CN110247438A - Active distribution network most optimum distribution of resources based on longicorn palpus algorithm - Google Patents
Active distribution network most optimum distribution of resources based on longicorn palpus algorithm 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/48—Controlling the sharing of the in-phase component
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention discloses a kind of active distribution network most optimum distributions of resources based on longicorn palpus algorithm, consider the timing that wind-force and photovoltaic distributed generation resource power output and load have, maximization consumption renewable energy is distributed rationally by resource, balance active power, most optimum distribution of resources is carried out using longicorn palpus algorithm, day part active power is balanced according to system requirements and economy principle, carrying out reactive compensation reduces system power dissipation, reduce voltage deviation, meet cost-effectiveness requirement, the invention has certain practical value, the ability that system safely and steadily runs horizontal and active distribution network consumption renewable energy can be improved.
Description
Technical field
The present invention relates to active distribution network most optimum distribution of resources technologies, in particular to consideration wind-force and photovoltaic distributed electricity
The active distribution network most optimum distribution of resources of longicorn palpus algorithm under the temporal characteristics of source power output and load.
Background technique
With the access of a large amount of distributed generation resources, conventional electrical distribution net is faced with new challenges.First to the structure of power distribution network and
Operation produces a very large impact, and generates the bidirectional power flow between transmission and distribution network, and load and power supply will have dual uncertainty, visitor
Family is by the dual identity with consumers and producers.Secondly, traditional power distribution network is with single-side power, radial pattern powering mode
Main, high permeability distributed generation resource access power distribution network will lead to voltage level raising, short circuit current increases, power supply reliability reduces
And a series of problems, such as power quality deterioration.After new normality is stepped into China's economic development, power network development mode has occurred great
Transformation, is changed into emphasis returns of investment by focusing on scale effect originally, and the tradition for exchanging reliability for by improving equipment nargin is advised
The method of drawing is difficult to support Sustainable Development of Enterprises.Under the conditions of high permeability, new energy accesses the power generation of power distribution network in a distributed manner
Mode brings three challenges: 1, the challenge that power is sent: largely sending phenomenon gradually to increase to substation.2, power generation characteristics are chosen
War: the problems such as voltage level increases, short circuit current increases, power supply reliability reduces and power quality deteriorates.3, using energy source
Challenge: the value utilization rate of distributed energy is not high, and demanding side of the electrical power net responding ability is insufficient.Active distribution network is that solution is above-mentioned
The core technology of challenge.
Active distribution network is can be with the distribution of comprehensively control distributed energy (distributed power generation, flexible load and energy storage)
Net can flexibly use effective management of network implementations trend.Distributed energy meet supervision and access criterion on the basis of,
The supporting role certain to System Take.With the extensive use of new energy power generation technology, the active containing numerous regulation resources
Power distribution network becomes the inexorable trend of power distribution network development.The access of distributed generation resource is possible to change distribution net work structure, to power distribution network
Pressure regulation and operation bring difficulty.Active distribution network can solve renewable energy consumption problem, form distributed generation resource and distribution
The mutual spare pattern of net.Research active distribution network most optimum distribution of resources be conducive to raising system safely and steadily run it is horizontal and
Active distribution network dissolves the ability of renewable energy, reaches energy-saving target.
The economic benefit for improving distributed generation resource access power distribution network guarantees main under a large amount of distributed generation resource access conditions
The safety of dynamic power distribution network is flexibly and economical operation, palpus emphasis solve the problems, such as active distribution network most optimum distribution of resources.For distribution
For net, the distribution system of early stage is passive network due to itself, and grid structure and part throttle characteristics are relatively fixed, and active distribution
Largely with the access of intermittent distributed generation resource under the conditions of net, to entire electric system bring it is many it is uncertain because
Element.Therefore according to the power output of distributed generation resource and the timing of load, reasonably optimizing resource distribution becomes raising active distribution network
Safe and stable, economy operation, promotes the important measures of renewable energy consumption.
