CN106602557B - A kind of multi-period optimal reconfiguration method of active distribution network containing electric car - Google Patents
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- H—ELECTRICITY
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Abstract
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car, updates particle position using binary particle swarm algorithm, selects globally optimal solution by niche technique, selects optimal compromise solution using fuzzy membership Decision Method.It the advantage is that, consider the access of distributed generation resource, and installation site and capacity are determined according to power loss sensitivity, consider the unordered charging of electric car and intelligent charge both of which, by the way that the network loss of power distribution network after reconstruct reduces, quality of voltage is improved, meanwhile the power distribution that simulation distribution formula power supply power output and electric car charging change over time, so that the present invention is more tallied with the actual situation.
Description
Technical field
The present invention relates to active distribution network field, especially a kind of multi-period optimization weight of the active distribution network containing electric car
Structure method.
Background technique
In recent years, traditional energy is increasingly depleted, and the growth of electricity needs is just driving power grid to develop towards sustainable mode,
So that construction iterative method of the country to smart grid, and in power distribution network side, the access amount of distributed generation resource and electric car
It is continuously increased, conventional electrical distribution net has been difficult to cope with, for this purpose, the concept of active distribution network is arisen spontaneously, purpose is exactly by matching
The novel load occurred in power grid carries out the control and management of active, reaches the reliability service for reducing via net loss, realizing power grid
Deng.
In order to cope with the power demand of a large amount of power loads, it is essential one that distributed generation resource, which accesses active distribution network,
A link can select it to connect the addressing constant volume of the distributed generation resource for the purpose of reducing network loss by power loss sensitivity
Enter position and capacity.It is counted according to related data, ends 2015, Chinese automobile user reaches 22.3 ten thousand, country
Also corresponding policy is put into effect, the year two thousand twenty is arrived, electric car ownership will reach 5,000,000, and electric car is as a kind of cleaning
The energy, it is considered to be the important technology approach of reduce carbon emission amount, administer haze etc..But electric car is solving energy
While source problem and problem of environmental pollution, pressure can be also brought to power grid as a kind of electric load during access grid charging
The unordered charging of power, especially electric car car owner can be such that network voltage decline, power loss increases, so extensive electronic vapour
The networking research of vehicle is that active distribution network optimization runs an important factor for being considered.
In order to improve the influence of the electric car access unordered charging of power distribution network, there is scholar just to propose the side of intelligent charge
Method needs a large amount of electric car car owners to participate in although the method for intelligent charge increases to the stable operation of power distribution network
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 switching switch state come
Reduce via net loss, improve quality of voltage, balanced load etc., it realizes the stable operation of distribution system, also rarely has document to mention electricity
System running state is improved by network reconfiguration when electrical automobile unordered charging.
Summary of the invention
It is an object of the invention to: in view of extensive electric car accesses the influence after power grid to power distribution network, and provide
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car.Utilize binary particle swarm algorithm more new particle position
It sets, 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, consider that the unordered charging of electric car and intelligence are filled
Electric both of which, by the way that the network loss of power distribution network after reconstruct reduces, quality of voltage is improved, meanwhile, simulation distribution formula power supply goes out
The power distribution that power and electric car charging change over time, more tallies with the actual situation.
The technical scheme adopted by the invention is as follows:
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car, comprising the following steps:
Step (1): the objective function of active distribution network multiple-objection optimization reconstruct, including active loss minimization, voltage are determined
Offset is minimum and on-off times are minimum, specifically:
Wherein: N is the branch sum of system；klFor the switch on-off state of branch l, value 0 or 1 is opened or closed；
Pl、QlRespectively represent the active power and reactive power of branch；UlFor the head end busbar voltage of branch l；Δ T and Δ t are respectively indicated
EV charging time section and the standard time section for calculating network loss；VSI is systematic offset voltage index；M is system node sum；ViWith
VinFor the virtual voltage and voltage rating of node i；T is the period；y(t-1)iAnd z(t-1)jRespectively indicate the t-1 period in system
Block switch, interconnection switch current state, switch number of operations be 2 multiple；M is the block switch in power distribution network
Quantity；N is the quantity of interconnection switch in power distribution network；
Step (2): the constraint condition for meeting power distribution network reconfiguration is established, including the constraint of trend constraint, node voltage, electric current are about
Beam and network topology constraint, specifically:
gk∈Gk (7)
Wherein: Pi、QiThe respectively active power and reactive power of node i injection；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 is injected to node i；Ui、UjRespectively
The voltage magnitude of node i, j；Y is branch admittance matrix；The respectively lower and upper limit of node i voltage magnitude；For branch l current-carrying capacity upper limit value；gkFor Switch State Combination in Power Systems；GkFor the collection for constituting the switch position combinations for radiating l network
It closes；
Step (3): population scale, greatest iteration is arranged in parameter initialization, including power distribution network node data, branch data
Number and distributed generation resource power output and load power data；
Step (4): population at individual initialization calculates its target function value to the particle of generation, is stored into external disaggregation
Space；
Step (5): the shared fitness value of individual 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 individual amount in microhabitat；fsh(def) it is Sharing Function；defIndicate the Europe between microhabitat individual
Formula distance；σshareFor the microhabitat radius value of particle e, updated according to the space length dynamic between particle e and f；
Step (6): whether particle rapidity and personal best particle update, replaced according to Pareto non-dominated ranking method choice
The individual in step (4) is changed, wherein particle position is updated by Sigmoid function, and advantage is that its binary number can indicate
The state switched in power distribution network；
Step (7): extracting population elite disaggregation, as next generation's initialization individual；
Step (8): judging whether to reach maximum number of iterations, if so, current iteration terminates；If it is not, being returned to
It is calculated again to step (5).
