CN106602557A - Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles - Google Patents
Multi-period optimization reconstruction method of active power distribution network comprising electric automobiles Download PDFInfo
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- 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
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- 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
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Abstract
The invention discloses a multi-period optimization reconstruction method of an active power distribution network comprising electric automobiles. Particle positions are updated by use of a binary-system particle swarm algorithm, a global optimal solution is selected through a small niche technology, and an optimal comprise solution is selected by use of a fuzzy grade of membership decision-making method. The method has the following advantages: access of a distributed power source is taken into consideration, installation positions and capacities are determined according to network loss sensitivity, an electric automobile disordered charging mode and an intelligent charging mode are taken into consideration, after reconstruction, the network loss of the power distribution network is reduced, the voltage quality is improved, at the same time, distributed power output and power distribution of the electric automobiles during charging along with time are simulated, and the method is more suited to actual conditions.
Description
Technical field
The present invention relates to active distribution network field, particularly a kind of active distribution network containing electric automobile is multi-period to optimize weight
Structure method.
Background technology
In recent years, traditional energy increasingly depleted, the growth of electricity needs is just driving electrical network to develop towards continuable mode,
So that construction iterative method of the country to intelligent grid, and in the access amount of power distribution network side, distributed power source and electric automobile
It is continuously increased, conventional electrical distribution net is difficult to tackle, for this purpose, the concept of active distribution network is arisen spontaneously, purpose is exactly by matching somebody with somebody
The novel load occurred in electrical network carries out the control and management of active, reduces via net loss, realizes the reliability service of electrical network
Deng.
In order to tackle the power demand of a large amount of power loads, it is requisite one that distributed power source accesses active distribution network
Individual link, for the addressing constant volume of the distributed power source for the purpose of reduction network loss, can select it to connect by power loss sensitivity
Enter position and capacity.Counted according to related data, end 2015, the automobile user of China reaches 22.3 ten thousand, country
Also corresponding policy is put into effect, to the year two thousand twenty, electric automobile recoverable amount will reach 5,000,000, and electric automobile is used as a kind of cleaning
The energy, it is considered to be reduce carbon emission amount, administer the important technology approach of the aspects such as haze.But, electric automobile is solving energy
While source problem and problem of environmental pollution, to access and also can bring pressure to electrical network as a kind of electric load during grid charging
The unordered charging of power, especially electric automobile car owner declines can line voltage, power attenuation is increased, so extensive electronic vapour
The networking research of car is active distribution network optimization operation key factor to be considered.
In order to improve the impact that electric automobile accesses the unordered charging of power distribution network, there is scholar just to propose the side of intelligent charge
Method, although stable operation of the method for intelligent charge to power distribution network increases, however it is necessary that a large amount of electric automobile car owners participate in
The United Dispatching of grid company, just actually from the point of view of have certain difficulty, and power distribution network reconfiguration be exactly by switch on off state come
Reduce via net loss, improve quality of voltage, balanced load etc., realize the stable operation of distribution system, also rarely have document to mention electricity
System running state is improved by network reconfiguration during electrical automobile unordered charging.
The content of the invention
It is an object of the invention to：The impact after electrical network to power distribution network is accessed in view of extensive electric automobile, and is provided
A kind of multi-period optimal reconfiguration method of the active distribution network containing electric automobile.Using binary particle swarm algorithm more new particle position
Put, globally optimal solution is selected by niche technique, optimal compromise solution is selected using fuzzy membership Decision Method.Consider distribution
The access of formula power supply, and installation site and capacity are determined according to power loss sensitivity, it is considered to the unordered charging of electric automobile and intelligence are filled
Electric both of which, the network loss of power distribution network is reduced after reconstruct, quality of voltage is improved, meanwhile, simulation distribution formula power supply goes out
Power and the time dependent power distribution of charging electric vehicle, more tally with the actual situation.
The technical scheme that the present invention takes is：
A kind of multi-period optimal reconfiguration method of the active distribution network containing electric automobile, comprises the following steps：
Step (1)：Determine the object function that active distribution network multiple-objection optimization is reconstructed, including active loss minimization, voltage
Skew is minimum and on-off times are minimum, specially：
Wherein：N is total for the branch road of system；kiFor the switch on-off state of branch road i, value 0 or 1 is opened or closed；
Pi、QiThe active power and reactive power of branch road are represented respectively；UiFor the head end busbar voltage of branch road i；Δ T and Δ t are represented respectively
The standard time section of EV charging intervals section and calculating network loss；VSI (Voltage Stability Index) is systematic offset voltage
Index；M is system node sum；ViAnd VinFor the virtual voltage and rated voltage of node i；K is the time period；y(k-1)iAnd z(k-1)j
The block switch of the time period of kth -1, the current state of interconnection switch in expression system respectively, switching manipulation number of times be 2 times
Number；M is the quantity of the block switch in power distribution network；N is the quantity of interconnection switch in power distribution network.
