CN109460844B - GIS-based power-conserving vehicle optimized scheduling method - Google Patents

GIS-based power-conserving vehicle optimized scheduling method Download PDF

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CN109460844B
CN109460844B CN201810315902.2A CN201810315902A CN109460844B CN 109460844 B CN109460844 B CN 109460844B CN 201810315902 A CN201810315902 A CN 201810315902A CN 109460844 B CN109460844 B CN 109460844B
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power
node
conserving
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vehicle
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CN109460844A (en
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陈嵘
陆竑
周浩
郑伟军
俞涯
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State Grid Zhejiang Electric Power Co Ltd
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
Jiaxing Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Zhejiang Huayun Information Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention relates to the field of power system automation, in particular to an optimal dispatching method for a power-conserving vehicle based on a GIS. The GIS-based power-conserving vehicle optimal scheduling method is characterized by comprising the following steps of: A) importing a power grid topological structure, road network information and electric vehicle attachment information, and solving an electric target set; B) taking the intersection as a node, taking the electricity-conserving target as a target node, using the road section to pass through time consumption as a connecting line, establishing a time-consuming node model, and placing a movable node representing an electricity-conserving vehicle to obtain the target model; C) solving an objective model by taking the minimum sum of the arrival time of the power-conserving vehicles obtained by any power-conserving objective as an objective; D) when a power-conserving target fails, a power-conserving vehicle with the shortest arrival time is assigned, and an optimal route is displayed. The invention has the beneficial effects that: the dispatching scheme which enables the efficiency of the power-conserving vehicle to be highest is obtained through a scientific algorithm, and the rationality and the scientificity of dispatching the power-conserving vehicle are improved.

Description

GIS-based power-conserving vehicle optimized scheduling method
Technical Field
The invention relates to the field of power system automation, in particular to an optimal dispatching method for a power-conserving vehicle based on a GIS.
Background
The electricity utilization condition of important users is the work key point of power supply enterprises, the important users have important positions in local society, politics and economy although the number of the users is small, and the interruption of power supply can cause great political influence, great economic loss, even serious disorder of social public order and the like. Therefore, the power supply reliability of important users is improved, a perfect and feasible power conservation scheme is established and effectively implemented, and the method has important practical significance for power supply companies. In the current power supply protection work, although a large number of guard personnel can be arranged on the site, the types of power protection emergency repair vehicles are more, the number of each type is not large enough to be arranged on the whole site, so that the power protection emergency repair vehicles are often arranged on the position which is subjectively considered to be suitable by power protection planning personnel, scientific planning is lacked, the power protection emergency repair vehicles are considered to be out of consideration, and the power protection emergency repair vehicles are not favorable for timely repairing sudden faults. There is thus a need for a system that can intelligently schedule a power conservation maintenance vehicle.
Chinese patent CN 101105891 a, published 2008, 1 month and 16 days, a GPS vehicle monitoring and scheduling system applied to power system, which includes vehicle-mounted terminal equipment, wireless communication network, monitoring and scheduling center and task assignment system with intelligent scheduling algorithm and fault processing time statistical model, monitors and assigns maintenance tasks through the task assignment system with intelligent scheduling algorithm and fault processing time statistical model, realizes monitoring and scheduling of GPS vehicle, improves scheduling efficiency, and realizes whole process monitoring, so that monitoring of service work is more effective, and assessment is more objective and fair, but it does not disclose intelligent scheduling algorithm.
Disclosure of Invention
The technical problem to be solved by the invention is that the current dispatching of the power-conserving vehicle has subjectivity and lacks scientific planning. The optimal dispatching method of the power-conserving vehicle based on the GIS and capable of intelligently calculating the optimal dispatching scheme is provided.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: the GIS-based power-conserving vehicle optimal scheduling method comprises the following steps: A) importing a power grid topological structure and electric vehicle attachment information, solving an electric target set, and acquiring road network information by a GIS (geographic information system); B) taking a road intersection as a node, taking a power-conserving target as a target node, taking a road section passing time consumption as a connecting line, connecting the connecting line with the node and the target node based on a road network topological structure, establishing a time-consuming node model, and randomly putting a power-conserving vehicle into the time-consuming node model as a movable node to obtain a target model; C) solving an objective model to obtain the optimal dispatching of the power-conserving vehicles by taking the minimum time sum of the power-conserving vehicles obtained after any power-conserving target in the power-conserving target set breaks down as an objective; D) when the power-conserving targets in the power-conserving target set have faults, assigning the power-conserving vehicle with the shortest arrival time and displaying an optimal route; E) and monitoring the execution condition of the power-conserving vehicle, and updating the scheduling.
