CN110059897B - Active power distribution network intraday rolling optimization method based on mixed integer PSO algorithm - Google Patents

Active power distribution network intraday rolling optimization method based on mixed integer PSO algorithm Download PDF

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CN110059897B
CN110059897B CN201910436490.2A CN201910436490A CN110059897B CN 110059897 B CN110059897 B CN 110059897B CN 201910436490 A CN201910436490 A CN 201910436490A CN 110059897 B CN110059897 B CN 110059897B
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period
particle
gear
formula
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CN110059897A (en
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吴红斌
曾希
徐斌
骆晨
丁津津
陈洪波
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Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
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Electric Power Research Institute of State Grid Anhui Electric Power Co Ltd
Hefei University of Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H02J3/383—
    • H02J3/386—
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses an active power distribution network intraday rolling optimization method based on a mixed integer PSO algorithm, which comprises the following steps of: 1, determining the time scale of rolling optimization in the day, establishing a gear time sequence of an optimization stage in the day, and establishing a gear change period variable; 2, establishing a day rolling optimization mathematical model considering gear correction; and 3, outputting an optimization result through a mixed integer PSO algorithm. The invention can correct the gear of the discrete regulation and control equipment and overcome the influence of randomness and uncertainty of distributed generation, thereby ensuring the minimization of the active loss and the regulation and control cost of the power distribution network in the worst distributed generation output scene.

Description

Active power distribution network intraday rolling optimization method based on mixed integer PSO algorithm
Technical Field
The invention relates to the field of optimized operation of a power distribution network of a power system, in particular to an active power distribution network intraday rolling optimization method based on a mixed integer PSO algorithm.
Background
At present, China is undergoing a new energy revolution, and governments are popularizing new energy technologies in ways of photovoltaic poverty-relieving engineering, new energy industry subsidy and the like. According to the forecast of domestic professional institutions, the installed capacity of the distributed power supply in 2030 years can reach about 17% of the total installed capacity of the whole nation at the same period. The distributed photovoltaic power generation and the distributed wind power generation are widely applied and technically mature, so that a large amount of practical application is achieved. The distributed power generation has the characteristics of strong randomness, large fluctuation and intermittence, and a large amount of distributed power generation is connected into the power distribution network, so that the safety and stability of the power distribution network and the economical efficiency of operation are greatly influenced, and the distributed power generation becomes a key research subject in the field of optimized operation of the power distribution network.
The optimized operation of the power distribution network comprises a day-ahead scheduling stage and an intra-day optimizing stage. The day-ahead scheduling stage is optimized based on the distributed generation output prediction with a longer time period, the distributed generation output prediction precision is limited due to factors such as the prediction period, and the gear sequence of the discrete regulation and control equipment determined in the day-ahead scheduling stage is not optimal. In the actual operation of the power distribution network, if the deviation between the actual distributed generation output and the predicted output in the day-ahead scheduling stage is large, the discrete regulation and control equipment is excessively regulated, and the active loss and the voltage of the power distribution network are increased and unstable.
There are two main methods for day-to-day optimization operation of an active power distribution network: a robust optimization method and a rolling optimization method. The robustness optimization method considers the uncertainty of distributed generation output, ensures that the safety and stability of the power distribution network can be still maintained in the worst scene, but has conservative optimization results and poor economy. The rolling optimization method carries out rolling optimization according to the distributed generation output prediction in a short time period, when the prediction is accurate, the optimization effect is good, but the prediction error and the uncertainty of the distributed generation are inevitable, and the optimization effect of the rolling optimization method is greatly influenced by the prediction precision.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides an active power distribution network intraday rolling optimization method based on a mixed integer PSO algorithm, so that the gear of discrete regulation and control equipment can be corrected, the influence of randomness and uncertainty of distributed power generation is overcome, and the active loss and the regulation and control cost of the power distribution network are minimized in the worst distributed power generation output scene.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to an active power distribution network intraday rolling optimization method based on a mixed integer PSO algorithm, wherein a distributed generation DG, discrete regulation and control equipment and continuous regulation and control equipment are connected to an active power distribution network; the distributed generation DG comprises distributed photovoltaic generation and distributed wind power generation; the discrete regulation and control equipment comprises an on-load tap changer (OLTC) and a switched Capacitor Bank (CB); the continuous regulating and controlling equipment comprises a static var generator (SVC); the method is characterized in that the intraday rolling optimization method of the active power distribution network is carried out according to the following steps:
step one, establishing a gear time sequence of an optimization stage in the day:
step 1.1, determining the time scale of rolling optimization in the day:
scheduling phase D with T day aheadDOn a time scale of HD×TDFor the interval length of the day-ahead scheduling phase, where HDIs an integer multiple;
intra-day rolling optimization by TINOn a time scale of HIN×TINInterval length optimized for rolling in days, where HINIs an integer multiple; and has TD＝M×TINM is an integer multiple, and the superscript IN represents the IN-day optimization stage;
step 1.2, establishing a gear time sequence of a day-ahead scheduling stage:
the results are optimized by the known day-ahead scheduling phase,respectively establishing gear time sequences of the on-load tap changer OLTC in the day-ahead scheduling stage D
Figure GDA0002789606640000021
Gear time sequence of switched capacitor bank CB in day-ahead scheduling stage D
Figure GDA0002789606640000022
Interaction power time sequence of distribution network and large power grid in day-ahead scheduling stage D
Figure GDA0002789606640000023
Wherein N isOLTCRepresenting the total number of on-load tap changers OLTC,
Figure GDA0002789606640000024
the gear of the on-load tap changer OLTC with the number of i in the t period of the day-ahead scheduling stage D is represented;
Figure GDA0002789606640000025
the gear of the switched capacitor bank CB with the number i in the period t of the scheduling phase D before the day,
Figure GDA0002789606640000026
the interaction power of the power distribution network and the large power grid in the t period of the scheduling stage D before the day is represented, and the subscript grid represents the interaction of the power distribution network and the large power grid;
step 1.3, establishing a gear time sequence of an optimization stage in the day:
according to time scale TDAnd TINAccording to the setting condition of the on-load tap changer (OLTC), the gear time sequence of the OLTC in the optimization stage in the day is respectively obtained from each time sequence of the day-ahead scheduling stage
Figure GDA0002789606640000027
Gear time sequence of switched capacitor bank CB in optimization stage in day
Figure GDA0002789606640000028
The power distribution network and the large power grid are excellent in the dayInteractive power time series of chemical phases
Figure GDA0002789606640000029
Wherein the content of the first and second substances,
Figure GDA00027896066400000210
and
Figure GDA00027896066400000211
respectively representing the gear of the on-load tap changer OLTC with the number of i in the t period of the optimization stage in the day and the gear of the switched capacitor bank CB with the number of i in the t period of the optimization stage in the day,
Figure GDA00027896066400000212
representing the interactive power of the distribution network and the large power grid in the time t of the optimization stage in the day;
step two, rolling optimization initialization in a day:
step 2.1, establishing a gear change period variable:
establishing a serial number i and a device type i according to the number of gear change of the discrete regulation and control device
Figure GDA00027896066400000213
The variation of the s-th gear change period of the discrete regulating and controlling equipment
Figure GDA0002789606640000031
And is
Figure GDA0002789606640000032
The values of (a) are the corresponding time interval number and the equipment type when the s-th gear change occurs
Figure GDA0002789606640000033
The value is OLTC or CB, which respectively represents an on-load tap changer and a switched capacitor bank;
defining the gear time sequence of the intraday optimization stage obtained from the gear time sequence of the day-ahead scheduling stage as the initial gear time sequence of the intraday optimization stage, and establishing a gear time sequence with the number i and the number iThe standby type is
Figure GDA0002789606640000034
The variation of the s-th initial gear change period of the discrete regulation and control equipment