Step (2)：Foundation meets the constraints of power distribution network reconfiguration, including the constraint of trend constraint, node voltage, electric current are about
Beam and network topology are constrained, specially：
gk∈Gk (7)
Wherein：Pi、QiActive power and reactive power that respectively node i is injected；PDG,i、QDG,iDG is followed successively by node i
The active and reactive power of input；PEV,i、QEV,iIt is followed successively by the active and reactive power that EV injects to node i；Ui、UjRespectively
The voltage magnitude of node i, j；Y is branch admittance matrix；The respectively lower limit and the upper limit of node i voltage magnitude；For branch road l current-carrying capacity higher limits；gkFor Switch State Combination in Power Systems；GkTo constitute the collection of the switch position combinations of radiation l network
Close.
Step (3)：Parameter initialization, including power distribution network node data, branch data, arrange population scale, greatest iteration
Number of times, and distributed power source exerts oneself and load power data.
Step (4)：Population at individual is initialized, i.e., the particle to generating calculates its target function value, is stored into outside disaggregation
Space.
Step (5)：Individual shared fitness value is calculated according to microhabitat shared mechanism, according to proportional to fitness value
Wheel disc bet method choose global optimum position, niche technique mathematic(al) representation is as follows：
Wherein：NbFor the domestic individual amount of your pupil；fsh(dij) it is Sharing Function；dijMicrohabitat individuality X is represented in the textiWith
Individual XjBetween Euclidean distance；σshareFor the microhabitat radius value of particle i, according to the equivalent distance dynamic between particle i and j
Update.
Step (6)：Whether particle rapidity and personal best particle update, replaced according to Pareto non-dominated rankings method choice
The individuality changed in step (4), wherein particle position are updated by Sigmoid functions, and advantage is that its binary number can be represented
The state of power distribution network breaker in middle.
Step (7)：Population elite disaggregation is extracted, it is individual as initialization of future generation.
Step (8)：Judge whether to reach maximum iteration time, if just carrying out next step, if not moving back to
Step (5) is calculated again.
A kind of multi-period optimal reconfiguration method of the active distribution network containing electric automobile, selects to be satisfied with by Fuzzy Decision Theory
The maximum solution of degree, the optimal compromise solution that as this is calculated.
A kind of multi-period optimal reconfiguration method of the active distribution network containing electric automobile, system includes distributed power source and electricity
The access of electrical automobile, distributed power source includes wind-power electricity generation and photovoltaic generation, and its position and capacity is sensitive according to active power loss
Degree determines that electric automobile accessed and charge in power distribution network comprising unordered charging and intelligent charge both of which；Establish distributed electrical
The multi-period probabilistic model that source is exerted oneself with charging electric vehicle.
A kind of multi-period optimal reconfiguration method of active distribution network containing electric automobile of the present invention, technique effect is as follows：
1：Algorithmically, particle position is updated by Sigmoid functions, makes algorithm be more suitable for power distribution network reconfiguration；Introduce
Niche technique, strengthens the global optimizing ability of algorithm, overcomes basic particle group algorithm to be easily trapped into lacking for locally optimal solution
Fall into；Population elite disaggregation is chosen by Pareto non-dominated rankings, the result of calculation of each object function is taken into account.
2. optimal compromise solution is selected using fuzzy evaluation Decision Method, for the reconfiguration scheme that policymaker provides a science.
3. consider the dynamic power probability distribution of wind-power electricity generation, photovoltaic generation and charging electric vehicle, make model more
Meet realistic meaning.
4. in the unordered charging of electric automobile car owner, the runnability for improving electrical network is considered from supply of electric power side, it is to avoid
Electric automobile car owner participates in power scheduling and brings unnecessary trouble to power consumer.
Description of the drawings
Fig. 1 is the prioritization scheme figure that distributed power source of the present invention is exerted oneself with charging electric vehicle.
Fig. 2 is improvement multi-objective particle swarm algorithm flow chart of the present invention.
Fig. 3 is the employing IEEE33 partition of nodes topology diagram of the present invention.
Fig. 4 is the active capability diagram of day part wind-power electricity generation of the present invention and photovoltaic generation.
Fig. 5 is day part charging electric vehicle load chart under different charge modes of the present invention.
Fig. 6 is Performance comparision box traction substation before and after multi-objective particle swarm algorithm of the present invention improvement.
