination method described in any one of the above.

[0014] According to one aspect of an embodiment of the present invention, there is provided a computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enabling the electronic device to execute the regional power supply parameter determination method described in any one of the above.

[0015] In the embodiment of the present invention, through the above steps S102 - S110, area parameters corresponding to the fault area are obtained, where the area parameters include area position parameters, area power consumption parameters, and area environment parameters; according to the area position parameters, a plurality of traffic routes corresponding to the fault area are determined; according to the area environment parameters and the plurality of traffic routes, group power supply parameters corresponding to a plurality of mobile energy storage device groups are determined, where the plurality of mobile energy storage device groups correspond one-to-one with the plurality of traffic routes; according to the group power supply parameters corresponding to the plurality of mobile energy storage device groups respectively, the fault area is divided to obtain sub-fault areas corresponding to the plurality of mobile energy storage device groups respectively, where the plurality of mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic routes; according to the area power consumption parameters and the group power supply parameters corresponding to the plurality of mobile energy storage device groups respectively, target power supply parameters corresponding to the plurality of sub-fault areas are obtained to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas according to the corresponding target power supply parameters. By determining a plurality of traffic routes corresponding to the fault area, it helps to realize the advance planning of the travel of sub-mobile energy storage devices, and further helps to make full use of the power supply attribute of sub-mobile energy storage devices subsequently, improving the power supply recovery speed of the entire fault area. By according to the group power supply parameters corresponding to the mobile energy storage device groups on different traffic routes, it can ensure the reasonable scheduling of the mobile energy storage device groups and avoid equipment damage caused by over-power supply of the mobile energy storage device groups. By the division of sub-fault areas, the power restoration scheduling is more targeted, reducing unnecessary delay and resource waste in the restoration process. By determining the target power supply parameters, it ensures that the corresponding mobile energy storage device groups can provide sufficient and stable power without damaging their own equipment health, thus helping the sub-fault areas to restore power supply. That is, by comprehensively determining the target power supply parameters based on the area power consumption parameters and the group power supply parameters, while ensuring that each sub-fault area can obtain stable and reliable power supply, unnecessary power supply and the power supply recovery time of the fault area are reduced, thereby improving the fast recovery ability of the distribution network in the face of disasters or faults, and further solving the technical problem in the related art that when relying on electric vehicles to cooperate with the distribution network for power generation, due to the power supply uncertainty of electric vehicles, etc., the determination of area power supply parameters is inaccurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0017] Figure 1 is a flowchart of the method for determining area power supply parameters in the embodiment of the present invention;

[0018] Figure 2 It is a flowchart of the distribution network islanding division strategy in an alternative embodiment of the present invention;

[0019] Figure 3 It is a schematic diagram of the topological result of islanding division under Strategy 1 in an alternative embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of the topological result of islanding division under Strategy 2 in an alternative embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of the operation result of the electric vehicle cluster scheduling in an alternative embodiment of the present invention;

[0022] Figure 6 It is a schematic diagram of the load recovery amount in an alternative embodiment of the present invention;

[0023] Figure 7 It is a schematic diagram of the elastic recovery index of Island 1 and the state of charge of the EV cluster in an alternative embodiment of the present invention;

[0024] Figure 8 It is a schematic diagram of the load recovery amount and the output of the energy storage power station in an alternative embodiment of the present invention;

[0025] Figure 9 It is a schematic diagram of the elastic recovery index of Island 2 in an alternative embodiment of the present invention;

[0026] Figure 10 It is a block diagram of the structure of the device for determining the regional power supply parameters in an embodiment of the present invention. Detailed implementation manners

[0027] 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.

[0028] It should be noted that the terms "first", "second", etc. in the description, claims and the 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 "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising 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.

[0029] First, some nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations:

[0030] Non-convex non-linear model: A non-convex non-linear model means that at least one of the objective function or the constraint conditions is non-linear, and the feasible solution space of the entire model is non-convex, that is, the feasible solution space of the model is not a convex set. That is, in the solution space, the line connecting any two points may not be completely within the feasible solution space.

[0031] Convex relaxation method: The convex relaxation method is a method of transforming a non-convex optimization problem into a convex optimization problem. By relaxing some constraints of the original problem or modifying the objective function, the feasible solution space of the problem becomes a convex set, thereby transforming the non-convex problem into a convex problem.

[0032] Relaxed constraint conditions: Relaxed constraint conditions refer to relaxing certain constraints in an optimization problem to expand the feasible solution space, making the problem easier to solve, or avoiding the situation where the problem has no feasible solution.

[0033] Second-order cone transformation: Second-order cone transformation is to transform some non-linear constraints or objective functions into the form of second-order cone constraints.

[0034] Second-order cone programming: Second-order cone programming (SOCP) is a special convex optimization problem, whose objective function is linear, and the constraint conditions include linear constraints and second-order cone constraints.

[0035] MATLAB: MATLAB (Matrix Laboratory) is a high-performance numerical calculation and visualization software, which provides a rich mathematical function library and supports matrix operations, numerical analysis, signal processing, image processing, etc.

[0036] Cplex: Cplex is an optimization solver that supports various optimization problems such as linear programming (LP), mixed integer linear programming (MILP), quadratic programming (QP), and second-order cone programming (SOCP).

[0037] Yalmip: Yalmip is a MATLAB-based modeling language used to define and solve optimization problems. It provides a unified interface that can call various optimization solvers, including Cplex, etc.

[0038] Monte Carlo sampling method: The Monte Carlo method is a numerical calculation method based on random sampling, used to solve probability and statistics problems, optimization problems, or integral problems.

[0039] Clustering modeling: Clustering modeling is a data processing method that divides data into several clusters (or groups) so that the data within the same cluster has high similarity and the data between different clusters has low similarity.

[0040] Box uncertainty set: The box uncertainty set is a common form of uncertainty set in robust optimization, used to describe the variation range of uncertain parameters.

[0041] Embodiment 1

[0042] According to an embodiment of the present invention, an embodiment of a method for determining regional power supply parameters 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 a different order than here.

[0043] Figure 1 is a flowchart of the method for determining regional power supply parameters according to an embodiment of the present invention, as Figure 1 shown, the method includes the following steps:

[0044] S102. Obtain regional parameters corresponding to the fault area, where the regional parameters include regional location parameters, regional power consumption parameters, and regional environmental parameters;

[0045] In step S102 provided in the present application, regional parameters corresponding to the fault area are obtained.

[0046] Among them, the fault area is involved. The fault area is an area where part of the power distribution network or transmission network is powered off or operates abnormally due to natural disasters, equipment damage, or operational errors, etc. For example, after a natural disaster, a certain power distribution network loses its connection with the main network, resulting in insufficient power supply support for the power distribution network, thereby affecting the power supply in a certain area. Then, this area can be considered a fault area.

[0047] Among them, regional parameters are involved. The regional parameters are parameters used to reflect the characteristics and status of the fault area. These regional parameters help to understand the power demand, supply capacity, and operating environment of the fault area.

[0048] Among them, regional location parameters are involved. The regional location parameters are parameters used to reflect the location of the fault area. The regional location parameters may include parameters related to the geographical location of the area, the location of power consumption nodes in the area, traffic route information, etc.

[0049] Among them, regional power consumption parameters are involved. The regional power consumption parameters are parameters used to reflect the power consumption demand of the fault area, and may include related parameters such as load type, load magnitude, power factor of the load, and the change pattern of the load over time.

[0050] Among them, regional environment parameters are involved. The regional environment parameters are parameters used to reflect the environmental information of the fault area, and may include temperature, wind speed, light intensity, and traffic convenience.

[0051] By obtaining the regional parameters corresponding to the fault area, it helps to subsequently understand the power consumption demand situation of the fault area, thereby helping to subsequently provide targeted scheduling of power supply resources, minimizing the scheduling cost and the fault recovery time.

[0052] S104. According to the regional location parameters, determine multiple traffic routes corresponding to the fault area;

[0053] In step S104 provided in this application, multiple traffic routes corresponding to the fault area are determined.

[0054] Among them, multiple traffic routes are involved. The multiple traffic routes are routes connecting each key node (such as a power outage node, a wind power generation node, a photovoltaic power generation node, a distribution network energy storage node) in the fault area. Each of the multiple traffic routes has corresponding geographical information and traffic conditions, such as the length of the road, road conditions, etc.

[0055] By determining multiple traffic routes corresponding to the fault area, it helps to achieve advance planning for the travel of sub-mobile energy storage devices, ensuring that the sub-mobile energy storage devices can be dispatched quickly, safely, and efficiently. Thus, it helps to subsequently analyze the problem of making full use of the sub-mobile energy storage devices to help the fault area restore power supply, and further helps to make full use of the power supply attribute of the sub-mobile energy storage devices, realizing the effective utilization of the sub-mobile energy storage devices and improving the power supply restoration speed of the entire fault area.