Summary of the invention
The purpose of the present invention is the digestion capabilities for active distribution network to distributed generation resource, consider distributed generation resource power output
Have the characteristics that timing with load, bring difficulty is run and optimized to active distribution network, proposes based on longicorn palpus algorithm
Active distribution network most optimum distribution of resources, maximize dissolve renewable energy in the case where, carried out according to principle of economic benefit
Optimizing resource allocation balances active power, and optimizes configuration, Jin Erti to the idle power output of each resource with longicorn palpus algorithm
The digestion capability of high active distribution network renewable resource and the operation level and economy of power distribution network are established inclined with network loss, voltage
Difference is the mathematical model of objective function, carries out multiple-objection optimization.
Above-mentioned purpose is implemented with the following technical solutions in the present invention.Active distribution network resource optimization based on longicorn palpus algorithm
Configuration, which is characterized in that its step are as follows:
1) in the case where fully consumption renewable energy, active power is balanced, Power Balance Model is as follows:
Net_load=Pload(t)-pPV(t)-PWG(t)-Pb(t) (2)
In formula: PDR(i, t) indicates i-th of flexible modulation resource in the online electric power of t period；Net_load (t) is
T period flexible modulation resource needs the net burden with power compensated；Grid_loss is the loss factor of active distribution network；
PloadIt (t) is the original loads of active distribution network；PPV(t) the photovoltaic power generation power output of t period active distribution network；PWG(t) t
The wind power generation output of period active distribution network；Pb(t) power provided for upper level power transmission network；
2) single goal model, i.e. fitness function are converted by multiple target using weigthed sums approach are as follows:
In formula: wiFor multiple target weighted value, the trade-off relationship of economical in reaction and voltage stabilization becomes preference heterogeneity, and m is
Objective function number, usuallyMultiple objective function is as follows:
A) objective function 1:AND voltage deviation is minimum:
In formula: i=1,2,3 ..., N, N are power distribution network node total number；VNFor voltage rating；I, j is respectively the head end of branch
And endpoint node；ΔVij(x) component is indulged for branch i, j voltage landing；δVijFor the horizontal component of branch i, j voltage landing；X is control
Variable can flexible modulation resource idle power output；
B) objective function 2:AND loss minimization:
In formula: i=1,2,3 ..., NbFor the branch sum in network；WithRespectively flow through branch biIt is active
Power and reactive power；Branch biBranch resistance；VbiFor branch biTerminal voltage；
3) algorithm parameter initializes；
4) initialize longicorn control position, that is, initialize each can flexible modulation resource idle power output, formula is such as
Under:
Xi=Ximin+(Ximax-Ximin)×rand (6)
In formula: i representation dimension, XiminIndicate i-th can the idle power output minimum value of flexible modulation resource, XimaxIt indicates i-th
Can the idle power output of flexible modulation resource maximum value；
5) position of longicorn or so antenna is found out according to the position of longicorn, and longicorn or so touching is found out according to fitness function
Fitness value corresponding to the position of palpus, calculation formula are as follows:
A) position of longicorn or so antenna calculates:
Dir=rands (k, 1)
In formula: k indicates the dimension in total of control variable；Dir indicates the random vector of the direction of longicorn；Xleft indicates left
The position vector of antenna；D indicates fixed range, that is, longicorn size between longicorn antenna；X is that control variable vector can be flexible
Adjust the idle power output of resource；Xright indicates the position vector of right antenna；
Formula (7), which is used to generate a random vector, indicates that longicorn direction is normalized for the direction of longicorn palpus, formula (8),
Formula (9) and formula (10) are respectively intended to calculate the position of left and right longicorn antenna；
B) left and right antenna fitness value calculation:
F (X)=af1(X)+bf2(X) (11)
In formula: a, b are objective function f respectively1(X) and f2(X) weighted value；
6) compare the size of the fitness value of left and right antenna, and longicorn position be updated, mathematical model is as follows:
X=X-step × dir × sign (fleft-fright)+w (12)
In formula: step indicates step-length；Sign is sign function, and fleft and fright are that left and right antenna is corresponding suitable respectively
Angle value is answered, w is random movement minimum step；
7) new fitness value, and adaptation corresponding with a upper longicorn are found out according to the updated longicorn position of step 6)
Angle value, which compares, obtains optimal fitness value；
8) judge whether the number of iterations termination condition for meeting setting, do not meet such as, repeat step 5)；As met, then select
Select most optimum distribution of resources strategy corresponding to adaptive optimal control angle value, i.e., the power output of each flexible resource.