Fig. 7 is day part network loss value comparison diagram under different scenes of the present invention.
Fig. 8 is day part node minimum amount of voltage that curve chart under different scenes of the present invention.
Specific embodiment
Technical scheme is specifically described below in conjunction with accompanying drawing.
A kind of multi-period optimal reconfiguration method of the active distribution network containing electric automobile, comprises the following steps：
(1)：Determine the object function that active distribution network multiple-objection optimization is reconstructed, including active loss minimization, variation
Minimum and on-off times are minimum, specially：
Wherein：N is total for the branch road of system；kiFor the switch on-off state of branch road i, value 0 or 1 (is opened or closed)；
Pi、QiThe active power and reactive power of branch road are represented respectively；UiFor the head end busbar voltage of branch road i；Δ T and Δ t are represented respectively
The standard time section of EV charging intervals section and calculating network loss；VSI is systematic offset voltage index；M is system node sum；ViWith
VinFor the virtual voltage and rated voltage of node i；K is the time period；y(k-1)iAnd z(k-1)jTime period of kth -1 in expression system respectively
Block switch, the current state of interconnection switch, switching manipulation number of times is 2 multiple；M is the block switch in power distribution network
Quantity；N is the quantity of interconnection switch in power distribution network.
(2)：Foundation meets the constraints of power distribution network reconfiguration, including the constraint of trend constraint, node voltage, restriction of current with
And network topology constraint, specially：
gk∈Gk (7)
Wherein：Pi、QiActive power and reactive power that respectively node i is injected；PDG,i、QDG,iDG is followed successively by node i
The active and reactive power of input；PEV,i、QEV,iIt is followed successively by the active and reactive power that EV injects to node i；Ui、UjRespectively
The voltage magnitude of node i, j；Y is branch admittance matrix；The respectively lower limit and the upper limit of node i voltage magnitude；For branch road l current-carrying capacity higher limits；gkFor Switch State Combination in Power Systems；GkTo constitute the collection of the switch position combinations of radiation l network
Close.
(3)：Parameter initialization, including power distribution network node data, branch data, arrange population scale, maximum iteration time,
And distributed power source is exerted oneself and charging electric vehicle power data, wherein distributed power generation includes that wind-power electricity generation and photovoltaic are sent out
Electricity, charging electric vehicle includes unordered charging and intelligent charge.
(4)：Population at individual is initialized, i.e., the particle to generating calculates its target function value, is stored into outside disaggregation space.
(5)：Individual shared fitness value is calculated according to microhabitat shared mechanism, according to the wheel proportional to fitness value
Disk bet method chooses global optimum position, and niche technique mathematic(al) representation is as follows：
Wherein：NbFor the domestic individual amount of your pupil；fsh(dij) it is Sharing Function；Dij represents in the text microhabitat individuality Xi
And the Euclidean distance between individual Xj；σshareFor the microhabitat radius value of particle i, moved according to the equivalent distance between particle i and j
State updates.
(6)：Particle rapidity and personal best particle update, and according to Pareto non-dominated rankings method choice step whether is replaced
Suddenly the individuality in (4), wherein particle position is updated by Sigmoid functions, and advantage is that its binary number can represent distribution
The state of net breaker in middle.
(7)：Population elite disaggregation is extracted, it is individual as initialization of future generation.
(8)：Judge whether to reach maximum iteration time, if just carrying out next step, if not moving back to step
(5) calculate again.
(9)：The solution of Maximum Satisfaction, the optimal compromise solution that as this is calculated are selected according to Fuzzy Decision Theory.
According to the above-mentioned a kind of multi-period optimal reconfiguration method of active distribution network containing electric automobile, the system includes
The mathematical model of the access of distributed power source and electric automobile, distributed power source and electric automobile is specially：
(1), the probabilistic model that distributed power source is exerted oneself, including wind-power electricity generation (WT) mathematical model and photovoltaic generation (PV) number
Learn model：
1)：The active probability density function of wind-power electricity generation can be represented with Weibull distributions, as follows
Wherein：kwFor shape index, reflect the distribution character of this area's wind speed, usual value is between 1.8 to 2.3；cwTable
Show scale parameter, embody the mean wind speed of this area, the k of different regionswAnd cwIt is general different；PWTFor the output work of blower fan
Rate；k1And k2For coefficient；PrIt is blower fan rated output power；vci、vco、vrRespectively cut wind speed, cut-out wind speed and specified wind
Speed.
2), the active probability density of photovoltaic generation can be distributed with Beta and be represented, as follows
Wherein：α and β is the form parameter of Beta distributions；PpvFor photovoltaic generation active power of output；PpvmFor photovoltaic array
Peak power output；N is battery component sum；An、ηnThe area and photoelectric transformation efficiency of respectively m-th photovoltaic module.
