CN113052362A - Main distribution collaborative maintenance plan time window optimization method and system - Google Patents
Main distribution collaborative maintenance plan time window optimization method and system Download PDFInfo
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Abstract
Aiming at the problems that repeated power failure is caused by the fact that association cross influence exists among a plurality of maintenance plans in a power grid maintenance window and the safe operation of the power grid is influenced, evaluation indexes considering various factors such as safety constraint, mutual exclusion characteristic and power supply reliability are provided, a maintenance time window multi-objective optimization model is established by taking the infeasibility of maintenance mutual exclusion and the minimum degree of violation of maintenance company resource constraint as targets, a simplex normal programming method is adopted for solving, and a main distribution cooperative maintenance plan optimization strategy in the maintenance time window is formed. The method and the system are suitable for the field of multi-constraint condition target optimization of the maintenance plans of the power system, and provide a method and a system for reasonably arranging the maintenance plans by taking the minimum influence of the maintenance plans on the power grid in a time window as an index, so as to ensure the safe and reliable operation of the power grid.
Description
Technical Field
The invention belongs to the field of electric power system scheduling automation, and particularly relates to a main distribution collaborative maintenance plan time window optimization method and system.
Background
The equipment maintenance is a conventional business in a power system and relates to a plurality of professional fields such as planning, transportation, scheduling and the like. Each work area submits maintenance application according to the operation condition of the equipment, and scheduling and operation departments adjust the maintenance application in a mode based on the safety of a power grid and the reliable power supply of users, and constraint objects and targets considered by people in different professions are inconsistent, so that a unified platform is lacked for integrated analysis of each flow of maintenance business, and the optimization processing of the maintenance business cannot be carried out.
Because the power system is a full network structure of the distribution, transmission and distribution interconnection, the decommissioning of the devices is not isolated and is mutually influenced. Due to the layered and partitioned management mechanism of the power grid, when a plan maker arranges a maintenance plan, major influences of maintenance of main and distribution network equipment are judged according to experience, even the main and distribution network equipment performs own functions, so that the main and distribution cross influence of multi-equipment maintenance cannot be cooperatively handled through intelligent analysis, and repeated power failure is caused. For example, the main network equipment maintenance can influence the power loss of the distribution network, only the safety of the main network side is considered when the maintenance mode is arranged, the switching capacity of the distribution network cannot be considered, high-risk operation of the distribution network side is caused, and certain power failure risk is brought to power-protection users and important users.
Because each work area submits the maintenance application form isolated, the equipment maintenance produces the associated cross influence and can not be foreseen, including the repeated power failure caused by short time, the high risk operation of the power grid, and meanwhile, the centralized maintenance in the same time interval also brings certain pressure to the operation of the power grid and the personnel configuration. Therefore, the maintenance schedule needs to be optimized and analyzed, and the time is arranged comprehensively.
Disclosure of Invention
In order to solve the technical problem, the invention provides a main distribution collaborative maintenance plan time window optimization method and a system, which are characterized in that: the method comprises the steps of providing evaluation indexes considering various factors such as safety constraint, mutual exclusion characteristic and power supply reliability, establishing a maintenance time window multi-objective optimization model by taking the minimum degree of infeasibility of maintenance mutual exclusion, repeated power failure loss and violation of maintenance company resource constraint as targets, and solving by adopting a simplex normal programming method to form a main distribution cooperative maintenance plan optimization strategy in a maintenance time window. The method and the system are suitable for the field of multi-constraint condition target optimization of the maintenance plans of the power system, and provide a method and a system for reasonably arranging the maintenance plans by taking the minimum influence of the maintenance plans on the power grid in a time window as an index, so as to ensure the safe and reliable operation of the power grid.
