CN110034560A - Power distribution network multi-target optimum operation method based on intelligent Sofe Switch - Google Patents
Power distribution network multi-target optimum operation method based on intelligent Sofe Switch Download PDFInfo
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Abstract
The invention discloses a kind of power distribution network multi-target optimum operation methods based on intelligent Sofe Switch, propose a kind of intelligent Sofe Switch mathematical model in the intelligent Sofe Switch of research on the basis of being that power distribution network brings benefit ability in multiple target according to different operational objectives.Then, on this basis, it is difficult to trade-off problem for the multiple target of power distribution network emerged in operation, proposed to reduce via net loss, equally loaded and improvement voltage are the Optimized model of target.Then to determine the intelligent optimal power output of Sofe Switch, a kind of comprehensive multi-objective particle and taxi algorithm are proposed in the optimization method of one.The present invention combines intelligent Sofe Switch mathematical model, optimization aim model, multi-objective particle swarm algorithm and taxi algorithm, effectively improves the power quality of power distribution network, has adapted to the access of large-scale distributed power supply.
Description
Technical field
The invention discloses a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch, specific steps are as follows:
To guarantee distribution network electric energy quality and adapting to the access of large-scale distributed power supply (Distributed Generation, DG),
Propose a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch.The present invention is by intelligent Sofe Switch mathematical modulo
Type, Optimized model, particle swarm algorithm and taxi algorithm combine, and effectively improve the power quality of power distribution network, and fit
The access of large-scale distributed power supply is answered.
Background technique
With being continuously increased for Distributed Generation in Distribution System (DG) quantity, various operation problems, as network loss increases, voltage
The problems such as out-of-limit, overload, becomes increasingly conspicuous.In order to guarantee power quality, and the access of large-scale distributed power supply (DG) is adapted to,
It needs using new trend and voltage control method.It the use of power electronic devices is to solve the problems, such as this one of approach.This hair
It is bright to propose a kind of Multipurpose Optimal Method to improve the fortune of the power distribution network with distributed generation resource and intelligent Sofe Switch (SOP)
Row.Intelligent Sofe Switch is a kind of power electronic equipment, has real-time, accurate active and reactive power flow control ability.The present invention
A kind of hybrid algorithm for combining multi-objective particle with taxi optimization algorithm is proposed, to reduce power damage
Consumption, balanced feeder line load and improvement voltage are target, it is determined that the optimal power output of intelligent Sofe Switch.Mixing proposed by the present invention is calculated
Method combining local searching technology, the local solution obtained to global search technology are finely adjusted, and overcome multi-objective particle swarm algorithm
Deficiency in terms of local optimum capture.
Summary of the invention
Goal of the invention: a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch is provided.
Technical solution: the power distribution network multi-target optimum operation method based on intelligent Sofe Switch includes the following steps:
Step 1 constructs intelligent soft switch power injection model；
Step 2, building are to reduce via net loss, equally loaded and improve voltage as the Optimized model of target；
Step 3, according to Pareto optimality, obtain the local solution between one group of different target, then use global search technology
It is optimized with intelligent Sofe Switch power output of the local search technique to power distribution network.
In a further embodiment, the step 1 is further are as follows:
It establishes its power injection model and the active power of SOP and reactive power is injected into power grid tide using the model
In stream；The electric network swim is calculated by following recurrence equation:
In formula, PiAnd QiIt is the active power and reactive power that i+1 bus is flowed to from i bus respectively；Pload(i)And Qload(i)
It is the active and reactive requirement of bus i, Ploss(i,i+1)And Qloss(i,i+1)It is the power damage between branch connection bus i and i+1
Consumption, riAnd xiFor resistance and reactance；ViFor the voltage of bus i, NbusFor the sum of network median generatrix；
The running boundary of SOP are as follows:
PC1=Pp-Ploss(p,C1)；PC2=Pq-Ploss(q,C2)；
In formula, PC1And PC2For the effective power flow of each VSC of SOP；Ploss(p,C1)It is the power loss between bus p and VSC1,
Ploss(q,C2)It is the power loss between bus q and VSC2；
The active power exchange of two VSC has following constraint:
PC1+PC2+PSOPloss=0；
In formula, PSOP, lossFor the power loss at SOP；These power losses are opposite compared with the power loss of whole network
It is lower, therefore can be ignored above formula and simplify are as follows:
PC1=-PC2
The constraint condition of SOP capacity-constrained and voltage are as follows:
Vmin≤|VC1|≤Vmax；Vmin≤|VC2|≤Vmax
In formula, QC1And QC2The idle injection rate of each VSC of respectively SOP；SC1And SC2It is the rated capacity of each VSC；
VminAnd VmaxIt is the minimum voltage and maximum voltage that network allows；VC1And VC2It is the voltage on each SOP terminal.
