CN-121998160-A - Global optimization allocation method, system and medium for mobile emergency resources

CN121998160ACN 121998160 ACN121998160 ACN 121998160ACN-121998160-A

Abstract

The invention relates to a global optimization allocation method, a global optimization allocation system and a global optimization allocation medium for mobile emergency resources, wherein the method comprises the steps of obtaining structural parameters of a traffic system after disaster, modeling a traffic network, and dividing a road in the traffic network into a plurality of uniform cells based on a cell transmission model; in a traffic network, taking minimized vehicle weighted running time as an objective function, taking traffic flow conservation constraint as a constraint condition, giving different weights according to the importance degrees of different types of mobile emergency resources, constructing an emergency resource scheduling model based on dynamic traffic flow, solving to obtain running paths and running time of all mobile emergency resources, constructing a power distribution network recovery model by taking maximized load supply quantity as the objective function, substituting the running paths and the running time of all mobile emergency resources, and solving to obtain a power distribution network recovery scheme. Compared with the prior art, the invention is beneficial to fully utilizing the power generation resources, thereby providing emergency power supply service for more loads.

Inventors

WEI XINCHI
LI MINGXUAN
XU YIN
SHI SHANSHAN
WANG YING
WEI WEI
LIU SHU
WU XIANGYU
FENG NAN
ZHANG QIQI

Assignees

国网上海市电力公司

Dates

Publication Date: 20260508
Application Date: 20251217

Claims (10)

1. The global optimization allocation method for the mobile emergency resources is characterized by comprising the following steps of: Obtaining structural parameters of a traffic system after disaster, modeling a traffic network, dividing a road in the traffic network into a plurality of uniform cells based on a cell transmission model, and describing dynamic changes of traffic flow; in the traffic network, taking minimized vehicle weighted running time as an objective function, taking traffic flow conservation constraint as constraint conditions, giving different weights according to the importance degrees of different types of mobile emergency resources, constructing an emergency resource scheduling model based on dynamic traffic flow, and obtaining running paths and running time of all mobile emergency resources after solving; and constructing a power distribution network recovery model by taking the maximized load supply quantity as an objective function, substituting all the driving paths and the driving time of the mobile emergency resources, and solving to obtain a power distribution network recovery scheme.
2. The global optimal allocation method for mobile emergency resources according to claim 1, wherein the length of each cell is a distance travelled by a vehicle at a free speed for a unit period of time.
3. The global optimization deployment method of mobile emergency resources according to claim 1, wherein the expression of the cell transmission model describing the dynamic change of traffic flow is: in the formula, Representing cells At the position of The flow rate of the vehicle at the moment in time, Representing cells At the position of The flow rate of the vehicle at the moment in time, Is shown in From time to time by cell Flow to cells Is used for controlling the vehicle flow rate of the vehicle, Is shown in From time to time by cell Flow to cells Is used for controlling the vehicle flow rate of the vehicle, Representing all and cells A collection of connected upstream cells, Representing all and cells A collection of connected downstream cells, For a set of all of the cells, A set of all time periods within the time window; Finger cell At the position of The maximum flow that can flow in or out at the moment, Representing cells At the position of The state of propagation of the blockage at the moment, , Is a cellular unit At the position of The free running speed of the vehicle at the moment, Is a cellular unit At the position of The speed of the vehicle at the moment under the influence of traffic jam propagation, Is that Time of day may be stored in a cell Maximum traffic flow of (2).
4. The global optimization deployment method for mobile emergency resources according to claim 3, wherein said cell transmission model is used for traffic flow of post-disaster traffic system And density of Constructing when meeting cell transmission constraint, wherein the expression of the cell transmission constraint is as follows: in the formula, For the free running speed of the vehicle on the road section, For the maximum traffic flow for that road segment, For the driving speed of the vehicle under the influence of traffic congestion propagation, Is the maximum vehicle density, i.e., jam density.
5. The global optimization deployment method for mobile emergency resources according to claim 3, wherein the expression of the emergency resource scheduling model based on the dynamic traffic flow is: in the formula, For the value of the objective function, 、 Respectively a set of connecting lines between different types of vehicles and cells; a set of cells for termination; Represent the first Priority weights of class vehicles; Representing cells At the position of Time of day Traffic flow of the class vehicle; Is shown in From time to time by cell Flow to cells Is the first of (2) Traffic flow of the class vehicle; representing initial time cells Is the first of (2) The flow rate of the class-vehicle, For initial time cell connection Interlude (C) Traffic flow of class vehicle.
6. The global optimal allocation method of mobile emergency resources according to claim 5, wherein the different types of vehicles include mobile emergency power supplies, maintenance personnel, dispatchable public buses and common vehicles.
7. The global optimization deployment method for mobile emergency resources according to claim 1, wherein the expression of the objective function of the power distribution network recovery model is: in the formula, In order to optimize the number of problem periods, For the number of nodes of the system, Is a node The value per unit load of the load, Is a node At the moment of time Active load supply of all mobile emergency resources.
8. The global optimization deployment method for mobile emergency resources according to claim 1, wherein the constraint conditions of the power distribution network recovery model include a fixed power generation resource output constraint, a mobile power generation resource output constraint, a tide constraint and a power distribution network radial constraint.
9. A mobile emergency resource global optimization deployment system comprising a memory storing a computer program and a processor invoking the computer program to perform the steps of the method of any of claims 1 to 8.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which is executed by a processor by the method according to any of claims 1 to 8.