A main-distribution collaborative maintenance plan time window optimization method is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a main and auxiliary cooperative analysis integrated model, and inputting a current maintenance plan;
step 2: collecting real-time operation data, load prediction data and historical operation data of a power grid;
step 3, according to the historical operation data obtained in the step 2, correlation coefficients between the maintenance equipment in the maintenance window and the line and the transformer are calculated quantitatively;
step 4, establishing a maintenance plan risk evaluation index, and calculating a maintenance plan risk value of the single maintenance equipment during maintenance at the moment t according to the correlation coefficient calculated in the step 3, the real-time operation data and the load prediction data;
step 5, judging whether the maintenance plan has risks or not according to the maintenance plan risk value calculated in the step 4; when the calculation result of the step 4 is greater than 0, the maintenance is considered to have risks, and a step 6 is executed; otherwise, the maintenance is considered to have no risk, and the result of the original plan can be output;
step 6, establishing a maintenance time window multi-objective optimization model by taking maintenance which causes the least power failure of equipment to the power grid, the least risk of the power grid operation mode and maintenance time window balance as targets;
and 7: setting a maintenance start time mutual exclusion constraint condition and a maintenance company resource constraint condition for the maintenance time window multi-objective optimization model in the step 6, and solving;
and 8, outputting a result when the objective function of the maintenance time window optimization model in the step 6 is the minimum value based on the step 7, and obtaining a time window optimization strategy.
The invention further adopts the following preferred technical scheme:
in the step 1, a main and auxiliary cooperative analysis integrated model is constructed based on a data fusion technology, and cross-system data sharing is realized by combining transparent access service;
the maintenance plan comprises maintenance starting time, maintenance duration and human and material resources occupied by maintenance.
In step 3, a pearson correlation coefficient model is established, and correlation coefficients between the overhaul equipment and the line and between the overhaul equipment and the transformer are respectively calculated according to the following formulas based on the overhaul equipment power, the load power and the new energy power generation power in the historical operation data:
in the formula, λcA correlation coefficient representing a correlation factor X, Y, Xt，YtRepresenting the historical operating data of the relevant factors X, Y at the corresponding time t,
it represents the average of X, Y over a period of T.
In the step 4, the maintenance plan risk assessment indexes comprise power failure loss rate, new energy power abandonment rate, line out-of-limit rate and transformer out-of-limit rate.
In step 4, for the kth maintenance plan, calculating a line out-of-limit rate by the following formula:
in the formula: lambda [ alpha ]cktl,jIndicating the out-of-limit rate index of the jth line at the time t, if lambdacktl,jIf < 0, forcibly making λctl,j＝0，λcktlRepresents the line out-of-limit rate index, Δ P, at time tktShowing the flow variation of the kth overhaul equipment at the moment t, Ireal,j，INRespectively representing the actual value and the rated value, lambda, of the current in the j-th linecLjRepresents the correlation coefficient, N, of the corresponding overhaul equipment of the jth linelAnd the number of the loads of the power grid is represented.
In step 4, for the kth maintenance plan, calculating the out-of-limit rate of the transformer by the following formula:
in the formula: lambda [ alpha ]cktt,jIndicating the line overload margin index of the jth main transformer at the time t, if lambdacktt,jIf < 0, forcibly making λctt,j＝0，λckttRepresents the out-of-limit rate index, I, of the transformer at the time treal,j，INRespectively representing the actual value and the rated value, lambda, of the main high/medium voltage measuring current of the jth transformercQjRepresents the correlation coefficient, N, of the corresponding overhaul equipment of the jth transformertAnd the number of the loads of the power grid is represented.
In step 4, for the kth maintenance plan, calculating the power failure loss rate by the following formula:
in the formula: lambda [ alpha ]cktd,jIndicating the j-th load stop at time tIndex of electrical loss rate, lambdacktdIndicates the power failure loss rate index at time t, Δ Pdt,jThe power loss of the load caused by the kth overhaul equipment at the time t is delta P under the condition of no power lossdt＝0，NdAnd the number of the loads of the power grid is represented.