Preferably, the solving of the power conservation target set comprises the following steps: A1) acquiring a power protection user set and a power grid topological structure; A2) solving a power supply source and a power supply line by adopting a breadth-first algorithm; A3) and taking the power supply equipment contained in the power supply source and the power supply line as a power protection target to obtain a power protection target set.
Preferably, the establishing of the time-consuming node model comprises the following steps: B1) taking a road intersection as a node, predicting the passage time consumption of a road section according to the historical data of the road section, and connecting the nodes by taking the passage time consumption as a connecting line to form a time-consuming node topological structure; B2) adding the electricity-preserving target into a road topological structure as a node, and recalculating a corresponding time-consuming connection line; B3) and taking the electricity-preserving vehicle as a movable node and randomly placing the movable node in a time-consuming node topological structure to form a time-consuming node model.
Preferably, the solving of the objective model comprises the following steps: C1) calculating the shortest time for a movable node i representing a power-conserving vehicle to reach a target node g representing a power-conserving target according to a time-consuming node model
Figure BDA0001623751540000021
The minimum value is taken as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure BDA0001623751540000022
And further writing a calculation formula of the target function F:
Figure BDA0001623751540000023
wherein G is a target node set; C2) taking a positive real value as an initial value T of the control parameter T0The count N is set to 0; C3) randomly moving a movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, calculating the difference delta F between the value of the target function F' after moving and the value of the target function F before moving, if the delta F is less than 0, accepting the position of the movable node after moving as a new solution and setting the value of N as 0, otherwise, calculating the probability p,
Figure BDA0001623751540000024
comparing a random value epsilon in the interval [0, 1) with p, if p is larger than epsilon, accepting the position of the movable node after movement as a new solution and setting the value of N as 0, otherwise, maintaining the solution before movement and adding 1 to the value of N; C4) calculating a value T' of a new control parameter T, T-lnT; C5) and judging whether a termination condition is met, wherein the termination condition is that T is less than or equal to 1 and N is more than or equal to 20, if the termination condition is not met, the step C3-C4 is repeated by taking T' as the value of the control parameter T, if the termination condition is met, the solution is ended, and the position of the current movable node is taken as the optimal solution.
Preferably, the step of assigning the electricity-conserving vehicle with the shortest arrival time comprises the following steps: D1) according to the time-consuming node model, exhausting all routes from the movable node i representing the power-conserving vehicle to the target node g representing the power-conserving target and calculating the time consumption
Figure BDA00016237515400000211
Wherein X is the set of all routes, taken
Figure BDA00016237515400000210
Is taken as the shortest time consumption for the corresponding movable node i to reach the target node g
Figure BDA0001623751540000025
Get
Figure BDA0001623751540000026
The line corresponding to the minimum value of (a) is taken as the optimal line gamma of g-ig-i(ii) a D2) Get
Figure BDA0001623751540000027
The movable node i corresponding to the minimum value of (a) is taken as an assigned power-conserving vehicle, and the route gamma is taken asg-iAnd displaying as an optimal route, wherein I belongs to I, and I is a set of power-conserving vehicles.
Preferably, the solving of the objective model comprises the following steps: C11) acquiring historical average fault rate psi of power-conserving target g from big data platformg(ii) a C12) Calculating the shortest time for a movable node i representing a power-conserving vehicle to reach a target node g representing a power-conserving target according to a time-consuming node model
Figure BDA0001623751540000028
The minimum value is taken as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure BDA0001623751540000029
Historical mean fault rate psi considering power conservation objective ggWriting a calculation formula of the target function F:
Figure BDA00016237515400000212
wherein G is a target node set; C13) taking a positive real value as an initial value T of the control parameter T0The count N is set to 0; C14) randomly moving a movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, calculating the difference delta F between the value of the target function F' after moving and the value of the target function F before moving, if the delta F is less than 0, accepting the position of the movable node after moving as a new solution and setting the value of N as 0, otherwise, calculating the probability p,
Figure BDA0001623751540000031
comparing a random value epsilon in the interval [0, 1) with p, if p is larger than epsilon, accepting the position of the movable node after movement as a new solution and setting the value of N as 0, otherwise, maintaining the solution before movement and adding 1 to the value of N; C15) calculating a value T' of a new control parameter T, T-lnT; C16) and judging whether a termination condition is met, wherein the termination condition is that T is less than or equal to 1 and N is more than or equal to 20, if the termination condition is not met, the step C14-C15 is repeated by taking T' as the value of the control parameter T, if the termination condition is met, the solution is ended, and the position of the current movable node is taken as the optimal solution.