3), the idle injection rate of WT and PV is expressed as follows
Wherein：Setting φ=arcsin (0.9).
4) installation site and capacity of distributed power source, are determined according to power loss sensitivity principle, based on to the active net of system
The impact of damage is minimum, it is considered to which distributed power source is exerted oneself in the case of nargin, meets the ability that system to be received distributed power source,
Meet the service requirement of active distribution network；
(2), the probabilistic model of charging electric vehicle, including unordered charge mode and intelligent charge pattern：
1)：Under unordered charge mode, the electric automobile car owner center unification that is not scheduled arranges to charge, i.e., the mould " with to filling "
Formula, its mathematical model is expressed as follows
Wherein：fsX () starts to charge up moment probability-distribution function for electric automobile；σs=3.4；μs=17.6；X is charging
Moment；F (d) is daily travel distribution；σd=0.88；μd=3.2；F (t) is charging electric vehicle power between 2～3kW
Charging interval probability distribution；fstRepresent the probability density distribution of EV charging moment and time span, fst=fs·ft；PcIt is
EV charge powers (kW).
2)：Under intelligent charge pattern, electric automobile car owner needs the arrangement for obeying control centre, by control centre a few days ago
Reasonable arrangement, plan next day private car owner charging plan, realize the reliability service of electrical network, electric automobile under intelligent charge
Arrangement can be represented by the formula
Wherein：EiFor calculated EV quantity allotteds, according to the proportional distribution of the loading at each moment；E is electronic vapour
Car sum；Pl.iFor the load value of moment i.
One calculated examples is provided as a example by following IEEE33 Node power distribution systems：
There are 33 nodes in IEEE33 node systems, 37 circuits, 5 interconnection switches, power distribution system load is always active
It is idle for 2300kvar for 3715kW, access distributed power source and the network topology structure after electric automobile is as shown in Figure 3.Point
Cloth plant-grid connection position and installed capacity such as following table, comprising two wind power generating sets and a photovoltaic generator in system
Group.
| DG positions (DG types) | 25(WT1) | 30(WT2) | 32(PV) | It is total |
| Output calculating value/kW | 293.891 | 646.406 | 359.953 | 1300.25 |
| Specified installed capacity/the kW of DG | 300 | 700 | 400 | 1400 |
General private savings electric automobile car owner concentrates on resident load area, as shown in figure 3, setting respectively in Liang Ge resident loads area
A fixed electric automobile charging station, it is assumed that have 1000 electric automobiles and concentrate in the two cells, in order that the present invention is more
Plus tally with the actual situation, taken into full account the randomness of distributed power source and charging electric vehicle, will be divided into for 1 day 12 it is each when
Section, distributed power source is exerted oneself with charging electric vehicle load power respectively as shown in Figure 4 and Figure 5.
Fig. 6 is the calculating performance comparison diagram for improving multi-objective particle swarm algorithm and basic multi-objective particle swarm algorithm, is improved
Particle cluster algorithm afterwards, better than the algorithm before improving, is more suitable for the optimal reconfiguration of active distribution network in optimizing performance.
After the on-position of distributed power source and electric automobile determines, point four kinds of scenes illustrate system during charging electric vehicle
Optimization and the Contrast on effect not optimized.
Scene 1：It is not reconstructed under unordered charge mode；
Scene 2：It is not reconstructed under intelligent charge pattern；
Scene 3：It is reconstructed under unordered charge mode；
Scene 4：It is reconstructed under intelligent charge pattern.
Following table gives result of calculation of each desired value in 1 day before and after the unordered charging of electric automobile and intelligent charge reconstruct
| Scene | Network loss/kW | VSI | On-off times |
| 1 | 365.58 | 5.91 | 0 |
| 2 | 323.19 | 5.81 | 0 |
| 3 | 238.27 | 3.68 | 48 |
| 4 | 207.83 | 3.79 | 44 |
Network loss 323.19kW of scene 2, variation index 5.81, network loss 238.27kW respectively than scene 3, voltage are inclined
Move index 3.68 and be higher by 35.6% and 57.9%, illustrate from grid company side to consider, system can be made more to optimize, although need
To be optimized by switching manipulation, but disclosure satisfy that the need for electricity of electric automobile car owner.Further to 4 kinds of scene synthesis
Analysis, the network loss and variation desired value before and after being reconstructed when intelligent charge and unordered charging improves significantly, field
Scape 4 is fewer than the on-off times of scene 34 times, illustrates that the reconstructing method used by the present invention is effective.