In step 4, for the kth maintenance plan, calculating the new energy power abandonment rate by the following formula:
in the formula: lambda [ alpha ]cktg,jRepresents the new energy power abandon rate index lambda of the jth new energy at the time tcktgRepresents a new energy power abandon rate index delta P at the moment tgtDiscarding electric power for the new energy caused by the kth overhaul equipment at the time t, and if no electric power is discarded, determining the value of delta Pgt,j＝0，NgAnd the number of the loads of the power grid is represented.
Step 5 comprises the following steps:
step 501: calculating the comprehensive loss index of the overhaul equipment at the moment t by the following formula:
θkt＝λcktl+λcktt+λcktd+λcktg (6)
in the formula: thetaktThe comprehensive loss index of the kth overhaul equipment at the time t is represented;
step 502: when the calculation result of the step 501 is greater than 0, the maintenance is considered to have a risk, and the step 6 is executed; otherwise, the maintenance is considered to have no risk, and the result which can be carried out by the original plan is output.
In step 6, establishing an objective function of the maintenance time window multi-objective optimization model as follows:
wherein
γkt＝μααkt+μδδkt+μθθkt (8)
In the formula: gamma rayktRepresents the cross-impact index, alpha, of the kth overhaul at time tktFor the kth overhaul at time t, the exclusive infeasibility, mu, of the overhaulαIs alphaktThe weight of (2); deltaktTo the extent of violating the resource constraints of the overhaul company, μδIs deltaktWeight of (u)θTo synthesize the loss weights, ncIndicates the number of overhauls in the time window, ntAnd representing the number of time points between maintenance time windows.
In step 7, setting a maintenance start time mutual exclusion constraint based on the kth maintenance start time and the h-th maintenance start time:
αk＞αh+Dk+1 (9)
in the formula: alpha is alphakAnd alphahFor the start of overhaul time of the kth and h overhaul, DkSetting parameters for a maintenance plan for the kth equipment maintenance duration;
based on the resource condition of the maintenance company occupied by each maintenance, setting the resource constraint of the maintenance company:
in the formula: deltaktAnd setting parameters for the maintenance plan for resources such as manpower and material resources occupied at the kth maintenance time t and the maximum resource amount which can be maintained simultaneously.
And 8, solving the maintenance time window optimization objective function by adopting a simplex method to ensure the minimum value, and outputting the minimum value.
A main distribution collaborative maintenance plan time window optimization system based on the main distribution collaborative maintenance plan time window optimization method comprises an input module, a data acquisition module, a maintenance plan risk assessment module and a maintenance time window optimization module, and is characterized in that:
the input module receives a maintenance plan input by a user;
the data acquisition module acquires a maintenance plan, a main and distribution network model, real-time data and historical operation data;
the overhaul plan risk evaluation module calculates an integrated loss index of overhaul equipment at the time t according to the data acquired by the data acquisition module; and evaluating whether the overhaul has risks according to the calculation result;
and the maintenance time window optimization module calculates according to the received data by taking the purposes of minimum equipment power failure, minimum risk of a power grid operation mode and maintenance time window balance caused by maintenance on the power grid as targets.
When the calculation result is less than 0, the maintenance plan risk evaluation module considers that the maintenance has risks and transmits the acquired data to the maintenance time window optimization module;
otherwise, the maintenance is considered to have no risk, and the result which can be carried out by the original plan is directly output.
The invention achieves the following beneficial effects:
aiming at the problems that the equipment maintenance plan cannot meet the safe operation of a power grid and the consumption of new energy is limited due to randomness and volatility characteristics of two ends of source load, factors such as a power failure plan of a main distribution network, repeated power failure of a user, load transfer capacity, safety risks of the power grid and the like are comprehensively considered, the prearranged power failure plan of the main distribution network and the distribution network is optimized, the correlation characteristics of the maintenance equipment and the operation states of equipment such as new energy power generation, loads, lines, transformers and the like are mastered by combining historical operation information, the optimized arrangement of the maintenance plan is realized on the basis, a large amount of deterministic network analysis calculated quantity in a time window is reduced, the influence of maintenance on the operation of the power grid is reduced, and.
