CN105741193B - The multiple target Distribution system method of meter and distributed generation resource and negative rules - Google Patents
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Abstract
The invention discloses the multiple target Distribution system method of meter and distributed generation resource and negative rules, this method substantially envisages the uncertainty of wind power output, photovoltaic power generation and load.The present invention provides three important evaluation indexes of distribution optimization operation: active loss, node minimum amount of voltage that, load balancing degree, i.e. the three of reconstruction and optimization target first.Then, using the uncertain factor in scene analysis method processing wind power output, photovoltaic power output and load.Finally, obtaining final reconfiguration scheme using multiple target disturbance biogeography algorithm and fuzzy set theory.The present invention can be quickly found out the decision solution of multiple target Distribution system, and search efficiency with higher can be obviously improved the indices of network by network reconfiguration in the case where dissolving distributed generation resource, so that reconstruct is more in line with practice decision process.
Description
Technical field
The invention belongs to Operation of Electric Systems analysis and control technology field, in particular to it is a kind of meter and distributed generation resource and
The multiple target Distribution system method of negative rules.
Technical background
In recent years, wind-powered electricity generation, photovoltaic obtain in worldwide increasingly wider as pollution-free, reproducible green energy resource
General application, the specific gravity being incorporated to constantly increase.Inherently there are many uncertain factors such as load fluctuation in practical power systems,
And the grid-connected of wind-powered electricity generation, photovoltaic has been further exacerbated by uncertainty, proposes new challenge to traditional Distribution system technology.
Distribution system technology is one of key technology of power distribution automation as the following basis for implementing smart grid,
Important role in terms of ensureing power quality, reducing.Research Distribution system initial stage, mainly with
The a certain item evaluation index of distribution is as optimization aim, and as research deepens continuously, the research of single goal reconstruct has been tended to
It is kind.However, Distribution system is actually a complicated Nonlinear Multiobjective combinatorial optimization problem, recent domestic and foreign scholars are to this
There are some researchs, but in the selection of multiple target, previous research consideration is not comprehensive, in addition, uncertain factor in distribution
Processing it is also complex, thus the invention discloses the multiple targets of a kind of new meter and distributed generation resource and negative rules
Distribution system method.
Summary of the invention
Goal of the invention: in view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to propose a kind of meter and distributed generation resource
With the multiple target Distribution system method of negative rules, contributed using scene analysis method processing wind power output, photovoltaic and negative
Uncertain factor in lotus obtains final reconfiguration scheme using multiple target disturbance biogeography algorithm and fuzzy set theory,
Decision scheme is provided for operations staff.
Technical solution: the present invention provide it is a kind of meter and distributed generation resource and negative rules multiple target Distribution system side
Method, comprising the following steps:
Step 1: establishing three objective functions of reconstruction and optimization, it may be assumed that active loss, node minimum amount of voltage that, load balancing
Degree；
Step 2: using the uncertain factor in scene analysis method processing wind power output, photovoltaic power output and load；
Step 3: obtaining final reconfiguration scheme using multiple target disturbance biogeography algorithm and fuzzy set theory.
Further, the step 1 the following steps are included:
Step 101: establishing the first aim of reconstruction and optimization: distribution network systems active loss, i.e., are as follows:
In formula, NbIndicate circuitry number；RkIndicate the resistance of branch k；PkIndicate the active power of branch k；QkIndicate branch k
Reactive power；VkIndicate the terminal voltage of branch k；
Step 102: establishing the second target of reconstruction and optimization: maximizing the minimum node voltage of voltage, i.e., are as follows:
In formula, VjIndicate that the voltage per unit value of node j, N indicate number of nodes；
Step 103: establishing the third target of reconstruction and optimization: load balancing.It passes through network reconfiguration for the heavier line of load
It is transferred to the loaded portion of road on the lighter route of load, with system loading evenness index (system load
Balancing index, SLBI) it is measured, value is the smaller the better, i.e., are as follows:
In formula, SjExpression flows through the actual current size of branch j；Indicate the maximum current for allowing to flow through branch j；
In addition, in Load flow calculation, it is also necessary to consider following constraint condition:
In formula: Vmin, VmaxRespectively indicate the bound of node voltage when distribution operates normally；SkIndicate the current-carrying of branch k
Amount, SmaxIndicate the maximum carrying capacity of route k.