The network loss value that 1 day day part different scenes draw is as shown in fig. 7, day part minimum amount of voltage that is as shown in figure 8, from figure
In it can be seen that electric automobile access after system loading the 9th to the 12nd time period (i.e. 18:00-24:00) peak the phase,
And now, network loss has larger improvement after electric automobile is reconstructed under two kinds of charge modes, in the minima of the period voltage
Also make moderate progress, be conducive to the stable operation of system.
The present invention is analyzed in IEEE33 power saving apparatus, it is contemplated that different type distribution is accessed in active distribution network
Formula power supply, it is also considered that the impact under different charge modes in electric automobile access system, by improved multi-objective particle swarm
Algorithm is calculated, and the system losses and variation index after reconstructing when electric automobile is accessed are greatly improved.
Claims (3)
1. a kind of multi-period optimal reconfiguration method of active distribution network containing electric automobile, it is characterised in that comprise the following steps：
Step (1)：Determine the object function that active distribution network multiple-objection optimization is reconstructed, including active loss minimization, variation
Minimum and on-off times are minimum, specially：
Wherein：N is total for the branch road of system；kiFor the switch on-off state of branch road i, value 0 or 1 is opened or closed；Pi、Qi
The active power and reactive power of branch road are represented respectively；UiFor the head end busbar voltage of branch road i；Δ T and Δ t represent that respectively EV fills
Electric time period and the standard time section of calculating network loss；VSI (Voltage Stability Index) refers to for systematic offset voltage
Number；M is system node sum；ViAnd VinFor the virtual voltage and rated voltage of node i；K is the time period；y(k-1)iAnd z(k-1)jPoint
The block switch of the time period of kth -1, the current state of interconnection switch not in expression system, switching manipulation number of times is 2 multiple；
M is the quantity of the block switch in power distribution network；N is the quantity of interconnection switch in power distribution network；
Step (2)：Foundation meets the constraints of power distribution network reconfiguration, including the constraint of trend constraint, node voltage, restriction of current with
And network topology constraint, specially：
gk∈Gk(7)；
Wherein：Pi、QiActive power and reactive power that respectively node i is injected；PDG,i、QDG,iIt is followed successively by DG to be input into node i
Active and reactive power；PEV,i、QEV,iIt is followed successively by the active and reactive power that EV injects to node i；Ui、UjRespectively node
The voltage magnitude of i, j；Y is branch admittance matrix；The respectively lower limit and the upper limit of node i voltage magnitude；For
Branch road l current-carrying capacity higher limits；gkFor Switch State Combination in Power Systems；GkTo constitute the set of the switch position combinations of radiation l network；
Step (3)：Parameter initialization, including power distribution network node data, branch data, arrange population scale, maximum iteration time,
And distributed power source is exerted oneself and load power data；
Step (4)：Population at individual is initialized, i.e., the particle to generating calculates its target function value, is stored into outside disaggregation space；
Step (5)：Individual shared fitness value is calculated according to microhabitat shared mechanism, according to the wheel proportional to fitness value
Disk bet method chooses global optimum position, and niche technique mathematic(al) representation is as follows：
Wherein：NbFor the domestic individual amount of your pupil；fsh(dij) it is Sharing Function；dijMicrohabitat individuality X is represented in the textiAnd individuality
XjBetween Euclidean distance；σshareFor the microhabitat radius value of particle i, according to the equivalent distance dynamic between particle i and j more
Newly；
Step (6)：Particle rapidity and personal best particle update, and according to Pareto non-dominated rankings method choice step whether is replaced
Suddenly the individuality in (4), wherein particle position is updated by Sigmoid functions, and advantage is that its binary number can represent distribution
The state of net breaker in middle；
Step (7)：Population elite disaggregation is extracted, it is individual as initialization of future generation；
Step (8)：Judge whether to reach maximum iteration time, if just carrying out next step, if not moving back to step
(5) calculate again.
2. a kind of multi-period optimal reconfiguration method of active distribution network containing electric automobile according to claim 1, its feature exists
In：The solution of Maximum Satisfaction, the optimal compromise solution that as this is calculated are selected by Fuzzy Decision Theory.
3. a kind of multi-period optimal reconfiguration method of active distribution network containing electric automobile according to claim 1, its feature exists
In：The system includes the access of distributed power source and electric automobile, and distributed power source includes wind-power electricity generation and photovoltaic generation,
Its position and capacity determines that electric automobile is accessed in power distribution network and charged comprising unordered charging and intelligence according to active power losses sensitivity
Can be charged both of which；Establish the multi-period probabilistic model that distributed power source is exerted oneself with charging electric vehicle.
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