Further, the multiple objective function is voltage deviation and system losses.
The present invention considers the power output of renewable resource and the timing of load, can as far as possible not by optimizing allocation of resources
It is maximized in the case where abandoning light and abandonment and dissolves wind-powered electricity generation, the renewable resources such as photoelectricity, and it is accurate by longicorn palpus algorithm comparison
Global optimizing optimizes configuration to resource, balances day part active power according to system requirements and economy principle, carries out nothing
Function compensation reduces system power dissipation, reduces voltage deviation, meets cost-effectiveness requirement, which has certain practical value, energy
Raising system safely and steadily runs the ability of horizontal and active distribution network consumption renewable energy.The present invention is suitable for actively matching
Power grid considers the timing of renewable resource power output and load, maximizes consumption renewable resource and to be able to maintain system steady safely
Surely the most optimum distribution of resources run.
Detailed description of the invention
Fig. 1 is the flow chart of active distribution network most optimum distribution of resources of the invention based on longicorn palpus algorithm.
Specific embodiment
Below in conjunction with drawings and examples, the present invention will be further described.Referring to Fig. 1, the active based on longicorn palpus algorithm
Power distribution network most optimum distribution of resources carries out resource according to principle of economic benefit and matches in the case where maximizing consumption renewable energy
Optimization is set, balances active power, and configuration, including following step are optimized to the idle power output of each resource with longicorn palpus algorithm
It is rapid:
Step 1: active balance.In the case where fully consumption renewable energy, most according to adjustable resource configuration cost
It is small to carry out optimizing resource allocation for economy principle, active power is balanced, Power Balance Model is as follows:
Net_load=Pload(t)-PPV(t)-PWG(t)-Pb(t) (2)
In formula: PDR(i, t) indicates i-th of flexible modulation resource in the online electric power of t period；Net_load (t) is
The net burden with power that flexible resource needs to compensate is adjusted in the t time；Grid_loss is the loss factor of active distribution network；
PloadIt (t) is the original loads of active distribution network；PPV(t) the photovoltaic power generation power output of t period active distribution network；PWG(t) t
The wind power generation output of period active distribution network；Pb(t) power provided for upper level power transmission network；
Step 2: converting single goal model, i.e. fitness function for multiple target using weigthed sums approach are as follows:
In formula: wiFor multiple target weighted value, the trade-off relationship of economical in reaction and voltage stabilization becomes preference heterogeneity, and m is
Objective function number, usuallyMultiple objective function is as follows:
A) objective function 1:AND voltage deviation is minimum:
In formula: i=1,2,3 ..., N, N are power distribution network node total number；VNFor voltage rating；I, j is respectively the head end of branch
And endpoint node；ΔVij(x) component is indulged for branch i, j voltage landing；δVijFor the horizontal component of branch i, j voltage landing；X is control
Variable can flexible modulation resource idle power output；
B) objective function 2:AND loss minimization:
In formula: i=1,2,3 ..., NbFor the branch sum in network；WithRespectively flow through branch biWattful power
Rate and reactive power；For branch biBranch resistance；For branch biTerminal voltage；
Step 3: algorithm parameter initialization include longicorn must maximum the number of iterations, the dimension of longicorn, minimum longicorn it is big