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car selects to be satisfied with by Fuzzy Decision Theory
Maximum solution is spent, as this optimal compromise solution calculated.
A kind of active distribution network containing electric car multi-period optimal reconfiguration method includes distributed generation resource and electricity in system
The access of electrical automobile, distributed generation resource include wind-power electricity generation and photovoltaic power generation, position and capacity it is sensitive according to active power loss
Degree determines that electric car accesses charging in power distribution network and includes unordered charging and intelligent charge both of which；Establish distributed electrical
The multi-period probabilistic model of source power output and electric car charging.
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car of the invention, technical effect are as follows:
1: algorithmically, particle position being updated by Sigmoid function, algorithm is made to be more suitable for power distribution network reconfiguration；It introduces
Niche technique strengthens the global optimizing ability of algorithm, and basic particle group algorithm is overcome to be easily trapped into lacking for locally optimal solution
It falls into；Population elite disaggregation is chosen by Pareto non-dominated ranking, takes into account the calculated result of each objective function.
2. selecting optimal compromise solution using fuzzy evaluation Decision Method, a scientific reconfiguration scheme is provided for policymaker.
3. considering the dynamic power probability distribution of wind-power electricity generation, photovoltaic power generation and electric car charging, make model more
Meet realistic meaning.
4. considering the runnability for improving power grid from power supply side in the unordered charging of electric car car owner, avoiding
Electric car car owner participates in power scheduling and brings unnecessary trouble to power consumer.
Detailed description of the invention
Fig. 1 is the prioritization scheme figure of distributed generation resource of the present invention power output and electric car charging.
Fig. 2 is improvement multi-objective particle swarm algorithm flow chart of the present invention.
Fig. 3 is of the invention using the partition of nodes IEEE33 topology diagram.
Fig. 4 is the active power output figure of day part wind-power electricity generation of the present invention and photovoltaic power generation.
Fig. 5 is day part electric car charging load chart under different charge modes of the present invention.
Fig. 6 is that performance compares box traction substation before and after multi-objective particle swarm algorithm of the present invention improves.
Fig. 7 is that day part network loss value compares figure under different scenes of the present invention.
Fig. 8 is day part node minimum amount of voltage that curve graph under different scenes of the present invention.
Specific embodiment
Technical solution of the present invention is specifically described below in conjunction with attached drawing.
A kind of multi-period optimal reconfiguration method of active distribution network containing electric car, comprising the following steps:
(1): determining the objective function of active distribution network multiple-objection optimization reconstruct, including active loss minimization, variation
Minimum and on-off times are minimum, specifically:
Wherein: N is the branch sum of system；klFor the switch on-off state of branch l, value 0 or 1 is opened or closed；
Pl、QlRespectively represent the active power and reactive power of branch；UlFor the head end busbar voltage of branch l；Δ T and Δ t are respectively indicated
EV charging time section and the standard time section for calculating network loss；VSI is systematic offset voltage index；M is system node sum；ViWith
VinFor the virtual voltage and voltage rating of node i；T is the period；y(t-1)iAnd z(t-1)jRespectively indicate the t-1 period in system
Block switch, interconnection switch current state, switch number of operations be 2 multiple；M is the block switch in power distribution network
Quantity；N is the quantity of interconnection switch in power distribution network；
(2): establish and meet the constraint condition of power distribution network reconfiguration, including the constraint of trend constraint, node voltage, restriction of current with
And network topology constraint, specifically:
gk∈Gk(7)；
Wherein: Pi、QiThe respectively active power and reactive power of node i injection；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 is injected to node i；Ui、UjRespectively
The voltage magnitude of node i, j；Y is branch admittance matrix；The respectively lower and upper limit of node i voltage magnitude；For branch l current-carrying capacity upper limit value；gkFor Switch State Combination in Power Systems；GkFor the collection for constituting the switch position combinations for radiating l network
It closes.