Preferably, the method for updating the schedule includes: E1) when the power-conserving vehicle is assigned to carry out maintenance tasks, deducting the assigned power-conserving vehicle and the power-conserving target with faults, establishing a target model again by using the residual power-conserving target and the power-conserving vehicle, and repeating the step C; E2) and D, when the power-protection vehicle completes the maintenance task, adding the power-protection vehicle which completes the maintenance and the power-protection target to be maintained, establishing the target model again, and repeating the step C.
Preferably, the shortest time for the mobile node i to reach the target node g is calculated
Figure BDA0001623751540000032
The method comprises the following steps: according to the time-consuming node model, exhausting all routes from the movable node i representing the power-conserving vehicle to the target node g representing the power-conserving target and calculating the time consumption
Figure BDA0001623751540000033
Wherein X is the set of all routes, taken
Figure BDA0001623751540000034
Is taken as the shortest time for the mobile node i to reach the target node g
Figure BDA0001623751540000035
Preferably, a plurality of power protection targets located at the same position of the road network topology structure are regarded as one power protection target.
The substantial effects of the invention are as follows: the dispatching scheme which enables the efficiency of the power-conserving vehicle to be highest is obtained through a scientific algorithm, and the rationality and the scientificity of dispatching the power-conserving vehicle are improved.
Drawings
Fig. 1 is a flow chart of a GIS-based power-conserving vehicle scheduling method.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
As shown in fig. 1, a flow chart of a power-conserving vehicle scheduling method based on a GIS is shown, and first, according to a power grid topology and power-conserving user information, a power supply and a transmission line of a power-conserving user are obtained by using a breadth-first algorithm, and a power supply target set is obtained by using power supply devices included in the power supply and the power supply line as power-conserving targets.
And importing the road network information by the GIS system to obtain a road network topological structure, and obtaining the average traffic time of each road section of the road network according to the GIS historical data. And (4) taking the road intersections as nodes and taking the road section time consumption as connecting lines, reconstructing a road network topological structure and constructing a time-consuming node model. And importing the electricity-protecting targets serving as target nodes into a time-consuming node model according to the geographic positions of the electricity-protecting targets, wherein a plurality of electricity-protecting targets located at the same position of the topological structure of the road network are regarded as one electricity-protecting target, and then importing the electricity-protecting vehicles serving as movable nodes into the time-consuming node model according to the attached information of the electricity-protecting vehicles to form the target model. And solving the target model by using a simulated annealing method to obtain an optimal solution which is used as a scheduling scheme of the power-conserving vehicle. And monitoring whether the electricity-guaranteeing target set has maintenance requirements or not, and attaching the electricity-guaranteeing vehicles to have vehicles which complete maintenance tasks or not, and updating the optimal scheduling scheme of the vehicles.
The method for solving the target model by using the simulated annealing method comprises the following steps:
s101) calculating the shortest time for a movable node i representing an electricity-conserving vehicle to reach a target node g representing an electricity-conserving target according to a time-consuming node model
Figure BDA0001623751540000041
The minimum value is taken as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure BDA0001623751540000042
And further writing a calculation formula of the target function F:
Figure BDA0001623751540000043
wherein G is a target node set;
s102) taking a positive real value as an initial value T of the control parameter T0The count N is set to 0;
s103) randomly moving a movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, calculating a difference value delta F between the value of the target function F' after moving and the value of the target function F before moving, if the delta F is less than 0, accepting the position of the movable node after moving as a new solution and setting the value of N as 0, otherwise, calculating the probability p,
Figure BDA0001623751540000044
comparing a random value epsilon in the interval [0, 1) with p, if p is larger than epsilon, accepting the position of the movable node after movement as a new solution and setting the value of N as 0, otherwise, maintaining the solution before movement and adding 1 to the value of N;
s104) calculating a value T' of the new control parameter T;
s105) judging whether a termination condition is met, wherein the termination condition is that T is less than or equal to 1 and N is greater than or equal to 20, if the termination condition is not met, the step S103-S104 is repeated by taking T' as the value of the control parameter T, if the termination condition is met, the solution is ended, and the position of the current movable node is taken as the optimal solution.
