CN109117570A - A kind of power distribution network optimized maintenance method based on distributed photovoltaic - Google Patents
A kind of power distribution network optimized maintenance method based on distributed photovoltaic Download PDFInfo
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Abstract
The power distribution network optimized maintenance method based on distributed photovoltaic that the invention discloses a kind of, comprising: the building of S1, repair time Optimized model；The building of S2, load transfer path Optimized model；The solution of S3, model.The present invention establishes the overhaul of the equipments time for considering a variety of constraint conditions and load transfer path combined optimization model, takes Revised genetic algorithum to be solved with load transfer path optimization problem for overhaul of the equipments is time-optimized, in the hope of global optimum.The overhaul of the equipments time is optimized first, then turns to be powered by other feeder lines by suitable path by the load influenced by maintenance by the optimization of load transfer path, to obtain optimal maintenance solution.Under the premise of guaranteeing system safety operation, the power supply reliability of system is improved to greatest extent, avoids repeating to have a power failure, reduces the loss of outage of user and power supply enterprise.
Description
Technical field
The present invention relates to grid maintenance technical field, in particular a kind of power distribution network optimized maintenance based on distributed photovoltaic
Method.
Background technique
In recent years, as policies at different levels are intensively put into effect, distributed photovoltaic power is developed rapidly.It is a large amount of distributed
Power grid makes power distribution network become the multiterminal network interconnected throughout power supply and user by the single-terminal network of conventional radiation formula,
The direction of energy no longer uniaxially flows to each load from substation bus bar, is possible to exist in the region of scheduled outage maintenance
The distributed generation resource of isolated operation causes anti-power transmission, becomes the new risk for threatening service personnel's safety.In addition, a large amount of distributed
Plant-grid connection low-voltage network, to power distribution network short circuit current level, power quality, relay protection, automation equipment movement and
System stabilization etc. can all generate certain influence.
Traditional power distribution network maintenance solution formulate power loss load caused by thinking mainly passes through interconnection switch and will overhaul with
Non- power loss route is connected, and gets around maintenance part and is powered, to reduce scope of power outage.Due to the limitation of line transmission capacity
And the requirement that node voltage is stable, part this may by the load that interconnection switch is powered in traditional maintenance solution not yet
It obtains and does not cut off, to ensure maintenance and power supply safety.
Summary of the invention
Technical problem to be solved by the present invention lies in provide a kind of power distribution network optimized maintenance side based on distributed photovoltaic
Method has reached repair time optimization and the optimization of load transfer path by genetic algorithm.
In order to solve the above-mentioned technical problem, technical scheme is as follows:
A kind of power distribution network optimized maintenance method based on distributed photovoltaic, this method comprises:
The building of S1, repair time Optimized model: repair time optimization aim is to reduce the sale of electricity loss of power supply enterprise,
It is as follows to establish repair time optimized mathematical model:
Objective function:
In formula: F is sale of electricity failure costs；P is electricity price；N is repair apparatus sum；T is maintenance period sum；pitFor t
Power failure load caused by i-th of overhaul of the equipments of period；UitFor the situation of i-th of repair apparatus of t period, when taking 0, indicate
Equipment operates normally, and when taking 1, indicates that equipment is stopped transport and overhauls；
Its constraint condition are as follows:
1) Line Flow constrains:
|Sl|≤Slmax
In formula: SlFor the trend of route l, SlmaxFor route l allow by trend limit value；
3) mutual exclusion maintenance constraint:
In order to avoid load point has a power failure in maintenance, some equipment cannot overhaul simultaneously, therefore formulate in maintenance plan
It cannot be arranged in the identical period in journey；
xj>xi+Di+1
In formula: xiAnd xjThe respectively beginning repair time of ith and jth equipment；DiIt is lasting for i-th of overhaul of the equipments
Duration (is uniformly converted to number of days)；
3) resource constraint is overhauled:
Since resource is limited, the limitation that maintenance plan is also contemplated that service ability, including service personnel's quantity and skill are formulated
Art ability, capacity of equipment etc.:
In formula: M is the equipment number that can be overhauled simultaneously, uitSituation is set for i-th of t period maintenance, when taking 0, is indicated
Equipment operates normally, and when taking 1, indicates that equipment is stopped transport and overhauls；
4) time adjustment constraint: | xi-xi0|≤Λi
In formula: x0iIt is declared for i-th of equipment and starts the repair time；ΛiFor the i equipment adjustment time limit value；
5) constraint is overhauled simultaneously:
In a system, primary the problem of can solve that have a power failure wants comprehensively solve, does not allow to occur because inconsiderate
It repeats to have a power failure and solves the problems, such as same system.Therefore, some equipment must overhaul simultaneously.In of that month all maintenance, it is all make it is same
The maintenance of route, same node point power loss, is regarded as repeating interruption maintenance weighing when carrying out maintenance plan time sequential routine
Multiple interruption maintenance was arranged in the identical period, i.e., only allows to have a power failure in this maintenance plan implementation procedure primary.
xi=xj
xiAnd xjThe respectively beginning repair time of ith and jth equipment；
Since power supply system covers biggish geographic area, it is contemplated that service work personnel and the quantity limit for overhauling fund
System will fully consider that service personnel overhauls the reasonability of route when formulating maintenance plan, former nearby according to geographical location as far as possible
Then reasonable arrangement maintenance sequence, the overhaul managements expense such as not only reduced labor intensity but also can reduce travel charge in this way is to improve
Economic benefit.
xj=xi+Di+1；
8) the maintenance constraint that can not be changed: the maintenance that the time cannot be changed includes: the maintenance that 1. higher level traffic department formulates
Plan；2. extending to the maintenance of this month last month；3. trouble hunting etc..The initial time of this kind of maintenance is regarded as determining, no
Participate in the layout of maintenance plan time.
xi=Bi
In formula: BiI-th of the equipment assigned for higher level's scheduling starts the repair time；
9) overhauls window constrains:
Scheduled overhaul is to carry out in the project for the unified regulation defined in the whole nation issued by authorities, period
Maintenance.Therefore the date of survey of equipment has regular hour limitation.
