CN110059897A - Active power distribution network based on MIXED INTEGER PSO algorithm in a few days rolling optimization method - Google Patents
Active power distribution network based on MIXED INTEGER PSO algorithm in a few days rolling optimization method Download PDFInfo
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Abstract
The invention discloses a kind of active power distribution network based on MIXED INTEGER PSO algorithm in a few days rolling optimization method, step include: 1 determine in a few days the time scale of rolling optimization, establish in a few days the gear time series of optimizing phase, segment variable when establishing gear change；2 establish rolling optimization mathematical model in consideration gear modified day；3 export optimum results by MIXED INTEGER PSO algorithm.The present invention can be modified the gear of discrete type adjusting device, overcome distributed power generation randomness and probabilistic influence, to ensure that the active loss of power distribution network and regulation and control cost minimize in the case where distributed power generation contributes most severe scene.
Description
Technical field
The present invention relates to system for distribution network of power to optimize operation field, and in particular to one kind is based on MIXED INTEGER PSO algorithm
Active power distribution network in a few days rolling optimization method.
Background technique
China is undergoing a new energy revolution at present, and government passes through photovoltaic aid-the-poor project and New Energy Industry subsidy
Etc. modes widely popularize new energy technology.According to domestic professional institution, it is expected that the year two thousand thirty distributed generation resource installed capacity can reach together
17% or so of phase whole nation total installation of generating capacity.Wherein, distributed photovoltaic power generation and distributed wind-power generator are due to its scope of application
Extensively, technology maturation has obtained a large amount of practical application.Since distributed power generation has, randomness is strong, fluctuation is big and intermittent
The characteristics of, a large amount of distributed power generations access power distribution network, the greatly economy of the safety and stability of influence power distribution network and operation,
Emphasis research topic as power distribution network optimization operation field.
Power distribution network optimization operation includes scheduling and in a few days optimizing phase a few days ago.Scheduling phase is based on week long period a few days ago
The distributed power generation power output prediction of phase optimizes, and because of factors such as predetermined period length, distributed power generation power output precision of prediction is limited,
It not is optimal in the gear sequence for the discrete type adjusting device that scheduling phase a few days ago determines.In the operation of practical power distribution network,
If actual distribution formula generated output and the prediction output deviation of scheduling phase a few days ago are larger, cause discrete type adjusting device excessive
It adjusts, causes power distribution network active loss increase and spread of voltage.
For active power distribution network in a few days optimization operation there are mainly two types of method: robust Optimal methods and rolling optimization side
Method.Robust Optimal methods consider that distributed power generation power output is uncertain, guarantee still to be able to maintain power distribution network under most severe scene
Safety and stability, but optimum results are more conservative, economy is poor.Distributed power generation of the rolling optimization method according to short cycle
Power output prediction carries out rolling optimization, and when predicting accurate, effect of optimization is good, but the prediction error of distributed power generation and its uncertain
Property is inevitable, and the effect of optimization of rolling optimization method is affected by precision of prediction.
Summary of the invention
The present invention is to overcome above-mentioned the shortcomings of the prior art, is provided a kind of based on MIXED INTEGER PSO algorithm
Active power distribution network in a few days rolling optimization method to be modified to the gear of discrete type adjusting device overcomes distribution
Formula generates electricity randomness and probabilistic influence, to ensure the active of in the case where distributed power generation contributes most severe scene power distribution network
Loss and regulation and control cost minimize.
In order to achieve the above object, the technical scheme adopted by the invention is as follows:
A kind of active power distribution network based on MIXED INTEGER PSO algorithm of the present invention in a few days rolling optimization method, it is described active to match
Power grid access is distributed formula power generation DG, discrete type adjusting device and continuous type adjusting device；The distributed power generation DG includes point
Cloth photovoltaic power generation and distributed wind-power generator；The discrete type adjusting device includes on-load regulator transformer OLTC and switching electricity
Container group CB；The continuous type adjusting device includes static reactive generator SVC；Its main feature is that the active power distribution network is in a few days
Rolling optimization method is to carry out as follows:
Step 1: establishing the gear time series of in a few days optimizing phase:
Step 1.1, the time scale for determining in a few days rolling optimization:
Scheduling phase D is a few days ago with TDFor time scale, with HD×TDFor the siding-to-siding block length of scheduling phase a few days ago, wherein HDFor
Integer multiple；
In a few days rolling optimization is with TINFor time scale, with HIN×TINFor the siding-to-siding block length of in a few days rolling optimization, wherein HINFor
Integer multiple；And there is TD=M × TIN, M is integer multiple, and subscript IN indicates the in a few days optimizing phase；
Step 1.2, the gear time series for establishing scheduling phase a few days ago:
By known scheduling phase optimum results a few days ago, on-load regulator transformer OLTC is established respectively in scheduling phase a few days ago
The gear time series of DSwitched capacitor group CB is in scheduling phase D a few days ago
Gear time seriesWith power distribution network and bulk power grid scheduling phase D a few days ago friendship
Cross-power time seriesWherein, NOLTCIndicate the total number of on-load regulator transformer OLTC,Indicate to number the on-load regulator transformer OLTC for being i in the gear of the t period of scheduling phase D a few days ago；It indicates
Gear of the switched capacitor group CB that number is i in the t period of scheduling phase D a few days ago,Indicate that power distribution network and bulk power grid exist
The interaction power of the t period of scheduling phase D a few days ago, subscript grid indicate that power distribution network is interacted with bulk power grid power；
Step 1.3, the gear time series for establishing the in a few days optimizing phase:
According to time scale TDAnd TINFacilities, by each time series of scheduling phase a few days ago respectively obtained load adjust
Gear time series of the pressure transformer OLTC in the in a few days optimizing phaseIt throws
Capacitor group CB is cut in the gear time series of in a few days optimizing phaseAnd distribution
The interaction power time series of net and bulk power grid in the in a few days optimizing phaseWherein,WithRespectively indicating and numbering the on-load regulator transformer OLTC for being i in the gear of the t period of in a few days optimizing phase, number is i's
Gear of the switched capacitor group CB in the t period of in a few days optimizing phase,Indicate that power distribution network and bulk power grid are in a few days optimizing rank
The interaction power of the t period of section；
Step 2: in a few days rolling optimization initializes:
Step 2.1, segment variable when establishing gear change:
The number of gear change occurs according to discrete type adjusting device, foundation number is i and device type isDiscrete type