Drawings
FIG. 1 is a flow chart of the inventive master-slave coordinated overhaul plan time window optimization method.
Detailed Description
The following describes a main-distribution collaborative overhaul plan time window optimization method and system in detail with reference to the accompanying drawings.
As shown in fig. 1, a master-slave cooperative maintenance schedule time window optimization method of the present invention includes the following steps:
step 1, establishing a main and auxiliary cooperative analysis integrated model based on a data fusion technology, and inputting a current maintenance plan. Specifically, the overhaul plan includes overhaul start time, overhaul duration time, and human and material resources occupied for overhaul. The main and auxiliary cooperative analysis integrated model can realize cross-system data sharing by combining with transparent access service.
And 2, collecting real-time operation data, load prediction data and historical operation data of the power grid as optimization basic data of the maintenance time window. Specifically, the power consumption information acquisition system acquires information including a maintenance plan, a main distribution network model, power generation plan data, load prediction data, real-time operation data, historical operation data and the like from a power grid dispatching Operation Management System (OMS), a power grid dispatching control system and a distribution network automatic master station system, and provides data support for maintenance plan optimization, wherein the real-time operation data include real-time measurement data, state estimation sections and the like, and the historical operation information includes periodically stored power grid operation data.
And 3, quantitatively calculating the correlation coefficient between the maintenance equipment in the maintenance window and the line and the transformer according to the historical operating data acquired in the step 1. The relevant factors of the maintenance equipment comprise maintenance equipment power, load power, new energy power generation power and the like.
Specifically, based on collected historical operation data of the power grid, data of various relevant factors under the same time scale are selected, a Pearson correlation coefficient model is established, and correlation coefficients between maintenance equipment and lines in a maintenance window and between the maintenance equipment and a transformer are respectively calculated in a quantitative mode, wherein the specific calculation method comprises the following steps:
in the formula, λcA correlation coefficient representing a correlation factor X, Y, Xt，YtExpress relevant factorsX, Y historical operating data at corresponding time t,
it represents the average of X, Y over a period of T.
And 4, establishing a maintenance plan risk assessment index, and calculating various risk indexes of single maintenance at each moment according to the correlation coefficient calculated in the step 3 and a power grid topological connection relation generated based on the real-time operation data and the load prediction data, wherein the maintenance plan risk assessment index comprises but is not limited to factors such as power failure loss rate, new energy power abandonment rate, line out-of-limit rate, transformer out-of-limit rate and the like.
For the kth service plan, the risk index is calculated as follows:
calculating the line out-of-limit rate:
in the formula: lambda [ alpha ]cktl,jIndicating the out-of-limit rate index of the jth line at the time t, if lambdacktl,jIf < 0, forcibly making λctl,j＝0，λcktlRepresents the line out-of-limit rate index, Δ P, at time tktShowing the flow variation of the kth overhaul equipment at the moment t, Ireal,j，INRespectively representing the actual value and the rated value, lambda, of the current in the j-th linecLjA correlation coefficient, N, representing the corresponding overhaul equipment of the jth linelAnd the number of the loads of the power grid is represented.
Calculating the out-of-limit rate of the transformer:
in the formula: lambda [ alpha ]cktt,jIndicating the line overload margin index of the jth main transformer at the time t, if lambdacktt,jIf < 0, forcibly making λctt,j＝0，λckttIndex I for representing out-of-limit rate of transformer at t momentreal,j，INRespectively representing the actual value and the rated value, lambda, of the main high/medium voltage measuring current of the jth transformercQjA correlation coefficient N representing the overhauling equipment corresponding to the jth transformertAnd the number of the loads of the power grid is represented.
And (3) calculating the power failure loss rate:
in the formula: lambda [ alpha ]cktd,jDenotes the power failure loss rate index, lambda, at the jth load time tcktdIndicates the power failure loss rate index at time t, Δ Pdt,jThe power loss of the load caused by the kth overhaul equipment at the time t is delta P under the condition of no power lossdt＝0，NdAnd the number of the loads of the power grid is represented.