Figure GDA0002789606640000035
Is a constant variable, and
Figure GDA0002789606640000036
has a value of
Figure GDA0002789606640000037
The initial value of (a), superscript 0 represents the initial information;
step 2.2, rolling optimization initialization in a day:
defining the rolling optimization interval in the Kth day of the intra-day optimization stage as [ K, K + H ]IN-1]K is the start period of the rolling in-day optimization, k + HIN-1 is the last period of the rolling optimization within the day, and K is defined as the maximum number of optimizations of the rolling optimization within the day, Kmax(ii) a Initializing rolling optimization in the current day into rolling optimization in the 1 st day, wherein the rolling optimization interval in the 1 st day is [1, H ]IN]；
Step three, establishing a day rolling optimization mathematical model considering gear correction:
step 3.1, establishing an uncertain output interval of the distributed generation DG:
optimizing interval [ k, k + H ] according to current rollingIN-1]The output prediction information of the distributed generation DG is obtained by using the formula (1) to obtain the output of the distributed generation DG with the number of i in the t period
Figure GDA0002789606640000038
The uncertain interval of (2):
Figure GDA0002789606640000039
in the formula (1), NDGIs the total number of distributed generation DGs in the distribution network,
Figure GDA00027896066400000310
and
Figure GDA00027896066400000311
respectively representing a predicted standard value and a predicted maximum value of the output of the distributed generation DG with the number of i in the t period;
step 3.2, obtaining a double-layer objective function of rolling optimization in the day by using the formula (2):
Figure GDA00027896066400000312
in the formula (2), fp denotes the branches of the head end node and the tail end node which are the node f and the node p respectively, phi denotes the set composed of all the branches, Ifp,tIs the resistance r flowing through the branch fp during the period tfpCurrent of (3), NSVCIs the total number of SVCs, CSVCIs the converted unit power output cost of the static var generator SVC,
Figure GDA00027896066400000313
the method comprises the steps of representing reactive power output decision variables of a static var generator (SVC) with the number of i in a t period, wherein alpha and beta are weight coefficients; pDGRepresents the set of all distributed generation DGs in each time interval of the current rolling optimization interval, PDGIs a decision variable for inner layer optimization;
Figure GDA00027896066400000314
represents the set of reactive power output decision variables of all static var generators SVC at each time interval of the current roll optimization interval,
Figure GDA00027896066400000315
represents the set of gear change period decision variables of all discrete regulating and controlling devices in each period of the current rolling optimization interval,
Figure GDA0002789606640000041
and
Figure GDA0002789606640000042
is the decision variable of the outer layer optimization;
step 3.3, obtaining the adjustment constraint of the gear change period decision variable of the discrete regulation and control equipment by using the formula (3):
Figure GDA0002789606640000043
in the formula (3), the reaction mixture is,
Figure GDA0002789606640000044
denotes the number i and the type of device is
Figure GDA0002789606640000045
The decision variable of the s-th gear change period of the discrete regulation and control equipment;
step 3.4, obtaining branch load flow constraint containing the on-load tap changing transformer OLTC in the t period by using the formula (4):
Figure GDA0002789606640000046
in the formula (4), Vf,tAnd Vp,tThe voltage amplitudes of node f and node P, P, respectively, during period tfp,tAnd Qfp,tActive and reactive power, x, on the t-period branch fp, respectivelyfpIs the reactance of the branch fp,
Figure GDA0002789606640000047
is the reactive power output of the static var generator SVC connected at node p during period t,
Figure GDA0002789606640000048
and
Figure GDA0002789606640000049
active and reactive power, K, respectively, during t periods of a load L connected to a node pfp,tIs the transformation ratio of the on-load tap changer OLTC connected to the branch fp during the period t,
Figure GDA00027896066400000410
is the reactive power output of the switched capacitor bank CB connected to the node p in the period t, U (p) represents the end node set of all branches taking the node p as the head end node, r belongs to U (p) represents the node r belonging to the set U (p);
and 3.5, obtaining the constraint of the SVC reactive power output numbered i in the t period by using the formula (5):
Figure GDA00027896066400000411
in the formula (5), the reaction mixture is,
Figure GDA00027896066400000412
and
Figure GDA00027896066400000413
the maximum output and the minimum output of the reactive power of the static var generator (SVC) with the serial number of i are respectively;
step 3.6, obtaining the constraint of the interaction power of the power distribution network and the large power grid at the time period t by using the formula (6):
Figure GDA00027896066400000414
in the formula (6), kgridRepresenting the degree of the interactive power of the power distribution network and the large power grid deviating from the day-ahead scheduling in the optimization stage in the day, NnodeIs the total number of nodes in the distribution network;
and 3.7, obtaining the safety constraint of the power distribution network in the t time period by using the formula (7) and the formula (8):
Figure GDA0002789606640000051
Figure GDA0002789606640000052
in the formula (7), the reaction mixture is,
Figure GDA0002789606640000053
and
Figure GDA0002789606640000054
respectively the minimum and maximum current allowed to flow by the branch fp;
in the formula (8), the reaction mixture is,
Figure GDA0002789606640000055
and
Figure GDA0002789606640000056
respectively, the minimum and maximum voltages allowed for node p;
solving the intraday rolling optimization model by using a mixed integer PSO algorithm:
step 4.1, particle coding:
judging the rolling optimization interval [ k, k + H ] in the current dayIN-1]Whether each discrete type regulating and controlling device has gear change or not and what is happening is the gear change for the second time, so that the decision variable of the relevant gear change time period of the discrete type regulating and controlling device with the gear change is brought into the particle code;
obtaining a particle vector consisting of a reactive power output decision variable of a static var generator (SVC), a gear change period decision variable of an on-load tap changer (OLTC) with gear change and a gear change period decision variable of a switched Capacitor Bank (CB) with gear change by using a formula (9)
Figure GDA0002789606640000057
Figure GDA0002789606640000058
In the formula (9), the reaction mixture is,
Figure GDA0002789606640000059
the expression number is NSVCK + H of static var generator SVCIN-a reactive power output decision variable for a1 time period; subscripts 1, …, NSVCRespectively represent NSVCThe equipment number of each static var generator SVC;
Figure GDA00027896066400000510
s-th of on-load tap changer OLTC with Ch numberCh+NCh1 gear change period decision variable, subscripts C1, C2, …, Ch respectively representing the current roll optimization interval [ k, k + H [ ]IN-1]The equipment numbers, subscripts S, of the inner h on-load tap changing transformers OLTCC1、…、SC1+NC1-1 denotes respectively the occurrence of the sth of the on-load tap changer OLTC numbered C1C1Sub, …, SC1+NC1-1 gear change; n is a radical ofC1Represents the current rolling optimization interval [ k, k + H ]IN-1]The OLTC with the internal number of C1 generates N in totalC1A secondary gear change;
Figure GDA00027896066400000511
s of switched capacitor group CB with number DgDg+NDg1 gear change period decision variable, subscripts D1, D2, …, Dg representing the current roll optimization interval [ k, k + H, respectivelyIN-1]The equipment numbers and subscripts S of the inner g switched capacitor bank CB with gear changeD1、…、SD1+ND1-1 denotes respectively the S th occurrence of the switched capacitor bank CB, numbered D1D1Sub, …, SD1+ND1-1 gear change; n is a radical ofD1Represents the current rolling optimization interval [ k, k + H ]IN-1]Switched capacitor bank CB with internal number D1 has N in commonD1A secondary gear change;
step 4.2, particle initialization:
step 4.2.1 for fractional amounts in particles
Figure GDA0002789606640000061
Figure GDA0002789606640000062
Taking the initial value of
Figure GDA0002789606640000063
A random value of;
step 4.2.2 for fractional amounts in particles
Figure GDA0002789606640000064
And
Figure GDA0002789606640000065
Figure GDA0002789606640000066
taking the initial value of
Figure GDA0002789606640000067
Random integer in the brace, max {. is the maximum value of all numbers in the brace, and min {. is the minimum value of all numbers in the brace;
step 4.2.3, initializing to obtain h + g original particles
Figure GDA0002789606640000068
Wherein the content of the first and second substances,
Figure GDA0002789606640000069
representing the original particle numbered w;
step 4.3, iterative initialization of particles:
initializing the current iteration algebra z as 1 and the maximum iteration algebra as zmax(ii) a The original particles
Figure GDA00027896066400000610
Simultaneously as the 1 st generation particle and the 1 st generation history optimal particle, one of h + g original particles is selected to minimize the objective function valueOptimizing particles for an initial population of particles
Figure GDA00027896066400000611
The superscript best, and the subscript PS for the population;
step 4.4, particle updating:
step 4.4.1, particle velocity update:
the motion velocity of the d-th component of the z-th generation particle using equation (10)
Figure GDA00027896066400000619
Updating to obtain the motion speed of the d component of the z +1 th generation particle
Figure GDA00027896066400000612
Figure GDA00027896066400000613
In the formula (10), the compound represented by the formula (10),
Figure GDA00027896066400000614
denotes the d-th component of the z-th generation particle numbered w,
Figure GDA00027896066400000615
and
Figure GDA00027896066400000616
respectively representing the d-th component of the z-th generation history optimal particle and the d-th component of the particle swarm optimal particle, pi,