In formula: XiAllow to start repair time set for i-th of equipment
The building of S2, load transfer path Optimized model: load transfer path optimization aim is to reduce sale of electricity loss, is subtracted
Few switch operation, makes full use of distributed photovoltaic power to turn on-load, improves power supply reliability, establish optimized mathematical model such as
Under:
Objective function:
1) sale of electricity loss is reduced:
2) switch operating cost is reduced:
F2=min { β nops}
β is that switch operates primary expense；Nops is the switch number of operations for carrying out load transfer；
3) the transfer path power supply reliability of meter and distributed photovoltaic, which improves, saves failure costs:
In formula: Q is the repair apparatus set for needing transfer load；PiFor the load that has a power failure caused by i-th of overhaul of the equipments；DiFor
The lasting duration of i-th of overhaul of the equipments (being uniformly converted to number of days), βiSystem reliability when for i-th of overhaul of the equipments reduces damage
It loses, a turns on-load with the presence or absence of photo-voltaic power supply for load transfer path, is to take 1, otherwise takes 0；
Its constraint condition are as follows:
1) effective power flow constrains:
|Sls|≤Slmax s
In formula: lSFor the effective power flow of route l, lmax s be route l allow by effective power flow limit value；
2) voltage out-of-limit constrains:
VK.min≤VK’≤VK.max
In formula: VK', VK, min, VK, max are respectively the voltage and voltage bound of each node after transfer load；
3) network topology constrains: the network after carrying out load transfer must still maintain radial operation.
g∈G
In formula: g is the network topology structure after transfer load；G is radial networks topological structure.
The solution of S3, model: divided by globally optimal solution or suboptimal solution of the genetic algorithm to above-mentioned objective function
Analysis, expression is defined as: SGA=(C, F (x), P0, M,Γ, ψ, T)；
In formula: the coding method of C-individual；
F (x)-individual fitness evaluation function；
P0-initial population selection；
M-population size；
- selection operator；
Γ-crossover operator；
ψ-mutation operator；
T-genetic operation termination condition；
For seeking the optimization problem of objective function minimum value, theoretically only need to simply increase it negative sign can
It is translated into the optimization problem for seeking objective function maximum value, it may be assumed that
Min f (x)=max (- f (x))
When optimization aim is to find a function maximum value, and when objective function always takes positive value, can directly set the suitable of individual
Response F (X) is equal to corresponding target function value f (x), it may be assumed that
.F (X)=f (x)
As one of preferred embodiment of the invention, in the solution of the model: to meet wanting for the negated negative value of fitness
It asks, basic genetic algorithmic generally uses one of following two method that target function value f (x) is transformed to individual fitness F
(X), for asking the optimization problem of objective function maximum value, transform method are as follows:
C in formulaminIt is one of following three kinds for a suitably relatively small number:
A: a preassigned lesser number；
B: it evolves to currently on behalf of minimum target functional value only；
C: the minimum target functional value in former generation or nearest several generations group；
For asking the optimization problem of objective function minimum value, transform method are as follows:
Cmax is the biggish number that suitably compares in formula, is one of following three kinds:
A: a preassigned biggish number；
B: it evolves to currently on behalf of maximum target functional value only；
C: the maximum target functional value in former generation or nearest several generations group.
As one of preferred embodiment of the invention, in the solution of the model, selection operator is ratio selection operator,
Its specific implementation procedure is:
1) summation of the fitness of all individuals in group is first calculated；
2) size of the relative adaptability degrees of each individual is secondly calculated, individual is genetic to general in next-generation group
Rate；
3) it finally reuses simulation disk operation (random number between i.e. 0 to 1) and determines selected time of each individual
Number.
As one of preferred embodiment of the invention, in the solution of the model, crossover operator is single point crossing operator,
Its specific implementation procedure is:
1) random pair two-by-two is carried out to the individual in group, if group size is M, shared [M/2] is to being mutually paired
Group of individuals.Wherein [x] indicates the maximum integer for being not more than x；
2) individual being mutually paired to every a pair, being randomly provided the position after a certain locus is crosspoint.Tinction
The length of body is n, then shares (n-1) a possible cross-point locations；
3) individual being mutually paired to every a pair is exchanged with each other two in its intersection according to the crossover probability A of setting
The chromosome dyad of body, to produce two new individuals.
As one of preferred embodiment of the invention, in the solution of the model, mutation operator is basic bit mutation calculation
Son, specific implementation procedure is:
1) to each locus of individual, specify it for change point according to mutation probability Pm；
2) change point specified to each negates operation to its genic value or is replaced with other allele values,
To produce a new individual.
As one of preferred embodiment of the invention, the operation process of the genetic algorithm are as follows:
(1) it initializes: input initial parameter；
(2) coding production initial population, genetic algebra Gen=1；
(3) stochastic simulation is carried out under constraint condition；
(4) fitness function value is calculated；
(5) convergence judgement is carried out, YES exports decoding result；It returns to selection when NO to intersect, make a variation, Gen=Gen+1, then
It is secondary to return to initialization input parameter.