[0056] S106. According to the regional environment parameters and the multiple traffic routes, determine the group power supply parameters corresponding to multiple groups of mobile energy storage devices, where the multiple groups of mobile energy storage devices correspond one-to-one to the multiple traffic routes;

[0057] In step S106 provided by this application, the group power supply parameters corresponding to multiple mobile energy storage device groups are determined.

[0058] Among them, the mobile energy storage device group is involved. The mobile energy storage device group is a set of sub-mobile energy storage devices on the corresponding traffic route. For example, when the sub-mobile energy storage device is an electric vehicle, the mobile energy storage device group is a set of multiple electric vehicles on the corresponding traffic route. These electric vehicles can be used as distributed power sources and are scheduled to different nodes to provide power supply according to the electricity demand.

[0059] Among them, the group power supply parameters are involved. The group power supply parameter is the power supply capacity of the mobile energy storage device group under specific conditions, and may include the state of charge (SOC), schedulable time window, maximum power and duration that can be released, charge-discharge efficiency, etc. For example, the group power supply parameters may include the remaining power of each electric vehicle, the upper limit of the discharge power, and the duration of the discharge time. This information is crucial for reasonably scheduling mobile energy storage devices to meet the load demand.

[0060] By relying on the group power supply parameters corresponding to the mobile energy storage device groups on different traffic routes, the reasonable scheduling of the mobile energy storage device groups can be ensured, so as to ensure the matching of power supply and demand, which helps to ensure the timely power supply subsequently while avoiding equipment damage caused by over-power supply of the mobile energy storage device groups.

[0061] S108. According to the group power supply parameters corresponding to multiple mobile energy storage device groups respectively, divide the fault area to obtain sub-fault areas corresponding to multiple mobile energy storage device groups respectively, where multiple mobile energy storage device groups are respectively sets of sub-mobile energy storage devices included in the corresponding traffic routes;

[0062] In step S108 provided by this application, sub-fault areas corresponding to multiple mobile energy storage device groups respectively are obtained.

[0063] Among them, the sub-fault area is involved. The sub-fault area is a sub-area obtained by further dividing the fault area according to the available mobile energy storage device groups. For example, in a large-scale power outage event, the entire power outage area may be divided into several sub-areas that can be powered by different mobile energy storage device groups, and the load capacity and power supply capacity within each sub-area can be balanced.

[0064] Among them, the sub-mobile energy storage device is involved. The sub-mobile energy storage device is a device that can be scheduled to the corresponding sub-fault area in the corresponding traffic route to provide power supply. For example, an electric vehicle or other movable energy storage devices.

[0065] By dividing the fault area into multiple sub-fault areas according to the group power supply parameters corresponding to multiple mobile energy storage device groups respectively, the division of sub-fault areas makes the power restoration scheduling more targeted, reduces unnecessary delays and resource waste during the restoration process, and speeds up the power restoration speed of the fault area. And by dividing into multiple sub-fault areas, it is ensured that each sub-fault area realizes power supply restoration according to actual conditions such as corresponding power consumption demands. At the same time, it can ensure that each sub-fault area can achieve a faster and more economical restoration effect and avoid waste of resources.

[0066] S110. Based on the regional power consumption parameters and the group power supply parameters corresponding to multiple mobile energy storage device groups respectively, obtain the target power supply parameters corresponding to multiple sub-fault areas respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas according to the corresponding target power supply parameters.

[0067] In step S110 provided in this application, the target power supply parameters corresponding to multiple sub-fault areas are obtained.

[0068] Among them, the target power supply parameters are involved. The target power supply parameter is the power supply parameter of the corresponding mobile energy storage device group determined to meet the power demand in the corresponding sub-fault area. The target power supply parameter can be parameters such as the charge-discharge power and discharge duration set for the mobile energy storage device group.

[0069] By determining the target power supply parameters, it is ensured that the corresponding mobile energy storage device group can provide sufficient and stable power without damaging the health of its own equipment, thereby helping the sub-fault area to restore power supply. That is, by comprehensively determining the target power supply parameters based on the regional power consumption parameters and the group power supply parameters, while ensuring that each sub-fault area can obtain stable and reliable power supply, unnecessary power supply and the power supply restoration time of the fault area are reduced, thereby improving the fast restoration ability of the distribution network in the face of disasters or faults.

[0070] Through the above steps S102 - S110, area parameters corresponding to the fault area are obtained, where the area parameters include area location parameters, area power consumption parameters, and area environment parameters; according to the area location parameters, multiple traffic routes corresponding to the fault area are determined; according to the area environment parameters and the multiple traffic routes, group power supply parameters corresponding to multiple mobile energy storage device groups are determined, where the multiple mobile energy storage device groups correspond one - to - one with the multiple traffic routes; according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, the fault area is divided to obtain sub - fault areas corresponding to the multiple mobile energy storage device groups respectively, where the multiple mobile energy storage device groups are respectively sets of sub - mobile energy storage devices included in the corresponding traffic routes; according to the area power consumption parameters and the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, target power supply parameters corresponding to the multiple sub - fault areas are obtained to control the corresponding mobile energy storage device groups to supply power to the corresponding sub - fault areas according to the corresponding target power supply parameters. By determining multiple traffic routes corresponding to the fault area, it helps to realize the advance planning of the travel of sub - mobile energy storage devices, and then helps to make full use of the power supply attribute of sub - mobile energy storage devices subsequently, improving the power supply restoration speed of the entire fault area. By according to the group power supply parameters corresponding to the mobile energy storage device groups on different traffic routes, it can ensure the reasonable scheduling of the mobile energy storage device groups and avoid equipment damage caused by over - power supply of the mobile energy storage device groups. Through the division of sub - fault areas, the power restoration scheduling becomes more targeted, reducing unnecessary delays and resource waste during the restoration process. By determining the target power supply parameters, it ensures that the corresponding mobile energy storage device groups can provide sufficient and stable power without damaging their own equipment health, thus helping the sub - fault areas to restore power supply. That is, by comprehensively determining the target power supply parameters based on the area power consumption parameters and the group power supply parameters, while ensuring that each sub - fault area can obtain stable and reliable power supply, it reduces unnecessary power supply and the power supply restoration time of the fault area, thereby improving the fast restoration ability of the distribution network in the face of disasters or faults, and then solving the technical problem in the related art that when relying on electric vehicles to cooperate with the distribution network for power generation, due to the power supply uncertainty of electric vehicles, etc., the determination of area power supply parameters is inaccurate.

[0071] As an optional embodiment, according to the area environment parameters and the multiple traffic routes, determining the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively includes: according to the area environment parameters and the multiple traffic routes, determining traffic flow parameters corresponding to the multiple traffic routes respectively, where the corresponding traffic flow parameter is a parameter used to represent the number of sub - mobile energy storage devices in the corresponding traffic route; according to the multiple traffic flow parameters, determining the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively.

[0072] In this embodiment, the specific steps of determining the group power supply parameters corresponding to multiple mobile energy storage device groups according to the regional environmental parameters and multiple traffic routes are described.

[0073] Among them, the traffic flow parameter is involved. The traffic flow parameter is a parameter describing the quantity characteristics of sub-mobile energy storage devices (such as electric vehicles or other mobile energy storage devices) in the traffic route.

[0074] In the steps involved in this embodiment, first, according to the regional environmental parameters and multiple traffic routes, the traffic flow parameters corresponding to the multiple traffic routes are determined. Then, according to the multiple traffic flow parameters, the group power supply parameters corresponding to the multiple mobile energy storage device groups are determined.

[0075] By relying on the regional environmental parameters (such as weather conditions, road conditions, etc.) and multiple traffic routes, the traffic capacity of each traffic route can be further evaluated, which helps to accurately determine the traffic flow parameters on each traffic route. By introducing the traffic flow parameters, the influence of environmental and traffic changes on the power supply capacity of sub-mobile energy storage devices (such as electric vehicles) is considered, which helps to accurately determine the number of sub-mobile energy storage devices on each traffic route within a predetermined time period, reduces the power supply shortage or surplus caused by prediction errors, ensures that even under complex and changeable environmental and traffic conditions, the restoration process of the distribution network is still controllable, and enhances the adaptability of the power supply restoration in the fault area to face uncertainties.

[0076] As an alternative embodiment, determining the traffic flow parameters corresponding to multiple traffic routes according to the regional environmental parameters and multiple traffic routes includes: determining the travel demand parameters corresponding to the multiple traffic routes according to the regional environmental parameters, where the corresponding travel demand parameter represents the degree of travel demand of the sub-mobile energy storage device on the corresponding traffic route; determining the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes according to the multiple traffic routes and the travel demand parameters corresponding to the multiple traffic routes; determining the traffic flow parameters corresponding to the multiple traffic routes according to the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes.

[0077] In this embodiment, the specific steps of determining the traffic flow parameters corresponding to multiple traffic routes according to the regional environmental parameters and multiple traffic routes are described.