In a further embodiment, the step 2 is further are as follows:
Establishing, which reduces power loss, load balancing and voltage, improves as Model for Multi-Objective Optimization:
JOB=min [obj1,obj2,obj3]；
Vmin≤|Vi|≤Vmaxi∈{1,2,...,Nbus}；
|Ik|≤Ik maxk∈{1,2,...,Nbranch}；
Wherein Ik、Vk、Pk、QkTo pass through the electric current of k branch, voltage drop, effective power flow, reactive power flow, rkFor the electricity of k branch
Resistance, NbranchFor branch sum；Wherein Ik maxThe maximum current allowed for k branch.
In a further embodiment, the step 3 is further are as follows:
When optimization process starts, range A is sky, and the position and speed of all particles is all random initializtion；
Since particle position may be except feasible zone, it is necessary to assure the decision of active power and the inactivity injection of SOP
Variable is constrained in SOP rated capacity；
This point is indicated using constraint function is enforced, and in the function, SC service ceiling and lower limit bind particle position.
Set the initial personal optimum position of all particles to their initial position；
Function superiority and inferiority degree is used to select local solution from all particles according to the concept of Pareto optimality, and is deposited
Storage is in range；
In each iteration, particle position and speed are updated using Eqs；Then, solution party is improved using mutation operation
The diversity of case.
A kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch, the present invention is by intelligent Sofe Switch mathematical modulo
Type, optimization aim model, multi-objective particle swarm algorithm and taxi algorithm combine, and effectively improve the electric energy of power distribution network
Quality, and the access of large-scale distributed power supply has been adapted to, specifically include following steps.
The invention discloses a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch, specific steps are as follows:
To guarantee power quality and adapting to the access of large-scale distributed power supply (Distributed Generation, DG), invention
A kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch.This patent is power distribution network band studying intelligent Sofe Switch
Come on the basis of many-sided benefit ability, proposes a kind of intelligent Sofe Switch mathematical model.Then, on this basis, for matching
It is difficult to the contradiction weighed between target in operation of power networks, proposes to reduce via net loss, equally loaded and improvement voltage characteristic
For the Optimized model of target.Then to determine intelligent Sofe Switch optimal setting point, it is excellent to propose a kind of comprehensive multi-objective particle swarm
Change algorithm and taxi algorithm in the optimization method of one.The present invention is by intelligent Sofe Switch mathematical model, optimization aim model, grain
Swarm optimization and taxi algorithm combine, and effectively improve the power quality of power distribution network and adapt to large-scale distributed electricity
Source.
2, a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch is required according to right 1, it is special
Sign is: on the basis of studying intelligent Sofe Switch is that power distribution network brings many-sided benefit ability, opening for sufficiently assessment intelligence is soft
The potential impact run to power distribution network is closed, the power injection mathematical model of intelligent Sofe Switch is established, analyzes in power distribution network
Active and reactive trend.
3, a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch is required according to right 1, it is special
Sign is: on the basis of the power injection model analysis to intelligent Sofe Switch, for the multiple target of power distribution network emerged in operation
It is difficult to trade-off problem, considers the constraint condition of intelligent Sofe Switch, proposes one kind to reduce via net loss, equally loaded and improvement
Voltage is the Optimized model of target.
4, a kind of power distribution network multi-target optimum operation method based on intelligent Sofe Switch is required according to right 1, it is special
Sign is: to determine optimal power output of the intelligent Sofe Switch in power distribution network, according to Pareto optimality, obtaining between one group of different target
Local solution, then use global search technology (i.e. multi-objective particle swarm algorithm method) and local search technique (i.e. taxi
Algorithm) the intelligent Sofe Switch power output of power distribution network is optimized.