Beneficial effect
The present invention establishes the overhaul of the equipments time for considering a variety of constraint conditions and load transfer path combined optimization model,
Revised genetic algorithum is taken to be solved with load transfer path optimization problem for overhaul of the equipments is time-optimized, to demand perfection
Office is optimal.The overhaul of the equipments time is optimized first, then passes through the load that the optimization of load transfer path will be influenced by maintenance
Turn to be powered by other feeder lines by suitable path, so that optimal maintenance solution is obtained, it is final to obtain power supply enterprise's sale of electricity damage
The smallest maintenance plan and power failure load are lost, number of operations and the smallest load transfer path scheme of system losses are switched.?
Under the premise of guaranteeing system safety operation, the power supply reliability of system is improved to greatest extent, avoids repeating to have a power failure, and reduces user
With the loss of outage of power supply enterprise, to achieve the effect that system and social two-win.
Detailed description of the invention
Fig. 1 is the operational flowchart of the genetic algorithm of the present embodiment 1；
Fig. 2 is the schematic diagram of the single point crossing operation of the present embodiment 1；
Fig. 3 is the schematic diagram of the basic bit mutation operation of the present embodiment 1；
Fig. 4 is the disk schematic diagram of the present embodiment 1.
Specific embodiment
The effect of to make to structure feature of the invention and being reached, has a better understanding and awareness, to preferable
Examples and drawings cooperate detailed description, be described as follows:
Mentality of designing and principle of the invention: the establishment of power distribution network maintenance plan actually belongs to a with constraint conditions
Optimization problem.When the maintenance plan acquired meets all constraint conditions, it is feasible for claiming this plan.Many feasible
In scheme, the scheme with optimization objective function value is required optimal solution.According to this guiding theory, can establish
The Optimized model of maintenance plan, the model of foundation generally require to solve by complicated mathematical algorithm implementation model, the knot of solution
Fruit is theoretic optimal distribution maintenance plan.
The present invention is started with from operable practical method, the establishment of the maintenance plan in routine work is studied, when examining
Under conditions of the external constraint of worry is relatively fewer, according to the plan simple possible that the method is worked out, there is practicability, still
In the more situation of external constraint condition, then this method is limited to, i.e. the cost concept part covering of maintenance benefit and load
The factors such as the optimization benefit embodiment of transfer path cannot be embodied in a model, containing a large amount of especially in Modern power distribution net
Distributed photovoltaic power in the case where, the establishment of maintenance plan seems more complicated, it is therefore desirable to it is excellent to construct a set of multiple target
Change model, realizes the optimization for adapting to the distribution maintenance plan of new energy access.
Compared with traditional power distribution network maintenance plan establishment, the low and medium voltage distribution network containing distributed photovoltaic power overhauls meter
Formulations drawn increases inspection operation step when cutting off photo-voltaic power supply in maintenance and power failure electrical verification workload and is examining
The actions such as Inform when done photovoltaic power generation enterprise restores electricity are repaired, the increased content of institute was all referred to repair time and inspection
Load transfer path while the key of optimization when repairing.
Therefore, (the distribution multiple target optimized maintenance model based on PV, PV are defined as point for the present invention constructs object module
Cloth photovoltaic) tentatively consider reliability objectives function and economic cost objective function synthesis.Specific optimum ideals are as follows:
The overhaul of the equipments time for considering a variety of constraint conditions and load transfer path combined optimization model are established, for setting
Standby repair time optimization and load transfer path optimization problem take Revised genetic algorithum to be solved, in the hope of global optimum.
The overhaul of the equipments time is optimized first, then the load influenced by maintenance is passed through by conjunction by the optimization of load transfer path
Suitable path turns to be powered by other feeder lines, so that optimal maintenance solution is obtained, it is final to obtain power supply enterprise's sale of electricity loss reduction
Maintenance plan and power failure load, switch number of operations and the smallest load transfer path scheme of system losses.Guaranteeing system
Under the premise of system safe operation, the power supply reliability of system is improved to greatest extent, avoids repeating to have a power failure, and reduces user and power supply
The loss of outage of enterprise, to achieve the effect that system and social two-win.
Based on the above mentality of designing and principle: referring to Fig. 1-3, a kind of distribution based on distributed photovoltaic of the present embodiment
Net optimized maintenance method, this method comprises:
The building of S1, repair time Optimized model: examining time-optimized target is in the premise for guaranteeing power distribution network safe operation
Under, sale of electricity caused by improving power grid power supply reliability to greatest extent and reducing power grid due to maintenance is lost.And power grid power supply is reliable
Property can be reflected by load frequency of power cut and power failure load.When being overhauled, when can be by the superior and the subordinate's overhaul of the equipments
Between cooperation, reduce repeat have a power failure and it is unnecessary power failure to reduce frequency of power cut.Since different periods load level is different,
Sale of electricity loss brought by overhauling in different periods has very big difference, therefore can pass through the repair time of optimization equipment
To reduce sale of electricity loss.