It is small, random movement minimum step, step-length, longicorn size and random movement minimum step decay factor, active distribution network it is original
The parameters such as data；
Step 4: initialization longicorn position, initialization longicorn position be initialize each can flexible modulation resource nothing
Function power output.The formula of longicorn position initialization is as follows:
Xi=Ximin+(XI, max-Ximin)×rand (6)
In formula: i representation dimension, XiminIndicate i-th can the idle power output minimum value of flexible modulation resource, XimaxIt indicates i-th
Can the idle power output of flexible modulation resource maximum value；
Step 5: finding out the position of longicorn or so antenna according to the position of longicorn, and longicorn is found out according to fitness function
Fitness value corresponding to the position of left and right antenna, calculation formula are as follows:
A) position of longicorn or so antenna calculates:
Dir=rands (k, 1) (7)
In formula: k indicates the dimension in total of control variable；Dir indicates the random vector of the direction of longicorn；Xleft indicates left
The position vector of antenna；D indicates fixed range, that is, longicorn size between longicorn antenna；X is that control variable vector can be flexible
Adjust the idle power output of resource；Xright indicates the position vector of right antenna；Formula (7) indicates day for generating a random vector
The direction of ox palpus, by longicorn towards being normalized, formula (9), (10) are respectively intended to calculate the position of left and right longicorn antenna formula (8)
It sets；
B) left and right antenna fitness value calculation
F (X)=af1(X)+bf2(X) (11)
In formula: a, b are objective function f respectively1(X) and f2(X) weighted value；
Step 6: comparing the size of the fitness value of left and right antenna, and longicorn position is updated, mathematical model is such as
Under:
X=X-step × dir × sign (fleft-fright)+w (12)
D=d × eta_d+d0 (13)
L=L × eta_L+L0 (14)
W=L × rands (k, 1) (15)
Step=step × eta_step (16)
In formula: step indicates step-length；Sign is sign function；Fleft and fright is that left and right antenna is corresponding suitable respectively
Answer angle value；W is random movement minimum step, and d is longicorn size；d0Minimum longicorn；Eta_d is longicorn size decay factor；
Eta_step is step-length decay factor；L is random movement minimum step control parameter；L0To originate the control of random movement minimum step
Parameter processed；K is longicorn dimension.Formula (12) is updated to the position of longicorn；Formula (13) is updated to the size of longicorn；Formula
(14) and formula (15) is updated to random movement minimum step；Formula (16) is updated to step-length；
Step 7: finding out new fitness value, and adaptation corresponding with a upper longicorn according to updated longicorn position
Angle value, which compares, obtains optimal fitness value；
Step 8: judging whether to meet termination condition, do not meet such as, repeats the 5th step and continue iteration；As met, then select
Most optimum distribution of resources strategy corresponding to adaptive optimal control angle value, i.e., the power output of each flexible resource；
Step 9: software realization, entire program is based on windows operating system, using mathwork company
Matlab2017a is programming development platform.
According to above-mentioned steps, considers the temporal characteristics of wind-force and photovoltaic distributed power supply power output and load, pass through resource
Distribute maximization consumption renewable energy rationally, balance active power, meet system and maximize consumption renewable energy and want
It asks, and configuration is optimized to the idle power output of adjustable resource by longicorn palpus algorithm, to realize reduction network loss, meet warp
Ji property requires；Node voltage deviation is reduced, the requirement of system security and stability operation is met.