Segment variable when the s times gear change of adjusting deviceAndValue be corresponding period volume when the s time gear change occurs
Number, device typeValue is OLTC or CB, respectively indicates on-load regulator transformer, switched capacitor group；
The gear time series for the in a few days optimizing phase that definition is obtained by the gear time series of scheduling phase a few days ago is day
The original speed position time series of interior optimizing phase, and establish that number is i and device type isDiscrete type adjusting device s
Segment variable when secondary original speed position changes It is constant value variable, andValue beInitial value, subscript 0 indicate just
Beginning information；
Step 2.2, in a few days rolling optimization initialize:
Definition in a few days the optimizing phase kth in a few days rolling optimization section be [k, k+HIN- 1], k is in a few days rolling optimization
Start periods, k+HIN- 1 is the most end period of in a few days rolling optimization, and has K=k, defines the largest optimization time of in a few days rolling optimization
Number is Kmax；The current in a few days rolling optimization of initialization is the 1st in a few days rolling optimization, the 1st time in a few days rolling optimization section be [1,
HIN]；
Rolling optimization mathematical model in gear modified day is considered Step 3: establishing:
Step 3.1 establishes distributed power generation DG power output indeterminacy section:
Optimize section [k, k+H according to current scrollingIN- 1] distributed power generation DG power output predictive information, is obtained using formula (1) on
To the power output for the t period for numbering the distributed power generation DG for being iIndeterminacy section:
In formula (1), NDGIt is the total number of distributed power generation DG in power distribution network,WithRespectively be number be i
The power output prediction standard value and prediction deviation maximum value of the t period of distributed power generation DG；
Step 3.2 obtains the dual objective functions of in a few days rolling optimization using formula (2):
In formula (2), fp indicates head end and endpoint node is the branch of node f and node p respectively, and Φ indicates all branch groups
At set, Ifp,tIt is the resistance r for flowing through branch fp the t periodfpOn electric current, NSVCIt is total of static reactive generator SVC
Number, CSVCIt is the unit power output cost of the static reactive generator SVC after conversion,Indicate the static reacance that number is i
The reactive power of the t period of generator SVC exports decision variable, and α, β are weight coefficients；PDGIndicate that all distributed power generation DG exist
Current scrolling optimizes the set of the day part power output in section, PDGElement be internal layer optimization decision variable；Indicate all
Static reactive generator SVC exports the set of decision variable in the reactive power of the day part in current scrolling optimization section,Table
Show that all discrete type adjusting devices optimize the set of the gear change period decision variable of the day part in section in current scrolling,WithElement be outer layer optimization decision variable；
The adjusting of step 3.3, the gear change period decision variable that discrete type adjusting device is obtained using formula (3) is constrained:
In formula (3),Indicate that number is i and device type isDiscrete type adjusting device the s times gear change when
Section decision variable；
Step 3.4, the Branch Power Flow constraint that the OLTC containing on-load regulator transformer of t period is obtained using formula (4):
In formula (4), Vf,tAnd Vp,tIt is the voltage magnitude of node f and node p in the t period, P respectivelyfp,tAnd Qfp,tIt is t respectively
Active and reactive power on period branch fp, xfpIt is the reactance of branch fp,It is the static reacance hair connect on node p
The reactive power output of the t period of raw device SVC,WithBe respectively connect the load L on node p t period active and
Reactive power, Kfp,tIt is the no-load voltage ratio for connecing the t period of the on-load regulator transformer OLTC on branch fp,It is to connect on node p
Switched capacitor group CB the t period idle output power, U (p) indicate it is all using node p as the end of the branch of headend node
End segment point set, r ∈ U (p) indicate the node r for belonging to set U (p)；
Step 3.5 obtains what the static reactive generator SVC reactive power that the number of t period is i exported using formula (5)
Constraint:
In formula (7),WithIt is the reactive power maximum for the static reactive generator SVC that number is i respectively
Output, minimum output；
Step 3.6, the power distribution network that the t period is obtained using formula (6) interact the constraint of power with bulk power grid:
In formula (6), kgridIndicate that power is interacted with bulk power grid in a few days optimizing phase power distribution network deviates the journey dispatched a few days ago
Degree, NnodeIt is the sum of power distribution network interior joint；
Step 3.7, obtained using formula (7) and formula (8) the t period power distribution network security constraint:
In formula (7),WithBe respectively branch fp allow to flow through minimum, maximum current；
In formula (8),WithIt is minimum, the maximum voltage of node p permission respectively；
Step 4: being solved using MIXED INTEGER PSO algorithm in a few days rolling optimization model:
Step 4.1, particle coding:
Judge in currently in a few days rolling optimization section [k, k+HIN- 1] whether each discrete type adjusting device occurs gear change in
Which change, and happens is that time gear change, so that the related gear for the discrete type adjusting device that gear change occurs be become
Change period decision variable and is included in particle coding；
It obtains exporting decision variable by the reactive power of static reactive generator SVC using formula (9), gear change occurs
The gear of the switched capacitor group CB of the gear change period decision variable and generation gear change of on-load regulator transformer OLTC
Change the particle vector of period decision variable composition
In formula (9),Indicate that number is NSVCStatic reactive generator SVC k+HIN- 1 period it is idle
Output power decision variable；Subscript 1 ..., NSVCRespectively indicate NSVCThe device numbering of a static reactive generator SVC
Indicate the S for the on-load regulator transformer OLTC that number is ChCh+NCh- 1 gear change period determines
Plan variable, subscript C1, C2 ..., Ch be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] interior h occurs gear change
The device numbering of on-load regulator transformer OLTC, subscript SC1、…、SC1+NC1- 1 respectively indicates the on-load voltage regulation transformation that number is C1
The S that device OLTC occursC1It is secondary ..., SC1+NC1- 1 gear change；NC1It indicates to optimize section [k, k+H in current scrollingIN-
1] N has occurred in the on-load regulator transformer OLTC that number is C1 in altogetherC1Secondary gear change；
Indicate the S for the switched capacitor group CB that number is DgDg+NDg- 1 time gear change period decision becomes
Amount, subscript D1, D2 ..., Dg be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] switching of interior g generation gear change
The device numbering of capacitor group CB, subscript SD1、…、SD1+ND1- 1 respectively indicates what the switched capacitor group CB that number is D1 occurred
SD1It is secondary ..., SD1+ND1- 1 gear change；ND1It indicates to optimize section [k, k+H in current scrollingIN- 1] number is D1 in
Switched capacitor group CB N has occurred altogetherD1Secondary gear change；
Step 4.2, particle initialization:
Step 4.2.1, for component in particle
Initial value takeInterior random value；
Step 4.2.2, for component in particleWith
Initial value takeInterior random integers,
Max { } expression takes the maximum value of all numbers in braces, and min { } indicates to take the minimum value of all numbers in braces；
Step 4.2.3, initialization obtains h+g predecessorWherein,It indicates to compile
Number be w predecessor；
Step 4.3, particle iteration initialization:
Current iteration algebra z=1 is initialized, greatest iteration algebra is zmax；By predecessorIt is used as 1st generation simultaneously
Particle and 1st generation history optimal particle select one to keep target function value the smallest as initial in h+g predecessor
Population optimal particleSubscript best indicates best, and subscript PS indicates population；
Step 4.4, particle update:
Step 4.4.1, particle rapidity updates:
Using formula (10) to z for the movement velocity of d-th of component of particleIt is updated, to obtain z+1
For the movement velocity of d-th of component of particle
In formula (10),Indicate number be w z for particle d-th of component,WithTable respectively
Show z for d-th of component of history optimal particle and d-th of component of population optimal particle, π,Respectively decay factor,
Studying factors,For the random number of [0,1] range；
The movement velocity range of the da component of particle isAnd da ∈ [1,
(NSVC×HIN)], subscript idaIndicate the device numbering of the corresponding adjusting device of the da component, kSVCIt is that movement velocity adjusts ginseng
Number；The movement velocity range of the dc component of particle is [- 1,1], and dc ≠ da；
Step 4.4.2, particle updates:
Using formula (11) to z for the da component of particleIt is updated, to obtain z+1 for the of particle
Da componentAnd the value range of the da component of particle is
Using formula (12), formula (13) to z for the dc component of particleIt is updated, to obtain z+1 generation
The dc component of particleThe value range of the dc component of particle isInterior integer:
In formula (12), sigmoid () indicates sigmoid function,WithFor the random number of [0-1] range, ρ is value
For 1 or -1 auxiliary variable；σ is the auxiliary variable that value is 0 or 1；
Step 4.4.3, update to obtain z+1 for particle by particle
Step 4.5, particle decoding:
Step 4.5.1, obtained z+1 is turned for the gear change period decision variable of the discrete type adjusting device of particle
It is changed to the gear time series of corresponding discrete type adjusting device:
The first situation, ifIt enablesValue be gn, enableValue be km, definition
For z+1 for particle decoding convert after obtain number is i's and device type isDiscrete type adjusting device t period
Gear then willValue be assigned toIt willValue be assigned toSubscript t1 indicates that the period compiles
Number, and meetWith t1 ∈ [k, k+HIN-1]；
Second situation, ifThen willValue be assigned toIt will's
Value is assigned toSegment number when subscript t2 is indicated, and meetWith t2 ∈ [k, k+HIN-1]；
The third situation, ifThen willValue be assigned toSegment number when subscript t3 is indicated,
And meet t3 ∈ [k, k+HIN-1]；
Step 4.5.2, z+1 is obtained for the gear time series of the corresponding all discrete type adjusting devices of particle
It is obtained by z+1 using formula (14) for the discrete type adjusting device gear time series obtained after particle decoding conversion
In formula (14),The number obtained after z+1 is converted for particle decoding is respectively indicated as i's
The gear of the t period of on-load regulator transformer OLTC, switched capacitor group CB；
It willOptimize section [k, k+H with current scrollingIN- 1] the discrete type adjusting device of gear change does not occur on
Gear time series joint, to obtain z+1 for the gear time series of the corresponding all discrete type adjusting devices of particle
Step 4.6 generates optimal particle:
Step 4.6.1, obtain connecing the no-load voltage ratio of the t period of the voltage adjustment of on-load transformer OLTC on branch fp using formula (15)
Kfp,t:
In formula (14), Kfp,0With Δ KfpBe respectively connect the on-load regulator transformer OLTC on branch fp standard no-load voltage ratio and
Step-length is adjusted,It indicatesMiddle correspondence connects the shelves of the t period of the on-load regulator transformer OLTC on branch fp
Position enables K when being free of on-load regulator transformer OLTC in branchfp,t=1；
Step 4.6.2, the idle output power of the t period for the switched capacitor group CB that number is i is obtained using formula (16)
In formula (15),It is the single group capacitor compensation power for the switched capacitor group CB that number is i；
Step 4.6.3, decision variable set is obtained using formula (17)WithTarget after the value of middle element is determining
Function:
In formula (17), objective function is single target function, constraint condition include formula (4), formula (6), formula (7), formula (8),
Formula (15) and formula (16), solve single target function, obtain z+1 for particleTarget function value, then with
Current z is for history optimal particleTarget function value be compared, if z+1 is for particleTarget letter
Numerical value is less than current z for history optimal particleTarget function value, then history optimal particle is updated, will
Z+1 is for particleAs z+1 for history optimal particleOtherwise without updating, i.e., by current z generation
History optimal particleAs z+1 for history optimal particleMesh is selected in all history optimal particles
Optimal particle of the smallest particle of offer of tender numerical value as population
Step 4.7, iteration terminate judgement and optimum results output:
Z+1 is assigned to z, and judges z > zmaxIt is whether true, if not, return step 4.4, if so, expression changes
In generation, terminates, and by population optimal particleThe gear of the k period of corresponding on-load regulator transformer OLTCThe gear of the k period of switched capacitor group CBWith static nothing
The idle output power of the k period of function generator SVCRespectively as on-load regulator transformer
The practical control of OLTC, switched capacitor group CB and static reactive generator SVC in the current k period exports, and when casting out remaining
The gear and idle output power of section；
Step 5: rolling optimization terminates to judge:
K+1 is assigned to K, and judges K > KmaxIt is whether true, if so, then indicate that rolling optimization terminates, otherwise, with
Current scrolling optimization section [k, the k+H arrivedIN- 1] population optimal particle onCorresponding all discrete type adjusting devices
Gear time seriesReplace in a few days optimizing phase section [k, k+HIN- 1] the gear time of the discrete type adjusting device on
SequenceWith
And return step 3.1.
Compared with the prior art, the beneficial effects of the present invention are embodied in:
1, the present invention calculates rolling optimization model in consideration gear modified day using MIXED INTEGER PSO algorithm,
To be modified to the gear of discrete type adjusting device, the real-time change of tracking distributed power generation power output, it is suppressed that uncertain
Adverse effect of the property to power distribution network, improves security of distribution network and performance driving economy.
2, the present invention combines robust Optimal methods and rolling optimization method, establishes min-max type rolling optimization target
Function ensures that the active loss of power distribution network and regulation and control cost minimize in the case where distributed power generation contributes most severe scene, it is suppressed that
The probabilistic adverse effect of distributed power generation power output, improves power distribution network static stability.
It is 3, of the invention by establishing gear change period decision making variable and its constraint in a few days rolling optimization model,
Influence of the gear change in a few days rolling optimization objective function is considered, to realize in the in a few days optimizing phase to discrete type tune
The gear of control equipment is modified, and is avoided discrete type adjusting device and is excessively adjusted, reduces active loss, improve power distribution network
Safety.
4, the present invention is only limited to the amendment to the gear change period of right time to the gear amendment of discrete type adjusting device, and only
Allow to adjust the period that gear change occurs in a small range, is not related to the amendment of gear size, avoids because gear is excessively repaired
Just cause to improve the practicability of gear correction strategy with the Serious conflicts of scheduling phase relevant Decision a few days ago.