[0078] Among them, the travel demand parameter is involved. The travel demand parameter is used to measure the degree of travel demand of users of sub-mobile energy storage devices (such as electric vehicles) for a certain traffic route under specific environmental conditions in the fault area. The travel demand parameter takes into account the influence of environmental factors on the travel willingness of users of sub-mobile energy storage devices.

[0079] Among them, traffic inflow parameters are involved, and the traffic inflow parameter is used to represent the number of sub-mobile energy storage devices entering a certain traffic route within a specific time.

[0080] Among them, traffic outflow parameters are involved, and the traffic outflow parameter is used to represent the number of sub-mobile energy storage devices leaving a certain traffic route within a specific time.

[0081] In the steps involved in this embodiment, first, according to regional environmental parameters (such as weather, road conditions, time, etc.), travel demand parameters related to each traffic route are determined. Then, based on the known traffic routes and the predicted travel demand parameters, the traffic inflow parameters and traffic outflow parameters of each traffic route are determined. Finally, by integrating the traffic inflow parameters and traffic outflow parameters, the traffic flow parameters corresponding to each traffic route are determined.

[0082] By determining the travel demand parameters related to each traffic route, the influence of environmental factors in the fault area on users' travel willingness is considered, which helps to accurately predict the flow of sub-mobile energy storage devices on each traffic route. By determining the traffic inflow parameters and traffic outflow parameters of each traffic route within a predetermined time period, it helps to understand the flow information of sub-mobile energy storage devices entering and leaving each traffic route at different time points, so as to more accurately predict the flow of sub-mobile energy storage devices on the traffic route, reduce the scheduling failures or power supply problems caused by prediction errors, and thus can more reasonably adjust the charging and discharging strategies of sub-mobile energy storage devices to ensure the stable supply of power during the power restoration process.

[0083] As an alternative embodiment, based on the regional power consumption parameters and the group power supply parameters respectively corresponding to multiple mobile energy storage device groups, target power supply parameters respectively corresponding to multiple sub-fault areas are obtained, including: determining group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups according to the group power supply parameters respectively corresponding to multiple mobile energy storage device groups; determining multiple power-loss nodes in the fault area, where the multiple power-loss nodes are nodes that lose power supply in the fault area; determining power consumption weight parameters respectively corresponding to the multiple power-loss nodes, where the corresponding power consumption weight parameter is used to represent the importance degree of the power consumption demands respectively corresponding to the multiple power-loss nodes; dividing the fault area based on the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices.

[0084] In this embodiment, the specific steps of obtaining the target power supply parameters respectively corresponding to multiple sub-fault areas based on the regional power consumption parameters and the group power supply parameters respectively corresponding to multiple mobile energy storage device groups are described.

[0085] Among them, a group power supply adjustment coefficient is involved. This group power supply adjustment coefficient is a coefficient used to adjust the power supply capacity of a mobile energy storage device group (such as an electric vehicle cluster) to a specific sub-fault area at a specific time point.

[0086] Among them, a plurality of power-loss nodes are involved. These multiple power-loss nodes are load points in the fault area that have lost normal power supply due to faults or disasters.

[0087] Among them, an electricity consumption weight parameter is involved. This electricity consumption weight parameter is used to quantify the importance or urgency of the electricity consumption demand corresponding to the power-loss node. This electricity consumption weight parameter can be determined according to factors such as load type and load usage. For example, in the power restoration priority ranking, the electricity consumption weight parameters of key infrastructures such as hospitals and fire stations may be set to relatively high values, while the electricity consumption weight parameters of ordinary residential areas may be set to relatively low values, which reflects the principle of preferentially supplying power to key load points in the power restoration strategy.

[0088] In the steps involved in this embodiment, first, according to the group power supply parameters corresponding to each mobile energy storage device group, the group power supply adjustment coefficient corresponding to each mobile energy storage device group is determined. Then, a plurality of power-loss nodes in the fault area and the electricity consumption weight parameters corresponding to the plurality of power-loss nodes are determined. Finally, according to the regional electricity consumption parameters, the electricity consumption weight parameters corresponding to the plurality of power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients corresponding to each mobile energy storage device group, the fault area is divided to obtain sub-fault areas corresponding to each mobile energy storage device.

[0089] By determining the group power supply adjustment coefficient corresponding to each mobile energy storage device group, the power supply stability of the mobile energy storage device group is ensured. By identifying a plurality of power-loss nodes in the fault area and determining the electricity consumption weight parameters corresponding to the plurality of power-loss nodes, the quantification of the importance or urgency of different load points is realized, which helps to provide a reference basis for the scheduling order of the mobile energy storage device group. By comprehensively considering the regional electricity consumption parameters, the electricity consumption weight parameters corresponding to the plurality of power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients corresponding to each mobile energy storage device group, the mobile energy storage device group can be more reasonably allocated, avoiding ineffective scheduling in the resource scheduling process, improving the resource utilization efficiency, and thus enabling the power supply of key load points to be restored faster.

[0090] As an alternative embodiment, according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups, the fault area is divided to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices, including: according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups, determine the power supply access parameters respectively corresponding to multiple power-loss nodes, where the corresponding power supply access parameter is a parameter used to indicate whether to connect the corresponding power-loss node to the power supply; according to the power supply access parameters respectively corresponding to multiple power-loss nodes, divide the fault area to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices.

[0091] In this embodiment, the specific steps of dividing the fault area according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices are described.

[0092] Among them, the power supply access parameter is involved. The power supply access parameter is a parameter used to determine whether to connect a specific power-loss node to the temporary power supply network provided by the mobile energy storage device group. The power supply access parameter can be represented by a binary value (for example, 0 means not connected, 1 means connected). For example, if a power-loss node is evaluated to have a high power consumption demand and a low connection cost, its power supply access parameter can be set to 1, indicating that the mobile energy storage device group will supply power to this node.

[0093] In the steps involved in this embodiment, first, according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups, determine the power supply access parameters respectively corresponding to multiple power-loss nodes. Then, according to the power supply access parameters respectively corresponding to multiple power-loss nodes, divide the fault area to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices.

[0094] By dividing the fault area according to the power supply access parameter, the balance between power demand and supply is ensured under the condition of limited power supply resources, which helps to realize the reasonable scheduling of the mobile energy storage device group, avoids the power supply shortage or surplus caused by improper resource allocation, and at the same time reduces the cost of power restoration.

[0095] As an alternative embodiment, according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups, the fault area is divided to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices, including: determining the line connection parameters respectively corresponding to multiple power loss nodes according to the power supply access parameters respectively corresponding to multiple power loss nodes; determining the initial sub-areas respectively corresponding to multiple mobile energy storage devices according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power loss nodes, the line connection parameters respectively corresponding to multiple power loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups; determining the initial node sets respectively corresponding to multiple initial sub-areas, wherein the corresponding initial node sets include multiple power loss nodes in the corresponding initial sub-areas; determining whether there is an intersection between each initial node set and other initial node sets to obtain a judgment result; in the case that the judgment result is that there is no intersection between each initial node set and other initial node sets, dividing the fault area according to the initial sub-areas respectively corresponding to multiple mobile energy storage devices to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices.

[0096] In this embodiment, the specific steps of dividing the fault area according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to multiple power loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to multiple mobile energy storage device groups to obtain sub-fault areas respectively corresponding to multiple mobile energy storage devices are described.

[0097] Among them, the line connection parameter is involved. The line connection parameter is a parameter used to represent whether there is a power supply connection between power loss nodes in the fault area. The line connection parameter can be represented by a binary value.

[0098] Among them, the initial sub-area is involved. The initial sub-area is a power supply area formed after a preliminary division of the fault area.

[0099] Among them, the initial node set is involved. The initial node set is a set composed of multiple power loss nodes in the corresponding initial sub-area;

[0100] Among them, the intersection is involved. The intersection is used to represent whether there are the same power loss nodes between different initial sub-areas. If the nodes included in two initial sub-areas overlap, that is, the power of the same power loss node needs to be restored in both initial sub-areas, then these overlapping power loss nodes constitute the intersection of the two initial sub-areas.

[0101] In the steps involved in this embodiment, first, according to the power supply access parameters respectively corresponding to multiple power-loss nodes, the line connection parameters respectively corresponding to the multiple power-loss nodes are determined. Then, according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power-loss nodes, the line connection parameters respectively corresponding to the multiple power-loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, the initial sub-regions respectively corresponding to the multiple mobile energy storage devices are determined. Then, the initial node sets respectively corresponding to the multiple initial sub-regions are determined, and it is determined whether each initial node set has an intersection with other initial node sets, obtaining a judgment result. Finally, in the case where the judgment result is that each initial node set has no intersection with other initial node sets, the fault area is divided according to the initial sub-regions respectively corresponding to the multiple mobile energy storage devices, obtaining the sub-fault areas respectively corresponding to the multiple mobile energy storage devices.