The utility model has the advantages that distribution network electric energy quality can be improved, and promote the permeability in the power distribution network of distributed generation resource, the party
In combination with local search technique, the local solution obtained to global search technology is finely adjusted, overcomes the hybrid algorithm for including in method
Deficiency of the multi-objective particle swarm algorithm in terms of local optimum capture.
Detailed description of the invention
Fig. 1 is the block diagram for being equipped with the power distribution network of Sofe Switch.
Fig. 2 is method schematic of the invention.
Fig. 3 is the renewal process schematic diagram of particle position and speed in population.
Fig. 4 is code schematic diagram of the invention.
Specific embodiment
(1) on the basis of studying intelligent Sofe Switch is that power distribution network brings many-sided benefit ability, intelligently soft open is established
It closes power and injects mathematical model.
(2) it is difficult to trade-off problem for the multiple target of power distribution network emerged in operation, considers the constraint condition of intelligent Sofe Switch,
It proposes a kind of to reduce via net loss, equally loaded and improve voltage as the Optimized model of target.
(3) one group of difference mesh is obtained according to Pareto optimality for optimal power output of the determining intelligent Sofe Switch in power distribution network
Then local solution between mark (is gone out using global search technology (i.e. multi-objective particle swarm algorithm method) and local search technique
Hire a car algorithm) the intelligent Sofe Switch power output of power distribution network is optimized.
Above-mentioned steps (1) include the following contents:
11) SOP can use different topological structures, and the present invention is opened up using back-to-back voltage source converter (B2B VSC)
Structure is flutterred, simple scheme of installation of the SOP in power distribution network is as shown in Figure 1.
SOP can be run in four quadrants, two exchange end reactive power be it is independent, can according to need into
Row control, this enables SOP to provide flexible reactive power for power grid.Further, it is also possible to quickly and accurately control SOP's
Active power.
In order to sufficiently assess SOP to the potential impact of stable state operation of power networks, its power injection mathematical model is established.It utilizes
The active power of SOP and reactive power are injected into electric network swim by the model, without considering setting in detail for Converter controller
Meter.By taking the feeder line 1 in Fig. 1 as an example, trend is calculated by following recurrence equation:
In formula, Pi and Qi are the active power and reactive power that i+1 bus is flowed to from i bus respectively.Pload (i) and
Qload (i) is the active and reactive requirement of bus i, and Ploss (i, i+1) and Qloss (i, i+1) are branch connection bus i and i+
Power loss between 1, ri and xi are resistance and reactance.Vi is the voltage of bus i, and Nbus is the sum of network median generatrix.
The running boundary of SOP are as follows:
PC1=Pp-Ploss(p,C1) (4)
PC2=Pq-Ploss(q,C2) (5)
In formula, PC1And PC2For the effective power flow of each VSC of SOP.Ploss(p,C1)It is the power loss between bus p and VSC1,
Ploss(q,C2)It is the power loss between bus q and VSC2.
The active power exchange of two VSC has following constraint:
PC1+PC2+PSOPloss=0 (6)
In formula, PSOP, lossFor the power loss at SOP.These power losses are opposite compared with the power loss of whole network
It is lower, therefore can be ignored.Therefore, formula (6) are simplified are as follows:
PC1=-PC2 (7)
The constraint condition of SOP capacity-constrained and voltage are as follows:
Vmin≤|VC1|≤Vmax (10.1)
Vmin≤|VC2|≤Vmax (10.2)
In formula, QC1And QC2The idle injection rate of each VSC of respectively SOP.SC1And SC2It is the rated capacity of each VSC.
VminAnd VmaxIt is the minimum voltage and maximum voltage that network allows.VC1And VC2It is the voltage on each SOP terminal.
In general, the exchange side of VSC can control under PV mode or PQ mode.The present invention, which considers, to be controlled under PQ mode
Situation.By selecting optimal SOP power output, the trend of network internal can be efficiently controlled.