5, the present invention calculates in a few days rolling optimization model using MIXED INTEGER PSO algorithm, solves because gear becomes
Change period decision making variable complex nonlinear characteristic and caused by a few days rolling optimization model be difficult to the problem of solving.
Detailed description of the invention
Fig. 1 is the method for the present invention flow diagram.
Specific embodiment
In the present embodiment, as shown in Figure 1, active power distribution network access is distributed formula power generation DG, discrete type adjusting device and company
Ideotype adjusting device；Distributed power generation DG includes distributed photovoltaic power generation and distributed wind-power generator；Discrete type adjusting device packet
OLTC containing on-load regulator transformer and switched capacitor group CB；Continuous type adjusting device includes static reactive generator SVC；The base
In the active power distribution network of MIXED INTEGER PSO algorithm in rolling optimization be in a few days to carry out as follows:
Step 1: establishing the gear time series of in a few days optimizing phase:
Step 1.1, the time scale for determining in a few days rolling optimization:
Scheduling phase D is a few days ago with TDFor time scale, with HD×TDFor the siding-to-siding block length of scheduling phase a few days ago, wherein HDFor
Integer multiple；
In a few days rolling optimization is with TINFor time scale, with HIN×TINFor the siding-to-siding block length of in a few days rolling optimization, wherein HINFor
Integer multiple；And there is TD=M × TIN, M is integer multiple, and subscript IN indicates the in a few days optimizing phase；
In practical applications, it is proposed that take TD=60min, HD=24, TIN=15min, HIN=24, M=4.If rolling optimization
Period is too short, and optimizing cycle then cannot can not preferably carry out shelves comprising the predictive information near enough gear change periods
Position amendment；
Step 1.2, the gear time series for establishing scheduling phase a few days ago:
By known scheduling phase optimum results a few days ago, on-load regulator transformer OLTC is established respectively in scheduling phase a few days ago
The gear time series of DSwitched capacitor group CB is in scheduling phase D a few days ago
Gear time seriesWith power distribution network and bulk power grid scheduling phase D a few days ago friendship
Cross-power time seriesWherein, NOLTCIndicate the total number of on-load regulator transformer OLTC,Indicate to number the on-load regulator transformer OLTC for being i in the gear of the t period of scheduling phase D a few days ago；It indicates
Gear of the switched capacitor group CB that number is i in the t period of scheduling phase D a few days ago,Indicate that power distribution network and bulk power grid exist
The interaction power of the t period of scheduling phase D a few days ago, subscript grid indicate that power distribution network is interacted with bulk power grid power；
It is Given information that the discrete type adjusting device gear of scheduling phase and power distribution network, which interact power with bulk power grid, a few days ago；
Step 1.3, the gear time series for establishing the in a few days optimizing phase:
According to time scale TDAnd TINFacilities, by each time series of scheduling phase a few days ago respectively obtained load adjust
Gear time series of the pressure transformer OLTC in the in a few days optimizing phaseIt throws
Capacitor group CB is cut in the gear time series of in a few days optimizing phaseAnd distribution
The interaction power time series of net and bulk power grid in the in a few days optimizing phaseWherein,WithRespectively indicating and numbering the on-load regulator transformer OLTC for being i in the gear of the t period of in a few days optimizing phase, number is i's
Gear of the switched capacitor group CB in the t period of in a few days optimizing phase,Indicate that power distribution network and bulk power grid are in a few days optimizing rank
The interaction power of the t period of section；
Each period of scheduling phase is divided M small periods in the in a few days optimizing phase a few days ago, therefore hasSame gear time series also has such corresponding relationship.
Step 2: in a few days rolling optimization initializes:
Step 2.1, segment variable when establishing gear change:
The number of gear change occurs according to discrete type adjusting device, foundation number is i and device type isDiscrete type
Segment variable when the s times gear change of adjusting deviceAndValue be corresponding period volume when the s time gear change occurs
Number, device typeValue is OLTC or CB, respectively indicates on-load regulator transformer, switched capacitor group；
The gear time series for the in a few days optimizing phase that definition is obtained by the gear time series of scheduling phase a few days ago is day
The original speed position time series of interior optimizing phase, and establish that number is i and device type isDiscrete type adjusting device s
Segment variable when secondary original speed position changes It is constant value variable, andValue beInitial value, subscript 0 indicate just
Beginning information；
In a few days the gear time series of optimizing phase will do it amendment after each rolling optimization,Value be that can occur
Variation；And segment variable when original gear changeIt is constant value variable；
Step 2.2, in a few days rolling optimization initialize:
Definition in a few days the optimizing phase kth in a few days rolling optimization section be [k, k+HIN- 1], k is in a few days rolling optimization
Start periods, k+HIN- 1 is the most end period of in a few days rolling optimization, and has K=k, defines the largest optimization time of in a few days rolling optimization
Number is Kmax；The current in a few days rolling optimization of initialization is the 1st in a few days rolling optimization, the 1st time in a few days rolling optimization section be [1,
HIN]；
Rolling optimization mathematical model in gear modified day is considered Step 3: establishing:
Step 3.1 establishes distributed power generation DG power output indeterminacy section:
Optimize section [k, k+H according to current scrollingIN- 1] distributed power generation DG power output predictive information, is obtained using formula (1) on
To the power output for the t period for numbering the distributed power generation DG for being iIndeterminacy section:
In formula (1), NDGIt is the total number of distributed power generation DG in power distribution network,WithRespectively be number be i
The power output prediction standard value and prediction deviation maximum value of the t period of distributed power generation DG；
Step 3.2 obtains the dual objective functions of in a few days rolling optimization using formula (2):
In formula (2), fp indicates head end and endpoint node is the branch of node f and node p respectively, and Φ indicates all branch groups
At set, Ifp,tIt is the resistance r for flowing through branch fp the t periodfpOn electric current, NSVCIt is total of static reactive generator SVC
Number, CSVCIt is the unit power output cost of the static reactive generator SVC after conversion,Indicate the static reacance that number is i
The reactive power of the t period of generator SVC exports decision variable, and α, β are weight coefficients；PDGIndicate that all distributed power generation DG exist
Current scrolling optimizes the set of the day part power output in section, PDGElement be internal layer optimization decision variable；Indicate all
Static reactive generator SVC exports the set of decision variable in the reactive power of the day part in current scrolling optimization section,Table
Show that all discrete type adjusting devices optimize the set of the gear change period decision variable of the day part in section in current scrolling,WithElement be outer layer optimization decision variable；
Objective function shown in formula (2) is min-max structure, and internal layer max indicates the most severe scene of distributed power generation power output,
The objective function indicates to find the regulation and control scheme for making the minimization of object function under most severe scene.