Figure GDA00027896066400000617
Are respectively an attenuation factor and a learning factor,
Figure GDA00027896066400000618
is [0,1 ]]A random number of ranges;
the range of the motion speed of the da-th component of the particle is
Figure GDA0002789606640000071
And da ∈ [1, (N)SVC×HIN)]Subscript idaDevice number k indicating the control device corresponding to the da-th componentSVCIs a motion speed adjustment parameter; the dc component of the particle has a velocity of motion in the range of-1, 1]And dc ≠ da;
step 4.4.2, particle update:
the da component of the z-th generation particle is calculated by equation (11)
Figure GDA0002789606640000072
Updating to obtain the da component of the z +1 th generation particle
Figure GDA0002789606640000073
And the value range of the da component of the particle is
Figure GDA0002789606640000074
Figure GDA0002789606640000075
The dc component of the z-th generation particle is determined by the equations (12) and (13)
Figure GDA0002789606640000076
Updating to obtain the dc component of the z +1 th generation particle
Figure GDA0002789606640000077
The value of the dc component of the particle ranges from
Figure GDA0002789606640000078
The integer of (a):
Figure GDA0002789606640000079
Figure GDA00027896066400000710
in equation (12), sigmoid (. cndot.) represents a sigmoid function,
Figure GDA00027896066400000718
and
Figure GDA00027896066400000719
is [0-1 ]]A random number of the range, ρ being an auxiliary variable taking the value 1 or-1; sigma is an auxiliary variable with the value of 0 or 1;
step 4.4.3 obtaining z +1 th generation particles by particle renewal
Figure GDA00027896066400000711
Figure GDA00027896066400000712
And 4.5, particle decoding:
step 4.5.1, converting the gear change period decision variable of the discrete regulation and control equipment of the z +1 th generation particle into a corresponding gear time sequence of the discrete regulation and control equipment:
in the first case, if
Figure GDA00027896066400000713
Figure GDA00027896066400000714
The value of (a) is gn (n),
Figure GDA00027896066400000715
has a value of km, define
Figure GDA00027896066400000716
The number obtained after the z +1 th generation particle decoding conversion is i and the type of equipment is
Figure GDA00027896066400000717
Of discrete typeRegulating the gear of the device in the t period
Figure GDA0002789606640000081
Is given to
Figure GDA0002789606640000082
Will be provided with
Figure GDA0002789606640000083
Is given to
Figure GDA0002789606640000084
The subscript t1 denotes a period number, and satisfies
Figure GDA0002789606640000085
And t1 ∈ [ k, k + H ]IN-1]；
In the second case, if
Figure GDA0002789606640000086
Then will be
Figure GDA0002789606640000087
Is given to
Figure GDA0002789606640000088
Will be provided with
Figure GDA0002789606640000089
Is given to
Figure GDA00027896066400000810
The subscript t2 denotes a period number, and satisfies
Figure GDA00027896066400000811
And t2 ∈ [ k, k + H ]IN-1]；
In the third case, if
Figure GDA00027896066400000812
Then will be
Figure GDA00027896066400000813
Is given to
Figure GDA00027896066400000814
The subscript t3 denotes the period number and satisfies t3 ∈ [ k, k + H ]IN-1]；
Step 4.5.2, obtaining gear time sequences of all discrete type regulation and control equipment corresponding to z +1 th generation particles
Figure GDA00027896066400000815
Obtaining a discrete type regulating and controlling equipment gear time sequence obtained by decoding and converting z +1 th generation particles by using the formula (14)
Figure GDA00027896066400000816
Figure GDA00027896066400000817
In the formula (14), the compound represented by the formula (I),
Figure GDA00027896066400000818
respectively representing the gears of the on-load tap changer OLTC with the number of i and the t time period of the switched capacitor bank CB obtained after the z + 1-th generation of particles are decoded and converted;
will be provided with
Figure GDA00027896066400000819
And the current rolling optimization interval [ k, k + H ]IN-1]Combining the gear time sequences of the discrete regulation and control equipment without gear change, thereby obtaining the gear time sequences of all the discrete regulation and control equipment corresponding to z +1 th generation particles
Figure GDA00027896066400000820
And 4.6, generating optimal particles:
step 4.6.1, obtaining the transformation ratio K of the on-load tap changing transformer OLTC connected to the branch fp in the t period by using the formula (15)fp,t：
Figure GDA00027896066400000821
In formula (15), Kfp,0And Δ KfpRespectively the standard transformation ratio and the regulation step length of the on-load tap changing transformer OLTC connected on the branch fp,
Figure GDA00027896066400000822
to represent
Figure GDA00027896066400000823
The step corresponding to the t-period of the on-load tap changing transformer OLTC connected to the branch fp, when the branch does not contain the on-load tap changing transformer OLTC, Kfp,t＝1；
Step 4.6.2, obtaining the reactive power output of the switched capacitor bank CB with the serial number of i in the t period by using the formula (16)
Figure GDA00027896066400000824
Figure GDA00027896066400000825
In the formula (16), the compound represented by the formula,
Figure GDA00027896066400000826
the compensation power of a single group of capacitors of the switched capacitor group CB with the number i;
step 4.6.3, obtaining decision variable set by using equation (17)
Figure GDA00027896066400000827
And
Figure GDA00027896066400000828
the value of the medium element is determined as the target function:
Figure GDA0002789606640000091
in the formula (17), the objective function is a single-layer objective function, the constraint conditions include the formula (4), the formula (6), the formula (7), the formula (8), the formula (15) and the formula (16), and the single-layer objective function is solved to obtain the z +1 th generation particles
Figure GDA0002789606640000092
The target function value of (1) is then compared with the current z-th generation history optimal particle
Figure GDA0002789606640000093
If the z +1 th generation particle is compared
Figure GDA0002789606640000094
Is less than the current z-th generation history optimal particle
Figure GDA0002789606640000095
The target function value of (2) is updated to the historical optimal particle, and the z +1 th generation particle is used
Figure GDA0002789606640000096
As the z +1 th generation history optimal particle
Figure GDA0002789606640000097
Otherwise, not updating, namely, the current z-th generation history optimal particle
Figure GDA0002789606640000098
As the z +1 th generation history optimal particle
Figure GDA0002789606640000099
Selecting the particles with the smallest objective function value from all the historical optimal particles as the optimal particles of the particle swarm
Figure GDA00027896066400000910
Step 4.7, iteration ending judgment and optimization result output:
assigning z +1 to z, and judging that z is larger than zmaxIf it is not, returningAnd 4.4, if yes, indicating that the iteration is ended, and enabling the particle swarm to be the optimal particles
Figure GDA00027896066400000911
Corresponding k-period gear of on-load tap changer (OLTC)
Figure GDA00027896066400000912
Gear of switching capacitor bank CB in k time period
Figure GDA00027896066400000913
And reactive power output of the SVC in k period
Figure GDA00027896066400000914
The voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC are respectively used as actual control output of the on-load voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC in the current k time period, and the gear and reactive power output of the rest time periods are omitted;
step five, judging the completion of rolling optimization:
assigning K +1 to K, and judging that K is larger than KmaxWhether the current rolling optimization interval is established or not is judged, if so, the rolling optimization is ended, otherwise, the obtained current rolling optimization interval [ k, k + H ] is usedIN-1]Upper particle group of optimal particles
Figure GDA00027896066400000915
Corresponding gear time sequence of all discrete type regulating and controlling equipment
Figure GDA00027896066400000916
Replacement intra-day optimization phase interval [ k, k + H ]IN-1]Gear time sequence of discrete regulating and controlling equipment
Figure GDA00027896066400000917
And
Figure GDA00027896066400000918
and returns to step 3.1.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the mixed integer PSO algorithm is used for calculating the intraday rolling optimization model considering gear correction, so that the gear of discrete regulation and control equipment is corrected, the real-time change of distributed generation output is tracked, the adverse effect of uncertainty on a power distribution network is inhibited, and the safety and the operation economy of the power distribution network are improved.
2. According to the invention, a robust optimization method and a rolling optimization method are combined, a min-max type rolling optimization objective function is established, the active loss and the regulation and control cost of the power distribution network are minimized in the worst distributed generation output scene, the adverse effect of uncertainty of distributed generation output is restrained, and the static stability of the power distribution network is improved.
3. According to the invention, the influence of gear change on the rolling optimization objective function in the day is considered by establishing the gear change period variable decision variable and the constraint thereof in the rolling optimization model in the day, so that the gear of the discrete regulation and control equipment is corrected in the optimization stage in the day, the excessive regulation of the discrete regulation and control equipment is avoided, the active loss is reduced, and the safety of the power distribution network is improved.
4. The gear correction of the discrete regulation and control equipment is only limited to the correction of the gear change occurrence time interval, the gear change occurrence time interval is only allowed to be adjusted within a small range, the correction of the gear size is not related, the serious conflict of decision related to the day-ahead scheduling stage caused by excessive gear correction is avoided, and the practicability of the gear correction strategy is improved.