Embodiment: certain distribution system: typical Daily treatment cost is 4.74MW, minimum load 2.45MW, photovoltaic power generation dress
Machine capacity is 2MW, and wind-power electricity generation installed capacity is 2MW, and substation, which presses, determines power 1.5MW to the system power transmission.System loading and
Timing can all be showed in raw energy power output, in order in guarantee system power supply and demand balance and renewable energy power generation it is complete
Volume consumption, intends the micro- gas turbine of configuration, energy-storage battery, charging pile, capacitor etc. can flexible modulation resource, wherein day of charging pile
Average charge amount is 0.1MW.Simulation calculation now is carried out using improved 33 node system of standard IEEE, is connect respectively in No. 5 nodes
Entering energy-storage battery, 33 nodes access micro- gas turbine, and No. 22 nodes accesses are charged electric stakes, and 18 nodes access photovoltaic power generation factory, and 28
Number node accesses wind power plant, and 6,11 and 31 nodes access capacitor group.
The system is because of the timing that load and renewable energy are contributed, so that system unbalanced supply-demand, different periods go out
Existing power supply is insufficient or power supply is superfluous, and the loss of power grid is very big.Implement methodology pair according to the present invention
System carries out most optimum distribution of resources.
Step 1: according to the minimum economy principle of adjustable resource configuration cost to can flexible modulation resource configure
Optimization balances active power, and power-balance formula is as follows:
Net_load=pload(t)-pPV(t)-PWG(t)-Pb(t) (2)
Active balance calculating is carried out according to above formula, finds out in 24 hours each hour power supply situation i.e.
Then the active power that adjustable resource needs to adjust preferentially is adjusted using operating cost minimum energy-storage battery and charging pile
Section.The active supply and demand for being computed system reaches balance, meets the per day charge volume 0.1MW of charging pile, when power supply deficiency
Active power output is provided by micro- gas turbine and energy-storage battery, it is active by energy-storage battery and charging pile absorption when power supply surplus
Power, the power output range of Micro turbine are [0,1.59] MW, and the power output range of energy-storage battery is [- 0.85,1.0]
MW, when when the charging pile charging time being 1 to 9 and when 22 to 24, charge power 0.00833MW/h, 24 hours active balances
Situation such as table 1；
Table 1
| Time/h | Energy-storage battery active power output (MW) | Micro- gas turbine active power output (MW) | Charging pile charges (MW) |
| When 1 | 0 | 0.996 | -0.00833 |
| When 2 | 0 | 0.687 | -0.00833 |
| When 3 | -0.522 | 0 | -0.00833 |
| When 4 | -0.623 | 0 | -0.00833 |
| When 5 | -0.241 | 0 | -0.00833 |
| When 6 | 0.083 | 0 | -0.00833 |
| When 7 | -0.718 | 0 | -0.00833 |
| When 8 | 0.196 | 0 | -0.00833 |
| When 9 | -0.491 | 0 | -0.00833 |
| When 10 | -0.010 | 0 | 0 |
| When 11 | 0.156 | 0 | 0 |
| When 12 | -0.258 | 0 | 0 |
| When 13 | -0.449 | 0 | 0 |
| When 14 | -0.540 | 0 | 0 |
| When 15 | -0.735 | 0 | 0 |
| When 16 | -0.857 | 0 | 0 |
| When 17 | -0.408 | 0 | 0 |
| When 18 | 0.231 | 0 | 0 |
| When 19 | 1.000 | 0.784 | 0 |
| When 20 | 1.000 | 0.439 | 0 |
| When 21 | 1.000 | 1.541 | 0 |
| When 22 | 1.000 | 1.595 | -0.00833 |
| When 23 | 0.720 | 0.000 | -0.00833 |
| When 24 | 0.465 | 0.693 | -0.00833 |
Step 2: converting single goal model, i.e. fitness function for multiple target using weigthed sums approach are as follows:
A) objective function 1:AND voltage deviation is minimum:
B) objective function 2:AND loss minimization:
Multiple objective function is converted to single goal model according to weigthed sums approach above, in the case where reducing network loss, is protected
The quality for demonstrate,proving voltage first carries out nondimensionalization processing, i.e. f to objective function before using weigthed sums approach1/f1min, f2/
f2min, f1minAnd f2minMinimum value when being 2 single optimization of objective function 1 and objective function respectively, respective weights coefficient value are
w1=0.2, w2=0.8；