By analyzing above, repair time optimization aim is to reduce the sale of electricity loss of power supply enterprise, and it is excellent to establish the repair time
It is as follows to change mathematical model:
Objective function:
In formula: F is sale of electricity failure costs；P is electricity price；N is repair apparatus sum；T is maintenance period sum；pitFor t
Power failure load caused by i-th of overhaul of the equipments of period；UitFor the situation of i-th of repair apparatus of t period, when taking 0, indicate
Equipment operates normally, and when taking 1, indicates that equipment is stopped transport and overhauls；
Its constraint condition are as follows:
1) Line Flow constrains:
|Sl|≤Slmax
In formula: SlFor the trend of route l, SlmaxFor route l allow by trend limit value；
2) mutual exclusion maintenance constraint:
In order to avoid load point has a power failure in maintenance, some equipment cannot overhaul simultaneously, therefore formulate in maintenance plan
It cannot be arranged in the identical period in journey；
xj>xi+Di+1
In formula: xiAnd xjThe respectively beginning repair time of ith and jth equipment；DiIt is lasting for i-th of overhaul of the equipments
Duration (is uniformly converted to number of days)；
3) resource constraint is overhauled:
Since resource is limited, the limitation that maintenance plan is also contemplated that service ability, including service personnel's quantity and skill are formulated
Art ability, capacity of equipment etc.:
In formula: M is the equipment number that can be overhauled simultaneously, uitSituation is set for i-th of t period maintenance, when taking 0, is indicated
Equipment operates normally, and when taking 1, indicates that equipment is stopped transport and overhauls；
4) time adjustment constraint: | xi-xi0|≤Λi
In formula: x0iIt is declared for i-th of equipment and starts the repair time；ΛiFor the i equipment adjustment time limit value；
5) constraint is overhauled simultaneously:
In a system, primary the problem of can solve that have a power failure wants comprehensively solve, does not allow to occur because inconsiderate
It repeats to have a power failure and solves the problems, such as same system.Therefore, some equipment must overhaul simultaneously.In of that month all maintenance, it is all make it is same
The maintenance of route, same node point power loss, is regarded as repeating interruption maintenance weighing when carrying out maintenance plan time sequential routine
Multiple interruption maintenance was arranged in the identical period, i.e., only allows to have a power failure in this maintenance plan implementation procedure primary.
xi=xj
xiAnd xjThe respectively beginning repair time of ith and jth equipment；
Since power supply system covers biggish geographic area, it is contemplated that service work personnel and the quantity limit for overhauling fund
System will fully consider that service personnel overhauls the reasonability of route when formulating maintenance plan, former nearby according to geographical location as far as possible
Then reasonable arrangement maintenance sequence, the overhaul managements expense such as not only reduced labor intensity but also can reduce travel charge in this way is to improve
Economic benefit.
xj=xi+Di+1；
6) the maintenance constraint that can not be changed: the maintenance that the time cannot be changed includes: the maintenance that 1. higher level traffic department formulates
Plan；2. extending to the maintenance of this month last month；3. trouble hunting etc..The initial time of this kind of maintenance is regarded as determining, no
Participate in the layout of maintenance plan time.
xi=Bi
In formula: BiI-th of the equipment assigned for higher level's scheduling starts the repair time；
7) overhauls window constrains:
Scheduled overhaul is to carry out in the project for the unified regulation defined in the whole nation issued by authorities, period
Maintenance.Therefore the date of survey of equipment has regular hour limitation.
In formula: XiAllow to start repair time set for i-th of equipment；
Processing to constraint condition
First, the processing to inequality constraints:
Inequality constraints is handled using the method for penalty function.
Second, the processing of peer-to-peer constraint: form optimized variable collection
The building of S2, load transfer path Optimized model: when equipment is overhauled, to guarantee that load does not have a power failure as far as possible,
Overhaul special work formulation maintenance plan simultaneously, corresponding load transfer path need to be provided.Since power distribution network wiring is complicated, inspection
It is more to repair task, certain transfer path can only often be selected according to self-operating experience by overhauling special work in actual operation.It is such
Transfer path is likely to only feasible path, and optimal selection is all not necessarily from safety and economy.Therefore it needs
It will be according to the actual conditions of service work, it is established that load transfer path Optimized model simultaneously proposes to be suitble to solve the optimization problem
Algorithm.Especially in the power distribution network of a large amount of distributed photovoltaic powers access, it can select to underestimate the load period as far as possible
It overhauls and load is transferred to the distributed photovoltaic for having power supply nargin except service area and power.
Load transfer path optimization aim is to improve power grid to greatest extent under the premise of guaranteeing power distribution network safe operation
Power supply reliability and grid switching operation that can be as few as possible transfers a load on All other routes, combines non-service area
The distribution and power output of photovoltaic power generation power supply, to avoid load point to have a power failure to the maximum extent and reduce system caused by load transfer
Network loss.
In terms of power grid power supply reliability: can be by selecting suitable load transfer path transfer load to the maximum extent
And the equipment having a power failure will be caused to be arranged in the maintenance of load valley period, have a power failure load caused by can reducing because of inspection and repair shop, thus
It is final to improve power supply reliability.Sale of electricity loss aspect: since overhaul of the equipments may cut off the company of certain load points and power supply point
Connect, load will be caused to have a power failure when this sub-load does not have backup path or backup path off-capacity, thus should as far as possible by
Corresponding maintenance task is arranged in the load valley period to reduce sale of electricity loss.
Sale of electricity loss aspect: since overhaul of the equipments may cut off the connection of certain load points and power supply point, when this part
Load does not have load will be caused to have a power failure when backup path or backup path off-capacity, so will should overhaul accordingly as far as possible
Task is arranged in the load valley period to reduce sale of electricity loss.
Switch number of operations: when load point is there are when a plurality of backup path, should selection operation on-off times as far as possible it is few
Transfer scheme, to reduce switch operating cost as far as possible.
Distributed photovoltaic turns band capability analysis: when load point is there are when a plurality of backup path, preferential selection is in non-maintenance area
There are the route or platform area transfer load of photovoltaic power generation plant-grid connection in domain, to guarantee power supply capacity and power supply reliability.Transfer is negative
Network loss caused by lotus: theoretically, causing load point to have a power failure when to avoid certain overhauls of the equipments, need to carry out corresponding load transfer,
Additional network loss is caused to power grid, therefore selects the path of less network loss as far as possible, but due in real process due to selection
Network loss variation is relatively small caused by path is different, ignores simplified processing herein.