(3): population scale, maximum number of iterations is arranged in parameter initialization, including power distribution network node data, branch data,
And distributed generation resource is contributed and electric car charge power data, wherein distributed power generation includes wind-power electricity generation and photovoltaic hair
Electricity, electric car charging include unordered charging and intelligent charge.
(4): population at individual initialization calculates its target function value to the particle of generation, is stored into external disaggregation space.
(5): the shared fitness value of individual 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 individual amount in microhabitat；fsh(def) it is Sharing Function；defIndicate the Europe between microhabitat individual
Formula distance；σshareFor the microhabitat radius value of particle e, updated according to the space length dynamic between particle e and f.
(6): particle rapidity and personal best particle update, and whether replace step according to Pareto non-dominated ranking method choice
Suddenly the individual in (4), wherein particle position is updated by Sigmoid function, and advantage is that its binary number can indicate distribution
The state switched in net.
(7): population elite disaggregation is extracted, as next generation's initialization individual.
(8): judging whether to reach maximum number of iterations, if so, current iteration terminates；If it is not, moving back to step
Suddenly (5) calculate again.
(9): the solution of Maximum Satisfaction, as this optimal compromise solution 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 car, include in the system
The mathematical model of the access of distributed generation resource and electric car, distributed generation resource and electric car specifically:
(1), the probabilistic model of distributed generation resource power output, including wind-power electricity generation (WT) mathematical model and photovoltaic power generation (PV) number
Learn model:
1): the active probability density function of wind-power electricity generation can be distributed with Weibull to be indicated, 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, embodies the mean wind speed of this area, the k of different regionswAnd cwIt is general different；PWTFor the output work of blower
Rate；k1And k2For coefficient；PrIt is blower rated output power；vci、vco、vrRespectively cut wind speed, cut-out wind speed and specified wind
Speed.
2), the active probability density of photovoltaic power generation can be distributed with Beta and be indicated, as follows
Wherein: α and β is the form parameter of Beta distribution；PpvFor photovoltaic power generation active power of output；PpvmFor photovoltaic array
Peak power output；N is battery component sum；An、ηnThe area and photoelectric conversion 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 generation resource, are determined according to power loss sensitivity principle, based on to the active net of system
The influence of damage is minimum, in the case where considering distributed generation resource power output nargin, meets the ability that system can receive distributed generation resource,
Meet the service requirement of active distribution network；
(2), the probabilistic model of electric car charging, including unordered charge mode and intelligent charge mode:
1): under unordered charge mode, the not scheduled centre punch one of electric car car owner arranges charging, i.e. the mould " with to filling "
Formula, mathematical model are expressed as follows
Wherein: fs(x) moment probability-distribution function is started to charge for electric car；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 electric car charge power between 2~3kW
Charging time probability distribution；fstIndicate the probability density distribution of EV charging moment and time span, fst=fs·ft；PcIt is
EV charge power (kW).
2): under intelligent charge mode, electric car car owner needs to obey the arrangement of control centre, a few days ago by control centre
Reasonable arrangement, plan next day private car owner charging plan, realize the reliability service of power grid, electric car under intelligent charge
Arrangement can be represented by the formula
Wherein: EiFor the EV quantity allotted being calculated, according to the proportional distribution of the load at each moment；E is electronic vapour
Vehicle sum；Pl.iFor the load value of moment i.
One calculated examples is provided for following IEEE33 Node power distribution system:
There are 33 nodes in IEEE33 node system, 37 routes, 5 interconnection switches, the power distribution system load is always active
For 3715kW, idle is 2300kvar, and the network topology structure after accessing distributed generation resource and electric car is as shown in Figure 3.Point
Cloth plant-grid connection position and installed capacity such as following table include two wind power generating sets and a photovoltaic generator in system
Group.
General private savings electric car car owner concentrates on resident load area, as shown in figure 3, setting respectively in Liang Ge resident load area
A fixed electric automobile charging station, it is assumed that share 1000 electric cars and concentrate in the two cells, in order to make the present invention more
Add and tally with the actual situation, fully considered the randomness of distributed generation resource and electric car charging, will be divided within 1 day 12 it is each when
Section, distributed generation resource power output and electric car charging load power difference are as shown in Figure 4 and Figure 5.
Fig. 6 is the calculated performance comparison diagram for improving multi-objective particle swarm algorithm and basic multi-objective particle swarm algorithm, is improved
Particle swarm 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 generation resource and electric car determines, point four kinds of scenes illustrate system when electric car charging
Optimization and the Contrast on effect not optimized.
Scene 1: without reconstruct under unordered charge mode；
Scene 2: without reconstruct under intelligent charge mode；
Scene 3: it is reconstructed under unordered charge mode；
Scene 4: it is reconstructed under intelligent charge mode.