5. The method utilizes the mixed integer PSO algorithm to calculate the intraday rolling optimization model, and solves the problem that the intraday rolling optimization model is difficult to solve due to the complex nonlinear characteristic of variable decision variables during gear change periods.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, an active power distribution network is connected to a distributed generation DG, a discrete regulation and control device, and a continuous regulation and control device; the distributed generation DG includes distributed photovoltaic power generation and distributed wind power generation; the discrete regulation and control equipment comprises an on-load tap changer (OLTC) and a switched Capacitor Bank (CB); the continuous regulating and controlling equipment comprises a static var generator (SVC); the active power distribution network intraday rolling optimization based on the mixed integer PSO algorithm is carried out according to the following steps:
step one, establishing a gear time sequence of an optimization stage in the day:
step 1.1, determining the time scale of rolling optimization in the day:
scheduling phase D with T day aheadDOn a time scale of HD×TDFor the interval length of the day-ahead scheduling phase, where HDIs an integer multiple;
intra-day rolling optimization by TINOn a time scale of HIN×TINInterval length optimized for rolling in days, where HINIs an integer multiple; and has TD＝M×TINM is an integer multiple, and the superscript IN represents the IN-day optimization stage;
in practical application, T is suggestedD＝60min，HD＝24，TIN＝15min，HIN24, M4. If the rolling optimization cycle is too short and the optimization cycle cannot contain enough prediction information near the gear change time period, gear correction cannot be performed well;
step 1.2, establishing a gear time sequence of a day-ahead scheduling stage:
respectively establishing gear time sequences of the OLTC in the day-ahead scheduling stage D according to the optimization results of the known day-ahead scheduling stage
Figure GDA0002789606640000101
Gear time sequence of switched capacitor bank CB in day-ahead scheduling stage D
Figure GDA0002789606640000111
Interaction power time sequence of distribution network and large power grid in day-ahead scheduling stage D
Figure GDA0002789606640000112
Wherein N isOLTCRepresenting the total number of on-load tap changers OLTC,
Figure GDA0002789606640000113
the gear of the on-load tap changer OLTC with the number of i in the t period of the day-ahead scheduling stage D is represented;
Figure GDA0002789606640000114
the gear of the switched capacitor bank CB with the number i in the period t of the scheduling phase D before the day,
Figure GDA0002789606640000115
the interaction power of the power distribution network and the large power grid in the t period of the scheduling stage D before the day is represented, and the subscript grid represents the interaction of the power distribution network and the large power grid;
the gear of discrete regulation and control equipment and the interaction power of the power distribution network and the large power grid in the day-ahead scheduling stage are known information;
step 1.3, establishing a gear time sequence of an optimization stage in the day:
according to time scale TDAnd TINAccording to the setting condition of the on-load tap changer (OLTC), the gear time sequence of the OLTC in the optimization stage in the day is respectively obtained from each time sequence of the day-ahead scheduling stage
Figure GDA0002789606640000116
Gear time sequence of switched capacitor bank CB in optimization stage in day
Figure GDA0002789606640000117
Interaction power time sequence of power distribution network and large power grid in optimization stage in day
Figure GDA0002789606640000118
Wherein the content of the first and second substances,
Figure GDA0002789606640000119
and
Figure GDA00027896066400001110
respectively indicate the numbers iThe gear of the on-load tap changer OLTC in the time period t of the intraday optimization stage, the gear of the switched capacitor bank CB with the serial number i in the time period t of the intraday optimization stage,
Figure GDA00027896066400001111
representing the interactive power of the distribution network and the large power grid in the time t of the optimization stage in the day;
each time interval of the day-ahead scheduling phase is divided into M small time intervals in the day optimization phase, so that the current scheduling phase has
Figure GDA00027896066400001112
The gear time series also has the corresponding relation.
Step two, rolling optimization initialization in a day:
step 2.1, establishing a gear change period variable:
establishing a serial number i and a device type i according to the number of gear change of the discrete regulation and control device
Figure GDA00027896066400001113
The variation of the s-th gear change period of the discrete regulating and controlling equipment
Figure GDA00027896066400001114
And is
Figure GDA00027896066400001115
The values of (a) are the corresponding time interval number and the equipment type when the s-th gear change occurs
Figure GDA00027896066400001116
The value is OLTC or CB, which respectively represents an on-load tap changer and a switched capacitor bank;
defining the gear time sequence of the intraday optimization stage obtained from the gear time sequence of the day-ahead scheduling stage as the initial gear time sequence of the intraday optimization stage, and establishing the gear time sequence with the number i and the equipment type I
Figure GDA00027896066400001117
Discrete tone ofS time interval variable of initial gear change of control equipment
Figure GDA00027896066400001118
Is a constant variable, and
Figure GDA00027896066400001119
has a value of
Figure GDA00027896066400001120
The initial value of (a), superscript 0 represents the initial information;
the gear time series of the intra-day optimization stage is modified after each rolling optimization,
Figure GDA00027896066400001121
the value of (b) will vary; while the original gear change period variable
Figure GDA00027896066400001122
Is a constant variable;
step 2.2, rolling optimization initialization in a day:
defining the rolling optimization interval in the Kth day of the intra-day optimization stage as [ K, K + H ]IN-1]K is the start period of the rolling in-day optimization, k + HIN-1 is the last period of the rolling optimization within the day, and K is defined as the maximum number of optimizations of the rolling optimization within the day, Kmax(ii) a Initializing rolling optimization in the current day into rolling optimization in the 1 st day, wherein the rolling optimization interval in the 1 st day is [1, H ]IN]；
Step three, establishing a day rolling optimization mathematical model considering gear correction:
step 3.1, establishing an uncertain output interval of the distributed generation DG:
optimizing interval [ k, k + H ] according to current rollingIN-1]The output prediction information of the distributed generation DG is obtained by using the formula (1) to obtain the output of the distributed generation DG with the number of i in the t period
Figure GDA0002789606640000121
The uncertain interval of (2):
Figure GDA0002789606640000122
in the formula (1), NDGIs the total number of distributed generation DGs in the distribution network,
Figure GDA0002789606640000123
and
Figure GDA0002789606640000124
respectively representing a predicted standard value and a predicted maximum value of the output of the distributed generation DG with the number of i in the t period;
step 3.2, obtaining a double-layer objective function of rolling optimization in the day by using the formula (2):
Figure GDA0002789606640000125
in the formula (2), fp denotes the branches of the head end node and the tail end node which are the node f and the node p respectively, phi denotes the set composed of all the branches, Ifp,tIs the resistance r flowing through the branch fp during the period tfpCurrent of (3), NSVCIs the total number of SVCs, CSVCIs the converted unit power output cost of the static var generator SVC,
Figure GDA0002789606640000126
the method comprises the steps of representing reactive power output decision variables of a static var generator (SVC) with the number of i in a t period, wherein alpha and beta are weight coefficients; pDGRepresents the set of all distributed generation DGs in each time interval of the current rolling optimization interval, PDGIs a decision variable for inner layer optimization;
Figure GDA0002789606640000127
represents the set of reactive power output decision variables of all static var generators SVC at each time interval of the current roll optimization interval,
Figure GDA0002789606640000128
represents the set of gear change period decision variables of all discrete regulating and controlling devices in each period of the current rolling optimization interval,
Figure GDA0002789606640000129
and
Figure GDA00027896066400001210
is the decision variable of the outer layer optimization;
the objective function shown in the formula (2) is a min-max structure, the inner-layer max represents the worst scene of distributed generation output, and the objective function represents a regulation and control scheme for minimizing the objective function in the worst scene.
Step 3.3, obtaining the adjustment constraint of the gear change period decision variable of the discrete regulation and control equipment by using the formula (3):
Figure GDA0002789606640000131
in the formula (3), the reaction mixture is,
Figure GDA0002789606640000132
denotes the number i and the type of device is
Figure GDA0002789606640000133
The decision variable of the s-th gear change period of the discrete regulation and control equipment;
in order to achieve the optimal value of the objective function in each rolling optimization cycle, the situation that a gear sequence has large changes in each rolling optimization cycle may occur, so that gears are excessively corrected, on one hand, voltage fluctuation is aggravated, stable operation of a power distribution network is seriously threatened, and on the other hand, the action frequency of the discrete type regulation and control equipment exceeds the daily maximum action frequency after the optimization stage in a day is finished (the daily maximum action frequency is generally set to be not more than 4 times and is an important constraint of a scheduling stage in the day);
step 3.4, obtaining branch load flow constraint containing the on-load tap changing transformer OLTC in the t period by using the formula (4):
Figure GDA0002789606640000134
in the formula (4), Vf,tAnd Vp,tThe voltage amplitudes of node f and node P, P, respectively, during period tfp,tAnd Qfp,tActive and reactive power, x, on the t-period branch fp, respectivelyfpIs the reactance of the branch fp,
Figure GDA0002789606640000135
is the reactive power output of the static var generator SVC connected at node p during period t,
Figure GDA0002789606640000136
and
Figure GDA0002789606640000137
active and reactive power, K, respectively, during t periods of a load L connected to a node pfp,tIs the transformation ratio of the on-load tap changer OLTC connected to the branch fp during the period t,
Figure GDA0002789606640000138
is the reactive power output of the switched capacitor bank CB connected to the node p in the period t, U (p) represents the end node set of all branches taking the node p as the head end node, r belongs to U (p) represents the node r belonging to the set U (p);
and 3.5, obtaining the constraint of the SVC reactive power output numbered i in the t period by using the formula (5):
Figure GDA0002789606640000139
in the formula (5), the reaction mixture is,
Figure GDA00027896066400001310
and
Figure GDA00027896066400001311
the maximum output and the minimum output of the reactive power of the static var generator (SVC) with the serial number of i are respectively;
step 3.6, obtaining the constraint of the interaction power of the power distribution network and the large power grid at the time period t by using the formula (6):
Figure GDA00027896066400001312
in the formula (6), kgridRepresenting the degree of the interactive power of the power distribution network and the large power grid deviating from the day-ahead scheduling in the optimization stage in the day, NnodeIs the total number of nodes in the distribution network;
the large power grid can reserve certain spare capacity according to the day-ahead scheduling result so as to cope with the change of the actual power consumption demand; in order to ensure the safety of the power grid, the change of the actual power demand is not allowed to exceed a certain preset range.