By analyzing above, load transfer path optimization aim is to reduce sale of electricity loss, reduces switch operation, sufficiently benefit
Turn on-load with distributed photovoltaic power, improve power supply reliability, it is as follows to establish optimized mathematical model:
Objective function:
1) sale of electricity loss is reduced:
2) switch operating cost is reduced:
F2=min { β nops}
β is that switch operates primary expense；Nops is the switch number of operations for carrying out load transfer；
3) the transfer path power supply reliability of meter and distributed photovoltaic, which improves, saves failure costs:
In formula: Q is the repair apparatus set for needing transfer load；PiFor caused by i-th of overhaul of the equipments
Power failure load；DiFor the lasting duration of i-th of overhaul of the equipments (being uniformly converted to number of days), βiFor i-th of overhaul of the equipments
When system reliability reduce loss, it is to take 1, otherwise that a, which is that load transfer path turns on-load with the presence or absence of photo-voltaic power supply,
Take 0；
Its constraint condition are as follows:
1) effective power flow constrains:
|Sls|≤Slmax s
In formula: lSFor the effective power flow of route l, lmax s be route l allow by effective power flow limit value；
2) voltage out-of-limit constrains:
VK.min≤VK’≤VK.max
In formula: VK', VK, min, VK, max are respectively the voltage and voltage bound of each node after transfer load；
3) network topology constrains: the network after carrying out load transfer must still maintain radial operation.
g∈G
In formula: g is the network topology structure after transfer load；G is radial networks topological structure.
The solution of S3, model: above-mentioned optimization problem is decoupled into and solves the sub- optimization problem of following two by solution throughway: inspection
Load path optimization problem when repairing time optimal problem and maintenance.Repair time optimizes input condition: load prediction information, inspection
Repair the maintenance resource information of company, the coordination maintenance the constraint relationship of corresponding maintenance procedure and equipment room.Pass through asking for the problem
The maintenance plan of available repair apparatus is solved, and using this as the input condition of maintenance load path transfer optimization problem.Inspection
Repair load transfer path optimization input condition: initial network structure, equipment static parameter (impedance, allow by most spring tide
Restriction value etc.), distributed photovoltaic power access point position, distributed photovoltaic power curve, load prediction information etc..
The repair time is optimized using genetic algorithm based on initial maintenance plan, the maintenance meter optimized
It draws, and carries out the optimization of load transfer path, available a variety of transfers for this as the input condition of load transfer path optimization
Scheme, according to power failure load daily load prediction curve, the potential loss avoided with sale of electricity loss, power supply reliability raising is opened
Closing operational losses is that objective function uses genetic algorithm optimization again, corrects the repair time, finally obtains sale of electricity loss reduction
Maintenance plan and power loss load, switch number of operations and the highest load transfer path of power supply reliability.
Genetic algorithm (GAS-genetic algorithms) is a kind of based on nature natural selection and naturally hereditary machine
1 kind of randomized optimization process of system, genetic algorithm (Genetic Algorithms, abbreviation GA) are a kind of former based on natural selection
Search (optimizing) algorithm of reason and natural genetic mechanism, it is the life concern mechanism simulated in nature, in manual system
Realize the optimization of specific objective.It is as a kind of global optimization search, with its simple general-purpose, strong robustness and it is implicit simultaneously
Row processing the advantages that and be used widely.
The algorithm requires the property of objective function, or even is all not necessarily intended to explicitly write out objective function,
Feature possessed by genetic algorithm is one group of record, it can recorde multiple solutions.Genetic algorithm becomes in the decision to problem
After amount coding, calculating process is fairly simple, and can comparatively fast obtain a satisfactory solution.Due to the above-mentioned model category of this section
In multiple-objection optimization, and need to seek the Model Group of globally optimal solution or suboptimal solution, therefore genetic algorithm is selected to ask as model
It is proper that solution method makees this solution.
By analyzing above, analyzed by globally optimal solution or suboptimal solution of the genetic algorithm to above-mentioned objective function,
Its expression is defined as: SGA=(C, F (x), P0, M,Γ, ψ, T)；
In formula: the coding method of C-individual；
F (x)-individual fitness evaluation function；
P0-initial population selection；
M-population size；
- selection operator；
Γ-crossover operator；
ψ-mutation operator；
T-genetic operation termination condition；
In genetic algorithm, determine that it is general in next-generation group that the individual is genetic to the size of individual adaptation degree
Rate.The fitness of individual is bigger, and it is also bigger which is genetic to follow-on probability；Conversely, the fitness of individual is smaller,
It is also smaller that the individual is genetic to follow-on probability.Basic genetic algorithmic use ratio selection operator is each in group to determine
Individual is genetic to the quantity in next-generation group.For the genetic probability for being computed correctly each individual under different situations, it is desirable that institute
There is the fitness of individual to be necessary for positive number or zero, cannot be negative.
It, theoretically only need to be simply to it by analyzing the optimization problem it is found that for seeking objective function minimum value above
The optimization problem for seeking objective function maximum value can be translated by increasing a negative sign, it may be assumed that
Min f (x)=max (- f (x))
When optimization aim is to find a function maximum value, and when objective function always takes positive value, can directly set the suitable of individual
Response F (X) is equal to corresponding target function value f (x), it may be assumed that
.F (X)=f (x)
But the target function value in actual optimization problem has just and also has negative, and optimization aim finds a function maximum value, also asks
Function minimum, it is clear that it is this requirement of nonnegative number that two formulas, which can not guarantee fitness individual under all situations all, above.So
It must seek a kind of general and effectively by target function value to the transformational relation individual adaptation degree, be guaranteed by it
The total negated negative value of individual adaptation degree.Therefore, as one of preferred embodiment of the invention, in the solution of the model: to meet
The requirement of the negated negative value of fitness, basic genetic algorithmic generally use one of following two method to become target function value f (x)
It is changed to the fitness F (X) of individual, for asking the optimization problem of objective function maximum value, transform method are as follows:
C in formulaminIt is one of following three kinds for a suitably relatively small number:
A: a preassigned lesser number；
B: it evolves to currently on behalf of minimum target functional value only；
C: the minimum target functional value in former generation or nearest several generations group；
For asking the optimization problem of objective function minimum value, transform method are as follows:
Cmax is the biggish number that suitably compares in formula, is one of following three kinds:
A: a preassigned biggish number；
B: it evolves to currently on behalf of maximum target functional value only；
C: the maximum target functional value in former generation or nearest several generations group.