The adjusting of step 3.3, the gear change period decision variable that discrete type adjusting device is obtained using formula (3) is constrained:
In formula (3),Indicate that number is i and device type isDiscrete type adjusting device the s times gear change when
Section decision variable；
In order to reach the objective function optimal value in each rolling optimization period, in fact it could happen that gear sequence is in each rolling
The case where optimizing cycle varies widely leads to over-correction gear, on the one hand aggravates voltage fluctuation, seriously threaten power distribution network
Stable operation, on the other hand cause discrete type adjusting device action frequency after the in a few days optimizing phase dynamic more than day maximum
Make number (day maximum actuation number be traditionally arranged to be no more than 4 times and be important restrictions of scheduling phase a few days ago)；
Step 3.4, the Branch Power Flow constraint that the OLTC containing on-load regulator transformer of t period is obtained using formula (4):
In formula (4), Vf,tAnd Vp,tIt is the voltage magnitude of node f and node p in the t period, P respectivelyfp,tAnd Qfp,tIt is t respectively
Active and reactive power on period branch fp, xfpIt is the reactance of branch fp,It is the static reacance hair connect on node p
The reactive power output of the t period of raw device SVC,WithBe respectively connect the load L on node p t period active and
Reactive power, Kfp,tIt is the no-load voltage ratio for connecing the t period of the on-load regulator transformer OLTC on branch fp,It is to connect on node p
Switched capacitor group CB the t period idle output power, U (p) indicate it is all using node p as the end of the branch of headend node
End segment point set, r ∈ U (p) indicate the node r for belonging to set U (p)；
Step 3.5 obtains what the static reactive generator SVC reactive power that the number of t period is i exported using formula (5)
Constraint:
In formula (7),WithIt is the reactive power maximum for the static reactive generator SVC that number is i respectively
Output, minimum output；
Step 3.6, the power distribution network that the t period is obtained using formula (6) interact the constraint of power with bulk power grid:
In formula (6), kgridIndicate that power is interacted with bulk power grid in a few days optimizing phase power distribution network deviates the journey dispatched a few days ago
Degree, NnodeIt is the sum of power distribution network interior joint；
Bulk power grid can retain certain spare capacity according to scheduling result a few days ago, to cope with the variation of practical power demand；
To guarantee power grid security, not allowing the variation of practical power demand is more than certain predetermined range.
Step 3.7, obtained using formula (7) and formula (8) the t period power distribution network security constraint:
In formula (7),WithBe respectively branch fp allow to flow through minimum, maximum current；
In formula (8),WithIt is minimum, the maximum voltage of node p permission respectively；
Step 4: being solved using MIXED INTEGER PSO algorithm in a few days rolling optimization model:
Step 4.1, particle coding:
Judge in currently in a few days rolling optimization section [k, k+HIN- 1] whether each discrete type adjusting device occurs gear change in
Which change, and happens is that time gear change, so that the related gear for the discrete type adjusting device that gear change occurs be become
Change period decision variable and is included in particle coding；
It obtains exporting decision variable by the reactive power of static reactive generator SVC using formula (9), gear change occurs
The gear of the switched capacitor group CB of the gear change period decision variable and generation gear change of on-load regulator transformer OLTC
Change the particle vector of period decision variable composition
In formula (9),Indicate that number is NSVCStatic reactive generator SVC k+HIN- 1 period it is idle
Output power decision variable；Subscript 1 ..., NSVCRespectively indicate NSVCThe device numbering of a static reactive generator SVC
Indicate the S for the on-load regulator transformer OLTC that number is ChCh+NCh- 1 gear change period determines
Plan variable, subscript C1, C2 ..., Ch be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] interior h occurs gear change
The device numbering of on-load regulator transformer OLTC, subscript SC1、…、SC1+NC1- 1 respectively indicates the on-load voltage regulation transformation that number is C1
The S that device OLTC occursC1It is secondary ..., SC1+NC1- 1 gear change；NC1It indicates to optimize section [k, k+H in current scrollingIN-
1] N has occurred in the on-load regulator transformer OLTC that number is C1 in altogetherC1Secondary gear change；
Indicate the S for the switched capacitor group CB that number is DgDg+NDg- 1 time gear change period decision becomes
Amount, subscript D1, D2 ..., Dg be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] switching of interior g generation gear change
The device numbering of capacitor group CB, subscript SD1、…、SD1+ND1- 1 respectively indicates what the switched capacitor group CB that number is D1 occurred
SD1It is secondary ..., SD1+ND1- 1 gear change；ND1It indicates to optimize section [k, k+H in current scrollingIN- 1] number is D1 in
Switched capacitor group CB N has occurred altogetherD1Secondary gear change；
Particle does not include the gear change that the discrete type adjusting device of gear change does not occur in current scrolling optimizing cycle
Change period decision variable.
Step 4.2, particle initialization:
Step 4.2.1, for component in particle Initial value
It takesInterior random value；
Step 4.2.2, for component in particleWith Initial value takeInterior random integers, max { } expression take in braces
The maximum value of all numbers, min { } indicate to take the minimum value of all numbers in braces；
Step 4.2.3, initialization obtains h+g predecessorWherein,It indicates to compile
Number be w predecessor；
Decision variable is exported using the t period reactive power that formula (A1) obtains the static reactive generator SVC that number is iInitial value:
In formula (A1), θ8It is the stochastic variable that value range is [0,1]；The selection of value also use similar approach；
Step 4.3, particle iteration initialization:
Current iteration algebra z=1 is initialized, greatest iteration algebra is zmax；By predecessorIt is used as 1st generation simultaneously
Particle and 1st generation history optimal particle select one to keep target function value the smallest as initial in h+g predecessor
Population optimal particleSubscript best indicates best, and subscript PS indicates population；
Step 4.4, particle update:
Step 4.4.1, particle rapidity updates:
Using formula (10) to z for the movement velocity of d-th of component of particleIt is updated, to obtain z+