And 3.7, obtaining the safety constraint of the power distribution network in the t time period by using the formula (7) and the formula (8):
Figure GDA0002789606640000141
Figure GDA0002789606640000142
in the formula (7), the reaction mixture is,
Figure GDA0002789606640000143
and
Figure GDA0002789606640000144
respectively the minimum and maximum current allowed to flow by the branch fp;
in the formula (8), the reaction mixture is,
Figure GDA0002789606640000145
and
Figure GDA0002789606640000146
respectively minimum and maximum allowed for node pA voltage;
solving the intraday rolling optimization model by using a mixed integer PSO algorithm:
step 4.1, particle coding:
judging the rolling optimization interval [ k, k + H ] in the current dayIN-1]Whether each discrete type regulating and controlling device has gear change or not and what is happening is the gear change for the second time, so that the decision variable of the relevant gear change time period of the discrete type regulating and controlling device with the gear change is brought into the particle code;
obtaining a particle vector consisting of a reactive power output decision variable of a static var generator (SVC), a gear change period decision variable of an on-load tap changer (OLTC) with gear change and a gear change period decision variable of a switched Capacitor Bank (CB) with gear change by using a formula (9)
Figure GDA0002789606640000147
Figure GDA0002789606640000148
In the formula (9), the reaction mixture is,
Figure GDA0002789606640000149
the expression number is NSVCK + H of static var generator SVCIN-a reactive power output decision variable for a1 time period; subscripts 1, …, NSVCRespectively represent NSVCThe equipment number of each static var generator SVC;
Figure GDA00027896066400001410
s-th of on-load tap changer OLTC with Ch numberCh+NCh1 gear change period decision variable, subscripts C1, C2, …, Ch respectively representing the current roll optimization interval [ k, k + H [ ]IN-1]The equipment numbers, subscripts S, of the inner h on-load tap changing transformers OLTCC1、…、SC1+NC1-1 each represents a number C1sSth generation of on-load tap changer OLTCC1Sub, …, SC1+NC1-1 gear change; n is a radical ofC1Represents the current rolling optimization interval [ k, k + H ]IN-1]The OLTC with the internal number of C1 generates N in totalC1A secondary gear change;
Figure GDA00027896066400001411
s of switched capacitor group CB with number DgDg+NDg1 gear change period decision variable, subscripts D1, D2, …, Dg representing the current roll optimization interval [ k, k + H, respectivelyIN-1]The equipment numbers and subscripts S of the inner g switched capacitor bank CB with gear changeD1、…、SD1+ND1-1 denotes respectively the S th occurrence of the switched capacitor bank CB, numbered D1D1Sub, …, SD1+ND1-1 gear change; n is a radical ofD1Represents the current rolling optimization interval [ k, k + H ]IN-1]Switched capacitor bank CB with internal number D1 has N in commonD1A secondary gear change;
the particles do not include gear change period decision variables of the discrete type regulating and controlling equipment without gear change in the current rolling optimization cycle.
Step 4.2, particle initialization:
step 4.2.1 for fractional amounts in particles
Figure GDA0002789606640000151
Figure GDA0002789606640000152
Taking the initial value of
Figure GDA0002789606640000153
A random value of;
step 4.2.2 for fractional amounts in particles
Figure GDA0002789606640000154
And
Figure GDA0002789606640000155
Figure GDA0002789606640000156
taking the initial value of
Figure GDA0002789606640000157
Random integer in the brace, max {. is the maximum value of all numbers in the brace, and min {. is the minimum value of all numbers in the brace;
step 4.2.3, initializing to obtain h + g original particles
Figure GDA0002789606640000158
Wherein the content of the first and second substances,
Figure GDA0002789606640000159
representing the original particle numbered w;
obtaining t-period reactive power output decision variable of static var generator (SVC) numbered i by using formula (A1)
Figure GDA00027896066400001510
Initial value of (d):
Figure GDA00027896066400001511
in the formula (A1), θ8Is a value range of [0,1]A random variable of (a);
Figure GDA00027896066400001512
the value of (A) is selected in a similar way;
step 4.3, iterative initialization of particles:
initializing the current iteration algebra z as 1 and the maximum iteration algebra as zmax(ii) a The original particles
Figure GDA00027896066400001513
Simultaneously as the 1 st generation particle and the 1 st generation history optimal particle, one of h + g original particles is selected to minimize the objective function valueOptimal particles as initial population of particles
Figure GDA00027896066400001514
The superscript best, and the subscript PS for the population;
step 4.4, particle updating:
step 4.4.1, particle velocity update:
the motion velocity of the d-th component of the z-th generation particle using equation (10)
Figure GDA0002789606640000161
Updating to obtain the motion speed of the d component of the z +1 th generation particle
Figure GDA0002789606640000162
Figure GDA0002789606640000163
In the formula (10), the compound represented by the formula (10),
Figure GDA0002789606640000164
denotes the d-th component of the z-th generation particle numbered w,
Figure GDA0002789606640000165
and
Figure GDA0002789606640000166
respectively representing the d-th component of the z-th generation history optimal particle and the d-th component of the particle swarm optimal particle, pi,
Figure GDA0002789606640000167
Are respectively an attenuation factor and a learning factor,
Figure GDA00027896066400001619
is [0,1 ]]A random number of ranges;
the range of the motion speed of the da-th component of the particle is
Figure GDA0002789606640000168
And da ∈ [1, (N)SVC×HIN)]Subscript idaDevice number k indicating the control device corresponding to the da-th componentSVCIs a motion speed adjustment parameter; the dc component of the particle has a velocity of motion in the range of-1, 1]And dc ≠ da;
step 4.4.2, particle update:
the da component of the z-th generation particle is calculated by equation (11)
Figure GDA0002789606640000169
Updating to obtain the da component of the z +1 th generation particle
Figure GDA00027896066400001610
And the value range of the da component of the particle is
Figure GDA00027896066400001611
Figure GDA00027896066400001612
The dc component of the z-th generation particle is determined by the equations (12) and (13)
Figure GDA00027896066400001613
Updating to obtain the dc component of the z +1 th generation particle
Figure GDA00027896066400001614
The value of the dc component of the particle ranges from
Figure GDA00027896066400001615
The integer of (a):
Figure GDA00027896066400001616
Figure GDA00027896066400001617
in equation (12), sigmoid (. cndot.) represents a sigmoid function,
Figure GDA00027896066400001620
and
Figure GDA00027896066400001621
is [0-1 ]]A random number of the range, ρ being an auxiliary variable taking the value 1 or-1; sigma is an auxiliary variable with the value of 0 or 1;
step 4.4.3 obtaining z +1 th generation particles by particle renewal
Figure GDA00027896066400001618
Figure GDA0002789606640000171
1 st to (N)SVC×HIN) The particle components correspond to a reactive power output decision variable of the static var generator (SVC), the dc (dc ≠ da) component corresponds to a gear change time period decision variable of the discrete regulation and control equipment, and is different from the continuous reactive power output by the SVC, and the value of the gear change time period is a discrete integer, so the value ranges of the two are different;
and 4.5, particle decoding:
step 4.5.1, converting the gear change period decision variable of the discrete regulation and control equipment of the z +1 th generation particle into a corresponding gear time sequence of the discrete regulation and control equipment:
in the first case, if
Figure GDA0002789606640000172
Figure GDA0002789606640000173
The value of (a) is gn (n),
Figure GDA0002789606640000174
has a value of km, define
Figure GDA0002789606640000175
The number obtained after the z +1 th generation particle decoding conversion is i and the type of equipment is
Figure GDA0002789606640000176
The gear of the discrete regulating and controlling equipment in the t period will be
Figure GDA0002789606640000177
Is given to
Figure GDA0002789606640000178
Will be provided with
Figure GDA0002789606640000179
Is given to
Figure GDA00027896066400001710
The subscript t1 denotes a period number, and satisfies
Figure GDA00027896066400001711
And t1 ∈ [ k, k + H ]IN-1]；
In the second case, if
Figure GDA00027896066400001712
Then will be
Figure GDA00027896066400001713
Is given to
Figure GDA00027896066400001714
Will be provided with
Figure GDA00027896066400001715
Is given to
Figure GDA00027896066400001716
The subscript t2 denotes a period number, and satisfies
Figure GDA00027896066400001717
And t2 ∈ [ k, k + H ]IN-1]；
In the third case, if
Figure GDA00027896066400001718
Then will be
Figure GDA00027896066400001719
Is given to
Figure GDA00027896066400001720
The subscript t3 denotes the period number and satisfies t3 ∈ [ k, k + H ]IN-1]；
Step 4.5.2, obtaining gear time sequences of all discrete type regulation and control equipment corresponding to z +1 th generation particles
Figure GDA00027896066400001721
Obtaining a discrete type regulating and controlling equipment gear time sequence obtained by decoding and converting z +1 th generation particles by using the formula (14)
Figure GDA00027896066400001722
Figure GDA00027896066400001723
In the formula (14), the compound represented by the formula (I),
Figure GDA00027896066400001724