Selection operator or the effect for replicating operator be select some more excellent individuals from the former generation group, and
It is copied into next-generation group.The most frequently used mutually most basic selection operator is ratio selection operator.So-called ratio selection is calculated
Son, refers to that individual is selected and the probability being genetic in next-generation group is directly proportional to the fitness size of the individual.Ratio choosing
It selects actually one kind and returns random selection.It is illustrated in figure 4 disk schematic diagram.Entire disk is divided into of different sizes
Some covering of the fans respectively correspond and are worth different some election contest articles.When the disk being rotating stops naturally,
Article on pointer meaning covering of the fan just returns victor all.Although the pointer of disk specifically stop at which covering of the fan be can not be pre-
It surveys, but it is that can estimate that pointer, which is directed toward the probability of each covering of the fan, it is directly proportional to the central angle size of each covering of the fan:
A possibility that fenestra angle is bigger, is parked in the covering of the fan is also bigger；A possibility that central angle is smaller, is parked in the covering of the fan is also smaller.With
This is similar, and in genetic algorithm, entire group is divided by each individual, and the fitness of each individual is in the suitable of all individuals
Proportion is also not of uniform size in the sum of response, this ratio value has carved up entire disk surface, they also determine it is each each and every one
The probability that body is genetic in next-generation group.Therefore, as one of preferred embodiment of the invention, in the solution of the model
In, selection operator is ratio selection operator, and specific implementation procedure is:
1) summation of the fitness of all individuals in group is first calculated；
2) size of the relative adaptability degrees of each individual is secondly calculated, individual is genetic to general in next-generation group
Rate；
3) it finally reuses simulation disk operation (random number between i.e. 0 to 1) and determines selected time of each individual
Number.
As one of preferred embodiment of the invention, in the solution of the model, crossover operator is single point crossing operator,
It is illustrated in figure 2 the schematic diagram (by taking binary coding as an example) of single point crossing operation, specific implementation procedure is:
1) random pair two-by-two is carried out to the individual in group, if group size is M, shared [M/2] is to being mutually paired
Group of individuals.Wherein [x] indicates the maximum integer for being not more than x；
2) individual being mutually paired to every a pair, being randomly provided the position after a certain locus is crosspoint.Tinction
The length of body is n, then shares (n-1) a possible cross-point locations；
3) individual being mutually paired to every a pair is exchanged with each other two in its intersection according to the crossover probability A of setting
The chromosome dyad of body, to produce two new individuals.
As one of preferred embodiment of the invention, in the solution of the model, mutation operator is basic bit mutation calculation
Son is illustrated in figure 3 the schematic diagram of basic bit mutation operation, and specific implementation procedure is:
1) to each locus of individual, specify it for change point according to mutation probability Pm；
2) change point specified to each negates operation to its genic value or replaces with other allele values
To produce a new individual.
As one of preferred embodiment of the invention, the operation process of the genetic algorithm are as follows:
(1) it initializes: input initial parameter；
(2) coding production initial population, genetic algebra Gen=1；
(3) stochastic simulation is carried out under constraint condition；
(4) fitness function value is calculated；
(5) convergence judgement is carried out, YES exports decoding result；It returns to selection when NO to intersect, make a variation, Gen=Gen+1, then
It is secondary to return to initialization input parameter:
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and what is described in the above embodiment and the description is only the present invention
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement is both fallen in the range of claimed invention.The present invention claims protection scope by appended claims and
Its equivalent defines.