Further, the step 2 the following steps are included:
Step 201: scene partitioning is carried out to blower power output, photovoltaic power output and load:
It is generally acknowledged that wind speed Follow Weibull Distribution at present, probability density function such as following formula:
In formula, fvFor the probability density function of wind speed, c and d are scale parameter and form parameter respectively, can be according to scene
The historical data identification of wind speed is surveyed, v indicates wind speed m/s.Under conditions of known wind speed random distribution parameter, it can find out and appoint
The probability of meaning wind speed interval, calculation formula are as follows:
Case in above formulaiIndicate i-th of scene, vnAnd vmIndicate the up-and-down boundary of wind speed scene interval.
Intensity of the sunlight can approximation regard as Beta distribution:
In formula, s and smax(W/m2) it is respectively actual light intensity and largest light intensity in a period, α and β are a period of time
The form parameter of interior Beta distribution.
Solar cell array output power are as follows:
Psun=sA η
Wherein, PsunIt is the solar cell array gross area (m for battery array output power (kW), A2), η turns for photoelectricity
Change efficiency.
Using scene partitioning method same as Wind turbines power output, light intensity section is divided, according to Intensity Probability Density letter
Number calculates the probability of different sections (scene).
In distribution network, daily load curve, monthly load curve and yearly load curve have biggish fluctuation, with distribution
Power grid, load prediction is more difficult, so that the uncertainty in Distribution system is stronger.Load is divided into three by the present invention
A typical scene: normality load scenarios, underload scene and high load scenarios.
Step 202: scene number determines the evaluation of strategy and schemes synthesis optimality:
Uncertain factor is transformed into certainty scene by scene analysis method, wind-powered electricity generation, it is grid-connected after, Distribution system
Middle uncertain factor is mainly from three aspects: Wind turbines output power, solar panel output power, load, because
And the scene selected in reconstruction model is the combination of scene contained by the above three.The present invention is using synchronous back substitution "flop-out" method to big rule
The scene of mould is cut down, for the comprehensive performance of scheme: compare the objective function desired value size based on scene probability of happening,
Such as following formula:
In formula: nsFor the scene number after reduction；PBkThe probability occurred for scene k；FikFor i-th of optimization under k-th of scene
The comprehensive evaluation value of scheme；DiFor i-th of prioritization scheme.E[F(Di)] be i-th of prioritization scheme objective function desired value.
Further, the step 3 the following steps are included:
Step 301: the present invention obtains multiple target reconfiguration scheme using disturbance biogeography algorithm.The algorithm is used and is disturbed
Dynamic transfer operator and cosine migration models design habitat suitability evaluation index according to the principle for minimizing each sub-goal to draw
Evolution of Population is led, the non-domination solution during evolving is saved with filing population.Wherein, disturbance transfer operator obtains as the following formula:
qi,j=pk,j+ceil(ξ(pk1,j-pk2,j))
In formula, ξ is the random number of 0-1；J-th of variable (p of k-th of habitat is determined with roulettek,j) move to i-th
J-th of variable (q of a habitati,j) position, k1, k2 ∈ { 1,2 ..., N }/{ i } are generated at random.
Cosine migration models are as follows:
In formula, λkTo move into probability, μkTo remove probability, I is maximum immigration probability, and E removes probability to be maximum.
It is defined as comprehensive evaluation index, for guiding Evolution of Population:
In formula, F indicates habitat suitability vector, fiIndicate the i-th target function value of habitat vector, fi,max, fi,minPoint
Do not indicate that the maximin of i-th of all habitats target, rank indicate that the non-dominant ranking vector in habitat, ratio indicate
Non-dominant grade is ratio shared by 1 individual in population, and iteration early period, the individual that dominance hierarchy is 1 is less, and dominance hierarchy is comprehensive
It is larger to close proportion in index, with population iterative evolution, the individual that non-dominant grade is 1 is more and more, and dominance hierarchy exists
Effect in comprehensive evaluation index reduces.w1, w2, w3Initial value is disposed as 1, according to the attention degree to each target, Ke Yiling
Setting weight living, three's and be 1.