Calculating the power abandonment rate of the new energy:
in the formula: lambda [ alpha ]cktg,jRepresents the new energy power abandon rate index lambda of the jth new energy at the time tcktgRepresents a new energy power abandon rate index delta P at the moment tgtAbandoning electric power for the new energy caused by the kth overhaul equipment at the time t, and abandoning the electric power if no new energy existsRate, then Δ Pgt,j＝0，NgAnd the number of the loads of the power grid is represented.
And 5: and (4) judging whether the maintenance plan has risks or not according to the maintenance plan risk evaluation index calculated in the step (3). Specifically, step 5 includes the steps of:
step 501: calculating the normalized comprehensive loss coefficient of the overhaul equipment at different time intervals to serve as an overhaul plan risk evaluation index, specifically calculating the comprehensive loss index of the overhaul equipment overhauled at the time t by the following formula:
θkt＝λcktl+λcktt+λcktd+λcktg (6)
in the formula: thetaktAnd (4) showing the comprehensive loss index of the kth overhaul equipment at the time t.
Step 502: when the calculation result θ of step 501 istWhen > 0, the overhaul is considered to be at risk and step 6 is performed. Otherwise, the maintenance is considered to have no risk, and the result can be executed according to the original plan by directly giving the maintenance plan.
And 6, establishing a maintenance time window multi-objective optimization model by taking the minimum equipment power failure, the minimum risk of a power grid operation mode and the balance of a maintenance time window caused by maintenance on the power grid as targets.
Specifically, the objective function of the established overhaul time window optimization function is as follows:
wherein
γkt＝μααkt+μδδkt+μθθkt (8)
In the formula: gamma rayktRepresents the cross-impact index, alpha, of the kth overhaul at time tktFor the kth overhaul at time t, the exclusive infeasibility, mu, of the overhaulαIs alphaktThe weight of (2); deltaktTo the extent of violating the resource constraints of the overhaul company, μδIs deltaktThe weight of (a) is calculated,μθto synthesize the loss weights, ncIndicates the number of overhauls in the time window, ntAnd representing the number of time points between maintenance time windows.
The weighted values are set according to different emphasis points of practical application, and the method belongs to manual parameter setting.
And 7: and (6) setting a maintenance start time mutual exclusion constraint condition and a maintenance company resource constraint condition for the maintenance time window multi-objective optimization model in the step 6, and solving.
Constraint conditions are set for the maintenance within the maintenance time window. The method comprises the following specific steps:
(1) setting a maintenance start time mutual exclusion constraint based on the kth maintenance start time and the h-th maintenance start time:
αk＞αh+Dk+1 (9)
in the formula: alpha is alphakAnd alphahFor the start of overhaul time of the kth and h overhaul, DkAnd setting parameters for the maintenance plan for the kth equipment maintenance duration.
(2) Based on the resource condition of the maintenance company occupied by each maintenance, setting the resource constraint of the maintenance company:
in the formula: deltaktAnd setting parameters for the maintenance plan for resources such as manpower and material resources occupied at the kth maintenance time t and the maximum resource amount which can be maintained simultaneously.
And 8, outputting a result when the objective function of the maintenance time window optimization model in the step 6 is the minimum value based on the step 7, and obtaining a time window optimization strategy. Specifically, in the invention, the method adopts a simplex method to solve the maintenance time window optimization objective function.
A main distribution collaborative maintenance plan time window optimization system based on the main distribution collaborative maintenance plan time window optimization method comprises an input module, a data acquisition module, a maintenance plan risk assessment module and a maintenance time window optimization module.
Specifically, the user inputs the service plan through the input module.
The data acquisition module acquires information including a maintenance plan, a main and distribution network model, real-time data, historical operating data and the like from a slave power grid dispatching Operation Management System (OMS), a power grid dispatching control system and a distribution network automatic master station system, and provides data support for maintenance plan optimization.