Claims (6)
1. a kind of power distribution network optimized maintenance method based on distributed photovoltaic, which is characterized in that this method comprises:
The building of S1, repair time Optimized model: repair time optimization aim is to reduce the sale of electricity loss of power supply enterprise, establishes inspection
It is as follows to repair time-optimized mathematical model:
Objective function:
In formula: F is sale of electricityFailure costs；P is electricity price；N is repair apparatus sum；T
For maintenance period sum；pitFor power failure load caused by i-th of the overhaul of the equipments of t period；UitIt is overhauled for i-th of the t period
The situation of equipment when taking 0, indicates that equipment operates normally, and when taking 1, indicates that equipment is stopped transport and overhauls；
Its constraint condition are as follows:
1) Line Flow constrains:
|Sl|≤SlmaxIn formula: SlFor the trend of route l, SlmaxFor route l allow by trend limit value；
2) mutual exclusion maintenance constraint:
In order to avoid load point has a power failure in maintenance, some equipment cannot overhaul simultaneously, therefore in maintenance plan formulation process
It cannot be arranged in the identical period；
xj>xi+Di+1
In formula: xiAnd xjThe respectively beginning repair time of ith and jth equipment；DiFor the lasting duration of i-th of overhaul of the equipments
(being uniformly converted to number of days)；
3) resource constraint is overhauled:
Since resource is limited, the limitation that maintenance plan is also contemplated that service ability is formulated:
In formula: M is the equipment number that can be overhauled simultaneously, uitSituation is set for i-th of t period maintenance, when taking 0, indicates equipment
It operates normally, when taking 1, indicates that equipment is stopped transport and overhaul；
4) time adjustment constraint: | xi-xi0|≤Λi
In formula: x0iIt is declared for i-th of equipment and starts the repair time；ΛiFor the i equipment adjustment time limit value；
5) constraint is overhauled simultaneously:
In a system, primary the problem of can solve that have a power failure wants comprehensively solve, does not allow to repeat because inconsiderate
Power failure solves the problems, such as same system, therefore:
xi=xj
xiAnd xjThe respectively beginning repair time of ith and jth equipment；
Since power supply system covers biggish geographic area, it is contemplated that service work personnel and the quantity limitation for overhauling fund,
To fully consider that service personnel overhauls the reasonability of route when formulating maintenance plan, it is reasonable according to geographical location nearby principle as far as possible
Maintenance sequence is arranged, thus:
xj=xi+Di+1；
6) the maintenance constraint that can not be changed:
xi=Bi
In formula: BiI-th of the equipment assigned for higher level's scheduling starts the repair time；
7) overhauls window constrains:
In formula: XiAllow to start repair time set for i-th of equipment
The building of S2, load transfer path Optimized model: it is as follows to establish optimized mathematical model:
Objective function:
1) sale of electricity loss is reduced:
2) switch operating cost is reduced:
F2=min { β nops}
β is that switch operates primary expense；Nops is the switch number of operations for carrying out load transfer；
3) the transfer path power supply reliability of meter and distributed photovoltaic, which improves, saves failure costs:
In formula: Q is the repair apparatus set for needing transfer load；PiFor the load that has a power failure caused by i-th of overhaul of the equipments；DiIt is i-th
The lasting duration of overhaul of the equipments, βiSystem reliability when for i-th of overhaul of the equipments reduces loss, and a is that load transfer path is
No there are photo-voltaic power supplies to turn on-load, is, takes 1, otherwise takes 0；
Its constraint condition are as follows:
1) effective power flow constrains:
|Sls|≤Slmax s
In formula: lSFor the effective power flow of route l, lmax s be route l allow by effective power flow limit value；
2) voltage out-of-limit constrains:
VK.min≤VK’≤VK.max
In formula: VK', VK, min, VK, max are respectively the voltage and voltage bound of each node after transfer load；
3) network topology constrains: the network after carrying out load transfer must still maintain radial operation.
g∈G
In formula: g is the network topology structure after transfer load；G is radial networks topological structure.
The solution of S3, model: it is analyzed by globally optimal solution or suboptimal solution of the genetic algorithm to above-mentioned objective function, table
Show formula is defined as:
In formula: the coding method of C-individual；
F (x)-individual fitness evaluation function；
P0-initial population selection；
M-population size；
- selection operator；
Γ-crossover operator；
ψ-mutation operator；
T-genetic operation termination condition；
For seeking the optimization problem of objective function minimum value, theoretically only need to simply increase it negative sign can be by its turn
Turn to the optimization problem for seeking objective function maximum value, it may be assumed that
Min f (x)=max (- f (x))
When optimization aim is to find a function maximum value, and when objective function always takes positive value, can directly set the fitness F of individual
(X) it is equal to corresponding target function value f (x), it may be assumed that
F (X)=f (x).
2. the power distribution network optimized maintenance method according to claim 1 based on distributed photovoltaic, which is characterized in that in the model
Solution in: for the requirement for meeting the negated negative value of fitness, for asking the optimization problem of objective function maximum value, transform method
Are as follows:
C in formulaminIt is one of following three kinds for a suitably relatively small number:
A: a preassigned lesser number；
B: it evolves to currently on behalf of minimum target functional value only；
C: the minimum target functional value in former generation or nearest several generations group；
For asking the optimization problem of objective function minimum value, transform method are as follows:
Cmax is the biggish number that suitably compares in formula, is one of following three kinds:
A: a preassigned biggish number；
B: it evolves to currently on behalf of maximum target functional value only；
C: the maximum target functional value in former generation or nearest several generations group.
3. the power distribution network optimized maintenance method according to claim 1 based on distributed photovoltaic, which is characterized in that in the model
Solution in, selection operator be ratio selection operator, specific implementation procedure is:
1) summation of the fitness of all individuals in group is first calculated；
2) size of the relative adaptability degrees of each individual, the probability that individual is genetic in next-generation group are secondly calculated；
3) it finally reuses simulation disk operation and determines the selected number of each individual.
4. the power distribution network optimized maintenance method according to claim 1 based on distributed photovoltaic, which is characterized in that in the model
Solution in, crossover operator be single point crossing operator, specific implementation procedure is:
1) random pair two-by-two is carried out to the individual in group, if group size is M, shared [M/2] is to being mutually paired
Body group.Wherein [x] indicates the maximum integer for being not more than x；
2) individual being mutually paired to every a pair, being randomly provided the position after a certain locus is crosspoint.Tinction body
Length is n, then shares (n-1) a possible cross-point locations；
3) individual that every a pair is mutually paired, according to setting crossover probability A its intersection be exchanged with each other two it is individual
Chromosome dyad, to produce two new individuals.
5. the power distribution network optimized maintenance method according to claim 1 based on distributed photovoltaic, which is characterized in that in the model
Solution in, mutation operator be basic bit mutation operator, specific implementation procedure is:
1) to each locus of individual, specify it for change point according to mutation probability Pm；
2) change point specified to each negates operation to its genic value or is replaced with other allele values, thus
Produce a new individual.