[0102] By determining the line connection parameters, it is ensured that the devices used for power supply (such as mobile energy storage device groups) are in a radial connection mode with multiple power-loss nodes, avoiding the situation where a power-loss node that supplies power to another power-loss node cannot achieve effective power supply restoration. By determining whether each initial node set has an intersection with other initial node sets, it is possible to effectively avoid duplicate power supply on the same power-loss node, improving the resource utilization efficiency and the coordination of power restoration.

[0103] As an alternative embodiment, based on the regional power consumption parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups, the target power supply parameters respectively corresponding to the multiple sub-fault areas are obtained, including: based on the regional power consumption parameters, determining the sub-power consumption parameters respectively corresponding to the multiple sub-fault areas; determining the power consumption constraint parameters respectively corresponding to the multiple sub-fault areas; based on the power consumption constraint parameters and sub-power consumption parameters respectively corresponding to the multiple sub-fault areas, determining the corrected power consumption parameters respectively corresponding to the multiple sub-fault areas; determining the power supply constraint parameters respectively corresponding to the multiple mobile energy storage device groups; based on the power supply constraint parameters and group power supply parameters respectively corresponding to the multiple mobile energy storage device groups, determining the corrected power supply parameters respectively corresponding to the multiple mobile energy storage device groups; based on the corrected power consumption parameters respectively corresponding to the multiple sub-fault areas and the corrected power supply parameters respectively corresponding to the multiple mobile energy storage device groups, obtaining the target power supply parameters respectively corresponding to the multiple sub-fault areas.

[0104] In this embodiment, the specific steps of obtaining the target power supply parameters respectively corresponding to the multiple sub-fault areas based on the regional power consumption parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups are described.

[0105] Among them, sub - electrical parameters are involved. The sub - electrical parameters are parameters for quantitatively evaluating the power demand of each sub - fault area. The sub - electrical parameters may include factors such as the power load of each sub - fault area, the type of load, the importance and urgency of the load, etc.

[0106] Among them, power - consumption constraint parameters are involved. The power - consumption constraint parameters are parameters used to reflect the power - consumption limitations of the sub - fault areas. For example, voltage constraint limitations, current constraint limitations, etc. for each sub - fault area.

[0107] Among them, corrected power - consumption parameters are involved. The corrected power - consumption parameters are power - consumption parameters obtained by adjusting the sub - electrical parameters after considering the actual situation of the power - supply capacity and the power - consumption limitations of the sub - fault areas.

[0108] Among them, power - supply constraint parameters are involved. The power - supply constraint parameters are parameters used to reflect the power - supply limitations of the mobile energy - storage device group. For example, the upper limit of the charge - discharge power of the mobile energy - storage device group.

[0109] Among them, corrected power - supply parameters are involved. The corrected power - supply parameters are power - supply parameters obtained by adjusting the group power - supply parameters after considering the actual situation of the power - supply capacity and the power - supply limitations of the mobile energy - storage device group.

[0110] In the steps involved in this embodiment, first, according to the regional power - consumption parameters, the sub - electrical parameters corresponding to multiple sub - fault areas are determined respectively, and the power - consumption constraint parameters corresponding to multiple sub - fault areas are determined. Then, according to the power - consumption constraint parameters and the sub - electrical parameters corresponding to multiple sub - fault areas respectively, the corrected power - consumption parameters corresponding to multiple sub - fault areas are determined. Next, the power - supply constraint parameters corresponding to multiple mobile energy - storage device groups are determined, and according to the power - supply constraint parameters and the group power - supply parameters corresponding to multiple mobile energy - storage device groups respectively, the corrected power - supply parameters corresponding to multiple mobile energy - storage device groups are determined. Finally, according to the corrected power - consumption parameters corresponding to multiple sub - fault areas and the corrected power - supply parameters corresponding to multiple mobile energy - storage device groups, the target power - supply parameters corresponding to multiple sub - fault areas are obtained.

[0111] By determining the corrected power - consumption parameters, the stability of each sub - fault area when receiving power supply is ensured, and new fault problems caused by excessive power supply are avoided. By determining the corrected power - supply parameters, while ensuring that the power - supply capacity of the mobile energy - storage device group meets the corresponding power demand, it also helps to avoid problems such as shortened equipment life caused by excessive power supply of the mobile energy - storage device group. Furthermore, by determining the corresponding target power - supply parameters according to the corrected power - consumption parameters and the corrected power - supply parameters, the stability of the power supply of the mobile energy - storage device is further improved, and the reliability of power restoration is ensured.

[0112] Based on the above embodiments and optional embodiments, an optional implementation is provided, which is described in detail below.

[0113] In the related art, when extreme events such as large-scale power outages or system failures occur, the normal power supply of the distribution network in the area is affected. In the related art, when relying on electric vehicles to cooperate with the distribution network to generate electricity, due to the uncertainty of the power supply of electric vehicles, etc., there is a technical problem of inaccurate determination of regional power supply parameters.

[0114] To address the above-mentioned problems, no effective solution has been proposed yet.

[0115] In view of this, a method for determining regional power supply parameters is provided in an optional embodiment of the present invention, which can also be called a method for determining distribution network island division and elastic recovery strategy based on electric vehicle cluster scheduling, which can effectively solve the technical problem of inaccurate determination of regional power supply parameters due to uncertainty in power supply of electric vehicles, etc. in related technologies when relying on electric vehicles to cooperate with distribution networks to generate electricity.

[0116] Figure 2 is a flow chart of the distribution network island division strategy in an optional implementation mode of the present invention, such as Figure 2 As shown, a detailed description is given below.

[0117] S1. Obtaining regional parameters corresponding to the fault area, wherein the regional parameters include regional location parameters, regional power parameters, and regional environmental parameters;

[0118] For example, fault information is obtained (same as the above-mentioned regional parameters), and a distributed energy resource model of the distribution network is established, including establishing a distribution network energy storage device model, and establishing a distribution network wind power and photovoltaic power source model.

[0119] The distribution network energy storage device model is established as follows:

[0120]

[0121] α f +α c ≤1,α f ,α c ∈{1,0}

[0122]

[0123] in:

[0124] are the charging and discharging power of the energy storage device respectively;

[0125] α c , α f Respectively represent the charge and discharge coefficient states of the energy storage device;

[0126] represents the maximum charging power of the energy storage device;

[0127] represents the maximum discharging power of the energy storage device;

[0128] represents the state of charge of the energy storage device at time period t;

[0129] Δt represents a predetermined time period.

[0130] Establish the wind power and photovoltaic power source models of the distribution network as follows:

[0131]

[0132] Where:

[0133] is the predicted output value of the distributed wind and solar power sources;

[0134] is the predicted error value of the output of the distributed wind and solar power sources;

[0135] is the actual output value of the distributed wind and solar power sources;

[0136] g num is the uncertainty coefficient;

[0137] Ω DG is the set of distributed power generation units;

[0138] β is the uncertainty cost. By adjusting the value of β, the volatility of the uncertainty of the distributed power sources can be controlled. num is the serial number of the distributed power generation unit in the distributed power generation units.

[0139] S2. Determine multiple traffic routes corresponding to the fault area according to the regional location parameters;

[0140] S3. Determine the group power supply parameters corresponding to multiple mobile energy storage device groups according to the regional environmental parameters and the multiple traffic routes, where the multiple mobile energy storage device groups correspond to the multiple traffic routes one by one;

[0141] Specifically, S3 further includes:

[0142] S31. Determine the travel demand parameters corresponding to the multiple traffic routes according to the regional environmental parameters, where the corresponding travel demand parameters represent the demand degree of the sub-mobile energy storage device for traveling on the corresponding traffic route;

[0143] S32. Determine traffic inflow parameters and traffic outflow parameters corresponding to multiple traffic routes respectively based on the multiple traffic routes and the travel demand parameters corresponding to the multiple traffic routes respectively;

[0144] S33. Determine traffic flow parameters corresponding to multiple traffic routes respectively based on the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes respectively.

[0145] S34. Determine the group power supply parameters corresponding to multiple mobile energy storage device groups respectively based on the multiple traffic flow parameters.

[0146] For example, establish an aggregated battery model for an electric vehicle cluster, specifically including establishing a dynamic traffic flow model and an electric vehicle charging and discharging model for a single vehicle.