And the overhaul plan risk evaluation module calculates the comprehensive loss index of the overhaul equipment at the moment t according to the data acquired by the data acquisition module. And evaluating whether the overhaul has risk according to the calculation result. Specifically, when the calculation result is less than 0, the maintenance is considered to have a risk, and the collected data is transmitted to the maintenance time window optimization module. Otherwise, the maintenance is considered to have no risk, and a command capable of being carried out is directly output.
And the maintenance time window optimization module calculates according to the received data by taking the purposes of minimum equipment power failure, minimum risk of a power grid operation mode and maintenance time window balance caused by maintenance on the power grid as targets.
Aiming at the problems that the equipment maintenance plan cannot meet the safe operation of a power grid and the consumption of new energy is limited due to randomness and volatility characteristics of two ends of source load, factors such as a power failure plan of a main distribution network, repeated power failure of a user, load transfer capacity, safety risks of the power grid and the like are comprehensively considered, the prearranged power failure plan of the main distribution network and the distribution network is optimized, the correlation characteristics of the maintenance equipment and the operation states of equipment such as new energy power generation, loads, lines, transformers and the like are mastered by combining historical operation information, the optimized arrangement of the maintenance plan is realized on the basis, a large amount of deterministic network analysis calculated quantity in a time window is reduced, the influence of maintenance on the operation of the power grid is reduced, and.
While the best mode for carrying out the invention has been described in detail and illustrated in the accompanying drawings, it is to be understood that the same is by way of illustration and example only and is not to be taken by way of limitation, the scope of the invention should be determined by the appended claims and any changes or modifications which fall within the true spirit and scope of the invention should be construed as broadly described herein.
Claims (14)
1. A main-distribution collaborative maintenance plan time window optimization method is characterized by comprising the following steps: the method comprises the following steps:
step 1, establishing a main and auxiliary cooperative analysis integrated model, and inputting a current maintenance plan;
step 2: collecting real-time operation data, load prediction data and historical operation data of a power grid;
step 3, according to the historical operating data obtained in the step 2, correlation coefficients between the maintenance equipment in the maintenance window and the line and the transformer are calculated quantitatively;
step 4, establishing a maintenance plan risk evaluation index, and calculating a maintenance plan risk value of the single maintenance equipment during maintenance at the moment t according to the correlation coefficient calculated in the step 3, the real-time operation data and the load prediction data;
step 5, judging whether the maintenance plan has risks or not according to the maintenance plan risk value calculated in the step 4; when the calculation result of the step 4 is greater than 0, the maintenance is considered to have risks, and a step 6 is executed; otherwise, the maintenance is considered to have no risk, and the result of the original plan can be output;
step 6, establishing a maintenance time window multi-objective optimization model by taking maintenance which causes the least power failure of equipment to the power grid, the least risk of the power grid operation mode and maintenance time window balance as targets;
and 7: setting a maintenance start time mutual exclusion constraint condition and a maintenance company resource constraint condition for the maintenance time window multi-objective optimization model in the step 6, and solving;
and 8, outputting a result when the objective function of the maintenance time window optimization model in the step 6 is the minimum value based on the step 7, and obtaining a time window optimization strategy.
2. The master-slave cooperative overhaul plan time window optimization method according to claim 1, wherein:
in the step 1, a main and auxiliary cooperative analysis integrated model is constructed based on a data fusion technology, and cross-system data sharing is realized by combining transparent access service;
the maintenance plan comprises maintenance starting time, maintenance duration and human and material resources occupied by maintenance.
3. The master-slave cooperative overhaul plan time window optimization method according to claim 1, wherein:
in step 3, a pearson correlation coefficient model is established, and correlation coefficients between the overhaul equipment and the line and between the overhaul equipment and the transformer are respectively calculated according to the following formulas based on the overhaul equipment power, the load power and the new energy power generation power in the historical operation data:
in the formula, λcA correlation coefficient representing a correlation factor X, Y, Xt，YtRepresenting the historical operating data of the relevant factors X, Y at the corresponding time t,
it represents the average of X, Y over a period of T.