6. the power distribution network optimized maintenance method according to claim 1 based on distributed photovoltaic, which is characterized in that the heredity is calculated
The operation process of method are as follows:
(1) it initializes: input initial parameter；
(2) coding production initial population, genetic algebra Gen=1；
(3) stochastic simulation is carried out under constraint condition；
(4) fitness function value is calculated；
(5) convergence judgement is carried out, YES exports decoding result；It returns to selection when NO to intersect, make a variation, Gen=Gen+1 is turned again to
Initialization input parameter.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110263992A (en) * | 2019-06-12 | 2019-09-20 | 苏州智睿新能信息科技有限公司 | It is a kind of based on client to the optimal plan power failure method of electric service susceptibility |
| CN111950786A (en) * | 2020-08-06 | 2020-11-17 | 上海交通大学 | Two-stage maintenance scheduling method for steel enterprise equipment under complex constraint |
| CN112149842A (en) * | 2020-09-04 | 2020-12-29 | 深圳供电局有限公司 | Power grid maintenance management method and device |
| CN112418499A (en) * | 2020-11-16 | 2021-02-26 | 广东电网有限责任公司 | Power grid maintenance planning optimization method and device and computer readable storage medium |
| CN112488379A (en) * | 2020-11-26 | 2021-03-12 | 北京交通大学 | Method and system for optimizing maintenance plan of high-speed railway motor train unit |
| CN112488354A (en) * | 2020-10-28 | 2021-03-12 | 国网宁夏电力有限公司经济技术研究院 | Low-cost configuration method for operation and maintenance of power distribution network after clean energy access |
| CN113191575A (en) * | 2021-06-16 | 2021-07-30 | 广东电网有限责任公司 | Power distribution network maintenance power failure mode optimization method and device, terminal and storage medium |
| CN113505915A (en) * | 2021-06-18 | 2021-10-15 | 国网吉林省电力有限公司吉林供电公司 | Method for optimizing maintenance plan of power distribution network containing distributed power supply |
| CN113988390A (en) * | 2021-10-18 | 2022-01-28 | 浙江大学 | Unit maintenance optimization method based on support vector machine |
| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
| CN116486969A (en) * | 2023-06-25 | 2023-07-25 | 广东工业大学 | Genetic algorithm-based material optimal correlation relation acquisition method and application |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105279578A (en) * | 2015-10-27 | 2016-01-27 | 天津大学 | Power supply optimization configuration bilevel programming method in active distribution network region |
| CN105552896A (en) * | 2015-12-31 | 2016-05-04 | 国家电网公司 | Distributed photovoltaic power generation based power distribution network peak load control method |
| CN105574611A (en) * | 2015-12-14 | 2016-05-11 | 国家电网公司 | Overhaul plan optimization method containing distributed power distribution network |
-
2018
- 2018-08-24 CN CN201810975803.7A patent/CN109117570A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105279578A (en) * | 2015-10-27 | 2016-01-27 | 天津大学 | Power supply optimization configuration bilevel programming method in active distribution network region |
| CN105574611A (en) * | 2015-12-14 | 2016-05-11 | 国家电网公司 | Overhaul plan optimization method containing distributed power distribution network |
| CN105552896A (en) * | 2015-12-31 | 2016-05-04 | 国家电网公司 | Distributed photovoltaic power generation based power distribution network peak load control method |
Non-Patent Citations (6)
| Title |
|---|
| M. A. FOTOUHI 等: "Strategic maintenance scheduling of distributed generations in oligopolistic electricity markets", 《2009 IEEE ELECTRICAL POWER & ENERGY CONFERENCE (EPEC)》 * |
| 刘剑平 等: "《普通高等教育十二五规划教材 深基坑支护理论与应用》", 30 June 2013, 冶金工业出版社 * |
| 张姿姿 等: "考虑天气及电价的配网计划检修双层优化模型", 《电力科学与工程》 * |
| 朱新菊 等: "配电网检修计划优化问题的研究", 《陕西电力》 * |
| 编委会 著: "《高等学校应用型本科创新人才培养计划指定教材 智能制造信息系统开发》", 31 August 2017, 西安电子科技大学出版社 * |
| 颜磊: "计及分布式电源接入的配电网检修计划优化方法", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
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| CN110263992B (en) * | 2019-06-12 | 2023-07-04 | 苏州智睿新能信息科技有限公司 | Optimal planned power outage method based on customer sensitivity to power supply service |
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| CN112149842A (en) * | 2020-09-04 | 2020-12-29 | 深圳供电局有限公司 | Power grid maintenance management method and device |
| CN112488354A (en) * | 2020-10-28 | 2021-03-12 | 国网宁夏电力有限公司经济技术研究院 | Low-cost configuration method for operation and maintenance of power distribution network after clean energy access |
| CN112488354B (en) * | 2020-10-28 | 2022-05-13 | 国网宁夏电力有限公司经济技术研究院 | Low-cost configuration method for operation and maintenance of power distribution network after clean energy access |
| CN112418499A (en) * | 2020-11-16 | 2021-02-26 | 广东电网有限责任公司 | Power grid maintenance planning optimization method and device and computer readable storage medium |
| CN112488379A (en) * | 2020-11-26 | 2021-03-12 | 北京交通大学 | Method and system for optimizing maintenance plan of high-speed railway motor train unit |
| CN112488379B (en) * | 2020-11-26 | 2024-03-19 | 北京交通大学 | Maintenance plan optimization method and system for high-speed railway motor train unit |
| CN113191575B (en) * | 2021-06-16 | 2024-01-23 | 广东电网有限责任公司 | Optimization method, device, terminal and storage medium for power distribution network maintenance power failure mode |
| CN113191575A (en) * | 2021-06-16 | 2021-07-30 | 广东电网有限责任公司 | Power distribution network maintenance power failure mode optimization method and device, terminal and storage medium |
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| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
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