[0147] Considering the time-varying travel demand of electric vehicles (the same as the above travel demand parameters) and the dynamic traffic flow, establish a dynamic traffic flow model as follows:

[0148]

[0149] Where:

[0150] r refers to the set of routes, that is, multiple routes to the destination, including multiple traffic arcs;

[0151] b is a traffic arc;

[0152] x a (t) is the traffic flow of traffic arc a at time period t (the same as the above traffic flow parameters);

[0153] u a (t) is the flow rate flowing into traffic arc a (the same as the above traffic route) at time period t (the same as the above traffic inflow parameters);

[0154] v a (t) is the flow rate flowing out of traffic arc a at time period t (the same as the above traffic outflow parameters);

[0155] e w (t) represents the traffic flow reaching destination w at time period t;

[0156] E r,w (t) represents the cumulative flow rate reaching destination w at time t through the routes in set r using the state variable;

[0157] C(j) is the set of arcs flowing into node j;

[0158] D(j) is the set of arcs flowing out of node j;

[0159] x a,w (t) is the number of vehicles departing from arc a and reaching destination w;

[0160] x b,w ( ) is the number of vehicles departing from arc b and arriving at destination w;

[0161] x ar,w (t) is the number of vehicles passing through arc a at time t and finally planning to reach destination w through the routes in set r;

[0162] t a (t) is the average delay time of the vehicles passing through traffic arc a at time t.

[0163] From the above dynamic traffic flow model, the time-varying vehicle flow propagation volume considering the road conditions can be obtained, and then the charging and discharging model of a single electric vehicle can be obtained as follows:

[0164]

[0165] T a = [T i,ar , T i,eq

[0166] Where:

[0167] T a represents the time period when the electric vehicle is connected to the distribution network;

[0168] η ln represents the EV charging efficiency;

[0169] P i,ln,t represents the charging active power output of the i-th electric vehicle at time t;

[0170] P i,gn,t represents the discharging active power output of the i-th electric vehicle at time t;

[0171] η gn represents the EV discharging efficiency;

[0172] represents the state of charge of the i-th electric vehicle (EV) when leaving the grid;

[0173] represents the state of charge of the i-th electric vehicle (EV) when connecting to the grid;

[0174] represents the electric energy capacity of the electric vehicle;

[0175] represents the minimum charging active power output of the i-th electric vehicle (EV) at time t;

[0176] represents the maximum charging active power output of the i-th electric vehicle (EV) at time t;​

[0177] S i,t represents the state of charge of the i-th EV at time t;

[0178] S i,t-1 represents the state of charge of the i-th EV at time t-1;

[0179] represents the active power output of the m-th EV;

[0180] T i,ar represents the grid connection time period of the i-th EV;

[0181] T i,eq represents the grid disconnection time period of the i-th EV;

[0182] Δt represents the unit time step.

[0183] Based on the above, the aggregated battery model of the electric vehicle cluster is obtained:

[0184]

[0185] Wherein:

[0186] P ln,t represents the total power consumption;

[0187] η ln represents the EV charging efficiency;

[0188] H ev,i,t represents the connection status of the i-th EV to the distribution network at time t, H ev,i,t =0 indicates that the i-th EV is not connected to the distribution network, H ev,i,t =1 indicates that the i-th EV is connected to the distribution network;

[0189] P i,ln,t represents the power consumption of the i-th electric vehicle;

[0190] P gn,t represents the dischargeable power;

[0191] η gn represents the EV discharge efficiency.

[0192] S4. According to the group power supply parameters corresponding to multiple mobile energy storage device groups, divide the fault area to obtain sub-fault areas corresponding to multiple mobile energy storage device groups, where multiple mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic routes;

[0193] Specifically, S4 further includes:

[0194] S41. Determine the group power supply adjustment coefficients corresponding to multiple mobile energy storage device groups respectively according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively;

[0195] S42. Determine multiple power - lost nodes in the fault area, where the multiple power - lost nodes are the nodes that have lost power supply in the fault area;

[0196] S43. Determine the power consumption weight parameters corresponding to the multiple power - lost nodes respectively, where the corresponding power consumption weight parameters are used to represent the importance levels of the power consumption demands corresponding to the multiple power - lost nodes respectively;

[0197] S44. Determine the power supply access parameters corresponding to the multiple power - lost nodes respectively according to the regional power consumption parameters, the power consumption weight parameters corresponding to the multiple power - lost nodes respectively, and the group power supply parameters and group power supply adjustment coefficients corresponding to the multiple mobile energy storage device groups respectively, where the corresponding power supply access parameters are the parameters used to represent whether to connect the corresponding power - lost nodes to the power supply;

[0198] S45. Divide the fault area according to the power supply access parameters corresponding to the multiple power - lost nodes respectively to obtain sub - fault areas corresponding to the multiple mobile energy storage devices respectively.

[0199] Specifically, S45 further includes:

[0200] S451. Determine the line connection parameters corresponding to the multiple power - lost nodes respectively according to the power supply access parameters corresponding to the multiple power - lost nodes respectively;

[0201] S452. Determine the initial sub - areas corresponding to the multiple mobile energy storage devices respectively according to the regional power consumption parameters, the power consumption weight parameters corresponding to the multiple power - lost nodes respectively, the line connection parameters corresponding to the multiple power - lost nodes respectively, and the group power supply parameters and group power supply adjustment coefficients corresponding to the multiple mobile energy storage device groups respectively;

[0202] S453. Determine the initial node sets corresponding to the multiple initial sub - areas respectively, where the corresponding initial node sets include the multiple power - lost nodes in the corresponding initial sub - areas;

[0203] S454. Determine whether each initial node set has an intersection with other initial node sets to obtain a judgment result;

[0204] S455. In the case that the judgment result is that each initial node set has no intersection with other initial node sets, divide the fault area according to the initial sub - areas corresponding to the multiple mobile energy storage devices respectively to obtain sub - fault areas corresponding to the multiple mobile energy storage devices respectively.

[0205] For example, establish the following objective function for distribution network islanding division:

[0206] With the goal of minimizing the number of power - off nodes, as many power - off nodes as possible are included in the island. The island division range, the output of wind - solar - storage power sources, and the output of electric vehicles are used as decision variables, and the state of charge of the EV aggregation battery model is added as a penalty term λ (the same as the above - mentioned group power supply adjustment coefficient) to the objective function:

[0207]

[0208] Among them:

[0209] f1 represents the number of power - off nodes;

[0210] Ω t is the set of fault - duration periods;

[0211] ilo represents the load - node number including the electric - vehicle load;

[0212] ω ilo represents the weight of load - node ilo (the same as the above - mentioned electricity - consumption weight parameter), which is determined by the importance of the load connected to this node;

[0213] θ ilo,t represents whether the load at node ilo is cut off at time t (the same as the above - mentioned power - supply access parameter), θ ilo,t = 1 means that load - node ilo is cut off at time t, θ ilo,t = 0 means that node ilo is included in the operation range of the distribution network;

[0214] L ilo,t represents the magnitude of the load of node ilo at time t;

[0215] λ is the penalty coefficient;

[0216] Ω V is the set of EVs in the distribution network;

[0217] E i,t represents the electrical energy of the i - th EV that goes off - grid at time t;

[0218] E i,max represents the maximum electrical - energy capacity of the i - th EV.

[0219] To reduce the number of branch break - offs and lower the break - off cost, a second objective function is set:

[0220]

[0221] Among them:

[0222] f2 represents the break - off cost;

[0223] z li,neThe decision state variable indicating the opening of a line (same as the above line connection parameters), z li,ne When z li,ne = 1, it indicates that the line (li, ne) is closed, and when z

[0224] Ω l represents the set of lines.

[0225] S5. Based on the regional power consumption parameters and the group power supply parameters corresponding to multiple mobile energy storage device groups respectively, obtain the target power supply parameters corresponding to multiple sub-fault areas respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas respectively according to the corresponding target power supply parameters.

[0226] Specifically, S5 further includes:

[0227] S51. Based on the regional power consumption parameters, determine the sub-power consumption parameters corresponding to multiple sub-fault areas respectively;

[0228] S52. Determine the power consumption constraint parameters corresponding to multiple sub-fault areas respectively;

[0229] S53. Based on the power consumption constraint parameters and sub-power consumption parameters corresponding to multiple sub-fault areas respectively, determine the corrected power consumption parameters corresponding to multiple sub-fault areas respectively;

[0230] S54. Determine the power supply constraint parameters corresponding to multiple mobile energy storage device groups respectively;

[0231] S55. Based on the power supply constraint parameters and group power supply parameters corresponding to multiple mobile energy storage device groups respectively, determine the corrected power supply parameters corresponding to multiple mobile energy storage device groups respectively;

[0232] S56. Based on the corrected power consumption parameters corresponding to multiple sub-fault areas respectively and the corrected power supply parameters corresponding to multiple mobile energy storage device groups respectively, obtain the target power supply parameters corresponding to multiple sub-fault areas respectively.

[0233] For example, obtaining the distribution network island elastic restoration strategy by solving models such as the objective function includes: establishing the island power balance constraint; establishing the distribution network island division topology constraint; establishing the electric vehicle load charging and discharging constraint; establishing the output constraints of wind power and photovoltaic power sources; establishing the power flow and system security constraints; solving models such as the objective function to obtain the distribution network island elastic restoration method.