Step 302: in actual distribution operation, policymaker needs to determine an optimal reconstructed operation from candidate solution
Scheme, the present invention determine that best compromise solution by each sub-goal Fuzzy processing, is defined as follows using fuzzy set theory:
In formula: i ∈ 1,2 ..., Nobj}；fiFor objective function；NobjFor objective function number；Respectively
The maximum value of i objective function, minimum value；hi0 and 1 is taken to respectively indicate completely dissatisfied and be entirely satisfactory.Non-domination solution is concentrated each
The satisfaction of solution can use following formula scalarization:
The maximum solution of h value is set to best compromise solution.
Working principle: the present invention provides three important evaluation indexes of distribution optimization operation first: active loss, node are most
Small voltage value, load balancing degree, i.e. the 3 of reconstruction and optimization target.Then, wind power output, photovoltaic are handled using scene analysis method
Uncertain factor in power output and load.Finally, being obtained using multiple target disturbance biogeography algorithm with fuzzy set theory
Obtain final reconfiguration scheme.The present invention can be quickly found out the decision solution of multiple target Distribution system, search efficiency with higher,
In the case where dissolving distributed generation resource, the indices of network can be obviously improved by network reconfiguration, so that reconstruct more accords with
Close practice decision process.
The utility model has the advantages that compared with existing reconfiguration technique, the invention has the advantages that and technical effect:
(1) multiple target reconstruct can comprehensively consider via net loss, network voltage quality, load balancing degree, so that reconstruct is more
Application of load practical situations；
(2) reconstruction strategy for considering wind-powered electricity generation, photovoltaic and negative rules, can be the case where dissolving distributed generation resource
Under, the indices of network can be obviously improved by network reconfiguration；
(3) the multiple target disturbance biogeography algorithm that the present invention uses can be quickly found out determining for multiple target Distribution system
Plan solution ensure that the formation efficiency of strategy.
Detailed description of the invention
Fig. 1 is 69 node distribution topological diagrams of the invention；
Fig. 2 is Wind turbines power characteristic of the present invention；
Fig. 3 is cosine migration models figure of the present invention；
Fig. 4 is that multiple target of the present invention disturbs biogeography algorithm flow chart；
Fig. 5 is network loss convergence curve of the present invention；
Fig. 6 is voltage distribution graph of the present invention.
Specific embodiment:
In the following with reference to the drawings and specific embodiments, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate
It the present invention rather than limits the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention each
The modification of kind equivalent form falls within the application range as defined in the appended claims.
The present invention is using 69 node distribution network systems as shown in Figure 1, which contains 73 branches, 5 basic rings,
Reference power 10kVA, reference voltage 12.66kV, system-wide load 3.90MW and 2.70Mvar.The current-carrying capacity of every branch point
Not are as follows: No. 1-9 is 400A, and No. 46-49 and No. 52-64 is 300A, other route 200A.Under primary condition, branch 69,70,
71, it 72,73 disconnects, network loss 224.93kW.Program is write using MATLABR2013a, calculates environment are as follows: Intel (R) Core
(TM) i3-2120 [email protected], 4GBRAM.
Multiple target disturbance biogeography algorithm parameter is provided that population scale Np=70, maximum number of iterations Kmax=
50, maximum sudden change rate mmax=0.05, maximum entry/leave rate takes E=I=1 (cosine migration models are shown in Fig. 3).Blower and too
Positive energy distribution parameter k=1.99, c=9.94, α=0.28, β=2.05.The incision of blower, specified, cut-out wind speed are respectively 3m/
S, 14m/s, 25m/s (Wind turbines power characteristic such as Fig. 2), rated power 400kW；Photovoltaic battery matrix single component face
Product 2.16m2, photoelectric conversion efficiency 13.44%, 400 components one photovoltaic battery matrixes of composition, maximum intensity of sunshine 0.5w/
m2.3 typhoon power generators are accessed at node 8,13,18 respectively, are respectively connected to 50 photovoltaic cell sides in node 20,26,29
Battle array.High underload is respectively 1.1 times of conventional load, 0.9 times, and height Load Probability takes 0.2, and conventional load probability takes 0.6.