The movement velocity of d-th of component of 1 generation particle
In formula (10),Indicate number be w z for particle d-th of component,WithTable respectively
Show z for d-th of component of history optimal particle and d-th of component of population optimal particle, π,Respectively decay factor,
Studying factors,For the random number of [0,1] range；
The movement velocity range of the da component of particle isAnd da ∈ [1,
(NSVC×HIN)], subscript idaIndicate the device numbering of the corresponding adjusting device of the da component, kSVCIt is that movement velocity adjusts ginseng
Number；The movement velocity range of the dc component of particle is [- 1,1], and dc ≠ da；
Step 4.4.2, particle updates:
Using formula (11) to z for the da component of particleIt is updated, to obtain z+1 for particle
The da componentAnd the value range of the da component of particle is
Using formula (12), formula (13) to z for the dc component of particleIt is updated, to obtain z+1 generation
The dc component of particleThe value range of the dc component of particle isInterior integer:
In formula (12), sigmoid () indicates sigmoid function,WithFor the random number of [0-1] range, ρ is value
For 1 or -1 auxiliary variable；σ is the auxiliary variable that value is 0 or 1；
Step 4.4.3, update to obtain z+1 for particle by particle
1st to (NSVC×HIN) a particle component corresponds to the reactive power output decision of static reactive generator SVC and become
Amount, dc (dc ≠ da) a component correspond to the gear change period decision variable of discrete type adjusting device, are different from static reacance
The continuous reactive power of generator SVC output, the value of gear change period is discrete integer, therefore the value range of the two is not
With；
Step 4.5, particle decoding:
Step 4.5.1, obtained z+1 is turned for the gear change period decision variable of the discrete type adjusting device of particle
It is changed to the gear time series of corresponding discrete type adjusting device:
The first situation, ifIt enablesValue be gn, enableValue be km, definitionFor
Z+1 for particle decoding convert after obtain number is i's and device type isDiscrete type adjusting device the t period shelves
Position then willValue be assigned toIt willValue be assigned toSubscript t1 indicates that the period compiles
Number, and meetWith t1 ∈ [k, k+HIN-1]；
Second situation, ifThen willValue be assigned toIt will's
Value is assigned toSegment number when subscript t2 is indicated, and meetWith t2 ∈ [k, k+HIN-1]；
The third situation, ifThen willValue be assigned toSegment number when subscript t3 is indicated,
And meet t3 ∈ [k, k+HIN-1]；
Step 4.5.2, z+1 is obtained for the gear time series of the corresponding all discrete type adjusting devices of particle
It is obtained by z+1 using formula (14) for the discrete type adjusting device gear time series obtained after particle decoding conversion
In formula (14),The number obtained after z+1 is converted for particle decoding is respectively indicated as i's
The gear of the t period of on-load regulator transformer OLTC, switched capacitor group CB；
It willOptimize section [k, k+H with current scrollingIN- 1] the discrete type adjusting device of gear change does not occur on
Gear time series joint, to obtain z+1 for the gear time series of the corresponding all discrete type adjusting devices of particle
The gear time series for the discrete type adjusting device that gear change does not occur is indicated using formula (A2)
Gear time series of the z+1 for the corresponding all discrete type adjusting devices of particleByWith
It constitutes jointly, and each component is rearranged by device numbering sequence and period sequence；
Step 4.6 generates optimal particle:
Step 4.6.1, obtain connecing the no-load voltage ratio of the t period of the voltage adjustment of on-load transformer OLTC on branch fp using formula (15)
Kfp,t:
In formula (14), Kfp,0With Δ KfpBe respectively connect the on-load regulator transformer OLTC on branch fp standard no-load voltage ratio and
Step-length is adjusted,It indicatesMiddle correspondence connects the shelves of the t period of the on-load regulator transformer OLTC on branch fp
Position enables K when being free of on-load regulator transformer OLTC in branchfp,t=1；
Step 4.6.2, the idle output power of the t period for the switched capacitor group CB that number is i is obtained using formula (16)
In formula (15),It is the single group capacitor compensation power for the switched capacitor group CB that number is i；
Step 4.6.3, decision variable set is obtained using formula (17)WithTarget after the value of middle element is determining
Function:
In formula (17), objective function is single target function, constraint condition include formula (4), formula (6), formula (7), formula (8),
Formula (15) and formula (16), solve single target function, obtain z+1 for particleTarget function value, then with
Current z is for history optimal particleTarget function value be compared, if z+1 is for particleTarget letter
Numerical value is less than current z for history optimal particleTarget function value, then history optimal particle is updated, will
Z+1 is for particleAs z+1 for history optimal particleOtherwise without updating, i.e., by current z generation
History optimal particleAs z+1 for history optimal particleMesh is selected in all history optimal particles
Optimal particle of the smallest particle of offer of tender numerical value as population
Max type objective function shown in formula (17) is not the equivalent target letter of min-max type objective function shown in formula (2)
Number, the purpose of max type target function value shown in calculating formula (17) only calculate the target function value of particle；
Step 4.7, iteration terminate judgement and optimum results output:
Z+1 is assigned to z, and judges z > zmaxIt is whether true, if not, return step 4.4, if so, expression changes
In generation, terminates, and by population optimal particleThe gear of the k period of corresponding on-load regulator transformer OLTCThe gear of the k period of switched capacitor group CBWith static nothing
The idle output power of the k period of function generator SVCRespectively as on-load regulator transformer
The practical control of OLTC, switched capacitor group CB and static reactive generator SVC in the current k period exports, and when casting out remaining
The gear and idle output power of section；
Step 5: rolling optimization terminates to judge:
K+1 is assigned to K, and judges K > KmaxIt is whether true, if so, then indicate that rolling optimization terminates, otherwise, with
Current scrolling optimization section [k, the k+H arrivedIN- 1] population optimal particle onCorresponding all discrete type adjusting devices
Gear time seriesReplace in a few days optimizing phase section [k, k+HIN- 1] the gear time of the discrete type adjusting device on
SequenceWith
And return step 3.1.