Above-mentioned steps (2) include the following contents
In order to determine the optimal power output of power distribution network SOP, propose a kind of collection global search (multi-objective particle swarm algorithm) and
The power distribution network SOP optimization method of local search (taxi algorithm) Yu Yiti.From network power losses, feeder line load balancing, electricity
Pressure improvement etc. is compared with traditional multi-objective particle swarm algorithm method has carried out performance indicator.
For this purpose, it is Model for Multi-Objective Optimization that the present invention, which initially sets up reduction power loss, load balancing and voltage to improve,
Expression formula is as indicated at 11.
JOB=min [obj1,obj2,obj3] (11)
Power loss expression formula
Wherein Ik、Vk、Pk、QkTo pass through the electric current of k branch, voltage drop, effective power flow, reactive power flow, rkFor the electricity of k branch
Resistance, NbranchFor branch sum.
Load balancing expression formula
Load balancing is by minimizing load balancing index (LBI) Lai Shixian, and LBI is defined as
Wherein the rated current of k branch is IkValue
Improve voltage expression: the improvement of voltage characteristic be by minimize voltage characteristic index (VPI) Lai Shixian,
VPI is defined as:
Wherein Vi,refFor the nominal voltage of bus i, i.e. 1p.u is the V of all busesi,ref。
The constraint condition of Model for Multi-Objective Optimization
Other than above-mentioned constraint (1)-(10), it is also contemplated that following constraint:
Busbar voltage limitation
Vmin≤|Vi|≤Vmaxi∈{1,2,...,Nbus} (15)
Tributary capacity limitation
|Ik|≤Ik maxk∈{1,2,...,Nbranch} (16)
Wherein Ik maxThe maximum current allowed for k branch.
DG infiltration
The influence run to power distribution network is permeated in order to evaluate DG, it is contemplated that DG penetration range.DG permeability is defined as DG note
The ratio of the minimum active demand of the active power and network entered.Such case is considered as the worst situation, provides fragility
Operation condition of network.
It include the following contents according to step (3): global present invention employs combining to determine the optimal power output of intelligent Sofe Switch
The method of search technique (i.e. multi-objective particle swarm algorithm method) and local search technique (i.e. taxi algorithm), as shown in Figure 3.
Global search is carried out using least square particle swarm algorithm (multi-objective particle swarm algorithm) first, obtains one group of Pareto optimality
Solution.Then, in order to avoid falling into local optimum, obtained Pareto solution is adjusted using taxi algorithm.Utilize taxi
Vehicle algorithm has further excavated the search space around each Pareto optimal solution.
Multi-objective particle swarm optimization (multi-objective particle swarm algorithm): particle group optimizing (PSO)
PSO algorithm is a kind of multiple spot search technique based on population that Eberhart and Kennedy are developed in nineteen ninety-five.It searches
Suo Congyi group random search point (referred to as particle) starts.By position vector (x) coding comprising M dimension information, (i.e. M is each parameter
The quantity of decision variable).In the present invention, by SOP [PC1, QC1, QC2] (since PC2 is determined by PC1, so not including
PC2 active and idle injection) is used as decision variable.Position vector (x) is updated in subsequent iteration using the speed of particle.?
In each iteration, the velocity vector (v) of two optimum value more new particles is used.First is that each particle itself reaches a
Body/individual optimum position (pbest).Another kind is Arbitrary Particles global optimum position (gbest) obtained in population, it is
Guidance population moves towards optimal guide.The speed and location updating equation of i-th of particle are as follows:
Wherein ω is the inertia weight for controlling particle from previous velocity to present speed, i.e. phase Tongfang of the particle in its traveling
The trend continued up.c1It is a kind of cognitive learning factor, it represents particle to the attraction of itself optimum state.c2It is a kind of
Social learning's factor, it represents particle to attraction possessed by particle best in its neighbour.c1And c2It is normally defined just
Constant.r1And r2It is two random numbers in the range of [0,1], the two random numbers are used to avoid being entrapped in optimal conditions, and
Allow the diversity of particle in search space.The renewal process of particle swarm optimization algorithm is as indicated at 3.