respectively representing the gears of the on-load tap changer OLTC with the number of i and the t time period of the switched capacitor bank CB obtained after the z + 1-th generation of particles are decoded and converted;
will be provided with
Figure GDA00027896066400001725
And the current rolling optimization interval [ k, k + H ]IN-1]Combining the gear time sequences of the discrete regulation and control equipment without gear change, thereby obtaining the gear time sequences of all the discrete regulation and control equipment corresponding to z +1 th generation particles
Figure GDA00027896066400001726
The formula (A2) represents the gear time sequence of the discrete type regulating and controlling equipment without gear change
Figure GDA0002789606640000181
Figure GDA0002789606640000182
Gear time sequence of all discrete type regulating and controlling equipment corresponding to z +1 th generation particles
Figure GDA0002789606640000183
By
Figure GDA0002789606640000184
And
Figure GDA0002789606640000185
the components are combined and rearranged according to the equipment numbering sequence and the time period sequence;
and 4.6, generating optimal particles:
step 4.6.1, obtaining the transformation ratio K of the on-load tap changing transformer OLTC connected to the branch fp in the t period by using the formula (15)fp,t：
Figure GDA0002789606640000186
In formula (15), Kfp,0And Δ KfpRespectively the standard transformation ratio and the regulation step length of the on-load tap changing transformer OLTC connected on the branch fp,
Figure GDA0002789606640000187
to represent
Figure GDA0002789606640000188
On-load voltage regulation and transformation connected with branch fpThe t-period gear of the OLTC, when the branch does not contain the OLTC, Kfp,t＝1；
Step 4.6.2, obtaining the reactive power output of the switched capacitor bank CB with the serial number of i in the t period by using the formula (16)
Figure GDA0002789606640000189
Figure GDA00027896066400001810
In the formula (16), the compound represented by the formula,
Figure GDA00027896066400001811
the compensation power of a single group of capacitors of the switched capacitor group CB with the number i;
step 4.6.3, obtaining decision variable set by using equation (17)
Figure GDA00027896066400001812
And
Figure GDA00027896066400001813
the value of the medium element is determined as the target function:
Figure GDA00027896066400001814
in the formula (17), the objective function is a single-layer objective function, the constraint conditions include the formula (4), the formula (6), the formula (7), the formula (8), the formula (15) and the formula (16), and the single-layer objective function is solved to obtain the z +1 th generation particles
Figure GDA00027896066400001815
The target function value of (1) is then compared with the current z-th generation history optimal particle
Figure GDA00027896066400001816
If the z +1 th generation particle is compared
Figure GDA00027896066400001817
Is less than the current z-th generation history optimal particle
Figure GDA00027896066400001818
The target function value of (2) is updated to the historical optimal particle, and the z +1 th generation particle is used
Figure GDA00027896066400001819
As the z +1 th generation history optimal particle
Figure GDA00027896066400001820
Otherwise, not updating, namely, the current z-th generation history optimal particle
Figure GDA00027896066400001821
As the z +1 th generation history optimal particle
Figure GDA00027896066400001822
Selecting the particles with the smallest objective function value from all the historical optimal particles as the optimal particles of the particle swarm
Figure GDA00027896066400001823
The max-type objective function expressed by the expression (17) is not an equivalent objective function of the min-max-type objective function expressed by the expression (2), and the objective of calculating the max-type objective function value expressed by the expression (17) is only to calculate the objective function value of the particle;
step 4.7, iteration ending judgment and optimization result output:
assigning z +1 to z, and judging that z is larger than zmaxIf not, returning to the step 4.4, if so, indicating that the iteration is ended, and optimizing the particle swarm to obtain the optimal particles
Figure GDA0002789606640000191
Corresponding k-period gear of on-load tap changer (OLTC)
Figure GDA0002789606640000192
Gear of switching capacitor bank CB in k time period
Figure GDA0002789606640000193
And reactive power output of the SVC in k period
Figure GDA0002789606640000194
The voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC are respectively used as actual control output of the on-load voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC in the current k time period, and the gear and reactive power output of the rest time periods are omitted;
step five, judging the completion of rolling optimization:
assigning K +1 to K, and judging that K is larger than KmaxWhether the current rolling optimization interval is established or not is judged, if so, the rolling optimization is ended, otherwise, the obtained current rolling optimization interval [ k, k + H ] is usedIN-1]Upper particle group of optimal particles
Figure GDA0002789606640000195
Corresponding gear time sequence of all discrete type regulating and controlling equipment
Figure GDA0002789606640000196
Replacement intra-day optimization phase interval [ k, k + H ]IN-1]Gear time sequence of discrete regulating and controlling equipment
Figure GDA0002789606640000197
And
Figure GDA0002789606640000198
and returns to step 3.1.

Claims (1)

1. An active power distribution network day-interior rolling optimization method based on a mixed integer PSO algorithm is characterized in that a distributed generation DG, discrete regulation and control equipment and continuous regulation and control equipment are connected to the active power distribution network; the distributed generation DG comprises distributed photovoltaic generation and distributed wind power generation; the discrete regulation and control equipment comprises an on-load tap changer (OLTC) and a switched Capacitor Bank (CB); the continuous regulating and controlling equipment comprises a static var generator (SVC); the method is characterized in that the intraday rolling optimization method of the active power distribution network is carried out according to the following steps:
step one, establishing a gear time sequence of an optimization stage in the day:
step 1.1, determining the time scale of rolling optimization in the day:
scheduling phase D with T day aheadDOn a time scale of HD×TDFor the interval length of the day-ahead scheduling phase, where HDIs an integer multiple;
intra-day rolling optimization by TINOn a time scale of HIN×TINInterval length optimized for rolling in days, where HINIs an integer multiple; and has TD＝M×TINM is an integer multiple, and the superscript IN represents the IN-day optimization stage;
step 1.2, establishing a gear time sequence of a day-ahead scheduling stage:
respectively establishing gear time sequences of the OLTC in the day-ahead scheduling stage D according to the optimization results of the known day-ahead scheduling stage
Figure FDA0002789606630000011
Gear time sequence of switched capacitor bank CB in day-ahead scheduling stage D
Figure FDA0002789606630000012
Interaction power time sequence of distribution network and large power grid in day-ahead scheduling stage D
Figure FDA0002789606630000013
Wherein N isOLTCRepresenting the total number of on-load tap changers OLTC,
Figure FDA0002789606630000014
the gear of the on-load tap changer OLTC with the number of i in the t period of the day-ahead scheduling stage D is represented;
Figure FDA0002789606630000015
indicating the projection of number iThe capacitor bank CB is switched to the gear at the t period of the schedule phase D in the day ahead,
Figure FDA0002789606630000016
the interaction power of the power distribution network and the large power grid in the t period of the scheduling stage D before the day is represented, and the subscript grid represents the interaction of the power distribution network and the large power grid;
step 1.3, establishing a gear time sequence of an optimization stage in the day:
according to time scale TDAnd TINAccording to the setting condition of the on-load tap changer (OLTC), the gear time sequence of the OLTC in the optimization stage in the day is respectively obtained from each time sequence of the day-ahead scheduling stage
Figure FDA0002789606630000017
Gear time sequence of switched capacitor bank CB in optimization stage in day
Figure FDA0002789606630000018
Interaction power time sequence of power distribution network and large power grid in optimization stage in day
Figure FDA0002789606630000019
Wherein the content of the first and second substances,
Figure FDA00027896066300000110
and
Figure FDA00027896066300000111
respectively representing the gear of the on-load tap changer OLTC with the number of i in the t period of the optimization stage in the day and the gear of the switched capacitor bank CB with the number of i in the t period of the optimization stage in the day,
Figure FDA00027896066300000112
representing the interactive power of the distribution network and the large power grid in the time t of the optimization stage in the day;
step two, rolling optimization initialization in a day:
step 2.1, establishing a gear change period variable:
establishing a serial number i and a device type i according to the number of gear change of the discrete regulation and control device
Figure FDA0002789606630000021
The variation of the s-th gear change period of the discrete regulating and controlling equipment
Figure FDA0002789606630000022
And is
Figure FDA0002789606630000023
The values of (a) are the corresponding time interval number and the equipment type when the s-th gear change occurs