Step 3: algorithm parameter initializes.The number of iterations is set as n=100, takes minimum longicorn d0=0.0001, longicorn
Initial size d=0.02, longicorn size decay factor eta_d=0.95, random movement minimum step control parameter L=0, rise
Beginning random movement minimum step control parameter L0=0.0001, longicorn dimension k=5, step-length decay factor eta_step=0.95
Deng；
Step 4: initialization longicorn control variable, that is, initialize each can flexible modulation resource idle power output.It is public
Formula is as follows:
Xi=Ximin+(Ximax-Ximin)×rand (6)
Only micro- gas turbine, energy-storage battery and three group capacitors of Reactive-power control can be carried out, so the position of longicorn palpus
By this five can the power output of flexible modulation resource determine that the power factor (PF) of energy-storage battery and micro- gas turbine is adjustable, take least work
Rate factor is 0.9, and energy-storage battery is idle, and power output range is determined by its active power output, and micro- gas turbine, which only issues, idle does not absorb nothing
Function, the idle range issued are determined that the idle power output range of capacitor group is [0,1.2] Mvar by its active power output.It uses
Initialization longicorn location formula can obtain in corresponding power output range each can flexible modulation resource it is random it is idle go out
Power；
Step 5: finding out the position of longicorn or so antenna according to the position of longicorn, and longicorn is found out according to fitness function
Fitness value corresponding to the position of left and right antenna, calculation formula are as follows:
A) position of longicorn or so antenna calculates:
Dir=rands (k, 1) (7)
Left and right antenna position is calculated according to the longicorn position that the 4th step acquires；
B) left and right antenna fitness value calculation:
F (X)=af1(X)+bf2(X) (11)
The left and right antenna positional value acquired in a) is brought into respectively in formula (11), objective function f1(X) and f2(X) value can lead to
Cross be added can the network Load flow calculation after flexible modulation resource obtain, wherein a=0.2, b=0.8, f1(X) and f2(X) it calculates
Nondimensionalization processing is carried out after coming and reuses weigthed sums approach, obtains first time iteration adaptive optimal control angle value；
Step 6: comparing the size of the fitness value of left and right antenna, and longicorn position is updated, mathematical model is such as
Under:
X=X-step × dir × sign (fleft-fright)+w (12)
D=d × eta_d+d0 (13)
L=L × eta_L+L0 (14)
W=L × rands (k, 1) (15)
Step=step × eta_step (16)
The corresponding fitness value of left and right antenna acquired according to the 5th step carries out the update of longicorn position, at no point in the update process
In order to improve iteration later period convergence precision, the size d and step-length step of longicorn constantly reduce, but also joined minimum longicorn size
d0Most short step-length w prevents search from falling into local optimum；
Step 7: finding out new fitness value, and adaptation corresponding with a upper longicorn according to updated longicorn position
Angle value, which compares, obtains optimal fitness value；
Step 8: judging whether the termination condition for meeting the number of iterations n=100, do not meet such as, repeats the 5th step and continue to change
Generation；As met, then most optimum distribution of resources strategy corresponding to adaptive optimal control angle value, i.e., the idle power output of each flexible resource are selected.
The optimal most optimum distribution of resources that each small period can be found out according to above eight steps obtains corresponding each flexible
The active and idle power output of resource, grid net loss and voltage deviation, are obtained by calculation data as shown in table 2.
Table 2
Optimized calculating, 24 hourly average network loss of system are 65.62kW, voltage deviation 0.378, compared to not carrying out
24 hourly average network loss 79.44kW of idle generating optimization, voltage deviation 0.561, network loss and voltage deviation have all obtained bright
Aobvious reduction meets the economy and safety of power distribution network operation.