[0234] Establish the island power balance constraint as follows:

[0235]

[0236] Wherein:

[0237] PDG,if,t Denotes the power generation of the if - th distributed power source in the t - th period;

[0238] P B,jc,t Denotes the power generation of the jc - th energy storage device in the t - th period;

[0239] P gn,t Denotes the power generation of electric vehicles in the t - th period;

[0240] P Load,t Is the load magnitude in the t - th period;

[0241] P ln,t Denotes the power consumption of EVs in the t - th period;

[0242] m represents the number of energy storage devices;

[0243] n represents the number of distributed power sources.

[0244] The topological constraints for island division of the distribution network are established as follows:

[0245] Each load node in the distribution network can only be included in one of the islands.

[0246]

[0247] Where:

[0248] e ilo,s Is the island - division status variable of node ilo (the same as the above - mentioned division status parameter);

[0249] Ω b Is the set of load nodes in the distribution network;

[0250] e ilo,s When e = 1, node i is included in island s, and when e ilo,s = 0, the load node is not included in island s;

[0251] S is the set of islands.

[0252] After dividing the islands, in addition to satisfying connectivity, the topology needs to remain radial. After linearizing this constraint, the formula for the target constraint is as follows:

[0253]

[0254] Where:

[0255] Denotes the line island - division status variable, When, the line (li, ne) is included in island s, When, the line is not included in island s;

[0256] Ω lRepresents the set of distribution network lines;

[0257] e li,s Represents the island division status variable of node li;

[0258] e ne,s Represents the island division status variable of node ne.

[0259] At the same time, the islands formed after the division of the distribution network should still satisfy connectivity and maintain the state of radial operation. The formula is as follows:

[0260]

[0261] Where:

[0262] z li,ne Represents the decision variable of whether the line from node li to node ne is opened as the island boundary;

[0263] Ω l Represents the set of distribution network lines;

[0264] |Ω b | is the number of all load nodes in the distribution network;

[0265] |S| is the number of islands formed after division.

[0266] Establish the charging and discharging constraints of electric vehicle loads (same as the above power supply constraint parameters) as follows:

[0267]

[0268] Where:

[0269] Is the charging and discharging power of the i-th electric vehicle load at time t, with discharging being positive and charging being negative;

[0270] Is the minimum charging and discharging power of the i-th electric vehicle at time t;

[0271] Is the minimum charging and discharging power of the i-th electric vehicle at time t;

[0272] E i,t Is the load energy size of the i-th electric vehicle at time t;

[0273] E i,t0 Is the initial state of charge of the i-th electric vehicle at time t;

[0274] And Are the maximum and minimum values of the electrical energy of the i-th EV at time t, respectively. The output constraints of wind power and photovoltaic power sources are as follows:

[0275]

[0276] Among them:

[0277] P DG,it , Q DG,it are respectively the active and reactive power output magnitudes of the wind and photovoltaic power sources at node i during period t; are the maximum and minimum values of the active power of the wind and photovoltaic power sources at node i during period t; S DG,it is the installed capacity of wind power and photovoltaic power connected to node i;

[0278] is the minimum power factor of the wind and photovoltaic power sources at node i.

[0279] Establish the power flow and system security constraints (the same as the above power consumption constraint parameters) as follows:

[0280] Among them, the system security operation voltage constraint is:

[0281]

[0282] Among them:

[0283] U t,ilo is the voltage at node ilo;

[0284] and are respectively the upper and lower limits of the voltage at node ilo.

[0285] The line operation capacity constraint is:

[0286]

[0287] Among them:

[0288] I t,li,ne is the current value of line (li, ne);

[0289] is the maximum value of the current of line (li, ne).

[0290] Improve the power flow constraint as follows:

[0291]

[0292] Among them:

[0293] P ne,k,t is the active power magnitude of the power flow of line (ne, k) during period t;

[0294] P li,ne,t is the active power magnitude of the power flow of line (li, ne) during period t;

[0295] R li,ne is the resistance value of the line (li, ne);

[0296] I li,ne,t is the magnitude of the current flowing from node li to node ne during time period t;

[0297] P ne,t is the magnitude of the active power injected into node ne during time period t;

[0298] f(li) and s(li) are the sets of the parent and child nodes of node li in the distribution network; Ω bran is the set of distribution network branches;

[0299] Q ne,k,t is the magnitude of the reactive power of the power flow of the line (ne, k) during time period t;

[0300] Q li,ne,t is the magnitude of the reactive power of the power flow of the line (li, ne) during time period t;

[0301] X li,ne is the reactance value of the line (li, ne);

[0302] Q ne,t is the magnitude of the reactive power injected into node ne during time period t;

[0303] P ne,t is the magnitude of the active power injected into node ne during time period t;

[0304] P DG,ne.t 、Q DG,ne,t are the magnitudes of the active and reactive powers injected by the distributed power source into node ne during time period t, respectively;

[0305] P Load,ne.t 、Q Load,ne.t are the magnitudes of the active and reactive powers of the active and reactive loads of node ne during time period t, respectively;

[0306] Q gn,t is the reactive power of the electric vehicle during time period t;

[0307] U ne,t is the voltage at node ne;

[0308] α li,ne,t is the charging and discharging coefficient state of the line (li, ne) during time period t;

[0309] U li,t is the magnitude of the voltage of node li during time period t;

[0310] P S,ne.t 、Q S,ne.tThey are the active and reactive power magnitudes released by the energy storage power station of the energy storage device at node ne during period t, respectively.

[0311] The method for obtaining the resilient restoration method of the distribution network island for solving models such as the objective function is as follows:

[0312] The proposed island division and resilient operation model is still a non-convex and non-linear model. Using the relaxation constraint conditions of the convex relaxation method, the disaster prevention model is converted into a second-order cone programming model by using second-order cone transformation. Further, through MATLAB programming, the Cplex+yalmip optimization solver is used for strategy solution.

[0313] Through the above steps, a specific example is as follows:

[0314] Taking the improved 48-node distribution network system of a certain city as the basic topology to verify the superiority of the distribution network island division strategy proposed by the present invention. The node load levels are shown in Table 1, and the weights of the first-level, second-level, and third-level loads are 100, 10, and 1 respectively. At the same time, the electric vehicle connection points 3, 11, and 34 are added. The parameters of wind power and photovoltaic power generation are shown in Table 2, and the parameters of the energy storage device are shown in Table 3. An emergency diesel generator is equipped at node 8, and its parameters are shown in Table 2.

[0315] Table 1

[0316]

[0317] Table 2

[0318]

[0319] Table 3

[0320]

[0321] In this simulation example, it is set that the charging and discharging efficiencies of the EVs connected to the distribution network are both 95%. A predetermined type of EV is selected. The maximum and minimum charging and discharging powers of this predetermined type of EV are both 6.5 kW, the battery electric energy capacity is 63 kW·h, and the battery capacity range during operation is [0.1,1] kW·h. The Monte Carlo sampling method is used to obtain the grid connection habits of individual electric vehicles, and the sampling parameters of the electric vehicle user behavior habits are shown in Table 4.

[0322] Table 4

[0323]

[0324] Among them, T i,ar is the arrival time, T i,eq is the equivalent time, and S i,ar is the state at arrival.

[0325] Assume that under the influence of extreme conditions, all 110 / 10 kV substations in the distribution system experience a full-station fault power outage accident from 12:00 to 18:00. The overall distribution network system needs to operate in island mode for 6 hours, with a time interval of 15 minutes, i.e., Δt = 0.25 h. The actual operating time of the island is divided into 24 time periods.

[0326] The principles for dividing the distribution network into islands are formulated as follows:

[0327] First, at the beginning of island planning, search for all energy storage devices and electric vehicle connection points that can serve as the main power source in the distribution network. With the goal of minimizing the lost power value and minimizing the number of branch breakings, and taking the power balance during island operation as the constraint condition, try to include all power-loss nodes in different islands as much as possible.

[0328] Second, use the method of clustering modeling to establish a model of available distributed energy 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 uncertainty set to establish a predicted output power model for wind and solar power sources, and use the battery principle to establish an energy storage device model.

[0329] Finally, with the goal of minimizing the power-loss time of load nodes within the divided islands 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.

[0330] To verify the effectiveness of the proposed strategy, when dividing the distribution network system into islands, two different strategies are used for island division and operation control of the power-loss area.

[0331] Strategy 1: Use the above steps to obtain the island division and flexible operation strategy. When dividing the islands, 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.

[0332] Strategy 2: Do not use the above steps. That is, when dividing the islands, 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.

[0333] The island division topologies under the two strategies are obtained respectively. Figure 3 It is a schematic diagram of the island division topology result under Strategy 1 in an alternative embodiment of the present invention. Figure 4 It is a schematic diagram of the island division topology result under Strategy 2 in an alternative embodiment of the present invention. As Figure 3 , Figure 4 shown.