P in multi-objective particle (multi-objective particle swarm algorithm)bestAnd gbestSelection: in particle group optimizing
In algorithm, pbestAnd gbestSelection dependent on the fitness value of particle determined by objective function.However, in multi-objective problem
In, for the concept of optimal location replaced one group of local solution, each local solution has potential directive function to particle.
pbestSelection it is very simple: if the current location of i-th of particle be more than its people optimum position, pbestIt will be by
Current location is substituted.If current optimum position and personal optimum position are mutually indepedent, pbestTo be had same
The one of both of ability is substituted, otherwise, pbestIt remains unchanged.
About gbestSelection, by all targets according to importance distribution mesh calibration method aggregate into a single function
Energy.Then it by being optimized to most important one, is solved according to the sequence of importance of distribution.It must be noted, however, that
The most of multi-objective particle swarm algorithm methods proposed at present are all based on selected by Pareto optimal solution.In the minimum of proposition
Two multiply in particle swarm algorithm, utilize Pareto optimality iteration more new range.Then using random device from the solution of the range
Select the guide of particle.If a is x in range AiThe solution of the position of particle.According to the method, if Axi=a ∈ A | a < xiRefer to
A is xiSolution, then xi best solution is random.With equiprobability from AxiMiddle selection.If xi∈ A, then
Obvious AxiFor sky.In this case, most suitable x is selected from entire scope Ai.Therefore:
Taxi algorithm: applying taxi algorithm, carries out limited step optimization, fast convergence rate to nonlinear function.It hires out
Vehicle algorithm does not need any information of objective function derivative, is scanned for by decision variable mobile on standard base vector.
The process of taxi algorithm is as follows:
Using taxi algorithm, optimization, fast convergence rate are iterated to nonlinear function.Taxi algorithm is not required to syllabus
Any information of scalar functions derivative, but scanned for by moving decision variable along standard base vector.
The process of taxi algorithm is as follows:
(1) particle is selected from range, its position is set as X；
(2) standard base vector em=[0 ..., 1m ..., 0] is initializedT, wherein m=1,2 ..., m, m are decision variable
Number；
(3) objective function obj to be optimized is selectedn, wherein n=1,2 ..., Nobj.If X0=X
(4) with X0For search starting point.On each base vector, objnIt is considered only as the function of a decision variable, wherein adopting
With linear search technology, Fibonacci method generates optimal step size gm.Search is by successively carrying out simultaneously edge along each base vector
Objective function generate a series of improvement values and be performed:
Xm=Xm-1+gmem (21)
(5) this process is continued until to obtain XMUntil.It stops search and n=n+1 is set, then return step 3
If
|objn(XM)-objn(XM-1)|≤convergence criteria (22)
Otherwise, X is set0=XmAnd return to step 4.
After each iteration, all particles taxi algorithm being applied in range.It is obtained by taxi algorithm
Any new solution not controlled by member any in range be added in range, and by new solution control in range
Any member of system will delete from range.It includes one group of partial solution that this, which ensures the solution in range always,.
Integrated approach (multi-objective particle swarm algorithm and taxi algorithm):
In integrated approach, using multi-objective particle swarm algorithm global search solution space, using taxi algorithm to more mesh
Non-dominant solution in mark particle swarm algorithm range is finely adjusted.The code of integrated approach is as shown in Figure 4.
When optimization process starts, range A is empty (the 1st row), and the position and speed of all particles is all random initializtion
(the 3rd row).Since particle position may be except feasible zone, it is therefore necessary to guarantee the active power and inactivity injection of SOP
Decision variable be constrained in SOP rated capacity.In the 4th row and the 13rd row, this is indicated using constraint function is enforced
A bit, in the function, SC service ceiling and lower limit bind particle position.It sets the initial personal optimum position of all particles to
Their initial position (the 5th row).Function superiority and inferiority degree (the 7th row and the 19th row) be used for according to the concept of Pareto optimality from
Local solution is selected in all particles, and is stored it in range.In each iteration, particle position and speed are updated using Eqs
Degree.Then, using the diversity of mutation operation (the 14th row) Lai Gaijin solution.20th row illustrates local search procedure,
Wherein taxi algorithm is applied to the non-principal solution in range.