Following table gives calculated result of each target value of the unordered charging of electric car and intelligent charge reconstruct front and back in 1 day
| 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 |
The network loss 323.19kW of scene 2, variation index 5.81, it is more inclined than network loss 238.27kW, the voltage of scene 3 respectively
It moves index 3.68 and is higher by 35.6% and 57.9%, illustrate to consider from grid company side, system can be made more to optimize, although needing
It to be optimized by switching operation, but can satisfy the power demand of electric car car owner.It is further comprehensive to 4 kinds of scenes
Analysis, when intelligent charge and unordered charging, the network loss of front and back was reconstructed and variation index value improves significantly, field
Scape 44 times fewer than the on-off times of scene 3, illustrate that reconstructing method used in the present invention is effective.
Network loss value that 1 day day part different scenes obtains as shown in fig. 7, day part minimum amount of voltage that as shown in figure 8, from figure
In it can be seen that electric car access after system loading peak the phase in the 9th to the 12nd period (i.e. 18:00-24:00),
And at this point, network loss has larger improvement after electric car is reconstructed under two kinds of charge modes, in the minimum value of the period voltage
Also it makes moderate progress, is conducive to the stable operation of system.
The present invention analyzes 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 influence under to different charge modes in electric car access system passes through improved multi-objective particle swarm
Algorithm calculates, and the system losses and variation index in electric car access after reconstruct are greatly improved.
Claims (3)
1. a kind of multi-period optimal reconfiguration method of active distribution network containing electric car, it is characterised in that the following steps are included:
Step (1): the objective function of active distribution network multiple-objection optimization reconstruct, including active loss minimization, variation are determined
Minimum and on-off times are minimum, specifically:
Wherein: N is the branch sum of system；klFor the switch on-off state of branch l, value 0 or 1 is opened or closed；Pl、Ql
Respectively represent the active power and reactive power of branch；UlFor the head end busbar voltage of branch l；Δ T and Δ t respectively indicates EV and fills
Electric period and the standard time section for calculating network loss；VSI is systematic offset voltage index；M is system node sum；ViAnd VinFor
The virtual voltage and voltage rating of node i；T is the period；y(t-1)iAnd z(t-1)jRespectively indicate point of t-1 period in system
The current state of Duan Kaiguan, interconnection switch, switch number of operations are 2 multiple；M is the number of the block switch in power distribution network
Amount；N is the quantity of interconnection switch in power distribution network；
Step (2): establishing and meet the constraint condition of power distribution network reconfiguration, including the constraint of trend constraint, node voltage, restriction of current with
And network topology constraint, specifically:
gk∈Gk (7)
Wherein: Pi、QiThe respectively active power and reactive power of node i injection；PDG,i、QDG,iDG is followed successively by input to node i
Active and reactive power；PEV,i、QEV,iIt is followed successively by the active and reactive power that EV is injected to node i；Ui、UjRespectively node
I, the voltage magnitude of j；Y is branch admittance matrix；The respectively lower and upper limit of node i voltage magnitude；For
Branch l current-carrying capacity upper limit value；gkFor Switch State Combination in Power Systems；GkFor the set for constituting the switch position combinations for radiating l network；
Step (3): population scale, maximum number of iterations is arranged in parameter initialization, including power distribution network node data, branch data,
And distributed generation resource is contributed and load power data；
Step (4): population at individual initialization calculates its target function value to the particle of generation, is stored into external disaggregation space；
Step (5): the shared fitness value of individual 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 individual amount in microhabitat；fsh(def) it is Sharing Function；defIndicate microhabitat individual between it is European away from
From；σshareFor the microhabitat radius value of particle e, updated according to the space length dynamic between particle e and f；
Step (6): particle rapidity and personal best particle update, and whether replace step according to Pareto non-dominated ranking method choice
Suddenly the individual in (4), wherein particle position is updated by Sigmoid function, and advantage is that its binary number can indicate distribution
The state switched in net；
Step (7): extracting population elite disaggregation, as next generation's initialization individual；
Step (8): judging whether to reach maximum number of iterations, if so, current iteration terminates；If it is not, moving back to step
Suddenly (5) calculate again.
2. a kind of multi-period optimal reconfiguration method of active distribution network containing electric car, feature exist according to claim 1
In: the solution of Maximum Satisfaction, as this optimal compromise solution calculated are selected by Fuzzy Decision Theory.
3. a kind of multi-period optimal reconfiguration method of active distribution network containing electric car, feature exist according to claim 1
In: it include the access of distributed generation resource and electric car in the system, distributed generation resource includes wind-power electricity generation and photovoltaic power generation,
Its position and capacity determines that it includes unordered charging and intelligence that electric car, which accesses charging in power distribution network, according to active power losses sensitivity
Can charge both of which；Establish the multi-period probabilistic model of distributed generation resource power output and electric car charging.
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