[0334] (1)Analysis of the sufficient islanding 1 elastic recovery results of the main power supply power under two strategies:

[0335] Figure 5 It is a schematic diagram of the dispatching operation results of an electric vehicle cluster in an optional implementation manner of the present invention. The charging and discharging power conditions of the EV cluster in island 1 under two strategies are as Figure 5 shown.

[0336] 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 EV cluster is coordinated and adjusted to achieve the division and operation of island 1.

[0337] When dividing the island under strategy 2, only the power balance between the source and the load is considered. During the period when the output of distributed resources is sufficient, the EV cluster is not controlled to charge and store energy. The system reserve capacity of island 1 is relatively low. Therefore, during the period when the output of wind and light power sources decreases, the EV cluster does not have enough electrical energy reserves to provide power restoration and it is difficult to cope with the island power deficit period.

[0338] Figure 6 It is a schematic diagram of the load recovery amount in an optional implementation manner of the present invention, Figure 7 It is a schematic diagram of the island 1 elastic recovery index and the state of charge of the EV cluster in an optional implementation manner of the present invention. Under the two strategies, the load recovery amount of island 1, the charging amount of the EV cluster, and the elastic indexes of each period of island 1 are respectively as Figure 6 and Figure 7 shown.

[0339] According to Figure 6 shown, both of the two island division strategies have achieved the recovery of all primary load amounts and most secondary load amounts in island 1. By fully coordinating and dispatching the EV cluster and matching it with the fluctuating output of distributed power sources, strategy 1 increases the charging power of the EV cluster before the output of distributed power sources decreases, thereby increasing the electrical energy reserve of electric vehicles.

[0340] According to Figure 7 shown, the overall charging amount of the EV cluster always remains above 50%, effectively meeting the vehicle usage needs of users in the distribution network. During the period when the power supply is low, by increasing the reserve power of the EV 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.

[0341] 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 EV cluster in advance. When the output of distributed power sources decreases while the total load increases, the discharge of the EV cluster surges, resulting in a sharp drop in the state of charge (SOC) of the EVs. Even during periods when the supply of distributed power sources is sufficient, no proactive charging measures are taken to increase the power reserve, resulting in an average SOC of the EV cluster of only 0.427, which fails to fully meet the off-grid demands of electric vehicle users. In addition, the unregulated discharge of the EV 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 restoration value.

[0342] Overall, the average island full-time elasticity index under Strategy 2 is 0.441. In contrast, Strategy 1 can more effectively restore the load value, improve the satisfaction of electric vehicle users, and make the island system more operationally resilient during island division and operation.

[0343] (2) Analysis of the island 2 elasticity restoration results for the main power shortage under the two strategies:

[0344] Figure 8 It is a schematic diagram of the load restoration amount and the output of the energy storage power station in an optional implementation manner of the present invention. Figure 9 It is a schematic diagram of the island 2 elasticity restoration index in an optional implementation manner of the present invention. The overall load restoration situation of island 2 and the output of the energy storage power station obtained under the two strategies are as Figure 8 shown, and the overall elasticity indexes of island 2 obtained under the two strategies are as Figure 9 shown.

[0345] As can be seen from Figure 8 , due to the large amount of load in island 2, the power supply resources are always in a relatively short supply state. In Strategy 1, the island load restoration state 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 restoration amount is relatively large, and even a small part of the tertiary load is restored. 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 restoration amount in the later stage, the output can only be reduced, and some secondary loads are cut off.

[0346] As can be seen from Figure 9It can be seen that the average value of the island full-time elasticity index of Strategy 1 is 0.72, and the average value of the island elasticity index of Strategy 2 is 0.65. Strategy 2 sacrifices the overall stability of island operation and increases the recovery volume 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 volume during the full-time operation of the island, and makes the island more operationally elastic.

[0347] Through the above optional implementation manners, at least the following beneficial effects can be achieved:

[0348] (1) Compared with the related technologies, the present invention determines multiple traffic routes corresponding to the fault area, which helps to realize the advance planning of the travel of the sub-mobile energy storage devices, and further helps to make full use of the power supply attribute of the sub-mobile energy storage devices subsequently. By according to the group power supply parameters corresponding to the mobile energy storage device groups on different traffic routes, the reasonable scheduling of the mobile energy storage device groups can be ensured, and the device damage caused by the over-power supply of the mobile energy storage device groups can be avoided. The division of the sub-fault areas makes the power restoration scheduling more targeted, reducing unnecessary delays and resource waste during the restoration process. By determining the target power supply parameters, it is ensured that the corresponding mobile energy storage device groups can provide sufficient and stable power without damaging their own device health, thereby helping the sub-fault areas to restore power supply. That is, by comprehensively determining the target power supply parameters based on the regional power consumption parameters and the group power supply parameters, while ensuring that each sub-fault area can obtain stable and reliable power supply, unnecessary power supply and the power supply restoration time of the fault area are reduced, thereby improving the rapid restoration ability of the distribution network in the face of disasters or faults, and further solving the technical problem in the related technologies that when relying on electric vehicles to cooperate with the distribution network for power generation, due to the power supply uncertainty of electric vehicles, etc., the determination of regional power supply parameters is inaccurate.

[0349] (2) Compared with the related technologies, the present invention can further evaluate the passing capacity of each traffic route by according to the regional environmental parameters and multiple traffic routes, and further helps to accurately determine the traffic flow parameters on each traffic route. By introducing the traffic flow parameters, the influence of environmental and traffic changes on the power supply capacity of the sub-mobile energy storage devices (such as electric vehicles) is considered, which helps to accurately determine the number of sub-mobile energy storage devices on each traffic route within a predetermined time period, reducing the power supply shortage or surplus caused by prediction errors, ensuring that even under complex and changeable environmental and traffic conditions, the restoration process of the distribution network is still controllable, and enhancing the adaptability of the power supply restoration in the fault area in the face of uncertainties.

[0350] (3) Compared with the related technology, the present invention determines the travel demand parameters, takes into account the influence of environmental factors in the fault area on the user's travel willingness, and helps to accurately predict the flow of sub-mobile energy storage devices on each traffic route. By determining the traffic inflow parameters and traffic outflow parameters of each traffic route within a predetermined time period, it helps to understand the flow information of sub-mobile energy storage devices entering and leaving each traffic route at different time points, so as to more accurately predict the flow of sub-mobile energy storage devices on the traffic route, reduce the scheduling failure or power supply problems caused by prediction errors, and thus can more reasonably adjust the charging and discharging strategies of sub-mobile energy storage devices to ensure the stable supply of power during the power recovery process.

[0351] (4) Compared with the related technology, the present invention determines the line connection parameters to ensure that the devices used for power supply (such as mobile energy storage device groups) are radially connected to multiple power-loss nodes, avoiding the situation where a power-loss node cannot achieve effective power restoration due to power supply to another power-loss node. By determining whether each initial node set has an intersection with other initial node sets, it can effectively avoid repeated power supply on the same power-loss node, improving the utilization efficiency of resources and the coordination of power restoration.

[0352] (5) Compared with the related technology, the present invention determines the corrected power consumption parameters to ensure the stability of each sub-fault area when receiving power supply, avoiding new fault problems caused by over-power supply. By determining the corrected power supply parameters, it ensures that the power supply capacity of the mobile energy storage device group not only meets the corresponding power demand, but also helps to avoid problems such as shortened device life caused by over-power supply of the mobile energy storage device group. Furthermore, by determining the corresponding target power supply parameters according to the corrected power consumption parameters and the corrected power supply parameters, the stability of the power supply of the mobile energy storage device is further improved, ensuring the reliability of power restoration.

[0353] (6) Compared with the related technology, the present invention effectively reduces the algorithm model variables by establishing an aggregated battery model for an EV 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. On the premise of meeting the satisfaction of electric vehicle users, it fully improves the load restoration volume. By proposing a distribution network island division strategy, adopting a clustering modeling method to establish a distributed energy model and a controllable load model, it fully explores the controllable power supply potential on the load side and improves the coordination between distributed energy and the load. At the same time, when dividing the island, the number of switch closures is minimized, significantly reducing the island division cost. By proposing an operation control strategy considering the coordination of source-load-storage, when the island is in a state of power shortage, by flexibly adjusting the charging and discharging of the energy storage power station and coordinating with the new energy power supply, it preferentially ensures the load restoration volume of important loads inside the island, extends the duration of load restoration, and improves the overall resilience index of the island.

[0354] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0355] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0356] Embodiment 2

[0357] According to an embodiment of the present invention, there is also provided a device for implementing the above method for determining regional power supply parameters. Figure 10 It is a structural block diagram of the device for determining regional power supply parameters according to an embodiment of the present invention. As Figure 10 shown, the device includes: an acquisition module 1002, a first determination module 1004, a second determination module 1006, a third determination module 1008, and a fourth determination module 1010. The device will be described in detail below.