Index value is desired value in table 1,2 table of table, and scheme is final decision scheme (the multiple target disturbance biogeography after blurring
Algorithm solves process such as Fig. 4).
Fig. 5 provides the convergence curve of network loss target desired value, and algorithm enters convergence state after iteration 11 times, has preferable
Convergence.
1 reconfiguration scheme contrast table of table
As shown in Table 1, the multiple target reconstruction result without DG has biggish promotion compared to each index under primitive network:
Active loss reduces by 52.97%, and minimum voltage promotes 4.3%, and load balancing degree reduces by 19.47%, and reconstruction and optimization result is brighter
It is aobvious.The reconstruct of the multiple target of meter and DG, so that network loss and load balancing degree further decrease, compared to the reconstruct for taking no account of DG, this two
Index reduces 14.45% and 7.14% respectively, in Fig. 6, disregards the network node voltage of the reconstruct of DG compared to primitive network
It has a distinct increment, after meter and DG, the voltage of part of nodes, which compares the optimum results for taking no account of DG, further promotion, thus table
Bright, DG appropriate is grid-connected and optimal reconfiguration, has not only dissolved renewable energy, but also the items that can be obviously improved system refer to
Mark.
During cutting down scene, it is thus necessary to determine that final scene number, table 2 give the reconstruction and optimization under three scene numbers
As a result.
Optimum results table under 2 different scenes number of table
As seen from table, when scene number is less, active loss is more, and load balancing degree is lower, and it is shorter to calculate the time；Scene
When number is larger, active loss is lower, and load balancing degree is higher, but it is longer to calculate the time；Different scenes number does not have minimum voltage
Have an impact.
To sum up, example illustrates correctness and practicability of the invention.
Claims (1)
1. it is a kind of meter and distributed generation resource and negative rules multiple target Distribution system method, it is characterised in that: including with
Lower step:
Step 1: establishing three objective functions of reconstruction and optimization, it may be assumed that active loss, node minimum amount of voltage that, load balancing degree；
The step 1 the following steps are included:
Step 101: establishing the first aim of reconstruction and optimization: distribution network systems active loss, i.e., are as follows:
In formula, NbIndicate circuitry number；RkIndicate the resistance of branch k；PkIndicate the active power of branch k；QkIndicate that branch k's is idle
Power；VkIndicate the terminal voltage of branch k；
Step 102: establishing the second target of reconstruction and optimization: maximizing the minimum node voltage of voltage, i.e., are as follows:
In formula, VjIndicate that the voltage per unit value of node j, N indicate number of nodes；
Step 103: the third target of reconstruction and optimization: load balancing is established, it will be on the heavier route of load by network reconfiguration
Loaded portion is transferred on the lighter route of load, is measured with system loading evenness index, and value is the smaller the better, i.e.,
Are as follows:
In formula, NbIndicate circuitry number；SjExpression flows through the actual current size of branch j；Indicate the maximum for allowing to flow through branch j
Electric current；
In addition, in Load flow calculation, it is also necessary to consider following constraint condition:
In formula: Vmin, VmaxRespectively indicate the bound of node voltage when distribution operates normally；SkIndicate the current-carrying capacity of branch k, Smax
Indicate the maximum carrying capacity of route k；
Step 2: using the uncertain factor in scene analysis method processing wind power output, photovoltaic power output and load；
In the step 2 the following steps are included:
Step 201: scene partitioning is carried out to blower power output, photovoltaic power output and load:
It is generally acknowledged that wind speed Follow Weibull Distribution at present, probability density function such as following formula:
In formula, fvFor the probability density function of wind speed, c and d are scale parameter and form parameter respectively, can be according to field measurement
The historical data identification of wind speed, v indicate that wind speed m/s can find out any wind under conditions of known wind speed random distribution parameter
The probability in fast section, calculation formula are as follows:
Case in above formulaiIndicate i-th of scene, vnAnd vmRespectively indicate the up-and-down boundary of wind speed scene interval；