Claims (2)
1. the active distribution network most optimum distribution of resources based on longicorn palpus algorithm, which is characterized in that its step are as follows:
1) in the case where fully consumption renewable energy, active power is balanced, Power Balance Model is as follows:
Net_load=Pload(t)-PPV(t)-PWG(t)-Pb(t) (2)
In formula: PDR(i, t) indicates i-th of flexible modulation resource in the online electric power of t period；When Net_load (t) is t
Between section flexible modulation resource need the net burden with power that compensates；Grid_loss is the loss factor of active distribution network；Pload(t)
For the original loads of active distribution network；PPV(t) the photovoltaic power generation power output of t period active distribution network；PWG(t) the t period
The wind power generation output of active distribution network；Pb(t) power provided for upper level power transmission network；
2) single goal model, i.e. fitness function are converted by multiple target using weigthed sums approach are as follows:
In formula: wiFor multiple target weighted value, the trade-off relationship of economical in reaction and voltage stabilization becomes preference heterogeneity, and m is target
Function number, usuallyMultiple objective function is as follows:
A) objective function 1:AND voltage deviation is minimum:
In formula: i=1,2,3 ..., N, N are power distribution network node total number；VNFor voltage rating；I, j is respectively head end and the end of branch
End node；ΔVij(x) component is indulged for branch i, j voltage landing；δVijFor the horizontal component of branch i, j voltage landing；X is control variable
Can flexible modulation resource idle power output；
B) objective function 2:AND loss minimization:
In formula: i=1,2,3 ..., NbFor the branch sum in network；WithRespectively flow through branch biActive power and
Reactive power；For branch biBranch resistance；VbiFor branch biTerminal voltage；
3) algorithm parameter initializes；
4) the control position for initializing longicorn, that is, initialize each can flexible modulation resource idle power output, formula is as follows:
Xi=Ximin+(Ximax-Ximin)×rand (6)
In formula: i representation dimension, XiminIndicate i-th can the idle power output minimum value of flexible modulation resource, XimaxI-th of expression can spirit
The maximum value living for adjusting the idle power output of resource；
5) position of longicorn or so antenna is found out according to the position of longicorn, and longicorn or so antenna is found out according to fitness function
Fitness value corresponding to position, calculation formula are as follows:
A) position of longicorn or so antenna calculates:
Dir=rands (k, 1) (7)
In formula: k indicates the dimension in total of control variable；Dir indicates the random vector of the direction of longicorn；Xleft indicates left antenna
Position vector；D indicates fixed range, that is, longicorn size between longicorn antenna；X is that control variable vector can flexible modulation
The idle power output of resource；Xright indicates the position vector of right antenna；
Formula (7), which is used to generate a random vector, indicates that longicorn direction is normalized for the direction of longicorn palpus, formula (8), formula (9)
It is respectively intended to calculate the position of left and right longicorn antenna with formula (10)；
B) left and right antenna fitness value calculation:
F (X)=af1(X)+bf2(X) (11)
In formula: a, b are objective function f respectively1(X) and f2(X) weighted value；
6) compare the size of the fitness value of left and right antenna, and longicorn position be updated, mathematical model is as follows:
X=X-step × dir × sign (fleft-fright)+w (12)
In formula: step indicates step-length；Sign is sign function, and fleft and fright are the corresponding fitness of left and right antenna respectively
Value, w are random movement minimum step；
7) new fitness value, and fitness value corresponding with a upper longicorn are found out according to the updated longicorn position of step 6)
It compares and obtains optimal fitness value；
8) judge whether the number of iterations termination condition for meeting setting, do not meet such as, repeat step 5)；As met, then select most
Most optimum distribution of resources strategy corresponding to excellent fitness value, i.e., the power output of each flexible resource.
2. the active distribution network most optimum distribution of resources according to claim 1 based on longicorn palpus algorithm, which is characterized in that institute
Stating multiple objective function is voltage deviation and system losses.
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