Another example of solving the target model using simulated annealing that takes into account historical failure rates is:
s201) acquiring historical mean fault rate psi of power-conserving target g from big data platformg；
S202) calculating that a movable node i representing a power-saving vehicle reaches a target node representing a power-saving target according to the time-consuming node modelMinimum time of point g
Figure BDA0001623751540000045
The minimum value is taken as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure BDA0001623751540000046
Historical mean fault rate psi considering power conservation objective ggWriting a calculation formula of the target function F:
Figure BDA0001623751540000047
wherein G is a target node set;
s203) taking a positive real value as an initial value T of the control parameter T0The count N is set to 0;
s204) randomly moving the movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, calculating the difference value delta F between the value of the target function F' after moving and the value of the target function F before moving, if delta F is less than 0, accepting the position of the movable node after moving as a new solution and setting the value of N as 0, otherwise, calculating the probability p,
Figure BDA0001623751540000048
comparing a random value epsilon in the interval [0, 1) with p, if p is larger than epsilon, accepting the position of the movable node after movement as a new solution and setting the value of N as 0, otherwise, maintaining the solution before movement and adding 1 to the value of N;
s205) calculating a value T', T ═ T-lnT of the new control parameter T;
s206) judging whether a termination condition is met, wherein the termination condition is that T is less than or equal to 1 and N is more than or equal to 20, if the termination condition is not met, the step C14-C15 is repeated by taking T' as the value of the control parameter T, and if the termination condition is met, the solution is ended, and the position of the current movable node is taken as the optimal solution.
Shortest time for mobile node i to reach target node g representing power-conserving object
Figure BDA0001623751540000051
The calculation method comprises the following steps: according to the time-consuming node model, exhausting all routes from the movable node i representing the power-conserving vehicle to the target node g representing the power-conserving target and calculating the time consumption
Figure BDA0001623751540000052
Wherein X is the set of all routes, taken
Figure BDA0001623751540000053
The shortest time taken for the movable node i corresponding to the minimum value of (a) to reach the target node g
Figure BDA0001623751540000054
The method for assigning the electricity-keeping vehicle with the shortest arrival time comprises the following steps:
s301) exhausting all routes from the movable node i representing the power-conserving vehicle to the target node g representing the power-conserving target according to the time-consuming node model and calculating time consumption
Figure BDA0001623751540000055
Wherein X is the set of all routes, taken
Figure BDA0001623751540000056
The shortest time taken for the movable node i corresponding to the minimum value of (a) to reach the target node g
Figure BDA0001623751540000057
Get
Figure BDA0001623751540000058
The line corresponding to the minimum value of (a) is taken as the optimal line gamma of g-ig-i(ii) a S302) taking
Figure BDA0001623751540000059
The movable node i corresponding to the minimum value of (a) is taken as an assigned power-conserving vehicle, and the route gamma is taken asg-iAnd displaying as an optimal route, wherein I belongs to I, and I is a set of power-conserving vehicles.
The method for updating the optimal scheduling of the vehicle comprises the following steps:
s401) when the power-conserving vehicle is assigned to carry out maintenance tasks, deducting the assigned power-conserving vehicle and the power-conserving target with faults, establishing a target model again by using the residual power-conserving target and the power-conserving vehicle, and solving the target model again to update an optimal solution;
s402) when the power-saving vehicle completes the maintenance task, adding the power-saving vehicle which completes the maintenance and the maintained power-saving target to establish the target model again and solving the target model again to update the optimal solution.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (9)

1. The GIS-based power-conserving vehicle optimized dispatching method is characterized in that,
comprises the following steps:
A) importing a power grid topological structure and electric vehicle attachment information, solving an electric target set, and acquiring road network information by a GIS (geographic information system);
B) taking a road intersection as a node, taking a power-conserving target as a target node, taking a road section passing time consumption as a connecting line, connecting the connecting line with the node and the target node based on a road network topological structure, establishing a time-consuming node model, and randomly putting a power-conserving vehicle into the time-consuming node model as a movable node to obtain a target model;
C) solving an objective model to obtain the optimal dispatching of the power-conserving vehicles by taking the minimum time sum of the power-conserving vehicles obtained after any power-conserving target in the power-conserving target set breaks down as an objective;
D) when the power-conserving targets in the power-conserving target set have faults, assigning the power-conserving vehicle with the shortest arrival time and displaying an optimal route;
E) and monitoring the execution condition of the power-conserving vehicle, and updating the scheduling.
2. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
the solving of the power conservation objective set comprises the following steps:
A1) acquiring a power protection user set and a power grid topological structure;
A2) solving a power supply source and a power supply line by adopting a breadth-first algorithm;
A3) and taking the power supply equipment contained in the power supply source and the power supply line as a power protection target to obtain a power protection target set.
3. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
the establishing of the time-consuming node model comprises the following steps:
B1) taking a road intersection as a node, predicting the passage time consumption of a road section according to the historical data of the road section, and connecting the nodes by taking the passage time consumption as a connecting line to form a time-consuming node topological structure;
B2) adding the electricity-preserving target into a road topological structure as a node, and recalculating a corresponding time-consuming connection line;
B3) and taking the electricity-preserving vehicle as a movable node and randomly placing the movable node in a time-consuming node topological structure to form a time-consuming node model.
4. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
the solving of the objective model comprises the following steps:
C1) calculating movable nodes representing power-conserving vehicles according to time-consuming node model
Figure 781719DEST_PATH_IMAGE001
To a target node representing a power conservation objectivegMinimum time of
Figure 292335DEST_PATH_IMAGE002
And taking the minimum value as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure 399968DEST_PATH_IMAGE003
Further write out the objective functionFThe calculation formula (c) is as follows:
Figure 998440DEST_PATH_IMAGE004
，
whereinGA target node set is obtained;
C2) taking a positive real value as a control parameterTInitial value of (2)T 0 Counting ofNSetting to 0;
C3) randomly moving a movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, and calculating a moved objective function
Figure 406288DEST_PATH_IMAGE005
Value of (D) and pre-shift objective functionFDifference of value of
Figure 697592DEST_PATH_IMAGE006
If, if
Figure 525477DEST_PATH_IMAGE007
Then the moved mobile node position is accepted as a new solution and willNIs set to 0, otherwise the probability is calculatedp，
Figure 52273DEST_PATH_IMAGE008
Taking the random value in the interval [0, 1)
Figure 314627DEST_PATH_IMAGE009
Andpin comparison, if
Figure 511253DEST_PATH_IMAGE010
Then the moved mobile node position is accepted as a new solution and willNIs set to 0, otherwise the solution before movement is maintained and will beNAdding 1 to the value of (c);
C4) calculating new control parametersTValue of (A)
Figure 859058DEST_PATH_IMAGE011
，
Figure 425431DEST_PATH_IMAGE012
；
C5) Judging whether a termination condition is met, wherein the termination condition is
Figure 417658DEST_PATH_IMAGE013
And is
Figure 175398DEST_PATH_IMAGE014
If the termination condition is not satisfied, the method will
Figure 885865DEST_PATH_IMAGE011
As control parametersTAnd (4) repeating the steps C3-C4, if the termination condition is met, ending the solution, and taking the position of the current movable node as the optimal solution.
5. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
assigning the power-conserving vehicle with the shortest arrival time comprises the following steps:
D1) exhaustive representation of mobile nodes of a power conservation vehicle according to a time-consuming node model
Figure 488885DEST_PATH_IMAGE001
To a target node representing a power conservation objectivegAll routes of and calculating time consuming
Figure 725831DEST_PATH_IMAGE015
Wherein
Figure 529839DEST_PATH_IMAGE016
For the set of all routes, take
Figure 993182DEST_PATH_IMAGE017
As a corresponding mobile node
Figure 916005DEST_PATH_IMAGE001
Reach target nodegMinimum elapsed time of
Figure 7458DEST_PATH_IMAGE002
Get it
Figure 247947DEST_PATH_IMAGE017
As a line corresponding to the minimum value of
Figure 792060DEST_PATH_IMAGE018
Of the optimal route
Figure 143407DEST_PATH_IMAGE019
；
D2) Get
Figure 89367DEST_PATH_IMAGE002
Movable node corresponding to the minimum value of
Figure 500756DEST_PATH_IMAGE001
Representative electric vehicles as assigned electric vehicles, lines
Figure 33631DEST_PATH_IMAGE019
As an optimal route display, wherein
Figure 923090DEST_PATH_IMAGE020
，
Figure 723556DEST_PATH_IMAGE021
Is a collection of power-conserving vehicles.
6. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
the solving of the objective model comprises the following steps:
C11) from large to largeData platform acquisition power-conserving targetgHistorical mean failure rate of
Figure 305847DEST_PATH_IMAGE022
；
C12) Calculating movable nodes representing power-conserving vehicles according to time-consuming node model
Figure 824553DEST_PATH_IMAGE001
To a target node representing a power conservation objectivegMinimum time of
Figure 517702DEST_PATH_IMAGE002
And taking the minimum value as a power-protection target to obtain the arrival time of the power-protection vehicle
Figure 907095DEST_PATH_IMAGE003
Considering the goal of power conservationgHistorical mean failure rate of
Figure 17877DEST_PATH_IMAGE022
Writing out the objective functionFThe calculation formula (c) is as follows:
Figure 899246DEST_PATH_IMAGE023
，
whereinGA target node set is obtained;
C13) taking a positive real value as a control parameterTInitial value of (2)T 0 Counting ofNSetting to 0;
C14) randomly moving a movable node in the time-consuming node model to any adjacent node or between any adjacent nodes, and calculating a moved objective function
Figure 520720DEST_PATH_IMAGE005
Value of (D) and pre-shift objective functionFDifference of value of
Figure 30199DEST_PATH_IMAGE006
If, if
Figure 423134DEST_PATH_IMAGE007
Then the moved mobile node position is accepted as a new solution and willNIs set to 0, otherwise the probability is calculatedp，
Figure 650853DEST_PATH_IMAGE008
Taking the random value in the interval [0, 1)
Figure 46324DEST_PATH_IMAGE009
Andpin comparison, if
Figure 675889DEST_PATH_IMAGE010
Then the moved mobile node position is accepted as a new solution and willNIs set to 0, otherwise the solution before movement is maintained and will beNAdding 1 to the value of (c);
C15) calculating new control parametersTValue of (A)
Figure 239725DEST_PATH_IMAGE011
，
Figure 954740DEST_PATH_IMAGE012
；
C16) Judging whether a termination condition is met, wherein the termination condition is
Figure 918017DEST_PATH_IMAGE013
And is
Figure 11875DEST_PATH_IMAGE014
If the termination condition is not satisfied, the method will
Figure 906800DEST_PATH_IMAGE011
As control parametersTAnd (4) repeating the steps C14-C15, if the termination condition is met, ending the solution, and taking the position of the current movable node as the optimal solution.
7. The GIS-based power-conserving vehicle optimized scheduling method of claim 1,
the method for updating and scheduling comprises the following steps:
E1) when the power-conserving vehicle is assigned to carry out maintenance tasks, deducting the assigned power-conserving vehicle and the power-conserving target with faults, establishing a target model again by using the residual power-conserving target and the power-conserving vehicle, and repeating the step C;
E2) and D, when the power-protection vehicle completes the maintenance task, adding the power-protection vehicle which completes the maintenance and the power-protection target to be maintained, establishing the target model again, and repeating the step C.
8. The GIS-based electric vehicle optimal scheduling method of claim 2, wherein a plurality of electric targets located at the same position of the topology of the road network are regarded as one electric target.
9. The GIS-based power-conserving vehicle optimal scheduling method of claim 4 or 6,
computing a mobile node
Figure 843532DEST_PATH_IMAGE001
Reach target nodegMinimum time of
Figure 485866DEST_PATH_IMAGE002
The method comprises the following steps: exhaustive representation of mobile nodes of a power conservation vehicle according to a time-consuming node model
Figure 824443DEST_PATH_IMAGE001
To a target node representing a power conservation objectivegAll routes of and calculating time consuming
Figure 120295DEST_PATH_IMAGE015
Wherein
Figure 685269DEST_PATH_IMAGE016
Is all thatSet of routes, take
Figure 491813DEST_PATH_IMAGE017
As a mobile node
Figure 825842DEST_PATH_IMAGE001
Reach target nodegMinimum time of
Figure 558175DEST_PATH_IMAGE002
。
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103337163A (en) * 2013-07-16 2013-10-02 国家电网公司 Scheduling method of vehicles in electric power overhaul
WO2014127849A1 (en) * 2013-02-25 2014-08-28 Nec Europe Ltd. Method and system for determining routes of vehicles

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014127849A1 (en) * 2013-02-25 2014-08-28 Nec Europe Ltd. Method and system for determining routes of vehicles
CN103337163A (en) * 2013-07-16 2013-10-02 国家电网公司 Scheduling method of vehicles in electric power overhaul

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Simulated annealing-based decision support system for routing problems;T. Tlili, S. Krichen，S. Faiz;《2014 IEEE International Conference on Systems, Man, and Cybernetics》;20141204;全文 *
基于GIS的电网抢修车辆综合调度技术研究;汤继生，项达冬，李奇，孙喜民;《电力信息与通用技术》;20160225;全文 *

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