Figure FDA0002789606630000024
The value is OLTC or CB, which respectively represents an on-load tap changer and a switched capacitor bank;
defining the gear time sequence of the intraday optimization stage obtained from the gear time sequence of the day-ahead scheduling stage as the initial gear time sequence of the intraday optimization stage, and establishing the gear time sequence with the number i and the equipment type I
Figure FDA0002789606630000025
The variation of the s-th initial gear change period of the discrete regulation and control equipment
Figure FDA0002789606630000026
Figure FDA0002789606630000027
Is a constant variable, and
Figure FDA0002789606630000028
has a value of
Figure FDA0002789606630000029
The initial value of (a), superscript 0 represents the initial information;
step 2.2, rolling optimization initialization in a day:
defining the rolling optimization interval in the Kth day of the intra-day optimization stage as [ K, K + H ]IN-1]K is the start period of the rolling in-day optimization, k + HIN-1 is the last period of the rolling optimization within the day, and K is defined as the maximum number of optimizations of the rolling optimization within the day, Kmax(ii) a Initializing rolling optimization in the current day into rolling optimization in the 1 st day, wherein the rolling optimization interval in the 1 st day is [1, H ]IN]；
Step three, establishing a day rolling optimization mathematical model considering gear correction:
step 3.1, establishing an uncertain output interval of the distributed generation DG:
optimizing interval [ k, k + H ] according to current rollingIN-1]The output prediction information of the distributed generation DG is obtained by using the formula (1) to obtain the output of the distributed generation DG with the number of i in the t period
Figure FDA00027896066300000210
The uncertain interval of (2):
Figure FDA00027896066300000211
in the formula (1), NDGIs the total number of distributed generation DGs in the distribution network,
Figure FDA00027896066300000212
and
Figure FDA00027896066300000213
respectively representing a predicted standard value and a predicted maximum value of the output of the distributed generation DG with the number of i in the t period;
step 3.2, obtaining a double-layer objective function of rolling optimization in the day by using the formula (2):
Figure FDA00027896066300000214
in the formula (2), fp denotes the branches of the head end node and the tail end node which are the node f and the node p respectively, phi denotes the set composed of all the branches, Ifp,tIs the resistance r flowing through the branch fp during the period tfpCurrent of (3), NSVCIs the total number of SVCs, CSVCIs the converted unit power output cost of the static var generator SVC,
Figure FDA00027896066300000215
the method comprises the steps of representing reactive power output decision variables of a static var generator (SVC) with the number of i in a t period, wherein alpha and beta are weight coefficients; pDGRepresents the set of all distributed generation DGs in each time interval of the current rolling optimization interval, PDGIs a decision variable for inner layer optimization;
Figure FDA00027896066300000216
represents the set of reactive power output decision variables of all static var generators SVC at each time interval of the current roll optimization interval,
Figure FDA0002789606630000031
represents the set of gear change period decision variables of all discrete regulating and controlling devices in each period of the current rolling optimization interval,
Figure FDA0002789606630000032
and
Figure FDA0002789606630000033
is the decision variable of the outer layer optimization;
step 3.3, obtaining the adjustment constraint of the gear change period decision variable of the discrete regulation and control equipment by using the formula (3):
Figure FDA0002789606630000034
in the formula (3), the reaction mixture is,
Figure FDA0002789606630000035
denotes the number i and the type of device is
Figure FDA0002789606630000036
The decision variable of the s-th gear change period of the discrete regulation and control equipment;
step 3.4, obtaining branch load flow constraint containing the on-load tap changing transformer OLTC in the t period by using the formula (4):
Figure FDA0002789606630000037
in the formula (4), Vf,tAnd Vp,tThe voltage amplitudes of node f and node P, P, respectively, during period tfp,tAnd Qfp,tActive and reactive power, x, on the t-period branch fp, respectivelyfpIs the reactance of the branch fp,
Figure FDA0002789606630000038
is the reactive power output of the static var generator SVC connected at node p during period t,
Figure FDA0002789606630000039
and
Figure FDA00027896066300000310
active and reactive power, K, respectively, during t periods of a load L connected to a node pfp,tIs the transformation ratio of the on-load tap changer OLTC connected to the branch fp during the period t,
Figure FDA00027896066300000311
is the reactive power output of the switched capacitor bank CB connected to the node p in the period t, U (p) represents the end node set of all branches taking the node p as the head end node, r belongs to U (p) represents the node r belonging to the set U (p);
and 3.5, obtaining the constraint of the SVC reactive power output numbered i in the t period by using the formula (5):
Figure FDA00027896066300000312
in the formula (5), the reaction mixture is,
Figure FDA00027896066300000313
and
Figure FDA00027896066300000314
the maximum output and the minimum output of the reactive power of the static var generator (SVC) with the serial number of i are respectively;
step 3.6, obtaining the constraint of the interaction power of the power distribution network and the large power grid at the time period t by using the formula (6):
Figure FDA00027896066300000315
in the formula (6), kgridRepresenting the degree of the interactive power of the power distribution network and the large power grid deviating from the day-ahead scheduling in the optimization stage in the day, NnodeIs the total number of nodes in the distribution network;
and 3.7, obtaining the safety constraint of the power distribution network in the t time period by using the formula (7) and the formula (8):
Figure FDA0002789606630000041
Figure FDA0002789606630000042
in the formula (7), the reaction mixture is,
Figure FDA0002789606630000043
and
Figure FDA0002789606630000044
respectively the minimum and maximum current allowed to flow by the branch fp;
in the formula (8), the reaction mixture is,
Figure FDA0002789606630000045
and
Figure FDA0002789606630000046
respectively, the minimum and maximum voltages allowed for node p;
solving the intraday rolling optimization model by using a mixed integer PSO algorithm:
step 4.1, particle coding:
judging the rolling optimization interval [ k, k + H ] in the current dayIN-1]Whether each discrete type regulating and controlling device has gear change or not and what is happening is the gear change for the second time, so that the decision variable of the relevant gear change time period of the discrete type regulating and controlling device with the gear change is brought into the particle code;
obtaining a particle vector consisting of a reactive power output decision variable of a static var generator (SVC), a gear change period decision variable of an on-load tap changer (OLTC) with gear change and a gear change period decision variable of a switched Capacitor Bank (CB) with gear change by using a formula (9)
Figure FDA0002789606630000047
Figure FDA0002789606630000048
In the formula (9), the reaction mixture is,
Figure FDA0002789606630000049
the expression number is NSVCK + H of static var generator SVCIN-a reactive power output decision variable for a1 time period; subscripts 1, …, NSVCRespectively represent NSVCThe equipment number of each static var generator SVC;
Figure FDA00027896066300000410
s-th of on-load tap changer OLTC with Ch numberCh+NCh1 gear change period decision variable, subscripts C1, C2, …, Ch respectively representing the current roll optimization interval [ k, k + H [ ]IN-1]The equipment numbers, subscripts S, of the inner h on-load tap changing transformers OLTCC1、…、SC1+NC1-1 denotes respectively the occurrence of the sth of the on-load tap changer OLTC numbered C1C1Sub, …, SC1+NC1-1 gear change; n is a radical ofC1Represents the current rolling optimization interval [ k, k + H ]IN-1]The OLTC with the internal number of C1 generates N in totalC1A secondary gear change;
Figure FDA00027896066300000411
s of switched capacitor group CB with number DgDg+NDg1 gear change period decision variable, subscripts D1, D2, …, Dg representing the current roll optimization interval [ k, k + H, respectivelyIN-1]The equipment numbers and subscripts S of the inner g switched capacitor bank CB with gear changeD1、…、SD1+ND1-1 denotes respectively the S th occurrence of the switched capacitor bank CB, numbered D1D1Sub, …, SD1+ND1-1 gear change; n is a radical ofD1Represents the current rolling optimization interval [ k, k + H ]IN-1]Switched capacitor bank CB with internal number D1 has N in commonD1A secondary gear change;
step 4.2, particle initialization:
step 4.2.1 for fractional amounts in particles
Figure FDA0002789606630000051
Figure FDA0002789606630000052
Taking the initial value of
Figure FDA0002789606630000053
A random value of;
step 4.2.2 for fractional amounts in particles
Figure FDA0002789606630000054
And
Figure FDA0002789606630000055
Figure FDA0002789606630000056
taking the initial value of
Figure FDA0002789606630000057
Random integer in the brace, max {. is the maximum value of all numbers in the brace, and min {. is the minimum value of all numbers in the brace;
step 4.2.3, initializing to obtain h + g original particles
Figure FDA0002789606630000058
Wherein the content of the first and second substances,
Figure FDA0002789606630000059