Description

Global optimization allocation method, system and medium for mobile emergency resources Technical Field The invention relates to the technical field of power grid dispatching, in particular to a global optimization dispatching method, a global optimization dispatching system and a global optimization dispatching medium for mobile emergency resources. Background When the mobile resources are utilized for fault recovery after the extreme event, the real-time running state of the traffic system needs to be considered to formulate a deployment scheme of the mobile emergency resources in the traffic network. Two challenges are given to how to simulate the influence of real-time traffic states on the scheduling of the mobile emergency resources and how to quickly make a recovery decision to realize online real-time emergency response. The invention discloses a method for recovering an urban power distribution network taking mobile emergency resource scheduling into account, which comprises the steps of establishing a mobile power generation resource constraint model taking the constraint of the time when a mobile power generation resource arrives at a fault position into consideration, establishing a topology constraint model taking the constraint of a repair line of a maintainer into consideration, taking the weighted power supply time maximization of a load as a target, taking the operation constraint, the mobile power generation resource constraint model, the topology constraint model and the load state constraint as constraint conditions, establishing a multi-period urban power distribution network fault recovery model taking mobile emergency resource scheduling into consideration, solving the multi-period urban power distribution network fault recovery model taking mobile emergency resource scheduling into consideration, and carrying out mobile emergency resource scheduling on the urban power distribution network according to the solving result. In the scheme, a mobile power generation resource constraint model is directly built according to the constraint of the time of the mobile power generation resource reaching the fault position, the space-time distribution of traffic flows is not considered, a multi-period urban power distribution network fault recovery model is built with the aim of maximizing the weighted power supply time of loads, the total load recovery amount is not considered, and the power generation resource cannot be fully utilized. Disclosure of Invention The invention aims to overcome the defects that the space-time distribution of traffic flows is not considered and the power generation resources cannot be fully utilized in the prior art, and provides a global optimization allocation method, a global optimization allocation system and a global optimization allocation medium for mobile emergency resources. The aim of the invention can be achieved by the following technical scheme: A global optimization allocation method for mobile emergency resources comprises the following steps: Obtaining structural parameters of a traffic system after disaster, modeling a traffic network, dividing a road in the traffic network into a plurality of uniform cells based on a cell transmission model, and describing dynamic changes of traffic flow; in the traffic network, taking minimized vehicle weighted running time as an objective function, taking traffic flow conservation constraint as constraint conditions, giving different weights according to the importance degrees of different types of mobile emergency resources, constructing an emergency resource scheduling model based on dynamic traffic flow, and obtaining running paths and running time of all mobile emergency resources after solving; and constructing a power distribution network recovery model by taking the maximized load supply quantity as an objective function, substituting all the driving paths and the driving time of the mobile emergency resources, and solving to obtain a power distribution network recovery scheme. Further, the length of the cell is the distance the vehicle travels for a unit period of time at free speed. Further, the expression of the cell transmission model describing the dynamic change of the traffic flow is as follows: in the formula, Representing cellsAt the position ofThe flow rate of the vehicle at the moment in time,Representing cellsAt the position ofThe flow rate of the vehicle at the moment in time,Is shown inFrom time to time by cellFlow to cellsIs used for controlling the vehicle flow rate of the vehicle,Is shown inFrom time to time by cellFlow to cellsIs used for controlling the vehicle flow rate of the vehicle,Representing all and cellsA collection of connected upstream cells,Representing all and cellsA collection of connected downstream cells,For a set of all of the cells,A set of all time periods within the time window; Finger cell At the position ofThe maximum flow that can flow in or out at the moment,Represe