4. The master-slave cooperative overhaul plan time window optimization method according to any one of claims 1 to 3, wherein:
in the step 4, the maintenance plan risk assessment indexes comprise power failure loss rate, new energy power abandonment rate, line out-of-limit rate and transformer out-of-limit rate.
5. The master-slave cooperative overhaul plan time window optimization method according to claim 4, wherein:
in step 4, for the kth maintenance plan, calculating a line out-of-limit rate by the following formula:
in the formula: lambda [ alpha ]cktl,jIndicating the out-of-limit rate index of the jth line at the time t, if lambdacktl,jIf < 0, forcibly making λctl,j＝0，λcktlRepresents the line out-of-limit rate index, Δ P, at time tktShowing the flow variation of the kth overhaul equipment at the moment t, Ireal,j，INRespectively representing the actual value and the rated value, lambda, of the current in the j-th linecLjA correlation coefficient, N, representing the corresponding overhaul equipment of the jth linelAnd the number of the loads of the power grid is represented.
6. The master-slave cooperative overhaul plan time window optimization method according to claim 4, wherein:
in step 4, for the kth maintenance plan, calculating the out-of-limit rate of the transformer by the following formula:
in the formula: lambda [ alpha ]cktt,jIndicating the line overload margin index of the jth main transformer at the time t, if lambdacktt,jIf < 0, forcibly making λctt,j＝0，λckttRepresents the out-of-limit rate index, I, of the transformer at the time treal,j，INRespectively representing the actual value and the rated value, lambda, of the main high/medium voltage measuring current of the jth transformercQjA correlation coefficient N representing the overhauling equipment corresponding to the jth transformertAnd the number of the loads of the power grid is represented.
7. The master-slave cooperative overhaul plan time window optimization method according to claim 4, wherein:
in step 4, for the kth maintenance plan, calculating the power failure loss rate by the following formula:
in the formula: lambda [ alpha ]cktd,jDenotes the power failure loss rate index, lambda, at the jth load time tcktdIndicates the power failure loss rate index at time t, Δ Pdt,jThe power loss of the load caused by the kth overhaul equipment at the time t is delta P under the condition of no power lossdt＝0，NdAnd the number of the loads of the power grid is represented.
8. The master-slave cooperative overhaul plan time window optimization method according to claim 4, wherein:
in step 4, for the kth maintenance plan, calculating the new energy power abandonment rate by the following formula:
in the formula: lambda [ alpha ]cktg,jRepresents the new energy power abandon rate index lambda of the jth new energy at the time tcktgRepresents a new energy power abandon rate index delta P at the moment tgtDiscarding electric power for the new energy caused by the kth overhaul equipment at the time t, and if no electric power is discarded, determining the value of delta Pgt,j＝0，NgIndicating the load of the power gridAnd (4) counting.
9. The master-slave cooperative overhaul plan time window optimization method according to any one of claims 5 to 8, wherein:
step 5 comprises the following steps:
step 501: calculating the comprehensive loss index of the overhaul equipment at the moment t by the following formula:
θkt＝λcktl+λcktt+λcktd+λcktg (6)
in the formula: thetaktThe comprehensive loss index of the kth overhaul equipment at the time t is represented;
step 502: when the calculation result of the step 501 is greater than 0, the maintenance is considered to have a risk, and the step 6 is executed; otherwise, the maintenance is considered to have no risk, and the result which can be carried out by the original plan is output.
10. The master-slave cooperative overhaul plan time window optimization method according to any one of claims 1 to 3, wherein:
in step 6, establishing an objective function of the maintenance time window multi-objective optimization model as follows:
wherein
γkt＝μααkt+μδδkt+μθθkt (8)
In the formula: gamma rayktRepresents the cross-impact index, alpha, of the kth overhaul at time tktFor the kth overhaul at time t, the exclusive infeasibility, mu, of the overhaulαIs alphaktThe weight of (2); deltaktTo the extent of violating the resource constraints of the overhaul company, μδIs deltaktWeight of (u)θTo synthesize the loss weights, ncIndicates the number of overhauls in the time window, ntAnd representing the number of time points between maintenance time windows.