representing the original particle numbered w;
step 4.3, iterative initialization of particles:
initializing the current iteration algebra z as 1 and the maximum iteration algebra as zmax(ii) a The original particles
Figure FDA00027896066300000510
Simultaneously as the 1 st generation particle and the 1 st generation history optimal particle, selecting one of h + g original particles as the initial particle swarm optimal particle with the minimum objective function value
Figure FDA00027896066300000511
Superscript best, subscriptPS represents a particle group;
step 4.4, particle updating:
step 4.4.1, particle velocity update:
the motion velocity of the d-th component of the z-th generation particle using equation (10)
Figure FDA00027896066300000519
Updating to obtain the motion speed of the d component of the z +1 th generation particle
Figure FDA00027896066300000512
Figure FDA00027896066300000513
In the formula (10), the compound represented by the formula (10),
Figure FDA00027896066300000514
denotes the d-th component of the z-th generation particle numbered w,
Figure FDA00027896066300000515
and
Figure FDA00027896066300000516
respectively representing the d-th component of the z-th generation history optimal particle and the d-th component of the particle swarm optimal particle, pi,
Figure FDA00027896066300000517
Respectively, attenuation factor, learning factor, theta1、θ2Is [0,1 ]]A random number of ranges;
the range of the motion speed of the da-th component of the particle is
Figure FDA00027896066300000518
And da ∈ [1, (N)SVC×HIN)]Subscript idaThe device number of the regulating device corresponding to the da-th component is represented,kSVCis a motion speed adjustment parameter; the dc component of the particle has a velocity of motion in the range of-1, 1]And dc ≠ da;
step 4.4.2, particle update:
the da component of the z-th generation particle is calculated by equation (11)
Figure FDA0002789606630000061
Updating to obtain the da component of the z +1 th generation particle
Figure FDA0002789606630000062
And the value range of the da component of the particle is
Figure FDA0002789606630000063
Figure FDA0002789606630000064
The dc component of the z-th generation particle is determined by the equations (12) and (13)
Figure FDA0002789606630000065
Updating to obtain the dc component of the z +1 th generation particle
Figure FDA0002789606630000066
The value of the dc component of the particle ranges from
Figure FDA0002789606630000067
The integer of (a):
Figure FDA0002789606630000068
Figure FDA0002789606630000069
in the formula (12), sigmoid (. cndot.) represents a sigmoid function,. theta.3And theta4Is [0-1 ]]A random number of the range, ρ being an auxiliary variable taking the value 1 or-1; sigma is an auxiliary variable with the value of 0 or 1;
step 4.4.3 obtaining z +1 th generation particles by particle renewal
Figure FDA00027896066300000610
Figure FDA00027896066300000611
And 4.5, particle decoding:
step 4.5.1, converting the gear change period decision variable of the discrete regulation and control equipment of the z +1 th generation particle into a corresponding gear time sequence of the discrete regulation and control equipment:
in the first case, if
Figure FDA00027896066300000612
Figure FDA00027896066300000613
The value of (a) is gn (n),
Figure FDA00027896066300000614
has a value of km, define
Figure FDA00027896066300000615
The number obtained after the z +1 th generation particle decoding conversion is i and the type of equipment is
Figure FDA00027896066300000616
The gear of the discrete regulating and controlling equipment in the t period will be
Figure FDA00027896066300000617
Is given to
Figure FDA00027896066300000618
Will be provided with
Figure FDA00027896066300000619
Is given to
Figure FDA00027896066300000620
The subscript t1 denotes a period number, and satisfies
Figure FDA00027896066300000621
And t1 ∈ [ k, k + H ]IN-1]；
In the second case, if
Figure FDA0002789606630000071
Then will be
Figure FDA0002789606630000072
Is given to
Figure FDA0002789606630000073
Will be provided with
Figure FDA0002789606630000074
Is given to
Figure FDA0002789606630000075
The subscript t2 denotes a period number, and satisfies
Figure FDA0002789606630000076
And t2 ∈ [ k, k + H ]IN-1]；
In the third case, if
Figure FDA0002789606630000077
Then will be
Figure FDA0002789606630000078
Is given to
Figure FDA0002789606630000079
The subscript t3 denotes the period number and satisfies t3 ∈ [ k, k + H ]IN-1]；
Step 4.5.2, obtaining gear time sequences of all discrete type regulation and control equipment corresponding to z +1 th generation particles
Figure FDA00027896066300000710
Obtaining a discrete type regulating and controlling equipment gear time sequence obtained by decoding and converting z +1 th generation particles by using the formula (14)
Figure FDA00027896066300000711
Figure FDA00027896066300000712
In the formula (14), the compound represented by the formula (I),
Figure FDA00027896066300000713
respectively representing the gears of the on-load tap changer OLTC with the number of i and the t time period of the switched capacitor bank CB obtained after the z + 1-th generation of particles are decoded and converted;
will be provided with
Figure FDA00027896066300000714
And the current rolling optimization interval [ k, k + H ]IN-1]Combining the gear time sequences of the discrete regulation and control equipment without gear change, thereby obtaining the gear time sequences of all the discrete regulation and control equipment corresponding to z +1 th generation particles
Figure FDA00027896066300000715
And 4.6, generating optimal particles:
step 4.6.1, obtaining the transformation ratio K of the on-load tap changing transformer OLTC connected to the branch fp in the t period by using the formula (15)fp,t：
Figure FDA00027896066300000716
In formula (15), Kfp,0And Δ KfpRespectively the standard transformation ratio and the regulation step length of the on-load tap changing transformer OLTC connected on the branch fp,
Figure FDA00027896066300000717
to represent
Figure FDA00027896066300000718
The step corresponding to the t-period of the on-load tap changing transformer OLTC connected to the branch fp, when the branch does not contain the on-load tap changing transformer OLTC, Kfp,t＝1；
Step 4.6.2, obtaining the reactive power output of the switched capacitor bank CB with the serial number of i in the t period by using the formula (16)
Figure FDA00027896066300000719
Figure FDA00027896066300000720
In the formula (16), the compound represented by the formula,
Figure FDA00027896066300000721
the compensation power of a single group of capacitors of the switched capacitor group CB with the number i;
step 4.6.3, obtaining decision variable set by using equation (17)
Figure FDA00027896066300000722
And
Figure FDA00027896066300000723
the value of the medium element is determined as the target function:
Figure FDA00027896066300000724
in the formula (17), the objective function is a single-layer objective function, the constraint conditions include the formula (4), the formula (6), the formula (7), the formula (8), the formula (15) and the formula (16), and the single-layer objective function is solved to obtain the z +1 th generation particles
Figure FDA00027896066300000725
The target function value of (1) is then compared with the current z-th generation history optimal particle
Figure FDA0002789606630000081
If the z +1 th generation particle is compared
Figure FDA0002789606630000082
Is less than the current z-th generation history optimal particle
Figure FDA0002789606630000083
The target function value of (2) is updated to the historical optimal particle, and the z +1 th generation particle is used
Figure FDA0002789606630000084
As the z +1 th generation history optimal particle
Figure FDA0002789606630000085
Otherwise, not updating, namely, the current z-th generation history optimal particle
Figure FDA0002789606630000086
As the z +1 th generation history optimal particle
Figure FDA0002789606630000087
Selecting the particles with the smallest objective function value from all the historical optimal particles as the optimal particles of the particle swarm
Figure FDA0002789606630000088
Step 4.7, iteration ending judgment and optimization result output:
assigning z +1 to z, and judging that z is larger than zmaxIf not, returning to the step 4.4, if so, indicating that the iteration is ended, and optimizing the particle swarm to obtain the optimal particles
Figure FDA0002789606630000089
Corresponding k-period gear of on-load tap changer (OLTC)
Figure FDA00027896066300000810
Gear of switching capacitor bank CB in k time period
Figure FDA00027896066300000811
And reactive power output of the SVC in k period
Figure FDA00027896066300000812
The voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC are respectively used as actual control output of the on-load voltage-regulating transformer OLTC, the switched capacitor bank CB and the static var generator SVC in the current k time period, and the gear and reactive power output of the rest time periods are omitted;
step five, judging the completion of rolling optimization:
assigning K +1 to K, and judging that K is larger than KmaxWhether the current rolling optimization interval is established or not is judged, if so, the rolling optimization is ended, otherwise, the obtained current rolling optimization interval [ k, k + H ] is usedIN-1]Upper particle group of optimal particles
Figure FDA00027896066300000813
Corresponding gear time sequence of all discrete type regulating and controlling equipment
Figure FDA00027896066300000814
Replacement intra-day optimization phase interval [ k, k + H ]IN-1]Gear time sequence of discrete regulating and controlling equipment
Figure FDA00027896066300000815
And
Figure FDA00027896066300000816
and returns to step 3.1.
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