The preferred embodiment of the present invention has been described above in detail, still, during present invention is not limited to the embodiments described above
Detail a variety of equivalents can be carried out to technical solution of the present invention within the scope of the technical concept of the present invention, this
A little equivalents all belong to the scope of protection of the present invention.
Claims (4)
1. the power distribution network multi-target optimum operation method based on intelligent Sofe Switch, which comprises the steps of:
Step 1 constructs intelligent soft switch power injection model；
Step 2, building are to reduce via net loss, equally loaded and improve voltage as the Optimized model of target；
Step 3, according to Pareto optimality, obtain the local solution between one group of different target, then use the drawn game of global search technology
Portion's search technique optimizes the intelligent Sofe Switch power output of power distribution network.
2. the power distribution network multi-target optimum operation method according to claim 1 based on intelligent Sofe Switch, which is characterized in that
The step 1 is further are as follows:
It establishes its power injection model and the active power of SOP and reactive power is injected into electric network swim using the model
In；The electric network swim is calculated by following recurrence equation:
In formula, PiAnd QiIt is the active power and reactive power that i+1 bus is flowed to from i bus respectively；Pload(i)And Qload(i)It is female
The active and reactive requirement of line i, Ploss(i,i+1)And Qloss(i,i+1)It is the power loss between branch connection bus i and i+1, riWith
xiFor resistance and reactance；ViFor the voltage of bus i, NbusFor the sum of network median generatrix；
The running boundary of SOP are as follows:
PC1=Pp-Ploss(p,C1)；PC2=Pq-Ploss(q,C2)；
In formula, PC1And PC2For the effective power flow of each VSC of SOP；Ploss(p,C1)It is the power loss between bus p and VSC1,
Ploss(q,C2)It is the power loss between bus q and VSC2；
The active power exchange of two VSC has following constraint:
PC1+PC2+PSOPloss=0；
In formula, PSOP, lossFor the power loss at SOP；These power losses compared with the power loss of whole network relatively
It is low, therefore can be ignored, above formula simplifies are as follows:
PC1=-PC2
The constraint condition of SOP capacity-constrained and voltage are as follows:
Vmin≤|VC1|≤Vmax；Vmin≤|VC2|≤Vmax
In formula, QC1And QC2The idle injection rate of each VSC of respectively SOP；SC1And SC2It is the rated capacity of each VSC；VminWith
VmaxIt is the minimum voltage and maximum voltage that network allows；VC1And VC2It is the voltage on each SOP terminal.
3. the power distribution network multi-target optimum operation method according to claim 1 based on intelligent Sofe Switch, which is characterized in that
The step 2 is further are as follows:
Establishing, which reduces power loss, load balancing and voltage, improves as Model for Multi-Objective Optimization:
JOB=min [obj1,obj2,obj3]；
Vmin≤|Vi|≤Vmaxi∈{1,2,...,Nbus}；
|Ik|≤Ik maxk∈{1,2,...,Nbranch}；
Wherein Ik、Vk、Pk、QkTo pass through the electric current of k branch, voltage drop, effective power flow, reactive power flow, rkFor the resistance of k branch,
NbranchFor branch sum；Wherein Ik maxThe maximum current allowed for k branch.
4. the power distribution network multi-target optimum operation method according to claim 1 based on intelligent Sofe Switch, which is characterized in that
The step 3 is further are as follows:
When optimization process starts, range A is sky, and the position and speed of all particles is all random initializtion；
Since particle position may be except feasible zone, it is necessary to assure the decision variable of active power and the inactivity injection of SOP
It is constrained in SOP rated capacity；
This point is indicated using constraint function is enforced, and in the function, SC service ceiling and lower limit bind particle position.By institute
There is the initial personal optimum position of particle to be set as their initial position；
Function superiority and inferiority degree is used to select local solution from all particles according to the concept of Pareto optimality, and stores it in
In range；
In each iteration, particle position and speed are updated using Eqs；Then, solution is improved using mutation operation
Diversity.
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| CN110729765A (en) * | 2019-08-30 | 2020-01-24 | 四川大学 | Distribution network flexibility evaluation index system considering SOP and optimal scheduling method |
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