11. The master-slave cooperative overhaul plan time window optimization method according to claim 10, wherein:
in step 7, setting a maintenance start time mutual exclusion constraint based on the kth maintenance start time and the h-th maintenance start time:
αk＞αh+Dk+1 (9)
in the formula: alpha is alphakAnd alphahFor the start of overhaul time of the kth and h overhaul, DkSetting parameters for a maintenance plan for the kth equipment maintenance duration;
based on the resource condition of the maintenance company occupied by each maintenance, setting the resource constraint of the maintenance company:
in the formula: deltaktAnd setting parameters for the maintenance plan for resources such as manpower and material resources occupied at the kth maintenance time t and the maximum resource amount which can be maintained simultaneously.
12. The master-slave cooperative overhaul plan time window optimization method according to claim 11, wherein:
and 8, solving the maintenance time window optimization objective function by adopting a simplex method to ensure the minimum value, and outputting the minimum value.
13. A master-distribution collaborative overhaul plan time window optimization system based on the master-distribution collaborative overhaul plan time window optimization method of claims 1 to 12, the system comprising an input module, a data acquisition module, an overhaul plan risk assessment module, and an overhaul time window optimization module, wherein:
the input module receives a maintenance plan input by a user;
the data acquisition module acquires a maintenance plan, a main and distribution network model, real-time data and historical operation data;
the overhaul plan risk evaluation module calculates an integrated loss index of overhaul equipment at the time t according to the data acquired by the data acquisition module; and evaluating whether the overhaul has risks according to the calculation result;
and the maintenance time window optimization module calculates according to the received data by taking the purposes of minimum equipment power failure, minimum risk of a power grid operation mode and maintenance time window balance caused by maintenance on the power grid as targets.
14. The master-slave cooperative overhaul plan time window optimization system of claim 13, wherein:
when the calculation result is less than 0, the maintenance plan risk evaluation module considers that the maintenance has risks and transmits the acquired data to the maintenance time window optimization module;
otherwise, the maintenance is considered to have no risk, and the result which can be carried out by the original plan is directly output.
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| CN113919520A (en) * | 2021-09-24 | 2022-01-11 | 国网山东省电力公司威海供电公司 | Maintenance plan management method, device and equipment for power grid maintenance and storage medium |
| CN114841627A (en) * | 2022-07-04 | 2022-08-02 | 国电南瑞南京控制系统有限公司 | Maintenance plan checking method, device, equipment and storage medium |
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| CN109858823A (en) * | 2019-02-15 | 2019-06-07 | 广东电网有限责任公司 | Main distribution, which has a power failure, plans selection method and device |
| CN111695755A (en) * | 2020-04-21 | 2020-09-22 | 国网浙江江山市供电有限公司 | Power distribution network maintenance plan optimization method |
| CN112101667A (en) * | 2020-09-17 | 2020-12-18 | 国网江苏省电力有限公司 | Intelligent maintenance window optimization method for distribution network self-service power failure plan balance |
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| CN109858823A (en) * | 2019-02-15 | 2019-06-07 | 广东电网有限责任公司 | Main distribution, which has a power failure, plans selection method and device |
| CN111695755A (en) * | 2020-04-21 | 2020-09-22 | 国网浙江江山市供电有限公司 | Power distribution network maintenance plan optimization method |
| CN112101667A (en) * | 2020-09-17 | 2020-12-18 | 国网江苏省电力有限公司 | Intelligent maintenance window optimization method for distribution network self-service power failure plan balance |
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| CN114841627A (en) * | 2022-07-04 | 2022-08-02 | 国电南瑞南京控制系统有限公司 | Maintenance plan checking method, device, equipment and storage medium |
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