Intensity of the sunlight can approximation regard as Beta distribution:
In formula, s and smaxActual light intensity and largest light intensity in a respectively period, α and β are Beta distribution in a period of time
Form parameter；
Solar cell array output power are as follows:
Psun=sA η
Wherein, PsunFor battery array output power, A is the solar cell array gross area, and η is incident photon-to-electron conversion efficiency；
Using scene partitioning method same as Wind turbines power output, light intensity section is divided, according to Intensity Probability Density function meter
Calculate the different sections i.e. probability of different scenes；
In distribution network, daily load curve, monthly load curve and yearly load curve have biggish fluctuation, with distributed generation resource
Grid-connected, load prediction is more difficult, so that the uncertainty in Distribution system is stronger, load is divided into three typical scenes:
Normality load scenarios, underload scene and high load scenarios；
Step 202: scene number determines the evaluation of strategy and schemes synthesis optimality:
Uncertain factor is transformed into certainty scene by scene analysis method, wind-powered electricity generation, it is grid-connected after, in Distribution system not
Certainty factor is mainly from three aspects: Wind turbines output power, solar panel output power, load, thus again
The scene selected in structure model is the combination of scene contained by the above three, using synchronous back substitution "flop-out" method to large-scale scene into
Row is cut down, for the comprehensive performance of scheme: compare the objective function desired value size based on scene probability of happening, such as following formula:
In formula: nsFor the scene number after reduction；PBkThe probability occurred for scene k；FikFor i-th of prioritization scheme under k-th of scene
Comprehensive evaluation value；DiFor i-th of prioritization scheme, E [F (Di)] be i-th of prioritization scheme objective function desired value；
Step 3: obtaining final reconfiguration scheme using multiple target disturbance biogeography algorithm and fuzzy set theory；
The step 3 the following steps are included:
Step 301: multiple target reconfiguration scheme is obtained using disturbance biogeography algorithm；The algorithm is using disturbance transfer operator
With cosine migration models, according to the principle design habitat suitability evaluation index for minimizing each sub-goal with guide population into
Change, the non-domination solution during evolving is saved with filing population；Wherein, disturbance transfer operator obtains as the following formula:
qi,j=pk,j+ceil(ξ(pk1,j-pk2,j))
In formula, ξ is the random number of 0-1；J-th of variable (p of k-th of habitat is determined with roulettek,j) move to i-th and dwell
Cease j-th of variable (q on groundi,j) position, k1, k2 ∈ { 1,2 ..., N }/{ i } are generated at random；
Cosine migration models are as follows:
In formula, λkTo move into probability, μkTo remove probability, I is maximum immigration probability, and E removes probability to be maximum；
It is defined as comprehensive evaluation index, for guiding Evolution of Population:
In formula, F indicates habitat suitability vector, fiIndicate the i-th target function value of habitat vector, fi,max, fi,minTable respectively
Show that the maximin of i-th of all habitats target, rank indicate that the non-dominant ranking vector in habitat, ratio indicate population
In non-dominant grade be ratio shared by 1 individual, iteration early period, the individual that dominance hierarchy is 1 is less, and dominance hierarchy refers in synthesis
Proportion is larger in mark, and with population iterative evolution, the individual that non-dominant grade is 1 is more and more, and dominance hierarchy is in synthesis
Effect in evaluation index reduces；w1, w2, w3Initial value is disposed as 1, according to the attention degree to each target, can flexibly set
Set weight, three's and be 1；
Step 302: in actual distribution operation, policymaker needs to determine an optimal reconstructed operation side from candidate solution
Case determines that best compromise solution by each sub-goal Fuzzy processing, is defined as follows using fuzzy set theory:
In formula: i ∈ 1,2 ..., Nobj}；fiFor objective function；NobjFor objective function number；fi max,fi minRespectively the i-th mesh
The maximum value of scalar functions, minimum value；hi0 and 1 is taken to respectively indicate completely dissatisfied and be entirely satisfactory；Non-domination solution concentrates each solution
Satisfaction can use following formula scalarization:
The maximum solution of h value is set to best compromise solution.
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