Claims (1)
1. in a few days rolling optimization method, the active power distribution network access a kind of active power distribution network based on MIXED INTEGER PSO algorithm
It is distributed formula power generation DG, discrete type adjusting device and continuous type adjusting device；The distributed power generation DG includes distributed photovoltaic
Power generation and distributed wind-power generator；The discrete type adjusting device includes on-load regulator transformer OLTC and switched capacitor group
CB；The continuous type adjusting device includes static reactive generator SVC；It is characterized in that the active power distribution network in a few days roll it is excellent
Change method is to carry out as follows:
Step 1: establishing the gear time series of in a few days optimizing phase:
Step 1.1, the time scale for determining in a few days rolling optimization:
Scheduling phase D is a few days ago with TDFor time scale, with HD×TDFor the siding-to-siding block length of scheduling phase a few days ago, wherein HDFor integral multiple
Number；
In a few days rolling optimization is with TINFor time scale, with HIN×TINFor the siding-to-siding block length of in a few days rolling optimization, wherein HINFor integer
Multiple；And there is TD=M × TIN, M is integer multiple, and subscript IN indicates the in a few days optimizing phase；
Step 1.2, the gear time series for establishing scheduling phase a few days ago:
By known scheduling phase optimum results a few days ago, on-load regulator transformer OLTC is established respectively scheduling phase D's a few days ago
Gear time seriesShelves of the switched capacitor group CB in scheduling phase D a few days ago
Position time seriesWith power distribution network and bulk power grid scheduling phase D a few days ago interaction function
Rate time seriesWherein, NOLTCIndicate the total number of on-load regulator transformer OLTC,Table
Show on-load regulator transformer OLTC that number is i in the gear of the t period of scheduling phase D a few days ago；Indicate that number is i's
Gear of the switched capacitor group CB in the t period of scheduling phase D a few days ago,Indicate that power distribution network and bulk power grid are dispatching rank a few days ago
The interaction power of the t period of section D, subscript grid indicate that power distribution network is interacted with bulk power grid power；
Step 1.3, the gear time series for establishing the in a few days optimizing phase:
According to time scale TDAnd TINFacilities, by each time series of scheduling phase a few days ago respectively obtain on-load voltage regulation become
Gear time series of the depressor OLTC in the in a few days optimizing phaseSwitching electricity
Gear time series of the container group CB in the in a few days optimizing phaseWith power distribution network with
Interaction power time series of the bulk power grid in the in a few days optimizing phaseWherein,With
It respectively indicates and numbers the switching that the on-load regulator transformer OLTC for being i is i in the gear of the t period of in a few days optimizing phase, number
Gear of the capacitor group CB in the t period of in a few days optimizing phase,Indicate power distribution network and bulk power grid in the in a few days optimizing phase
The interaction power of t period；
Step 2: in a few days rolling optimization initializes:
Step 2.1, segment variable when establishing gear change:
The number of gear change occurs according to discrete type adjusting device, foundation number is i and device type isDiscrete type regulation
Segment variable when the s times gear change of equipmentAndValue it is corresponding when being the generation of the s time gear change when segment number,
Device typeValue is OLTC or CB, respectively indicates on-load regulator transformer, switched capacitor group；
The gear time series for the in a few days optimizing phase that definition is obtained by the gear time series of scheduling phase a few days ago is in a few days excellent
The original speed position time series in change stage, and establish that number is i and device type isThe s times of discrete type adjusting device at the beginning of
Segment variable when beginning gear changeIt is constant value variable, andValue beInitial value, subscript 0 indicates initially to believe
Breath；
Step 2.2, in a few days rolling optimization initialize:
Definition in a few days the optimizing phase kth in a few days rolling optimization section be [k, k+HIN- 1], k is the starting of in a few days rolling optimization
Period, k+HIN- 1 is the most end period of in a few days rolling optimization, and has K=k, and the largest optimization number for defining in a few days rolling optimization is
Kmax；The current in a few days rolling optimization of initialization is the 1st in a few days rolling optimization, the 1st time in a few days rolling optimization section be [1, HIN]；
Rolling optimization mathematical model in gear modified day is considered Step 3: establishing:
Step 3.1 establishes distributed power generation DG power output indeterminacy section:
Optimize section [k, k+H according to current scrollingIN- 1] distributed power generation DG power output predictive information, is compiled using formula (1) on
Number for i distributed power generation DG the t period power outputIndeterminacy section:
In formula (1), NDGIt is the total number of distributed power generation DG in power distribution network,WithIt is the distribution that number is i respectively
The power output prediction standard value and prediction deviation maximum value of the t period of formula power generation DG；
Step 3.2 obtains the dual objective functions of in a few days rolling optimization using formula (2):
In formula (2), fp indicates head end and endpoint node is the branch of node f and node p respectively, and Φ indicates all branch compositions
Set, Ifp,tIt is the resistance r for flowing through branch fp the t periodfpOn electric current, NSVCIt is the total number of static reactive generator SVC, CSVC
It is the unit power output cost of the static reactive generator SVC after conversion,Indicate the static reactive generator that number is i
The reactive power of the t period of SVC exports decision variable, and α, β are weight coefficients；PDGIndicate that all distributed power generation DG are rolled currently
The set of the day part power output in dynamic optimization section, PDGElement be internal layer optimization decision variable；Indicate all static nothings
Function generator SVC exports the set of decision variable in the reactive power of the day part in current scrolling optimization section,Indicate all
Discrete type adjusting device optimizes the set of the gear change period decision variable of the day part in section in current scrolling,With
Element be outer layer optimization decision variable；
The adjusting of step 3.3, the gear change period decision variable that discrete type adjusting device is obtained using formula (3) is constrained:
In formula (3),Indicate that number is i and device type isThe s times gear change period of discrete type adjusting device determine
Plan variable；
Step 3.4, the Branch Power Flow constraint that the OLTC containing on-load regulator transformer of t period is obtained using formula (4):
In formula (4), Vf,tAnd Vp,tIt is the voltage magnitude of node f and node p in the t period, P respectivelyfp,tAnd Qfp,tIt is the t period respectively
Active and reactive power on branch fp, xfpIt is the reactance of branch fp,It is the static reactive generator connect on node p
The reactive power of the t period of SVC exports,WithBe respectively connect the load L on node p the t period it is active and idle
Power, Kfp,tIt is the no-load voltage ratio for connecing the t period of the on-load regulator transformer OLTC on branch fp,It is the throwing connect on node p
The idle output power of the t period of capacitor group CB is cut, U (p) indicates all using node p as the end segment of the branch of headend node
Point set, r ∈ U (p) indicate the node r for belonging to set U (p)；
Step 3.5 obtains the constraint for the static reactive generator SVC reactive power output that the number of t period is i using formula (5):
In formula (7),WithRespectively be number be i static reactive generator SVC reactive power maximum it is defeated
Out, minimum output；
Step 3.6, the power distribution network that the t period is obtained using formula (6) interact the constraint of power with bulk power grid:
In formula (6), kgridIndicate that power is interacted with bulk power grid in a few days optimizing phase power distribution network deviates the degree dispatched a few days ago,
NnodeIt is the sum of power distribution network interior joint；
Step 3.7, obtained using formula (7) and formula (8) the t period power distribution network security constraint:
In formula (7),WithBe respectively branch fp allow to flow through minimum, maximum current；
In formula (8),WithIt is minimum, the maximum voltage of node p permission respectively；
Step 4: being solved using MIXED INTEGER PSO algorithm in a few days rolling optimization model:
Step 4.1, particle coding:
Judge in currently in a few days rolling optimization section [k, k+HIN- 1] whether each discrete type adjusting device occurs gear change in, with
And happens is which time gear change, thus by the related gear change period for the discrete type adjusting device that gear change occurs
Decision variable is included in particle coding；
Using formula (9) obtain by static reactive generator SVC reactive power output decision variable, gear change occur have load
The gear change of the switched capacitor group CB of the gear change period decision variable and generation gear change of adjustable transformer OLTC
The particle vector of period decision variable composition
In formula (9),Indicate that number is NSVCStatic reactive generator SVC k+HINThe idle output of -1 period
Power decision variable；Subscript 1 ..., NSVCRespectively indicate NSVCThe device numbering of a static reactive generator SVC
Indicate the S for the on-load regulator transformer OLTC that number is ChCh+NCh- 1 time gear change period decision becomes
Amount, subscript C1, C2 ..., Ch be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] interior h generation gear change has load
The device numbering of adjustable transformer OLTC, subscript SC1、…、SC1+NC1- 1 respectively indicates the on-load regulator transformer that number is C1
The S that OLTC occursC1It is secondary ..., SC1+NC1- 1 gear change；NC1It indicates to optimize section [k, k+H in current scrollingIN-1]
N has occurred in the on-load regulator transformer OLTC that interior number is C1 altogetherC1Secondary gear change；
Indicate the S for the switched capacitor group CB that number is DgDg+NDg- 1 gear change period decision variable,
Subscript D1, D2 ..., Dg be illustrated respectively in current scrolling optimization section [k, k+HIN- 1] the switching electricity of interior g generation gear change
The device numbering of container group CB, subscript SD1、…、SD1+ND1- 1 respectively indicates that the switched capacitor group CB that number is D1 occurs
SD1It is secondary ..., SD1+ND1- 1 gear change；ND1It indicates to optimize section [k, k+H in current scrollingIN- 1] number is D1's in
N has occurred in switched capacitor group CB altogetherD1Secondary gear change；
Step 4.2, particle initialization:
Step 4.2.1, for component in particle
Initial value takeInterior random value；
Step 4.2.2, for component in particleWith
Initial value takeInterior random integers, max
{ } indicates to take the maximum value of all numbers in braces, and min { } indicates to take the minimum value of all numbers in braces；
Step 4.2.3, initialization obtains h+g predecessorWherein,Indicate that number is w
Predecessor；
Step 4.3, particle iteration initialization:
Current iteration algebra z=1 is initialized, greatest iteration algebra is zmax；By predecessorIt is used as 1st generation particle simultaneously
With 1st generation history optimal particle, one is selected to keep target function value the smallest as initial particle in h+g predecessor
Group's optimal particleSubscript best indicates best, and subscript PS indicates population；
Step 4.4, particle update:
Step 4.4.1, particle rapidity updates:
Using formula (10) to z for the movement velocity of d-th of component of particleIt is updated, to obtain z+1 for grain
The movement velocity of d-th of component of son
In formula (10),Indicate number be w z for particle d-th of component,WithRespectively indicate
Z for d-th of component of history optimal particle and d-th of component of population optimal particle, π,Respectively decay factor, study
The factor, θ1、θ2For the random number of [0,1] range；
The movement velocity range of the da component of particle isAnd da ∈ [1, (NSVC×
HIN)], subscript idaIndicate the device numbering of the corresponding adjusting device of the da component, kSVCIt is movement velocity adjustment parameter；Particle
The movement velocity range of the dc component be [- 1,1], and dc ≠ da；
Step 4.4.2, particle updates:
Using formula (11) to z for the da component of particleIt is updated, so that the da for obtaining z+1 for particle is a
ComponentAnd the value range of the da component of particle is
Using formula (12), formula (13) to z for the dc component of particleIt is updated, to obtain z+1 for particle
The dc componentThe value range of the dc component of particle isInterior integer:
In formula (12), sigmoid () indicates sigmoid function, θ3And θ4For the random number of [0-1] range, ρ is that value is 1
Or -1 auxiliary variable；σ is the auxiliary variable that value is 0 or 1；
Step 4.4.3, update to obtain z+1 for particle by particle
Step 4.5, particle decoding:
Step 4.5.1, obtained z+1 is converted to for the gear change period decision variable of the discrete type adjusting device of particle
The gear time series of corresponding discrete type adjusting device:
The first situation, ifIt enablesValue be gn, enableValue be km, definitionFor z+
1 generation particle decoding convert after obtain number is i's and device type isDiscrete type adjusting device the t period gear,
Then willValue be assigned toIt willValue be assigned toSegment number when subscript t1 is indicated, and
MeetWith t1 ∈ [k, k+HIN-1]；
Second situation, ifThen willValue be assigned toIt willValue assign
It givesSegment number when subscript t2 is indicated, and meetWith t2 ∈ [k, k+HIN-1]；
The third situation, ifThen willValue be assigned toSegment number when subscript t3 is indicated, and it is full
Sufficient t3 ∈ [k, k+HIN-1]；
Step 4.5.2, z+1 is obtained for the gear time series of the corresponding all discrete type adjusting devices of particle
It is obtained by z+1 using formula (14) for the discrete type adjusting device gear time series obtained after particle decoding conversion
In formula (14),Respectively indicating the number obtained after z+1 is converted for particle decoding has load into i
The gear of the t period of adjustable transformer OLTC, switched capacitor group CB；
It willOptimize section [k, k+H with current scrollingIN- 1] gear of the discrete type adjusting device of gear change does not occur on
Time series joint, to obtain z+1 for the gear time series of the corresponding all discrete type adjusting devices of particle
Step 4.6 generates optimal particle:
Step 4.6.1, obtain connecing the no-load voltage ratio K of the t period of the voltage adjustment of on-load transformer OLTC on branch fp using formula (15)fp,t:
In formula (14), Kfp,0With Δ KfpIt is the standard no-load voltage ratio and adjusting for meeting the on-load regulator transformer OLTC on branch fp respectively
Step-length,It indicatesMiddle correspondence connects the gear of the t period of the on-load regulator transformer OLTC on branch fp, when
On-load regulator transformer OLTC is free of in branch, enables Kfp,t=1；
Step 4.6.2, the idle output power of the t period for the switched capacitor group CB that number is i is obtained using formula (16)
In formula (15),It is the single group capacitor compensation power for the switched capacitor group CB that number is i；
Step 4.6.3, decision variable set is obtained using formula (17)WithObjective function after the value of middle element is determining:
In formula (17), objective function is single target function, and constraint condition includes formula (4), formula (6), formula (7), formula (8), formula
(15) and formula (16), single target function is solved, obtains z+1 for particleTarget function value, then with work as
Preceding z is for history optimal particleTarget function value be compared, if z+1 is for particleObjective function
Value is less than current z for history optimal particleTarget function value, then history optimal particle is updated, by z
+ 1 generation particleAs z+1 for history optimal particleOtherwise without updating, i.e., in current z generation, is gone through
History optimal particleAs z+1 for history optimal particleThe selection target in all history optimal particles
Optimal particle of the smallest particle of functional value as population
Step 4.7, iteration terminate judgement and optimum results output:
Z+1 is assigned to z, and judges z > zmaxIt is whether true, if not, return step 4.4, if so, indicate iteration knot
Beam, and by population optimal particleThe gear of the k period of corresponding on-load regulator transformer OLTCThe gear of the k period of switched capacitor group CBWith static nothing
The idle output power of the k period of function generator SVCRespectively as on-load regulator transformer
The practical control of OLTC, switched capacitor group CB and static reactive generator SVC in the current k period exports, and when casting out remaining
The gear and idle output power of section；
Step 5: rolling optimization terminates to judge:
K+1 is assigned to K, and judges K > KmaxIt is whether true, if so, then indicate that rolling optimization terminates, otherwise, with what is obtained
Current scrolling optimizes section [k, k+HIN- 1] population optimal particle onThe gear of corresponding all discrete type adjusting devices
Time seriesReplace in a few days optimizing phase section [k, k+HIN- 1] the gear time series of the discrete type adjusting device onWithAnd it returns
Return step 3.1.
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