[0358] An acquisition module 1002, configured to acquire area parameters corresponding to a fault area, where the area parameters include area location parameters, area power consumption parameters, and area environment parameters; a first determination module 1004, connected to the acquisition module 1002, configured to determine multiple traffic routes corresponding to the fault area according to the area location parameters; a second determination module 1006, connected to the first determination module 1004, configured to determine group power supply parameters corresponding to multiple mobile energy storage device groups respectively according to the area environment parameters and the multiple traffic routes, where the multiple mobile energy storage device groups correspond to the multiple traffic routes one by one; a third determination module 1008, connected to the second determination module 1006, configured to divide the fault area according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, to obtain sub-fault areas corresponding to the multiple mobile energy storage device groups respectively, where the multiple mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic routes; a fourth determination module 1010, connected to the third determination module 1008, configured to obtain target power supply parameters corresponding to the multiple sub-fault areas respectively according to the area power consumption parameters and the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas respectively according to the corresponding target power supply parameters.

[0359] It should be noted here that the above acquisition module 1002, first determination module 1004, second determination module 1006, third determination module 1008, and fourth determination module 1010 correspond to steps S102 to S110 in the method for determining area power supply parameters. The instances and application scenarios implemented by the multiple modules and the corresponding steps are the same, but are not limited to the content disclosed in the above Embodiment 1.

[0360] Embodiment 3

[0361] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a processor; a memory for storing processor-executable instructions, where the processor is configured to execute the instructions to implement the method for determining area power supply parameters in any one of the above.

[0362] Embodiment 4

[0363] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the method for determining area power supply parameters in any one of the above.

[0364] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0365] In the above embodiments of the present invention, the descriptions of the various embodiments each have their own emphasis. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0366] In 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 merely 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 coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of units or modules can be in an electrical or other form.

[0367] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to 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.

[0368] In addition, in each embodiment of the present invention, the functional units 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 integrated units can be implemented in the form of hardware or in the form of software functional units.

[0369] If the above 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 to enable 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 each embodiment of the present invention. And the aforementioned 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.

[0370] The above are only the preferred embodiments 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 method for determining regional power supply parameters, characterized in that Including: Obtain area parameters corresponding to the fault area, where the area parameters include area location parameters, area power consumption parameters, and area environment parameters; Determine multiple traffic routes corresponding to the fault area according to the area location parameters; Determine the group power supply parameters corresponding to multiple mobile energy storage device groups respectively according to the area environment parameters and the multiple traffic routes, where the multiple mobile energy storage device groups correspond to the multiple traffic routes one by one; Divide the fault area according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively to obtain sub-fault areas corresponding to the multiple mobile energy storage device groups respectively, where the multiple mobile energy storage device groups are respectively the sets of sub-mobile energy storage devices included in the corresponding traffic routes; Obtain the target power supply parameters corresponding to multiple sub-fault areas respectively according to the area power consumption parameters and the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas respectively according to the corresponding target power supply parameters.

2. The method according to claim 1, characterized in that The determining the group power supply parameters corresponding to multiple mobile energy storage device groups respectively according to the area environment parameters and the multiple traffic routes includes: Determine the traffic flow parameters corresponding to the multiple traffic routes respectively according to the area environment parameters and the multiple traffic routes, where the corresponding traffic flow parameters are parameters used to represent the number of sub-mobile energy storage devices in the corresponding traffic routes; Determine the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively according to the multiple traffic flow parameters.

3. The method according to claim 2, wherein The determining the traffic flow parameters corresponding to the multiple traffic routes respectively according to the area environment parameters and the multiple traffic routes includes: Determine the travel demand parameters corresponding to the multiple traffic routes respectively according to the area environment parameters, where the corresponding travel demand parameters represent the degree of travel demand of the sub-mobile energy storage devices on the corresponding traffic routes; Determine the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes respectively according to the multiple traffic routes and the travel demand parameters corresponding to the multiple traffic routes; Determine the traffic flow parameters corresponding to the multiple traffic routes respectively according to the traffic inflow parameters and traffic outflow parameters corresponding to the multiple traffic routes.

4. The method according to claim 1, wherein The dividing the fault area according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively to obtain sub-fault areas corresponding to the multiple mobile energy storage device groups respectively includes: Determine the group power supply adjustment coefficients corresponding to the multiple mobile energy storage device groups respectively according to the group power supply parameters corresponding to the multiple mobile energy storage device groups; Determine multiple power loss nodes in the fault area, where the multiple power loss nodes are nodes that lose power supply in the fault area; Determine the power consumption weight parameters corresponding to the multiple power loss nodes respectively, where the corresponding power consumption weight parameters are used to represent the importance of the power consumption demands corresponding to the multiple power loss nodes respectively. According to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power - loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, divide the fault area to obtain sub - fault areas respectively corresponding to the multiple mobile energy storage devices.

5. The method according to claim 4, wherein The step of dividing the fault area according to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power - loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups to obtain sub - fault areas respectively corresponding to the multiple mobile energy storage devices includes: According to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power - loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, determine the power supply access parameters respectively corresponding to the multiple power - loss nodes, where the corresponding power supply access parameters are parameters used to indicate whether to connect the corresponding power - loss nodes to the power supply; According to the power supply access parameters respectively corresponding to the multiple power - loss nodes, divide the fault area to obtain sub - fault areas respectively corresponding to the multiple mobile energy storage devices.

6. The method according to claim 5, wherein The step of dividing the fault area according to the power supply access parameters respectively corresponding to the multiple power - loss nodes to obtain sub - fault areas respectively corresponding to the multiple mobile energy storage devices includes: According to the power supply access parameters respectively corresponding to the multiple power - loss nodes, determine the line connection parameters respectively corresponding to the multiple power - loss nodes; According to the regional power consumption parameters, the power consumption weight parameters respectively corresponding to the multiple power - loss nodes, the line connection parameters respectively corresponding to the multiple power - loss nodes, and the group power supply parameters and group power supply adjustment coefficients respectively corresponding to the multiple mobile energy storage device groups, determine the initial sub - areas respectively corresponding to the multiple mobile energy storage devices; Determine the initial node sets respectively corresponding to the multiple initial sub - areas, where the corresponding initial node sets include multiple power - loss nodes in the corresponding initial sub - areas; Determine whether each initial node set has an intersection with other initial node sets to obtain a judgment result; In the case where the judgment result is that each initial node set has no intersection with other initial node sets, divide the fault area according to the initial sub - areas respectively corresponding to the multiple mobile energy storage devices to obtain sub - fault areas respectively corresponding to the multiple mobile energy storage devices.

7. The method according to any one of claims 1 to 6, characterized in that The step of obtaining the target power supply parameters respectively corresponding to the multiple sub - fault areas according to the regional power consumption parameters and the group power supply parameters respectively corresponding to the multiple mobile energy storage device groups includes: According to the regional power consumption parameters, determine the sub - power consumption parameters respectively corresponding to the multiple sub - fault areas; Determine the power consumption constraint parameters respectively corresponding to the multiple sub - fault areas; According to the power consumption constraint parameters and sub - power consumption parameters respectively corresponding to the multiple sub - fault areas, determine the corrected power consumption parameters respectively corresponding to the multiple sub - fault areas; Determine the power supply constraint parameters respectively corresponding to the multiple mobile energy storage device groups; Determine the corrected power supply parameters corresponding to the multiple mobile energy storage device groups respectively according to the power supply constraint parameters and the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively; Obtain the target power supply parameters corresponding to the multiple sub-fault areas respectively according to the corrected power consumption parameters corresponding to the multiple sub-fault areas respectively and the corrected power supply parameters corresponding to the multiple mobile energy storage device groups respectively.

8. A device for determining regional power supply parameters, characterized in that, Comprising: An acquisition module, configured to acquire area parameters corresponding to a fault area, where the area parameters include area position parameters, area power consumption parameters, and area environment parameters; A first determination module, configured to determine a plurality of traffic routes corresponding to the fault area according to the area position parameters; A second determination module, configured to determine the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively according to the area environment parameters and the plurality of traffic routes, where the multiple mobile energy storage device groups correspond to the plurality of traffic routes one by one; A third determination module, configured to divide the fault area according to the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, and obtain sub-fault areas corresponding to the multiple mobile energy storage device groups respectively, where the multiple mobile energy storage device groups are respectively a set of sub-mobile energy storage devices included in the corresponding traffic routes; A fourth determination module, configured to obtain the target power supply parameters corresponding to the multiple sub-fault areas respectively according to the area power consumption parameters and the group power supply parameters corresponding to the multiple mobile energy storage device groups respectively, so as to control the corresponding mobile energy storage device groups to supply power to the corresponding sub-fault areas respectively according to the corresponding target power supply parameters.

9. An electronic device, characterized in that, Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the area power supply parameter determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the area power supply parameter determination method according to any one of claims 1 to 7.

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